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iii
Preface
Recent high-prole infectious disease outbreaks, such as those caused by the Ebola and
Zika viruses, serve as a reminder of the importance of preventing, promptly detect-
ing, and eectively limiting outbreaks, no matter where or when they emerge. is
report provides an assessment of potential future disease hot spots—those countries
that might be especially vulnerable to infectious disease outbreaks. is report builds
on a proof of concept that RAND Corporation researchers published in March 2015.
is report represents a more robust approach toward vulnerability assessment in four
ways: a more comprehensive evidence base, a more robust set of factors potentially
contributing to outbreak vulnerability and associated proxy measures, the use of
adjustable weights for these parameters, and an examination of all countries world-
wide. e assessment algorithm described in the report is inherently applicable to all
outbreak-prone infectious diseases. e report describes a user-friendly tool that can
help the U.S. Department of Defense, the U.S. Department of Health and Human
Services and other U.S. government agencies, and international partners set priorities
for technical and funding support to countries that may be most vulnerable to disease
outbreaks with transnational potential.
is research was conducted within the International Security and Defense Policy
Center of the RAND National Defense Research Institute, a federally funded research
and development center sponsored by the Oce of the Secretary of Defense, the Joint
Sta, the Unied Combatant Commands, the Navy, the Marine Corps, the defense
agencies, and the defense Intelligence Community.
For more information on the International Security and Defense Policy Center,
see www.rand.org/nsrd/ndri/centers/isdp or contact the director (contact information
is provided on the web page).
v
Contents
Preface ................................................................................................. iii
Figures and Tables ................................................................................... vii
Summary .............................................................................................. ix
Acknowledgments ..................................................................................xiii
CHAPTER ONE
Introduction ........................................................................................... 1
CHA PTER TWO
Methods ................................................................................................ 5
CHAPTER THREE
Developing a Framework to Assess Vulnerability .............................................. 9
Framework Foundation: Seven Domains and Associated Factors ................................ 9
Assembling the Framework and Assigning Weights ..............................................12
CHA PTER FOUR
Results .................................................................................................25
Initial Results ..........................................................................................25
Results from the Sensitivity Analysis ...............................................................29
Implications of the Findings ........................................................................ 34
Zika and Ebola as Empirical Examples ............................................................ 36
CHA PTER FIV E
Conclusions and Next Steps ...................................................................... 43
vi Identif ying Future Disease Hot Spots
APPEN DIXES
A. Overall Country Rankings ..................................................................... 45
B. Country Bins for Missing Data Imputation ................................................59
Abbreviations .........................................................................................71
Data Sources ..........................................................................................73
Bibliography ......................................................................................... 77
vii
Figures and Tables
Figures
S.1. Infectious Disease Vulnerability Index World Map .................................. x
3.1. Domains and Factors Associated with Disease Outbreak Vulnerability ..........10
4.1. Infectious Disease Vulnerability Index World Map ................................ 26
4.2. Infectious Disease Hot Spot Belt ..................................................... 28
4.3. Map of Infectious Disease Vulnerability for Brazil and Its Neighbors ........... 38
Tables
3.1. Demographic Factors, Hypotheses, Measures, and Weights .......................15
3.2. Health Care Factors, Hypotheses, Measures, and Weights .........................16
3.3. Public Health Factors, Hypotheses, Measures, and Weights .......................17
3.4. Disease Dynamics Factors, Hypotheses, Measures, and Weights..................18
3.5. Political-Domestic Factors, Hypotheses, Measures, and Weights .................19
3.6. Political-International Factors, Hypotheses, Measures, and Weights .............21
3.7. Economic Factors, Hypotheses, Measures, and Weights .......................... 22
4.1. 25 Most-Vulnerable Countries ........................................................ 27
4.2. 25 Least-Vulnerable Countries ....................................................... 30
4.3. Results of Sensitivity Testing: Adjusting Domain Weights .........................31
4.4. Countries Among Top 50 Most-Vulnerable at Outperform eir
Economic Indicators .....................................................................35
4.5. Countries at Most Underperform eir Economic Indicators ................. 36
4.6. Outbreak Summary for the Seven African Countries Experiencing Ebola
in 2014 ....................................................................................39
4.7. Summary of Vulnerability Scores for African Countries Experiencing Ebola
in 2014 ................................................................................... 40
A.1. Overall Country Rankings ............................................................ 46
B.1. Country Subgroups Classied by World Bank Income Group and Region ......59
ix
Summary
Recent high-prole outbreaks, such as those caused by the Ebola and Zika viruses,
have illustrated the transnational nature of infectious diseases and the need for
coordinated actions to curtail the outbreaks. Countries that are most vulnerable to
such outbreaks might be higher priorities for technical and funding support. To help
identify these countries, we created the Infectious Disease Vulnerability Index. is
index was designed as a tool for U.S. government and international agencies to provide
a clearer understanding of countries’ vulnerabilities to infectious disease and thereby
to help inform decisionmaking and actions about taking preemptive steps to mitigate
the eects of potential widespread outbreaks.
We employed a rigorous methodology to identify the countries most vulnerable
to disease outbreaks. is report builds on a proof of concept we published in the
context of the Ebola outbreak (Gelfeld etal., 2015). We conducted a comprehensive
review of relevant literature to identify factors inuencing vulnerability to infectious
disease outbreaks, which we organized into seven broad domains: demographic, health
care, public health, disease dynamics, political-domestic, political-international, and
economic. Using widely available data (e.g.,from the World Bank, the World Health
Organization, and other international organizations), we created a tool to generate an
index that allows us to identify and rank potentially vulnerable countries. e tool is
built to enable user-adjusted weights for individual parameters and for domains as a
whole. We drew from both the rigorous literature review and our extensive experiences
in epidemiology, global health, and the social sciences to create a baseline set of weights
and outputs and then carried out sensitivity analyses by systematically varying the
weights across all domains.
Key ndings from our assessment include a heat map reecting normed scores
for all countries worldwide with regard to their vulnerability to infectious disease
outbreaks (FigureS.1) and a ranked list of countries based on their vulnerability.
Unsurprisingly, 22 of the 25 most-vulnerable countries are in the Africa region
(within the Department of Defense’s U.S. Africa Command area of responsibility);
the other three are Afghanistan and Yemen (within U.S. Central Command) and
Haiti (within U.S. Southern Command). Sensitivity testing rst removed all weight-
ing (i.e.,all weights set to 1.0) and then systematically zeroed out (i.e.,weight set to
x Identifying Future Disease Hot Spots
zero), doubled and tripled each domain weight. is testing indicated that most of the
top-25 (i.e.,most-vulnerable) countries remained within that range, albeit at dierent
rankings, suggesting that the tool is highly robust to variability of parameter values
from the perspective of the ranking country’s vulnerability to infectious disease out-
breaks. Of particular concern are conict-aected countries, such as Somalia (ranked
1), Central African Republic (ranked 2), and South Sudan (ranked 4), all of which
play host to a dangerous combination of political instability and compromised health
systems.
To support our interpretation of the ndings, we compared health outcomes in seven
countries aected by Ebola in 2014. is comparison suggested that a high vulnerability
score alone does not necessarily condemn a country to poor outcomes with regard to disease
outbreaks.
We would encourage policymakers to focus on the most-vulnerable countries,
with an eye toward a potential “disease belt” in the Sahel region, which emerged from
the data. Of note, the vulnerability score for several countries was better than what
would have been predicted on the basis of economic indicators alone. is suggests that
FigureS.1
Infectious Disease Vulnerability Index World Map
NOTE: The color shading runs from deep red (most vulnerable) to deep green (least vulnerable).
RAND RR1605-S.1
Normed score
0.000 1.000
Summary xi
low-income countries can overcome economic challenges and become more resilient to
public health challenges.
Our aim in designing this algorithm is to provide a useful tool for U.S. federal
agencies and national and international health planners worldwide to help identify and
raise awareness of those countries that might be most vulnerable to infectious disease
outbreaks. e algorithm can be used to guide strategic planning and programming to
address vulnerabilities in health systems or other critical sectors and hone in on cases
of critical geographic, demographic, or regional importance. is tool highlights the
connections between economic development, political stability, and disease vulner-
ability. With this information in mind, the Department of Defense, the Department
of Health and Human Services (e.g., through the Centers for Disease Control and
Prevention), the U.S. Agency for International Development, and the international
community more broadly can take targeted actions to shore up weak health systems
and help countries prepare for future infectious disease outbreaks with the potential
for transnational spread. Such agencies should continue or ramp up programming to
strengthen public health systems (e.g.,disease surveillance, laboratory testing, outbreak
detection, rapid response reams for investigation and disease-control measures), as well
as medical care systems (e.g.,professional training and certication, clinic and hospi-
tal care). Aid organizations, such as the U.S. Agency for International Development,
should also continue to promote economic development and eorts to strengthen
governance. For example, better governance through democracy-promotion and anti-
corruption programs may lead to less vulnerability as states improve the coordination,
communication, and infrastructure systems that help to combat infectious disease
transmission. Finally, exercises, including tabletop exercises, can be used to help coun-
tries better understand actions and actors, and the coordination needed among them,
to best prepare systems to respond eectively to a disease threat that arises. With the
multitude of disease threats that exist and the expanded opportunities for transmission
in an increasingly globalized world, it is important to act now to better ensure that
countries around the world, and especially the most-vulnerable countries, develop the
enduring capabilities they need to eectively prevent, detect, and respond to disease
threats before they get out of hand.
xiii
Acknowledgments
We would like to thank RAND’s National Defense Research Institute for its generous
support of this project. We would also like to thank our RAND colleagues, Seth Jones
and Michael McNerney, for their leadership, insights, and inputs, and Stephanie Young
of RAND and CAPT Paul Reed of the Department of Defense for their careful and
thoughtful reviews of the manuscript.
1
CHAPTER ONE
Introduction
Recent high-prole outbreaks of Ebola, Middle East Respiratory Syndrome (MERS),
pandemic inuenza, and Zika, among others, have illustrated the transnational nature
of infectious diseases and the need for coordinated actions to curtail them. e Global
Health Security Agenda (GHSA), launched in February 2014 by the White House,
along with other partner countries and organizations, aims to help countries build
their capabilities to prevent, detect, and respond to infectious disease threats. GHSA
does not, however, include activities to assess the relative vulnerability of countries to
such threats. Which countries are most vulnerable to infectious disease outbreaks that
may cross national borders and spark regional or even global spread? Such countries
might be higher priorities for technical and funding support from U.S. government
agencies, as well as from other partner countries and organizations.
Global health security has become a policy priority of the United States and
countries around the world, enshrined explicitly in the U.S. National Security Strategy
(White House, 2015), the GHSA (White House, 2014), and the U.S. National Health
Security Strategy (U.S. Department of Health and Human Services [HHS], 2015).
e World Health Organization’s (WHO’s) International Health Regulations (WHO,
2005) also fundamentally aim to enhance global health security, albeit without using
the term explicitly. Past analyses have described the distribution of specic diseases and
the overall threat of infectious diseases worldwide (see, for example, Noah and Fidas,
2000; Christian etal., 2013), but we were unable to uncover past analyses in the more
than 30 studies we reviewed in the course of our literature review that systematically
aimed to identify country vulnerability to infectious disease outbreaks. Given the
unpredictability and potential enormity of such threats to global health security, we
developed the comprehensive Infectious Disease Vulnerability Index to assess potential
future disease hot spots. We created an interactive algorithm to estimate the relative
vulnerability of the world’s countries to infectious disease outbreaks and inform our
understanding of the most potentially vulnerable countries. By vulnerability, we refer
mainly to a country’s ability to limit the spread of outbreak-prone diseases. More-
vulnerable countries are less able to prevent, detect, and respond to disease spread,
whereas more-resilient countries are better able to do so. While the rst case (or cases)
of a disease may not be entirely preventable, countries should be able to quickly detect
2 Identifying Future Disease Hot Spots
the disease and limit its spread. e Ebola crisis in 2014–2015 illustrated both the
vulnerability of certain countries and the ability of others to react quickly to overcome
potential vulnerabilities and contain the disease. Most important, that crisis served as a
reminder of the interconnectedness of the global community with regard to outbreak-
prone infectious diseases and the importance of prevention; early detection; and timely,
eective response to outbreaks, wherever and whenever they may emerge.
Assessment of potential disease hot spots complements other eorts directed
toward global health security, such as direct eorts to help improve the prevention,
detection, and control of transnational infectious disease threats. e assessment
described here can inform prioritization for technical and funding support for such
eorts, before outbreaks emerge and as soon as possible once they do emerge, thereby
helping the most-vulnerable countries develop the enduring capabilities they need to
prevent and control such threats.
is report is a direct follow-up to RAND’s Mitigating the Impact of Ebola
in Potential Hot Zones (Gelfeld et al., 2015), which described a proof-of-concept
approach to help decisionmakers systematically assess the risk of the spread of the
Ebola virus to other potentially vulnerable countries and consider actions that could
be taken to mitigate the impact of Ebola in such countries. In our previous report,
we recommended the further development of the algorithm to incorporate a more
rigorous, quantitative methodology to systematically assess countries’ vulnerability
to infectious diseases. is report directly addresses that recommendation with an
algorithm that is more robust in four important ways: an evidence base that is more
thoroughly grounded in scholarly research and empirical studies; a more robust set
of factors potentially contributing to outbreak vulnerability, along with one or more
associated proxy measures for each factor; the introduction of adjustable weights
for these factors and measures; and the application of the algorithm to all countries
worldwide rather than just a select few.
Our aim in designing this algorithm is to provide a useful tool for U.S. federal
agencies and national and international health planners worldwide to help identify and
raise awareness of those countries that might be most vulnerable to infectious disease
outbreaks. e algorithm can be used to guide strategic planning and programming
to address health system vulnerabilities and hone in on cases of critical geographic,
demographic, or regional importance. With this additional information in mind, the
U.S. Department of Defense (DoD), HHS, other U.S. government agencies, and the
international community more broadly can take targeted actions to shore up weak
health systems and help countries prepare for future infectious disease outbreaks with
the potential for transnational spread. With the multitude of disease threats that exist
and the expanded opportunities for transmission in an increasingly globalized world,
it is important to act now to better ensure that countries around the world, and espe-
cially the most-vulnerable countries, develop the enduring capabilities they need to
eectively prevent, detect, and respond to disease threats before they get out of hand.
Introduction 3
is report describes the Infectious Disease Vulnerability Index tool and details
its potential applications as a decision-support tool for governments and international
organizations. Chapter Two details the methodology behind the design of the tool.
Chapter ree describes the development of the tool, its structure, and our approach
to weighting the various elements. Chapter Four presents our ndings from the
application of the tool to all 195 countries and discusses the interpretation of the
results. Chapter Five discusses potential applications of the tool for use by the United
States and other governments and international organizations.
5
CHAPTER TWO
Methods
To better measure the key concepts of vulnerability and resilience, which we consider
to be opposite sides of the same coin, we elected to use a methodology that combined
rigorous literature review and expert elicitation because it has worked in similar
studies and is rmly grounded in the relevant scholarly research. We rst conducted
an extensive literature review of the relevant scholarship on infectious disease outbreaks.
Specically, we comprehensively examined the scholarly literature that made connec-
tions between the performance of health systems and the incidence of infectious
disease, through a variety of social science lenses (see the bibliography). Based on
our ndings, we then created a framework of factors and associated measures that
followed from the central themes and conclusions identied in the relevant litera-
ture. We drew on numerous empirical studies and journal articles to identify major
themes and factors pertaining to infectious diseases and country vulnerability. For
each such factor (e.g.,medical care workforce, corruption), we stated the thesis from
the literature explaining the link between the factor and a country’s vulnerability to
disease outbreaks (or resilience). Based on key themes emerging from our literature
review, we divided these factors into seven overarching domains: demographic, health
care, public health, disease dynamics, political-domestic, political-international, and
economic.
To assign a value for each factor, we matched each one with one or more proxy
measures. We populated a matrix of all measures for all countries with widely available
data, drawn from such sources as the World Bank and the WHO. Of note, certain
data were not available for some countries. We normed the raw data for each measure
to produce a numerical score between 0 (worst) and 1 (best). Also, for purposes of
consistency, we arithmetically “ipped” measures for which a high score was inherently
worse (e.g.,infant mortality rate, corruption index).
To deal eectively with missing values, we imputed the values for missing data
for a country by taking the mean value of a measure for a similar subgrouping of
countries based on per capita gross domestic product (GDP) and geographic region (see
Appendix B for a full listing of country subgroups). is technique divided the data
into subcategories, rst based on per capita income (using the ve designations used
by the World Bank: low income; lower middle income; upper middle income; high
6 Identifying Future Disease Hot Spots
income, non-OECD [Organisation for Economic Co-operation and Development];
and high income, OECD) and then based on World Bank geographic region (sub-
Saharan Africa, South Asia, East Asia and Pacic, Latin America and the Caribbean,
the Middle East and North Africa, Europe and Central Asia, and North America).
By conditioning these means on smaller bins of relevant country data generated using
both per capita income and geography, we can ensure the closest matches possible and
that the imputed means will therefore be as close as possible to the missing country
value.
We assigned initial weights, usually between 0 and 1, to each factor and measure
based on six criteria that reect the factor’s quality and credibility. e rst four criteria
relate to the factor itself, and the nal two relate to the measure. e six criteria
are (1)strength of the correlation or association between the factor and disease risk;
(2)quality of the research supporting the factor; (3) face validity of the factor (does it
make intuitive sense?); (4) uniqueness (the extent to which the factor is duplicative to
other factors); (5) proxy value (the extent to which the measure is an eective proxy
for the factor—i.e.,reecting the factor); and (6) quality of the data available for
the measure. Weights for the rst and second criteria emerged from ndings during
the literature review. Weights for the third and fourth ones were derived from our
team’s assessment of the factor in question. Weights for the fth and sixth criteria
are specically related to the individual measures and are derived from the team
members’ evaluation according to their relevant area expertise. Where more than
one measure represents a factor, the measure weights are equally distributed across
the measures for the factor, to contribute to an overall factor score. We applied all of
these weights to the normed raw data to calculate an indexed vulnerability score for
each country and then normed that overall vulnerability score across all countries to
a value between 0 and 1, with 0 indicating the country most vulnerable to infectious
disease outbreaks and 1 indicating the most resilient country.
Our algorithm allows users to vary the weights based on their own assumptions,
priorities, or planning requirements. To assess the overall validity of our initial weights
and algorithm, we performed a number of sensitivity tests—systematically varying
the weights for each domain and comparing these results with those from our initial
baseline.
We recognize that there are other empirically valid ways that we might have gone
about structuring this study methodologically. One such way would have been to use
historical regression analysis to examine the eect on a dependent variable (infectious
disease vulnerability) of a host of independent variables to test their eects on the
outcome in question. While empirically rigorous, this methodology has challenges
of its own given that historical regression analysis is not unproblematic. ere are
counterfactual concerns in addition to establishing the necessary distinction between
vulnerability (the potential eects and extent of a disease outbreak should it occur) and
risk (the conuence of the likelihood of, the vulnerability to, and the consequences
Methods 7
of a disease outbreak) in this context. Furthermore, given the eort required and the
challenges faced because of missing data in assembling recent and contemporary data
for all countries, assembling global historical data for any signicant period would
have been nontrivial.
9
CHAPTER THREE
Developing a Framework to Assess Vulnerability
roughout the course of our literature review and research, we found that the
most-relevant concepts and associated measures fell into seven common domains
within the four broader thematic categories of demographic, health, political, and
economic factors. e seven domains are demographic, health care, public health,
disease dynamics, political-domestic, political-international, and economic. Factors
and associated measures within these seven domains provide the framework for our
quantitative analysis.
Framework Foundation: Seven Domains and Associated Factors
e sections that follow describe the factors within each domain, and Figure3.1
provides an overall summary. In the course of our initial literature review, we found
well-regarded academic studies linking every factor to vulnerability to infectious
diseases. We derived the domains by organizing the factors by theme. In total, we
used approximately three dozen dierent studies to establish these connections to
verify our hypotheses and lay the intellectual foundation for the tool. Our list of data
sources and bibliography (at the end of this report) are both organized by domain.
Demographic Factors
Several demographic factors inuence the degree of vulnerability of a country to
infectious disease outbreaks. e relevant literature emphasizes the role of such factors
as population density, growth and mobility, and the degree of urbanization. States with
densely packed, fast-growing urban areas and high population mobility across borders
are more vulnerable to the spread of contagious diseases. e level of education or
literacy can also play a helpful role in mitigating the spread and eects of infectious
diseases by enhancing the adoption of health behaviors or practices that reduce disease
transmission.
10 Identifying Future Disease Hot Spots
Health Care Factors
e strength and quality of a nation’s health care system have an obvious and direct
bearing on resiliency to infectious disease outbreaks. is is supported by a large body
of published evidence. From our extensive literature review, we included key factors that
indicate the strength of national health systems and selected proxy measures to represent
them. Factors include the size of the medical care workforce (doctors and nurses), health
care expenditures, and health care infrastructure (number of dierent types of health
care facilities). Based on the relevant research, we also included a measure—infant
mortality rate—as an indicator of the health status of a country.
Public Health Factors
Strong public health systems are needed to ensure that a country can prevent, promptly
detect, and eectively respond to infectious disease outbreaks. e ability of a govern-
ment to deliver basic health services (such as vaccinations) and the proportion of the
population with access to clean water and improved sanitation facilities reect how
well communities can prevent or respond to disease threats. e extent to which a
country has developed its core public health capacities in accordance with the WHO’s
International Health Regulations (IHR) or the degree of direct engagement in GHSA
Figure3.1
Domains and Factors Associated with Disease Outbreak Vulnerability
RAND RR1605-3.1
A nation’s ability to
PREVENT or CONTAIN
a disease outbreak
Economic
Political-Domestic
Political-International
• Medical workforce
• Medical expenditures
• Medical infrastructure
• Health outcomes
• Health service delivery
• Water, sanitation, hygiene
• Basic public health
infrastructure
• IHR core capacity score
• GHSA action packages
Health Care
Public Health
• Population density
• Urbanization
• Population growth
• Education/literacy
• Population mobility
Demographic
• Precipitation
• Temperature
• Land-use changes
• Aid support
• Aid dependence
• Aid continuity
• International org. support
• Governance
• Corruption
• Service provision
• Decentralization
• Democracy
• Stability
• Conflict
• Human rights
• International org./
bilateral donor
support
• Collaboration
Disease Dynamics
Economic:
• Strength
• Growth
• Development
Infrastructure:
• Transportation
• Technology
• Communications
Developing a Framework to Assess Vulnerability 11
should, in principle, also serve as important indicators of the adequacy of the country’s
public health system and, hence, its resilience against disease outbreaks.
Disease Dynamics Factors
Climate-related and ecological factors can also inuence a country’s vulnerability to
disease outbreaks. Patterns of precipitation and temperature can directly aect disease
transmission through impacts on the replication and movement (and perhaps evolution)
of disease microbes and vectors giving rise to water- or vector-borne diseases, such as
cholera, malaria, dengue, and West Nile virus. Furthermore, evidence suggests that
increases in anthropogenic activities—such as changes in the national patterns of land
use, including the extent of agricultural activities and deforestation—are associated
with the likelihood of emergence of zoonotic infectious diseases. is process happens
either by increasing proximity to conditions or, often, by changing the conditions that
favor an increased population of the microbe or its natural host. Accordingly, we have
accounted for these factors in our model.
Political-Domestic Factors
Domestic political factors can also aect the ability of a government to prepare for
and respond to infectious disease outbreaks and thus will have a signicant bearing on
its vulnerability to such threats. Backed by several academic studies, we have posited
that governance (measured by three dierent World Bank governance indicators) has a
positive eect on resiliency, while corruption has a deleterious one. Stable governments
without conicts within their borders are also likely to be more resilient in this regard,
as are governments that are perceived as providing quality services to their citizens.
Furthermore, greater levels of democracy and political decentralization, along with
respect for human rights, are associated with a state’s ability to defend itself against
disease threats. Greater levels of democracy within a country and empowerment of
local government often yield more-capable organization and response at the city, state,
or province level. Successful decentralization creates several levels of contingency
response throughout a country that can set up mechanisms to respond to a disease
outbreak. Civil society organizations, generally more vibrant and participative in a
democracy, can also help prepare for and respond to disease outbreaks. Furthermore,
respect for human rights means that citizens feel more empowered to provide feedback
regarding prevention and response eorts so that mechanisms in place to prevent the
spread of diseases are more fully vetted by citizen interest groups.
Political-International Factors
Along with domestic considerations, international political factors aect a country’s
resilience to infectious disease outbreaks. e consistent support of both bilateral
donors and international organizations can help strengthen a country’s health system
and provide critical funding, expertise, and personnel in preparing for and respond-
Figure3.1
Domains and Factors Associated with Disease Outbreak Vulnerability
RAND RR1605-3.1
A nation’s ability to
PREVENT or CONTAIN
a disease outbreak
Economic
Political-Domestic
Political-International
• Medical workforce
• Medical expenditures
• Medical infrastructure
• Health outcomes
• Health service delivery
• Water, sanitation, hygiene
• Basic public health
infrastructure
• IHR core capacity score
• GHSA action packages
Health Care
Public Health
• Population density
• Urbanization
• Population growth
• Education/literacy
• Population mobility
Demographic
• Precipitation
• Temperature
• Land-use changes
• Aid support
• Aid dependence
• Aid continuity
• International org. support
• Governance
• Corruption
• Service provision
• Decentralization
• Democracy
• Stability
• Conict
• Human rights
• International org./
bilateral donor
support
• Collaboration
Disease Dynamics
Economic:
• Strength
• Growth
• Development
Infrastructure:
• Transportation
• Technology
• Communications
12 Identifying Future Disease Hot Spots
ing to health crises. e response of the WHO; the U.S. Centers for Disease Control
and Prevention (CDC); and various nongovernmental organizations (NGOs), such as
Médecins Sans Frontières (Doctors Without Borders), among others, proved crucial in
containing the Ebola crisis in West Africa. While aid can certainly help mitigate the
eects of infectious diseases, studies have shown that aid can also create dangerous
dependencies, a fact that we have reected in our algorithm.
Economic Factors
e size and scope of a country’s economy often dictates the amount and quality
of resources it has to prepare for and respond to the threats posed by infectious
diseases. As a result, we incorporated certain measures of economic strength and
development into our model, including GDP per capita, economic growth rates, the
Human Development Index (from the United Nations Development Programme),
and national poverty ratios. Academic research into infectious diseases also pointed
to the importance of communications and transportation infrastructure and the
technological sophistication of a society; as result, we have included these factors
and associated measures into our model.
Assembling the Framework and Assigning Weights
Weights acted as multipliers in calculating the overall vulnerability score. Weighting
values were assigned separately for each parameter and also for a domain as a whole.
Tables 3.1–3.7 summarize the factors, hypotheses, measures, and initial assigned
weights for the seven domains. We drew from both our rigorous literature review and
our collective expertise and experiences in the elds of epidemiology, health, and social
science in assigning these initial weights. We adopted a convention of assigning weights
mostly in the range of 0 to 1.0 for each factor and measure, mostly in increments of
0.25 (e.g.,0.25, 0.50, 0.75) and preferably not all clustering around 1.0. A weight of 0
for any of the six criteria eectively eliminates the factor from the calculations. We did
not assign a zero weight to any parameter in our baseline scenario. As the perceived
value of each factor or measure increased, we increased the numerical value assigned
to the respective weights from 0 and approached 1.0 (or beyond, if weights greater
than 1.0 were assigned). We considered four factors to be of particular importance and
assigned baseline weights greater than 1.0 for the strength of correlation parameter
(ρ): We assigned a weight of 2.0 for a composite IHR core capacity score, governance,
and government stability and a weight of 1.5 to a factor reecting economic strength.
Because we deemed these to be the most-inuential factors in terms of their signicant,
direct impact on disease vulnerability, we assigned them weights greater than 1.0. e
signicance of these specic factors was determined through the conuence of their
importance in both the relevant literature and our professional judgment.
Developing a Framework to Assess Vulnerability 13
We assigned weights for six parameters related to each factor. Strength of corre-
lation (ρ) is intended to be the equivalent of a regression coecient, had the param-
eter been derived based on regression; ρ denotes the strength of correlation between
the factor and the outcome as identied in existing research. Quality of research (Qr)
captures our holistic assessment of the strength of evidence in support of the con-
tribution of the factor as marshaled in the existing research. Where multiple highly
rigorous studies inform the factor, this weight is (or approaches) 1.0; where fewer stud-
ies with thinner empirical foundation inform the factor, weights are lower. No factor
has a quality-of-research assessment lower than 0.5, because any factor with such a
slim foundation was entirely eliminated from consideration. Face validity (F) captures
our assessment of the face validity of the identied correlation. A face validity value
below 1 indicates that we are skeptical about the strength of correlation proposed in
the informing literature (that is, we believe that the factor is important but that it is not
as strongly correlated as existing research suggests) or that we can imagine alternative
mechanisms that diminish our condence in the importance of the factor. Uniqueness
(R) denotes the extent to which the factor is redundant with other factors. While each
factor is distinct, they are not necessarily discrete. We included uniqueness to reduce
the overall weight where we have included several similar and related factors. Proxy
value (X) denotes our assessment of the extent to which the measure used is a good
representative of the factor. A proxy value weight of 1.0 indicates that the measure is a
perfect match for the factor, while lower weights indicate that the best measure we were
able to nd for the factor is less than ideal. e quality of data weight (Qd) is similar,
being 1.0 where the data used to represent the factor are of the highest quality and
being lower where the data are beset by measurement error, low currency (that is, are
somewhat out-of-date), or extensive missing information.
ese six weights combine with the data representing each factor for each country
in the following way. First, the raw data for a measure of a factor are normed across
all countries over the range 0 to 1.0. en, the normed measure for each country is
multiplied by all six weights for the factor. Finally, the weighted measures are normed
again across all countries to produce a factor score for each country ranging between
0 and 1.0, where the worst country in the data scores 0 for the factor and the best
country scores 1.0. To generate scores for domains, all factor scores within a domain
are summed for each country and then normed again across countries. To generate
overall scores for each country, all factors across all domains are summed by country
and then normed across countries. So, at each level (individual factor, domain, or
overall), each score ranges between 0 and 1.0, with the worst country on that factor,
domain, or overall scoring 0 and the best country scoring 1.0.
e results presented here reect the strength of correlations in publications, the
quality of the data available, and judgments of the authors. e tool was constructed
to enable users to adjust weights, as we did with our initial weightings and the subsequent
sensitivity testing (described in Chapter Four). Because the method adopted for imputing
14 Identifying Future Disease Hot Spots
missing values rendered the tool less user-friendly, the tool is not published online but
is available by request from the authors. Dierent users may have dierent priori-
ties and hence may wish to assign signicantly dierent weights. One can imagine,
for example, that security sector planners (e.g.,DoD) might assign higher weights
to security-related parameters, whereas health sector planners (e.g.,HHS, the U.S.
Agency for International Development [USAID]) might assign higher weights to
health-related parameters, and yet others might consider economic factors to be most
important and wish to adjust the assigned weights accordingly.
Developing a Framework to Assess Vulnerability 15
Table3.1
Demographic Factors, Hypotheses, Measures, and Weights
Label Factor Hypothesis Measure
Weights
ρQrF R X Qd
DG -1 Population
density
A country with higher population
density is more susceptible to the
spread of emerging infectious
diseases via overcrowding
Persons per square km (high = bad;
ip measure value)
0.75 1.0 0 0.75 0.25 1.00 0.90
DG-2 Urbanization
(interaction
with water,
sanitation,
hygiene)
A country with densely populated
urban areas is more susceptible to
the spread of infectious diseases via
overcrowding and direct or indirect
contact with numerous persons
Percentage of persons living
in urban areas (high = bad; ip
measure value)
0.75 0.90 0.75 0.25 1.00 0.90
DG-3 Human
population
growth
A country with higher growth in
population is more susceptible to the
spread of emerging infectious
diseases via overcrowding
Annual population growth rate
(average annualpercentage change
in population) (high = bad; ip
measure value)
0.50 0.90 0.50 0.25 1.00 0.90
DG-4 Education/
literacy
A country with high rates of literacy
and education is less susceptible to
the spread of emerging diseases via
risky behaviors that may increase
exposure
Adult literac y rate (high = good) 0.75 0.90 1.00 1.0 0 1.00 0.90
Adult female literacy rate (high =
good)
0.75 0.90 1.00 1.0 0 1.00 0.90
DG-5 Population
mobility
A country with high migration and
mobility of peoples is more
susceptible to the spread of
infectious diseases
Net migration rate (average annual
number of migrants per 1,000
people) (high = bad; ip measure
value)
0.50 0.50 0.75 1.00 0.75 0.90
NOTES: Factor weights are ρ = strength of correlation; Qr = quality of research; F = face validity; R = redundancy (uniqueness). Measure weights are X =
proxy value; Qd = quality of data.
16 Identifying Future Disease Hot Spots
Table3.2
Health Care Factors, Hypotheses, Measures, and Weights
Label Factor Hypothesis Measure
Weights
ρQrF R X Qd
HC-1 Medical care
workforce
A country with more health care
providers is better able to limit
infectious disease outbreaks
Number of physicians per 1,000
population (high = good)
0.90 0.50 0.75 1.00 1.0 0 0.65
Number of nurses and
midwives per 1,000 population
(high = good)
0.90 0.50 0.75 1.00 1.0 0 0.65
HC-2 Medical care
expenditures
A country with greater spending on
health (specically, health care) is better
able to limit infectious disease
outbreaks
Percentage of GDP spent on
health (high = good)
0.50 0.90 0.50 0.33 1.00 0.90
Health expenditure per capita
(high = good)
0.50 0.90 0.50 0.33 1.00 0.90
HC-3 Medical care
infrastructure
A country with a better medical
infrastructure is better able to respond
to limit infectious disease outbreaks
Hospital beds per 1,000
population (high = good)
0.50 0.70 0.75 0.33 1.00 0.90
Health posts per 100,000
population (high = good)
0.50 0.70 0.75 0.33 1.00 0.90
Health centers per 100,000
population (high = good)
0.50 0.70 0.75 0.33 1.00 0.90
Hospitals per 100,000
population (high = good)
0.50 0.70 0.75 0.33 1.00 0.90
HC-4 Health status/
outcomes
A country with worse health status,
with infant mortality rate as a proxy,
reects less ability to deliver services
and in turn is less able to respond
effectively to prevent or limit infectious
disease outbreaks
Infant mortality rate (number
of deaths in <12 months per
1,000 live births) (high = bad;
ip measure value)
0.75 0.90 1.00 1.0 0 1.00 0.90
NOTES: Factor weights are ρ = strength of correlation; Qr = quality of research; F = face validity; R = redundancy (uniqueness). Measure weights are X =
proxy value; Qd = quality of data.
Developing a Framework to Assess Vulnerability 17
Table3.3
Public Health Factors, Hypotheses, Measures, and Weights
Label Factor Hypothesis Measure
Weights
ρQrF R X Qd
PH -1 Health service
delivery
A country that is better able to
deliver basic primary care services is
also better able to respond to limit
the spread of infectious disease
outbreaks
Percentage coverage with
third dose of DTP vaccine
(high = good)
0.90 0.50 1.0 0 0.33 1.0 0 0.90
Percentage coverage with rst
dose of measles vaccine
(high = good)
0.90 0.50 1.0 0 0.33 1.0 0 0.90
PH-2 Wa ter,
sanitation,
and hygiene
infrastructure
A country with more widespread
availability of potable water,
sanitar y conditions, and proper
hygiene is better protected against
the transmission of some infectious
diseases—e.g.,cholera
Population using improved
drinking-water sources (%)
(high = good)
1.00 0.90 1.00 1.00 1.0 0 0.90
Population using improved
sanitation facilities (%) (high =
good)
1.00 0.90 1.00 1.00 1.0 0 0.90
PH-3 Basic public
health
infrastructure
A country with a strong public
health infrastructure —e.g., having a
national public health institute—
is better able to prevent and
respond effectively to limit
infectious disease outbreaks
Country is member of the
International Association
of National Public Health
Institutes (binary; 1 = yes
[good])
0.50 0.75 1.0 0 1.00 0.75 0.90
PH-4 Composite IHR
core capacity
score
A country with stronger IHR core
capacities is better able to prevent
and respond effectively to limit
infectious disease outbreaks
Arithmetic average of score
across all IHR scores (high =
good)
2.00 0.90 1.00 1.0 0 1.00 0.90
PH-5 GHSA ac tion
packages
A country that is committed to lead
or contribute to a GHSA action
package will be better able to
contain infectious disease
outbreaks
Country leading or
contributing to >1 GHSA
action package (binary; 1 = yes
[good])
0.25 0.90 0.75 1.00 0.75 0.90
NOTES: Factor weights are ρ = strength of correlation; Qr = quality of research; F = face validity; R = redundancy (uniqueness). Measure weights are X =
proxy value; Qd = quality of data. DTP = diphtheria-tetanus-pertussis.
18 Identifying Future Disease Hot Spots
Table3.4
Disease Dynamics Factors, Hypotheses, Measures, and Weights
Label Factor Hypothesis Measure
Weights
ρQrF R X Qd
Environmental factor
DD -1 Precipitation/
rainfall
A country with greater precipitation can
have greater transmission of water- and
vector-borne diseases because of the
effects of precipitation on the replication
and movement (and perhaps evolution) of
disease microbes and vectors
Average rainfall per year
(mm) (high = bad; ip
measure value)
0.25 0.70 0.75 1.00 0.75 0.90
DD-2 Temperature A country with higher temperatures can
have greater transmission of water- and
vector-borne diseases because of the
effects of temperature on the replication
and movement (and perhaps evolution) of
disease microbes and vectors
Annual average temperature
(high = bad; ip measure
value)
0.25 0.70 0.75 1.00 0.75 0.75
Ecological factor
DD-3 Changes in
land use
Increasing anthropogenic activities is
associated with increased susceptibility to
and likelihood of emergence of zoonotic
infectious diseases —either by increasing
proximity or, often, by changing conditions
that favor an increased population of the
microbe or its natural host
Agricultural land (%) (high =
bad; ip measure value)
0.50 0.90 0.75 1.00 1.00 0.90
Forest area (%)
(high = bad; ip measure
value)
0.50 0.90 0.75 1.00 1.00 0.90
Global deforestation rates
(%) (high = bad; ip measure
value)
0.50 0.90 0.75 1.00 1.00 0.90
NOTES: Factor weights are ρ = strength of correlation; Qr = quality of research; F = face validity; R = redundancy (uniqueness). Measure weights are X =
proxy value; Qd = quality of data.
Developing a Framework to Assess Vulnerability 19
Table3.5
Political-Domestic Factors, Hypotheses, Measures, and Weights
Label Factor Hypothesis Measure
Weights
ρQrF R X Qd
P- D-1 Governance A country with a competent and strong
government is better able to contend with
an infectious disease outbreak
Worldwide Governance Indicators
Government Effectiveness Index
(high = good)
2.00 0.70 1.00 1.00 1.00 0.90
Worldwide Governance Indicators
Regulatory Quality Index (high =
good)
2.00 0.70 1.00 1.0 0 0.50 0.75
Worldwide Governance Indicators
Rule of Law Index (high = good)
2.00 0.70 1.00 1.00 0.75 0.90
P-D-2 Corruption A country with greater corruption has
worse health outcomes and greater
vulnerability to infectious disease
outbreaks
Transparency International Corruption
Perceptions Index (high = good)
0.75 1.0 0 0.75 0.50 1.0 0 0.90
P-D-3 Service
provision
A country with greater stability and better
quality of services has fewer barriers
(geographical, nancial, personnel, and
access) to health care for marginalized
populations
United Nations Development
Programme Human Development
Report Health Systems Survey (high =
good)
0.75 0.90 0.75 0.50 0.50 0.65
P-D-4 Decentral-
ization
Various dynamics of decentralization
(scal, political) are linked with positive
health outcomes
World Bank decentralization index
(high = good)
0.75 0.70 0.50 1.00 1.00 0.90
P-D-5 Democracy A country with a more democratic and
legitimate government is better able to
contend with an infectious disease
outbreak
Polity IV Project Democracy Index
(high = good)
0.50 0.90 0.75 1.00 1.0 0 0.75
P-D-6 Government
stability
State fragility increases vulnerability
to infectious disease outbreaks, while
infectious disease outbreaks can
exacerbate existing state weaknesses
Fund for Peace Fragile States Index
(high = bad; ip measure value)
2.00 0.70 1.00 0.50 1.0 0 0.90
20 Identif ying Future Disease Hot Spots
Table 3.5—Continued
Label Factor Hypothesis Measure
Weights
ρQrF R X Qd
P-D-7 Presence of
conict
Political stability—absence of conict and
state fragility—is associated with better
ability to deliver health care and better
health outcomes
Worldwide Governance Indicators
Political Stability and Absence of
Violence Index (World Bank) (high =
good)
0.90 0.70 1.00 0.50 0.75 0.75
P-D-8 Human
rights
A worse human rights record is linked with
worse health performance
Amnesty International Political Terror
Scale (high = bad; ip measure value)
0.50 0.58 0.50 1.0 0 0.50 0.25
U.S. Department of State via Amnesty
International Political Terror Scale
(high = bad; ip measure value)
0.50 0.58 0.50 1.0 0 0.50 0.25
NOTES: Factor weights are ρ = strength of correlation; Qr = quality of research; F = face validity; R = redundancy (uniqueness). Measure weights are X =
proxy value; Qd = quality of data.
Developing a Framework to Assess Vulnerability 21
Table 3.6
Political-International Factors, Hypotheses, Measures, and Weights
Label Factor Hypothesis Measure
Weights
ρQrF R X Qd
Successful cooperation with foreign partners
P-I-1 Aid support States receiving more donor aid are better
able to ensure health system functionalit y
World Bank Net Ofcial
Development Assistance per
capita (high = good)
0.50 0.50 0.75 0.33 0.75 0.75
P-I-2 Aid
dependence
Countries with a high proportion of donor
aid are less able to deal with health
emergencies on their own and therefore are
more vulnerable to infectious disease
outbreak
World Bank Net Ofcial
Development Assistance
received (% gross national
income) (high = bad; ip
measure value)
0.75 0.50 0.75 0.33 1.00 0.75
P-I-3 Aid continuity Consistent, predictable funding support can
promote better infectious disease control
through stronger health systems
Lagged correlation between
foreign aid and foreign direct
investment (high = good)
0.50 0.50 0.75 0.33 0.75 0.50
International cooperation and collaboration
P-I-4 International
organization
support
Greater involvement, funding, and assistance
by intergovernmental or bilateral partners
will lead to more -effective detection and
control of infectious disease outbreak
United Nations Development
Programme recipient funding
by country per capita (high =
good)
0.75 0.50 0.75 0.50 0. 25 0.25
P-I-5 International
organization/
bilateral
support for
health
International organization and bilateral
support to developing countries should lead
to health sec tor strengthening and better
resiliency against and response to infectious
disease outbreaks
Development assistance for
health by country 2011 (high =
good)
0.50 0.50 0.75 0.50 0.75 0.75
Development assistance for
health per capita (high = good)
0.50 0.50 0.75 0.50 0.75 0.75
P-I-6 Collaboration Collaboration across governments, donors,
and NGOs in program design and
implementation is associated with better
health systems and infec tious disease control
Involvement with multilateral
institutions (Jane’s) (high =
good)
0.75 0.50 0.75 1.0 0.50 0.50
NOTES: Factor weights are ρ = strength of correlation; Qr = quality of research; F = face validity; R = redundancy (uniqueness). Measure weights are X =
proxy value; Qd = quality of data.
22 Identif ying Future Disease Hot Spots
Table3.7
Economic Factors, Hypotheses, Measures, and Weights
Label Factor Hypothesis Measure
Weights
ρQrF R X Qd
Strong economy and strong economic growth
EC-1 Economic
strength
A strong economy is associated with better
health outcomes (lower infant mortality
and longer life expectancy) in all countries
GDP per capita
(high = good)
1.50 0.90 1.00 0.50 0.75 0.75
EC-2 Economic
growth
Greater economic growth has led to
signicant gains in control of infectious
disease outbreaks (tuberculosis, polio)
even in countries with weak institutional
environments (Democratic Republic of the
Congo [DRC], Myanmar, Haiti); the gains
from economic growth ow directly into
health gains, up to a certain threshold of
development
GDP per capita growth
rate (2010–2014) (high =
good)
0.50 0.50 0.75 1.00 1.0 0 0.75
EC-3 Economic
development
Countries with stronger economic
development have greater access to
diagnostic resources, making these
countries more able to detect and respond
to infectious disease outbreaks
United Nations
Development Programme
Human Development
Index (high = good)
0.75 0.70 0.90 0.50 0.75 0.75
Greater economic development has led to
better efforts to control infectious disease
as a result of more and higher-quality
resources with which to combat the spread
of infectious disease
Poverty headcount ratio
under $1.25 per day
(high = bad; ip measure
value)
0.75 0.90 0.90 0.50 0.75 0.75
Developing a Framework to Assess Vulnerability 23
Table 3.7—Continued
Label Factor Hypothesis Measure
Weights
ρQrF R X Qd
Infrastructure and technology
EC-4 Partner-nation
transpor tation
infrastructure
Good transportation infrastructure makes it
easier to deliver needed medical supplies to
a country and to distribute them
throughout the country
World Bank
Logistics Performance
Index (high = good)
0.50 0.50 0.75 1.00 0.50 0.65
Percentage of paved
roads (of total) (high =
good)
0.50 0.50 0.75 1.00 0.50 0.50
EC-5 Technological
sophistication
Greater technological penetration and
sophistication are associated with bet ter
infectious disease control
World Bank
Knowledge Economy
Index (high = good)
0.75 0.90 0.75 1.00 0.75 0.65
EC-6 Partner-nation
communications
infrastructure
Good communications infrastructure makes
it easier to deliver information about
infectious disease and control measures to
the population and outlying authorities
Televisions per 1,000
people (high = good)
0.50 0.50 0.75 1.00 0.25 0.50
Cell phone subscriptions
per 100 people (high =
good)
0.50 0.50 0.75 1.00 0.50 0.65
Internet users per 100
people (high = good)
0.50 0.50 0.75 1.00 0.25 0.65
NOTES: Factor weights are ρ = strength of correlation; Qr = quality of research; F = face validity; R = redundancy (uniqueness). Measure weights are X =
proxy value; Qd = quality of data.
25
CHAPTER FOUR
Results
e indexed scores—the overall score and domain-specic scores—for all countries
are presented as normed values between 0 and 1.0 and are listed in ranked order in
AppendixA. e global distribution of vulnerability can be seen visually in Figure4.1.
In this chapter, we rst present initial results and observations about the Infectious
Disease Vulnerability Index scores and then present the results of our sensitivity
analysis. We then discuss the implications of our ndings and use the Ebola and Zika
outbreaks as examples to illustrate these results.
Initial Results
To determine vulnerability proles, it is instructive to look at the most-vulnerable and
least-vulnerable countries, as indicated by the Infectious Disease Vulnerability Index,
and evaluate each list for commonalities and trends. We rst present the 25 most-
vulnerable countries according to our algorithm and then present, by way of contrast,
the 25 least-vulnerable countries.
Most-Vulnerable Countries
Examining the 25 most-vulnerable countries (Table4.1) reveals few surprises, with
22 of the 25 countries located in sub-Saharan Africa (covered by U.S. Africa Com-
mand [AFRICOM]). e other three countries—Haiti, Afghanistan, and Yemen—
have similar proles to the high-vulnerability African countries in terms of poor
access to resources, poor governance, and weak health systems. e tables here and
in AppendixA present the countries in rank order, from most to least vulnerable.
Across the 195 countries, the color shading of overall normed scores runs from deep
red (most vulnerable) through orange and yellow to light and deeper green (least
vulnerable). e 25 most-vulnerable countries have normed scores ranging from 0
(normed minimum value, for Somalia) to 0.26 (Mozambique), shaded from deep red
to light orange. e 25 least-vulnerable countries have normed scores ranging from
0.82 (Italy) to 1.0 (normed maximum value, for Norway).
26 Identifying Future Disease Hot Spots
Several notable trends emerge from the full results of our model (found in
Appendix A)—including overall index scores and domain-specic scores across all 195
countries—that can inform our understanding and possible responses. e rst and
most evident trend is the presence of conict or recent conict among more-vulnerable
countries. Seven of the ten most-vulnerable countries are in current conict zones
(Somalia, Central African Republic, South Sudan, Afghanistan) or have had a history
of recent conict (Angola, Madagascar, Chad). Certainly, conict undermines the
strength of a country’s health system and often reects weak, divided, or even failed
government. Because resources are destroyed in conict and trained professionals
are incentivized to leave, conict further exacerbates existing problem areas, creating
potential infectious disease hot spots.
Another concerning trend, which is already somewhat apparent in Figure4.1
but shown more clearly in Figure4.2, is geographic in nature: 24 of the 30 most-
vulnerable countries form a solid, near-contiguous belt from the edge of West Africa
in Mauritania, the Gambia, and Guinea through the Sahel countries of Mali, Niger,
Chad, and Sudan to the Horn of Africa in Somalia—a disease hot spot belt.
Were a communicable disease to emerge within this chain of countries, it could
easily spread across borders in all directions, abetted by high overall vulnerability and
Figure4.1
Infectious Disease Vulnerability Index World Map
RAND RR1605-4.1
Normed score
0.000 1.000
Results 27
Table4.1
25 Most-Vulnerable Countries
Rank
Combatant
Command Country or Territory Normed Score
1AFRICOM Somalia 0.000000
2AFRICOM Central African Republic 0.000061
3AFRICOM Chad 0.098450
4AFRICOM South Sudan 0.100 836
5AFRICOM Mauritania 0.107294
6AFRICOM Angola 0.148414
7SOUTHCOM Haiti 0.149 471
8CENTCOM Afghanistan 0 .157034
9AFRICOM Niger 0.166531
10 AFRICOM Madagascar 0.17 0787
11 AFRICOM Democratic Republic of the Congo 0 .181762
12 AFRICOM Mali 0.18 4254
13 AFRICOM Guinea-Bissau 0.187 841
14 AFRICOM Benin 0.206682
15 AFRICOM The Gambia 0.207809
16 AFRICOM Liberia 0.213114
17 AFRICOM Guinea 0.213225
18 AFRICOM São Tomé and Príncipe 0.223256
19 AFRICOM Sierra Leone 0.223397
20 AFRICOM Burkina Faso 0.2 31504
21 AFRICOM Comoros 0.238068
22 CENTCOM Ye me n 0.250277
23 AFRICOM Eritrea 0.252978
24 AFRICOM To go 0.259396
25 AFRICOM Mozambique 0.262501
NOTES: The color shading runs from deep red (most vulnerable) to deeper
green (least vulnerable). SOUTHCOM = U.S. Southern Command; CENTCOM =
U.S. Central Command.
28 Identif ying Future Disease Hot Spots
a string of weak national health systems along the way. Disease could also easily spread
to the south of Africa through the vulnerable border states of DRC and Angola and
to the greater Middle East from South Sudan, Eritrea, or Somalia through the gate-
way of Yemen. ough we have seen modern diseases rapidly transmitted all over the
world through interconnected travel, it is these vulnerable states with porous borders
and weak or conict-aected neighbors that face the greatest risks and potential health
challenges. As we have already witnessed with Ebola, it would not be long before these
Figure4.2
Infectious Disease Hot Spot Belt
RAND RR1605-4.2
Malawi
Côte
d’Ivoire
Yemen
Togo
Sudan
South
Sudan
South
Africa
Somalia
Sierra Leone
Saudi Arabia
Iran
Iraq
Nigeria
Niger
Mozambique
Mauritania
Mali
Libya
Liberia
Guinea
Guinea-Bissau
Eritrea
Egypt
Democratic
Republic of
the Congo
Chad
Central
African Republic
Burkina Faso
Benin
Angola
Algeria
The Gambia
Results 29
developing-world health problems appeared on the doorstep of the developed world.
Solutions could be far more quickly and cost-eectively implemented in a preemptive
fashion than a purely reactive one.
Least-Vulnerable Countries
Not surprisingly, the 25 least-vulnerable countries (i.e.,those ranked numbers 171–195
among the 195 countries examined; see Table4.2) are all highly developed nations in
Europe, North America, and Asia-Pacic with robust democracies, economies, and
health systems. e normed scores for these countries range from 0.82 (Italy) to 1.0
(Norway), and they are all shaded dark green in the red-orange-yellow-green spectrum.
e six least-vulnerable countries (i.e.,ranks 190 to 195) include all four Scandinavian
countries, which are at the top of many composite indicators, such as the Human
Development Index, and Germany and Canada; these are followed by Japan (189) and
the United States (188). In comparing the 25 least-vulnerable countries against the
next 25 and those progressively more vulnerable, the 25 least-vulnerable countries tend
to have larger medical workforces and medical expenditures; better health indicators;
less corrupt and more-stable (usually democratic) governments; better human rights;
and stronger economic development, transportation infrastructure, and technological
sophistication.
Results from the Sensitivity Analysis
Notwithstanding our extensive literature review and vetting of baseline weights
through multiple rounds of analysis and discussion, we realize that there remains
room for continued discussion in this area. To further validate our results, we systemati-
cally adjusted the domain weights to assess the nature and degree of the changes in
our results. First, to test the eect of any weighting of parameters, we assigned a value
of 1.0 to every parameter weight—eectively eliminating the subjective weighting
scheme—to examine the eect on the country vulnerability rankings. en, to assess
changes in the relative weight of the dierent domains and compare those results with
our baseline scenario, we systematically doubled, tripled, and zeroed out each domain
weight, leaving all parameter weights at baseline values and all other domains weighted
at a value of 1.0. e results of all these sensitivity tests are presented in Table4.3.
As shown in Table4.3, the reweighting of all parameter values to 1.0 (i.e.,
functionally eliminating weighting) resulted in four countries rising into the top 25
most vulnerable: Democratic People’s Republic of Korea (North Korea), which rose
from 46 to 3, the Republic of the Congo (Congo-Brazzaville), which rose from just
outside the top 25, at 26, to 15; Sudan, which rose from 30 to 21; and Côte d’Ivoire,
which rose from 28 to 24. e four countries that fell out of the top 25 most vulnerable
were São Tomé and Príncipe (18 to 41), Sierra Leone (19 to 27), Burkina Faso (20 to
30 Identifying Future Disease Hot Spots
Table4.2
25 Least-Vulnerable Countries
Rank
Combatant
Command Country or Territory Normed Score
171 EUCOM Italy 0.821690
172 EUCOM Czech Republic 0. 847175
173 EUCOM France 0.8554 07
174 EUCOM Belgium 0.870933
175 EUCOM Austria 0.874243
176 EUCOM Spain 0.875475
177 EUCOM Luxembourg 0.875694
178 PACOM Singapore 0.878289
179 PACOM Republic of Korea (South Korea) 0.879402
180 EUCOM Portugal 0.888782
181 EUCOM United Kingdom 0.897495
182 EUCOM Ireland 0.906320
183 EUCOM Iceland 0.908112
184 PACOM Australia 0 .912517
185 EUCOM Switzerland 0.915 839
186 PACOM New Zealand 0.916279
187 EUCOM Netherlands 0.918935
188 NORTHCOM United States 0.924939
189 PACO M Japan 0.926410
190 EUCOM Denmark 0.953641
191 EUCOM Sweden 0.955625
192 EUCOM Germany 0.966890
193 EUCOM Finland 0 .96 8274
194 NORTHCOM Canada 0.973400
195 EUCOM Norway 1.000000
NOTES: The color shading runs from deep red (most vulnerable) to deeper green
(least vulnerable). EUCOM = U.S. European Command; NORTHCOM = U.S. Northern
Command; PACOM = U.S. Pacic Command.
Results 31
Table4.3
Results of Sensitivity Testing: Adjusting Domain Weights
Baseline
Rank
Country or
Territory
Rank After Adjusting Weight as Indicated
Unweighted
(all
weights = 1)
Demographic Health Care Public Health
Disease
Dynamics
Political -
Domestic
Political -
International Economic
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
1 Somalia 5 2 2 1 2 2 1 1 1 2 2 2 1 1 1 2 1 1 2 2 2 1
2 Central
African
Republic
1 112 112 221 112 221 221 112
3Chad 6 444 335 453 443 444 333 335
4South Sudan 2 335 463 564 334 336 444 443
5Mauritania 12 563 554 3316 555 573 555 574
6Angola 17 912 6 6 4 10 7 7 11 786 899 667 896
7Haiti 8 8 8 7 8 11 6 898 668 7611 776 667
8Afghanistan 16 779 78813 17 5 9 12 7 6 5 16 888 759
9Niger 14 6 5 13 10 12 9 9 10 15 10 10 911 15 710 10 910 10 8
10 Madagascar 912 19 813 16 7 6 4 35 8 7 10 14 19 5 9 9 10 9 8 11
11 Democratic
Republic of
the Congo
415 17 10 9 7 15 17 20 611 912 9 8 19 11 11 11 11 11 12
12 Mali 13 10 912 11 914 11 12 12 13 13 11 12 16 10 12 13 12 13 13 10
13 Guinea-
Bissau
20 13 13 11 12 13 11 14 16 912 11 13 10 11 14 13 12 13 12 12 13
32 Identif ying Future Disease Hot Spots
Baseline
Rank
Country or
Territory
Rank After Adjusting Weight as Indicated
Unweighted
(all
weights = 1)
Demographic Health Care Public Health
Disease
Dynamics
Political -
Domestic
Political -
International Economic
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
14 Benin 18 11 11 18 15 14 17 12 11 26 14 15 16 25 30 815 16 14 16 17 14
15 The Gambia 11 16 15 16 18 19 13 15 14 19 16 18 14 17 17 13 14 15 15 15 15 15
16 Liberia 22 17 14 17 17 17 16 16 15 21 15 17 17 18 18 15 17 17 16 17 16 17
17 Guinea 10 14 10 21 16 15 18 22 26 719 19 15 13 13 21 16 14 17 14 14 18
18 São Tomé
and Príncipe
41 20 21 14 21 25 12 10 846 17 14 20 26 29 12 19 19 18 20 22 16
19 Sierra Leone 27 18 18 20 14 10 25 20 22 14 18 16 19 19 23 17 18 18 19 18 18 19
20 Burkina Faso 26 19 16 24 19 18 21 19 19 22 21 22 18 24 26 18 21 21 20 19 20 20
21 Comoros 19 22 26 15 20 20 22 18 18 28 20 21 22 22 25 20 20 20 21 21 21 21
22 Yem en 25 23 24 22 27 28 19 21 21 29 27 29 21 16 12 32 23 23 22 24 25 22
23 Eritrea 727 27 19 26 27 20 28 30 10 23 24 23 15 10 34 22 22 23 22 19 28
24 Tog o 23 25 25 25 25 24 24 27 28 17 22 20 25 23 21 27 24 24 25 23 23 24
25 Mozambique 35 24 22 26 24 23 26 23 25 27 24 23 24 29 32 22 25 25 24 25 24 27
Table 4.3—Continued
Results 33
Baseline
Rank
Country or
Territory
Rank After Adjusting Weight as Indicated
Unweighted
(all
weights = 1)
Demographic Health Care Public Health
Disease
Dynamics
Political -
Domestic
Political -
International Economic
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
Double
Triple
Zero
Not in Baseline, Top 25
46 Democratic
People’s
Republic of
Korea (North
Korea)
324 24
26 Republic of
the Congo
(Congo-
Brazzaville)
15 23 23 24 24 25 25 21 20 23
30 Sudan 21 18 20 14
28 Côte d’Ivoire 24 21 20 23 22 13 25
27 Nigeria 23 22 21 20 24
29 Malawi 25 23 23
58 Equatorial
Guinea
13
50 Cameroon 23
39 Burundi 25
51 Syria 22
34 Senegal 25
NOTE: Green cell = country originally among 25 most vulnerable, moved out of top 25; red cell = countr y originally not among top 25 most vulnerable,
moved into top 25.
Table 4.3—Continued
34 Identifying Future Disease Hot Spots
26), and Mozambique (25 to 35). Within the top 25, several countries increased in
vulnerability (e.g.,South Sudan, DRC, the Gambia, Guinea, Eritrea), while others
fell to lower levels among the top 25. While all of these countries are no doubt
highly vulnerable to infectious disease, the notable drop of seemingly more at-risk
countries, such as Mauritania, Angola, and Afghanistan, suggest that our initial, sub-
jective weighting better captured both the absolute and relative vulnerability of these
countries—i.e.,the baseline weighting scheme appeared to be a better reection of risk
than the completely unweighted scheme.
As shown in Table4.3, the doubling, tripling, and zeroing out of each domain
weight (which acted as a multiplier for the variable weights for the parameters within the
domain) did little to change the composition of the top-25 most-vulnerable countries.
Fourteen of the 16 most-vulnerable countries remained within the top 25 across all
perturbations of domain weights. For the others, they mostly fell slightly out of the
top 25 (seven of the 37 changes kept countries within the top-30 most vulnerable,
replaced by countries for which baseline ranks were only slightly outside the top25,
with just a few exceptions). ere were two particularly striking ndings from our
sensitivity analyses. First, no country fell out of the top-25 most vulnerable with
doubling, tripling, or zeroing out the domain weight for the political-international
domain, suggesting that this domain may contribute least to overall vulnerability.
Second, zeroing out the public health domain weight resulted in the largest number
of countries—six—falling out of the top 25, suggesting the greatest sensitivity to
elimination of that domain from our algorithm. Otherwise, zeroing out other domain
weights resulted in very little change to the top-25 most-vulnerable countries.
From these sensitivity tests, we concluded that the results are not strongly driven
by a single factor or the further weight assigned to any specic domain. For the most
part, the top-25 most-vulnerable countries remained in that range across each change
in weighting values; the great majority of changes kept countries within the top-30
most vulnerable. us, the minimal variance resulting from our sensitivity testing
would seem to validate the robustness of our tool and the resulting vulnerability scores.
Implications of the Findings
Our ndings suggest that a broad range of factors collectively shapes a country’s
resilience to infectious diseases, rather than a single factor or domain alone. is
rst became evident while examining the connection between a country’s overall
vulnerability score to its levels of economic strength and development. Some countries
outperformed their economic indicators—their overall normed vulnerability score
was better (i.e.,higher value) than their normed economic score alone might have
predicted. A few of the 25 least-vulnerable (i.e.,most resilient) countries were able to
achieve this: Portugal and Japan in particular were more resilient than their economic
Results 35
domain scores alone might have suggested. For these two countries, the very positive
score for the public health domain drove the overall score toward greater resilience.
is indicates that the countries’ health systems outperformed measures of income per
capita and economic productivity in terms of predicting the degree of vulnerability to
disease outbreaks. More relevant to the case of developing nations, however, may be
the countries among the 50 most vulnerable for which the overall vulnerability scores
were also substantially better than the economic domain scores, such as Eritrea (23),
Malawi (29), Zimbabwe (35), Lesotho (38), and Burundi (39), among others. Table4.4
presents the top-ten countries (among the 50 most vulnerable) that outperformed their
economic domain score, as shown by the relatively large positive dierence between the
overall normed score and the economic domain score. e better overall vulnerability
for most of these countries was driven more by the very low economic domain score
rather than a consistently higher score in another specic domain. However, as with
Portugal and Japan, the high (good) public health domain scores for Ethiopia and
Cameroon drove up the overall vulnerability score (toward greater resilience and lower
vulnerability). Vulnerable countries with weak health systems might look to these
countries as examples of ways in which health systems were improved with relatively
fewer resources, resulting in expectations for lower vulnerability to disease outbreaks.
Table4.5 presents the bottom 11 countries, with the largest dierences in the
opposite direction—i.e.,whose overall normed scores were even worse than predicted
Table4.4
Countries Among Top 50 Most Vulnerable That Outperform Their Economic Indicators
Rank for
Outperformance
of Economic
Domain Score
Country or
Territory
Rank—Overall
Vulnerability
Score
Normed
Overall
Score
Normed
Economic
Domain
Score
Difference
Between Overall
and Economic
Domain Scores
1Burundi 39 0.3541 0.0181 0.3360
2 Democratic
People’s Republic
of Korea (North
Korea)
46 0.3749 0.0901 0.2848
3Eritrea 23 0.2530 0.0000 0.2530
4Zimbabwe 35 0.3375 0 .1024 0.2350
5Rwanda 42 0.3553 0 .1253 0.2300
6Ethiopia 47 0.3820 0.1568 0.2253
7Uganda 44 0.3659 0 .14 42 0.2216
8Lesotho 38 0.3449 0.1247 0.2202
9Cameroon 50 0.3888 0.1755 0.2133
10 Malawi 29 0.2800 0.0831 0.19 69
36 Identif ying Future Disease Hot Spots
by their normed economic domain scores (dierences in the negative direction). is
list ranges from wealthy countries whose overall vulnerability scores did not match their
economic scores (e.g.,Brunei, Kuwait, United Arab Emirates, Luxembourg, Singapore)
to poorer countries for which the noneconomic domains drove the vulnerability scores
to even lower values, indicating greater vulnerability overall (e.g.,Equatorial Guinea,
South Sudan, Somalia, Gabon).
One important caveat that bears mention is that increased spending on public
health, in the short run, that compromises long-run gains in economic growth and
development could be detrimental to vulnerability to infectious disease in the future.
If the public health sector is to be shored up so that current disease vulnerability is
reduced, the government must try to do so in a way that does not compromise economic
gains, which are also important to long-run resiliency.
Zika and Ebola as Empirical Examples
We examined the cases of the Zika virus in the Americas (2015–2016) and the Ebola
virus in Africa (2014–2015) as empirical examples to assess the value and potential
limitations of our Infectious Disease Vulnerability Index.
Table4.5
Countries That Most Underperform Their Economic Indicators
Rank for
Outperformance of
Economic Domain
Score
Country or
Territory
Rank—Overall
Vulnerability
Score
Normed
Overall
Score
Normed
Economic
Domain Score
Difference
Between Overall
and Economic
Domain Scores
1Brunei 159 0.7629 0.8742 -0.1114
2Kuwait 131 0.6649 0.7724 -0.10 76
3 United Arab
Emirates
161 0.7652 0.8611 -0.0959
4Equatorial Guinea 58 0.4301 0.5190 -0.0890
5Luxembourg 177 0.8757 0.9627 -0.0870
6Singapore 178 0.8783 0.9642 -0.0859
7South Sudan 40.1 00 8 0.1866 -0.0858
8Turkmenistan 66 0.4867 0.5680 -0 .0 813
9 Somalia 1 0.0000 0.0757 -0.0757
10 Gabon 52 0.4030 0.4682 -0.0653
11 Taiwan 146 0.7097 0.7659 -0.0562
Results 37
Zika virus spread rapidly across the Americas in 2015–2016, aecting all South
American countries except Chile (which does not have the mosquito vector). By March
2016, Brazil and Colombia had reported the largest number of cases. Interestingly,
results from our tool indicate that all South American countries outperformed their
economic indicators—i.e.,their overall vulnerability scores were better than what would
have been predicted from their economic scores alone. While Brazil and Colombia do
not necessarily stand out in terms of overall infectious disease vulnerability (as seen
in Figure4.3 and in the full scores reported in Appendix A), they both have certain
factors that make them susceptible to a disease like the Zika virus. Zika ourishes
in population-dense areas with abundant stagnant or standing water in which the
disease-carrying mosquitoes can lay their eggs. In Brazil and Colombia, there are large
urban slums that feature poor sanitation and hygiene conditions that are ripe for Zika
to spread. Despite those countries’ overall satisfactory performances in the tool, the
high levels of inequality in Brazil and Colombia (both of which boast two of South
America’s highest Gini coecients—a common measure for inequality within a given
country) mean that health conditions and services vary widely within these countries.
Our model does not evaluate income inequality within countries, so this may be a
factor driving the rapid spread of the disease in underserved areas. In fact, both Brazil
and Colombia score poorly for government service provision relative to the rest of South
America. Poor service provision combined with high inequality and a fertile environ-
ment for a particular disease provide the conditions needed for a Zika virus outbreak.
e Zika virus example illustrates an important benet of our tool as well as a
limitation. e tool is designed to show vulnerability. at is, it will highlight which
states are least likely to be able to cope with a serious disease outbreak within their
country. It does not, however, strictly predict the likelihood of a disease arriving in a
country or what will actually happen once it does arrive. Brazil and Colombia, because
of unique economic and demographic challenges as well as the high prevalence of the
mosquito vector, have experienced high numbers of Zika cases. West African countries
were at greater risk for the spread of Ebola. What our model does show is which
countries will be better placed to react to such risks—more resilient—and with what
resources, though new diseases present new and dierent challenges, as the world
health community witnessed with both the Ebola and Zika viruses.
We used the Ebola experience in West Africa as an empirical case to compare
the three most-aected countries (Guinea, Liberia, Sierra Leone) with the four
countries that more successfully contained the outbreak and limited the number of
cases (Nigeria, DRC, Senegal, Mali), examining both the results from our algorithm
and other factors that might have contributed to the outcomes observed. All three
heavily aected countries and two of the four more-successful countries were among the
25 most-vulnerable countries (see Table4.1: Nigeria ranked 27 and Senegal ranked 34).
Table4.6 summarizes the key dates and number of cases for all seven African
countries experiencing Ebola during 2014, listed in order of date of rst reported case.
38 Identifying Future Disease Hot Spots
Figure4.3
Map of Infectious Disease Vulnerability for Brazil and Its Neighbors
NOTE: The color shading runs from deep red (most vulnerable) to deeper green (least vulnerable).
RAND RR1605-4.3
Venezuela
Uruguay
Suriname
Peru
Paraguay
Ecuador
Colombia
Chile
Brazil
Bolivia
Argentina
Normed score
0.000 1.000
Results 39
Of note, initial cases in Guinea, Liberia, and Sierra Leone preceded initial cases in
the other four countries by at least two months. e WHO’s declaration of Ebola as a
public health emergency of international concern in early August 2014 preceded initial
cases in three of those four countries (WHO, 2014a). ose countries might have been
better sensitized to the possibility of Ebola and better able to respond quickly given the
knowledge of the potential scope of the problem and the readiness of the international
community to provide timely support. e later timing of cases in those countries might
have contributed to their success with the Ebola outbreak, even though, according to
our vulnerability index, they are among the most-vulnerable countries in the world.
Table4.7 presents the outputs from our algorithm for these seven countries—the
normed scores overall, the scores for each domain, and the scores that reect countries’
degrees of core public health capacity under the WHO’s IHR. In line with the events
as they unfolded during the Ebola 2014 outbreak, the average vulnerability rank
for the three most heavily aected countries was 17, compared with 21 for the four
more-successful countries. Across these seven countries, the three most heavily aected
noticeably scored lower than the other four in the economic and political-international
domains. e other domains, including health care, public health (including the
composite IHR core capacity indicator), and political-domestic, did not neatly distin-
guish between the countries ultimately heavily aected by Ebola and those that more
successfully responded in 2014. e timing of initial cases seems to best distinguish
between the three heavily aected and four more-successful countries.
Table4.6
Outbreak Summary for the Seven African Countries Experiencing Ebola in 2014
Country
Date of
First Case
Date Declared
Ebola-Free Tot al C as es Total Deaths
Guinea (D ecemb er 2 013)
March 23, 2014
December 29, 2015 3,804 2,536
Liberia March 29, 2014 January 14, 2016 19,675 4,808
Sierra Leone May 25, 2014 March 17, 2016 14,12 2 3,955
Nigeria July 23, 2014 October 20, 2014 20 8
Democratic Republic
of the Congo
August 24, 2014 November 21, 2014 66 49
Senegal August 29, 2014 October 17, 2014 1 0
Mali Oc tober 23, 2014 January 18, 2015 8 6
SOURCE: CDC, 2016.
NOTE: The rst case in Guinea occurred in December 2013 but was not recognized and diagnosed as
Ebola until March 2014.
40 Identifying Future Disease Hot Spots
Table4.7
Summary of Vulnerability Scores for African Countries Experiencing Ebola in 2014
Rank
Country or
Territory
Normed Score
Overall
Demographic
Domain
Health Care
Domain
Public Health
Domain
Disease
Dynamic
Domain
Political-
Domestic
Domain
Political-
International
Domain
Economic
Domain
IHR
Composite
(factor score)
Heavily affected countries
16 Liberia 0.2131 0.19 62 0.2952 0.2980 0.3996 0.2429 0.3258 0.0958 0.5079
17 Guinea 0. 2132 0.0692 0.2207 0.4222 0.6631 0.1821 0.11 60 0.0242 0.5892
19 Sierra Leone 0.2234 0.2037 0.0556 0.3993 0.2549 0.2721 0.4294 0.10 87 0.6882
More-successful countries
11 Democratic
Republic of
the Congo
0.1818 0.3934 0 .1213 0.3694 0.3789 0 .10 94 0.3681 0.0530 0.7118
12 Mali 0.1843 0.1792 0.13 80 0.2497 0. 4911 0.2494 0.4501 0.149 6 0.5079
27 Nigeria 0.2707 0.3232 0.188 7 0.4070 0 .313 7 0.2655 0.5422 0 .2011 0.5686
34 Senegal 0.3292 0.169 0 0.3633 0.3844 0. 3619 0.4284 0.4952 0.18 43 0.5975
NOTE: The color shading runs from deep red (most vulnerable) to deeper green (least vulnerable).
Result s 41
Interestingly, DRC and Mali, which were among the more-successful countries,
appear to be more vulnerable than all three of the heavily aected countries. It is
important to keep in mind, however, what the vulnerability index scores and rankings
are meant to communicate. ey are meant to indicate the level of vulnerability (or
resilience) in a hypothetical scenario and suggest where extra eort might be needed to
successfully address an infectious disease challenge, such as Ebola. ey do not predict
what will actually happen in the face of an outbreak. For example, DRC’s success in
containing the Ebola outbreak has been partly attributed to the robust and extensive
response plans it already developed from its experiences with six previous outbreaks
(WHO, 2014b). Nigeria’s success came from several positive response elements, includ-
ing prompt identication of the index case, strong government response, successful
mobilization of internal and external funds, and eective coordination of response
eorts (Chamberlin etal., 2015).
An interesting lesson from the examination of Ebola-aected countries in 2014
is that the expected level of vulnerability of a country does not sentence it to fail in
controlling an infectious disease outbreak. Targeted, timely, and culturally sensitive
interventions in public health, health care, incident management, and governance, as
well as prompt global aid response, can help in mitigating an infectious disease out-
break, as seen in the examples of Nigeria, Senegal, and Mali (Chamberlin etal., 2015).
Furthermore, the success of a country during the recent Ebola outbreak is not
necessarily a testament to the extent of the country’s resilience. For example, while
Nigeria successfully controlled the Ebola outbreak within three months, with a total of
20 deaths, it has more recently been battling another infectious hemorrhagic illness—
Lassa fever. Although not as virulent as Ebola, more than 80 deaths were recorded
between August 2015 and January 2016 (WHO, 2016). As discussed, while eective
response interventions may help mitigate the spread of the outbreak, there may still be
glaring vulnerabilities that our tool exposes systematically.
Hence, this tool is designed to be more of an aid to suggest countries that may be
especially vulnerable and thus potential priorities for health engagement and capacity-
building activities, such as training and equipping laboratories, strengthening biosur-
veillance, or pandemic preparedness exercises. e tool does not strictly predict success
or failure in outbreak response.
43
CHAPTER FIVE
Conclusions and Next Steps
Because of the increasing risk to developing and developed countries alike posed
by a range of infectious diseases, it is essential to have a clear understanding of
current vulnerabilities at the country level across the globe—where the most-vulnerable
countries are and what contributes most to their vulnerabilities. e Ebola crisis and
the numerous infectious disease threats before it made it patently clear that infectious
diseases do not respect political borders, nor do they remain contained in certain regions
for very long in a hyper-connected world. RAND researchers developed the Infectious
Disease Vulnerability Index as a tool to help identify countries that are potentially most
vulnerable to poorly controlled infectious disease outbreaks because of a conuence of
factors ranging across multiple domains, including political, economic, public health,
medical, demographic, and disease dynamics. is information can help international
actors—including bilateral, multilateral, and organizational partners—prioritize their
respective programming to work with vulnerable countries to address weaknesses
proactively, before problems emerge and get out of hand, and certainly to rally quickly
to oer support to them when a disease threat does emerge. is report supports the rec-
ommendations of Gelfeld etal., 2015, in taking the next logical step to develop a more
rigorously and quantitatively based tool to help assess the vulnerability and resilience
of countries to infectious diseases. While the index cannot predict the occurrence or
response to outbreaks, it does point to countries that are most vulnerable to such threats,
for purposes of proactive programming to build country capabilities and the timely
oering of support to response eorts once an outbreak emerges.
RAND recommends that DoD, HHS, other U.S. government agencies, and other
international partners use this tool to inform their programming—to help identify
vulnerable countries and set priorities for helping those countries build the capabilities
they need to combat potential transnational disease outbreaks. As our results indicate,
there are countries within certain regions that are more vulnerable, and the potential
for infectious diseases to spread rapidly across highly vulnerable, contiguous countries
merits serious attention. e “infectious disease belt” that stretches from West Africa
to the Horn of Africa is particularly concerning.
e index highlights the connections between political stability and governance,
economic development, and disease vulnerability. With this information in mind
44 Identifying Future Disease Hot Spots
and by working with these and other vulnerable countries to improve health systems,
governance, and development outcomes, the international community can help shore
up the world’s defenses against diseases and foster the cooperative and communicative
networks that will lead to better, more-coordinated disease response. For example, the
U.S. government and its associated departments and agencies (e.g.,DoD, USAID, and
HHS, including the CDC) can work with governments of states that are particularly
vulnerable in order to improve their public health systems (e.g.,disease surveillance,
laboratory testing, outbreak detection, rapid response teams for investigation and
disease control measures) and medical care systems (e.g., professional training and
certication, clinic and hospital care). Aid organizations such as USAID also support
economic development and eorts to strengthen governance. For example, better
governance through democracy-promotion and anticorruption programs may lead to
less vulnerability as states improve coordination, communication, and infrastructure
systems that help to combat infectious disease transmission. Finally, exercises, includ-
ing tabletop exercises, can be used to help countries better understand actions and
actors, and the coordination needed among them, to best prepare their systems to
respond eectively to a disease threat that arises.
Given that resources for such endeavors are often scarce, we should take les-
sons from countries whose disease resilience has outperformed their level of economic
development to nd cost-eective, context-appropriate solutions that will work in these
challenging environments. In Rwanda, for example, improved governance and admin-
istration of aid, alongside reduced corruption, has allowed for higher vaccination rates
and greater investment in public health and community health care services (Hamblin,
2014).
While we have shown that this tool is robust to signicant changes in weights across
all domains, we designed it to be interactive: End users can change the weights to reect
their beliefs or changing realities on the ground. e Infectious Disease Vulnerability
Index is intended to inform actions addressing infectious disease preparedness and
response to foster greater resiliency of national, regional, and global systems. We have
already witnessed the devastating results of a reactive approach to infectious disease
control during the Ebola crisis. e international community would do well to take
more-extensive, preemptive measures to address the vulnerability at the country level
in advance of future disease crises.
45
APPENDIX A
Overall Country Rankings
Table A.1 presents, in ranked order, the normed overall scores for the 195 countries
examined, from most vulnerable (i.e., lowest score) to least vulnerable (highest score).
e color shading runs from deep red (most vulnerable) through orange and yellow to
light and deeper green (least vulnerable).
46 Identifying Future Disease Hot Spots
Table A.1
Overall Country Rankings
Rank
Country or
Territory
Combatant
Command
Overall
Score
Normed
Demographic
Domain
Score
Health Care
Domain
Score
Public
Health
Domain
Score
Disease
Dynamics
Domain
Score
Political-
Domestic
Domain
Score
Political-
International
Domain
Score
Economic
Domain
Score
1 Somalia AFRICOM 0.000000 0. 348761269 0.058301438 00.629775134 00.102 93 67 26 0.075656481
2 Central
African
Republic
AFRICOM 0.000061 0.15529298 00.045046954 0.548852541 0.053250913 0.315859162 0.005423805
3Chad AFRICOM 0.098450 0.21024639 0.0 49 051336 0.18881211 0.543405231 0.115586538 0.259990693 0.10676 029
4South Sudan AFRICOM 0.1 00836 0.0 07304932 0.231601644 0.18968877 0. 415040266 0.084988387 0.355877361 0.186605024
5Mauritania AFRICOM 0.107294 0.303529503 0.20019799 6 0.013730395 0.542586879 0.199660007 0.267845057 0.219330172
6Angola AFRICOM 0.148414 0.482859258 0.00080090 0.16 412 40 37 0.733107108 0.186137702 0.368890955 0.15088322
7Haiti SOUTHCOM 0.149 471 0.338437297 0.301310943 0.18504 92 8 0.50577236 0.126225355 0.400839569 0.082264511
8Afghanistan CENTCOM 0 .157034 0.1516373 25 0.199 60 7537 0. 3269 81557 0.707856514 0.073169519 0.362626952 0.077103098
9Niger AFRICOM 0.166531 00.254825107 0.1934135 72 0.573089179 0.269691628 0.41288 2392 0.095023518
10 Madagascar AFRICOM 0.17 0787 0.472647362 0.40269105 0.038220819 0.345020655 0.313667918 0.161904122 0.020461077
11 Democratic
Republic of
the Congo
AFRICOM 0 .181762 0.393402129 0.12133578 0.369430992 0.378852278 0 .10 93941 81 0.368097103 0.052965522
12 Mali AFRICOM 0.18 4254 0.179173821 0.138023501 0.2496769 0.4 911185 4 0.249390347 0.450073263 0.149 62 58 4 8
13 Guinea-Bissau AFRICOM 0.187 841 0.306480143 0.2515 06 69 0.283305699 0.347653436 0.18650 83 8 0.309947808 0.077079134
14 Benin AFRICOM 0.206682 0.102 42 92 04 0.2 05857213 0.198 58 7465 0.411218562 0.375126664 0.461867546 0.13270 0537
15 The Gambia AFRICOM 0.207809 0.223431605 0.330645436 0.250325387 0.531156832 0.250433571 0.26696682 0.074090725
16 Liberia AFRICOM 0.213114 0.196208895 0.295150785 0.298018392 0.399575501 0.2428 97448 0.3258 09636 0.095770949
Overall Country Rankings 47
Table A.1— Continued
Rank
Country or
Territory
Combatant
Command
Overall
Score
Normed
Demographic
Domain
Score
Health Care
Domain
Score
Public
Health
Domain
Score
Disease
Dynamics
Domain
Score
Political-
Domestic
Domain
Score
Political-
International
Domain
Score
Economic
Domain
Score
17 Guinea AFRICOM 0.213225 0.069168696 0.220723728 0.422224209 0.663071602 0.182076 73 6 0.116032771 0.024189584
18 São Tomé and
Príncipe
AFRICOM 0.223256 0.456315076 0.45499994 0.08756294 0.1991374 62 0.337999767 0.441651348 0.19 893970 4
19 Sierra Leone AFRICOM 0.223397 0.20367201 0.055611728 0.399252552 0.254903955 0.272075698 0.429440 571 0.1 08 72125 2
20 Burkina Faso AFRICOM 0.23150 4 0.141437335 0.237090446 0. 292575172 0.677980032 0.291887418 0.60014903 0.105762283
21 Comoros AFRICOM 0.238068 0.544585136 0.275448866 0.252023099 0.444342284 0.267647568 0.225866673 0.111809 438
22 Yem en CENTCOM 0.250277 0.433731647 0. 43165180 2 0.332075013 0.800080626 0.104453177 0.284363402 0.21272156
23 Eritrea AFRICOM 0.252978 0.51402076 8 0.410049982 0.45304433 0.482945814 0.0855761 0.09121531 0
24 Tog o AFRICOM 0.259396 0.405922983 0.301568399 0. 417204432 0 .15479948 3 0.210826371 0.273656224 0.115077926
25 Mozambique AFRICOM 0.262501 0.359585689 0.260453468 0.332816947 0 .3169115 72 0.313880616 0.463545305 0.108794995
26 Republic of
the Congo
(Congo-
Brazzaville)
AFRICOM 0.268887 0.570590666 0.437612936 0.316875454 0.28890352 0.17 80 93 753 0.30517 259 0.252505538
27 Nigeria AFRICOM 0.270681 0.323201474 0.18 87 28 449 0.40 7012765 0.313685957 0.265472276 0.5 42161976 0.201149401
28 Côte d’Ivoire AFRICOM 0.270743 0.149916663 0.198317769 0.480959396 0.293965416 0.244095631 0.2675316 0.214518 64
29 Malawi AFRICOM 0.279987 0.458224208 0.358702184 0.288097304 0.339690734 0. 3511682 0. 388453718 0 .083074415
30 Sudan AFRICOM 0.291580 0.588865802 0.33955302 0.494972938 0.568033919 0.086367456 0.236802875 0.173 8470 44
31 Djibouti AFRICOM 0.297892 0.516489993 0.308140349 0.360228068 0.494501563 0.275017476 0.4615 7537 9 0.186799289
32 Pakistan CENTCOM 0.308544 0.356313929 0.199267938 0.433297664 0.534440723 0.28 410 8175 0.399359647 0 .2920 54119
48 Identifying Future Disease Hot Spots
Table A.1— Continued
Rank
Country or
Territory
Combatant
Command
Overall
Score
Normed
Demographic
Domain
Score
Health Care
Domain
Score
Public
Health
Domain
Score
Disease
Dynamics
Domain
Score
Political-
Domestic
Domain
Score
Political-
International
Domain
Score
Economic
Domain
Score
33 Timor-Leste PACOM 0.31020 8 0.479566717 0.36300 6819 0.402401796 0.379571232 0.253405013 0.287238579 0.252405183
34 Senegal AFRICOM 0.329156 0.16895548 0.363332743 0.384433278 0.361880002 0.42 840 3911 0.4 9517516 9 0.184325671
35 Zimbabwe AFRICOM 0 .337478 0.697284238 0.343816002 0.485627407 0.653775962 0.19 93 49 79 4 0.410356268 0.102445378
36 Papua New
Guinea
PACOM 0.33918 4 0.525901322 0.358755958 0.461298605 0.131831164 0.302787534 0. 35 0931307 0.206087372
37 Tanzania AFRICOM 0.340445 0.515 88 4251 0.42046585 0.37514479 0.4 04598376 0.346796951 0.569472596 0.15939076
38 Lesotho AFRICOM 0.344860 0.699269239 0.193535354 0.3 81086761 0.494889941 0.403306048 0.485893078 0.124695851
39 Burundi AFRICOM 0 .35 4104 0.677770717 0.28284965 0.595976924 0.446364064 0.219291185 0.278333482 0.018062778
40 Laos PACO M 0. 35 5111 0.617197 869 0.303718578 0.470396949 0.316874844 0.303216875 0. 335972175 0.244014369
41 Cambodia PACOM 0.355133 0.603977974 0.501870 948 0.389329462 0.249954709 0.30794789 0.347829899 0. 268 33 9112
42 Rwanda AFRICOM 0.355300 0.485789716 0.462248927 0.428403858 0.316105011 0.3524 89703 0.416536032 0.125261052
43 Swaziland AFRICOM 0.358470 0.753558504 0.380812489 0.443187479 0.218933247 0.269991159 0.555068926 0.24 4366007
44 Uganda AFRICOM 0.365850 0.5886 81047 0.41351401 0.458546659 0.37389 8751 0.333622855 0. 332674624 0.144241411
45 Solomon
Islands
PACOM 0. 370311 0.707450052 0.534428456 0.376713 47 00.373395199 0.467899811 0.17 912 85 27
46 Democratic
People’s
Republic of
Korea (North
Korea)
PACOM 0.374870 0.907296141 0.613281963 0.586079979 0.611464602 0.054304985 00.090100341
47 Ethiopia AFRICOM 0.382021 0.28569545 0.39330463 0.622299852 0 .499217 243 0 .27 2946162 0.492468433 0.156768353
Overall Country Rankings 49
Table A.1— Continued
Rank
Country or
Territory
Combatant
Command
Overall
Score
Normed
Demographic
Domain
Score
Health Care
Domain
Score
Public
Health
Domain
Score
Disease
Dynamics
Domain
Score
Political-
Domestic
Domain
Score
Political-
International
Domain
Score
Economic
Domain
Score
48 Kenya AFRICOM 0.385436 0.610 453351 0.415088725 0.489142677 0.4 0613 032 5 0.3164 26717 0 .518 261714 0.20694373
49 Kiribati PACOM 0.388403 0.654475766 0.488085296 0.405577185 0.297866678 0.385759659 0.475592649 0.2051513 43
50 Cameroon AFRICOM 0.388770 0.519 03402 0.258084837 0.695554838 0.464468675 0.203603511 0.493377384 0.17 5481005
51 Syria CENTCOM 0.3 91337 0.766302674 0.625200685 0.600996701 0.624323285 0.0 401329 6 0.300755443 0.284843637
52 Gabon AFRICOM 0.402950 0.607270396 0.461092427 0.411908948 0.3915526 81 0.347626656 0.507560675 0.468207358
53 Nepal PACO M 0.404405 0.43755136 0.45526256 0.61031371 0.463079764 0. 278476918 0.285340204 0.207989533
54 Honduras SOUTHCOM 0.407296 0.65516817 8 0.568583253 0.402040524 0.401156848 0.371453871 0.548887432 0.271822983
55 Zambia AFRICOM 0.420459 0.43163488 0.365427589 0. 58 09 65131 0.52014 00 49 0.388834572 0.589087275 0.160896589
56 Bangladesh PACOM 0.422107 0.406154362 0.441798669 0.695174552 0 .4 6112604 5 0.259707056 0.364340005 0.176276411
57 Micronesia PACOM 0.425305 0.618409374 0.547928079 0.460395425 0.5414935 0.423698655 0.046091838 0.167556551
58 Equatorial
Guinea
AFRICOM 0.430054 0.86324907 0 .21161 08 92 0.637801825 0.400635817 0.215602062 0.220558685 0. 519 011312
59 Iraq CENTCOM 0.432182 0.503984852 0.515920977 0.701934137 0.502859325 0.168166417 0.402990831 0.320138576
60 Myanmar PACOM 0.4 48176 0.77384107 0.379347207 0.807929378 0.379456154 0.137665674 0.213666835 0.225276745
61 Palestine CENTCOM 0.4 50415 0.7436505 0.501813229 0.6423109 82 0.604772521 0.248676492 0.2 615214 04 0.223952424
62 Bhutan PACO M 0.460880 0.452080334 0.483140955 0.488859839 0.605787331 0.490043329 0.262023123 0.356907842
63 Ghana AFRICOM 0.462565 0.526023873 0.370472249 0.543649558 0 .4311 815 85 0.500767939 0.6166 86004 0.248491531
64 Guatemala SOUTHCOM 0.477179 0.628594639 0.518759849 0.579187293 0.575328055 0.367784506 0.52762574 0.347084061
65 Cape Verde AFRICOM 0. 48 6189 0.631853881 0.535080169 0.462030655 0.475776594 0. 521787662 0.538518732 0.347072706
50 Identif ying Future Disease Hot Spots
Table A.1— Continued
Rank
Country or
Territory
Combatant
Command
Overall
Score
Normed
Demographic
Domain
Score
Health Care
Domain
Score
Public
Health
Domain
Score
Disease
Dynamics
Domain
Score
Political-
Domestic
Domain
Score
Political-
International
Domain
Score
Economic
Domain
Score
66 Turkmenistan CENTCOM 0.486696 0.8 66733101 0.50185344 0.662080155 0.6754 31715 0.177345824 0.245358453 0.567962822
67 Namibia AFRICOM 0.490478 0.728321661 0.471954399 0.44645794 0.578010862 0.523755903 0.672243408 0.370783642
68 Vanuatu PACOM 0.490878 0.716342676 0.548938734 0.533251326 0.828054424 0.436297938 0.424226226 0.21720 03 62
69 Nicaragua SOUTHCOM 0.492491 0.638084587 0.566071409 0.660197738 0.232783952 0.364495143 0.442663666 0.291765645
70 Libya AFRICOM 0.493272 0.874139158 0.677912554 0.690021675 0.905000916 0.13 0266 55 0.266478037 0.354558564
71 India PACO M 0.493799 0.527272726 0.4116 87541 0. 6110 433 84 0.442952339 0.471933106 0.549585696 0.342247507
72 Algeria AFRICOM 0.49 6612 0.706 981418 0.558980692 0.611159 8 47 0.656533024 0.317721818 0.398632623 0.437323933
73 Dominican
Republic
SOUTHCOM 0.499533 0.651841382 0.52728257 0.561931812 0. 341170 0 8 0.4100 8651 0.751048964 0.487357273
74 Jamaica SOUTHCOM 0.499783 0.803953609 0.589106624 0.466499017 0 .19717428 6 0.474726908 0.544630277 0.474210427
75 Bolivia SOUTHCOM 0.500436 0.783350995 0.4 68876416 0.550798937 0.6 09 03124 6 0.436445721 0.4836 81062 0.359 40819
76 Tajikistan CENTCOM 0.507026 0.864911268 0.485530073 0.713000462 0.90740 4316 0. 237810489 0.349216002 0. 317040961
77 Uzbekistan CENTCOM 0.515492 0.9 2162 4713 0.576668339 0.664856243 0.853273942 0.245043684 0.310194908 0.383443552
78 Saint Lucia SOUTHCOM 0.516511 0.810879343 0.612550332 0. 4113 53 45 9 0.410794853 0.562974621 0.909730849 0.359239928
79 Bosnia and
Herzegovina
EUCOM 0.523079 0.913672874 0.730287281 0.477584357 0.3 28960 513 0.4335209 0.438477804 0.457486886
80 Egypt CENTCOM 0.530405 0.618601857 0.592082614 0.772762422 0.790643968 0.241970707 0.566291639 0.39614 8261
81 Venezuela SOUTHCOM 0.530692 0.897045602 0.619258291 0.690349672 0.39175468 0. 30915459 0.210215036 0.383245346
82 Tun isia AFRICOM 0.5354 51 0.696018316 0.636184194 0.554231019 0.776448056 0.434017521 0.545007557 0.450207048
83 Paraguay SOUTHCOM 0. 541167 0.887420329 0.585820732 0.596 008162 0.34557185 0.438463994 0.514414762 0.408028299
Overall Country Rankings 51
Table A.1— Continued
Rank
Country or
Territory
Combatant
Command
Overall
Score
Normed
Demographic
Domain
Score
Health Care
Domain
Score
Public
Health
Domain
Score
Disease
Dynamics
Domain
Score
Political-
Domestic
Domain
Score
Political-
International
Domain
Score
Economic
Domain
Score
84 Marshall
Islands
PACOM 0.544611 0.733122429 0.56898489 0.709406759 0.48890968 0.377615668 0.356184447 0.384185218
85 Philippines PACOM 0.544923 0.77982774 0.569482929 0.6075091 0.335072899 0.486973807 0.50580298 0.374891865
86 Lebanon CENTCOM 0.546332 0.795673254 0.703444198 0.598116034 0.6712 65282 0.359178936 0. 531402066 0.496274896
87 Botswana AFRICOM 0.548363 0.754044035 0.454855452 0.498290934 0.47607347 0. 614109149 0.663288916 0.470789877
88 Saint Vincent
and the
Grenadines
SOUTHCOM 0.5 49145 0.819932978 0.602106622 0.471895005 0.372237992 0.594954664 0.607374498 0.41767656
89 Azerbaijan EUCOM 0.550328 0.80274177 0.599807177 0.698233478 0.606958685 0.32056515 0.454723753 0.487222886
90 Belize SOUTHCOM 0.551546 0.771835129 0.602063419 0.649761602 0.463750568 0.48 6547326 0.705431728 0.181641854
91 Guyana SOUTHCOM 0.554987 0. 89113105 4 0.458067511 0.708 627172 0.296047164 0.416718306 0.628370039 0.391547761
92 Suriname SOUTHCOM 0.555320 0.954190989 0.606685909 0.590168054 0 .174 932096 0.4526610 43 0.59 820 3123 0.470173357
93 Kyrg yzs tan CENTCOM 0.555486 0.938771283 0.631595746 0.699298755 0.586997287 0.34098018 0.335903245 0. 331551371
94 Indonesia PACOM 0.562944 0.750619361 0.51111275 0.710992077 0.15555375 0.4786 630 61 0.445239925 0.395961649
95 Fiji PACOM 0.567238 0.912372761 0.550048845 0.793183785 0.338759095 0.315685028 0.457442072 0.404795695
96 Iran CENTCOM 0.567841 0.7844 83428 0.612522278 0.882104181 0 .6 24311112 0.198250398 0.230460389 0.44 2781114
97 Serbia EUCOM 0.568934 0.916239959 0.722244933 0.499384257 0.480581894 0.51660661 0.4 05971768 0.4936 0118
98 Morocco AFRICOM 0.569769 0.434646778 0.528876434 0.84548263 0.577067854 0.391024483 0.482941077 0.379778141
99 Sri Lanka PACOM 0.571001 0.824058144 0.711895651 0. 66 57118 87 0.324270477 0.403023718 0.408839796 0.464799504
100 Ecuador SOUTHCOM 0.575843 0.72 27 01117 0.595184777 0.707058492 0.6 78173471 0.428549787 0.427197843 0. 405 8118 0 4
52 Identif ying Future Disease Hot Spots
Table A.1— Continued
Rank
Country or
Territory
Combatant
Command
Overall
Score
Normed
Demographic
Domain
Score
Health Care
Domain
Score
Public
Health
Domain
Score
Disease
Dynamics
Domain
Score
Political-
Domestic
Domain
Score
Political-
International
Domain
Score
Economic
Domain
Score
101 Maldives PACOM 0.576299 0. 8418744 04 0.71580204 0.615057779 0.499207759 0.42243462 0.365921875 0.541604906
102 Samoa PACO M 0.580679 0.92150 8717 0.592935194 0.676569055 0.3663 83014 0.490973549 0.632269107 0.264505253
103 Colombia SOUTHCOM 0.583850 0.73590202 0.61253 4314 0.707762022 0.22902618