ArticlePDF Available

Assessing real-time Zika risk in the United States


Abstract and Figures

Background Confirmed local transmission of Zika Virus (ZIKV) in Texas and Florida have heightened the need for early and accurate indicators of self-sustaining transmission in high risk areas across the southern United States. Given ZIKV’s low reporting rates and the geographic variability in suitable conditions, a cluster of reported cases may reflect diverse scenarios, ranging from independent introductions to a self-sustaining local epidemic. Methods We present a quantitative framework for real-time ZIKV risk assessment that captures uncertainty in case reporting, importations, and vector-human transmission dynamics. Results We assessed county-level risk throughout Texas, as of summer 2016, and found that importation risk was concentrated in large metropolitan regions, while sustained ZIKV transmission risk is concentrated in the southeastern counties including the Houston metropolitan region and the Texas-Mexico border (where the sole autochthonous cases have occurred in 2016). We found that counties most likely to detect cases are not necessarily the most likely to experience epidemics, and used our framework to identify triggers to signal the start of an epidemic based on a policymakers propensity for risk. Conclusions This framework can inform the strategic timing and spatial allocation of public health resources to combat ZIKV throughout the US, and highlights the need to develop methods to obtain reliable estimates of key epidemiological parameters. Electronic supplementary material The online version of this article (doi:10.1186/s12879-017-2394-9) contains supplementary material, which is available to authorized users.
This content is subject to copyright. Terms and conditions apply.
R E S E A R C H A R T I C L E Open Access
Assessing real-time Zika risk in the United
Lauren A. Castro
, Spencer J. Fox
, Xi Chen
, Kai Liu
, Steven E. Bellan
, Nedialko B. Dimitrov
Alison P. Galvani
and Lauren Ancel Meyers
Background: Confirmed local transmission of Zika Virus (ZIKV) in Texas and Florida have heightened the need for
early and accurate indicators of self-sustaining transmission in high risk areas across the southern United States.
Given ZIKVs low reporting rates and the geographic variability in suitable conditions, a cluster of reported cases
may reflect diverse scenarios, ranging from independent introductions to a self-sustaining local epidemic.
Methods: We present a quantitative framework for real-time ZIKV risk assessment that captures uncertainty in case
reporting, importations, and vector-human transmission dynamics.
Results: We assessed county-level risk throughout Texas, as of summer 2016, and found that importation risk was
concentrated in large metropolitan regions, while sustained ZIKV transmission risk is concentrated in the
southeastern counties including the Houston metropolitan region and the Texas-Mexico border (where the sole
autochthonous cases have occurred in 2016). We found that counties most likely to detect cases are not necessarily
the most likely to experience epidemics, and used our framework to identify triggers to signal the start of an
epidemic based on a policymakers propensity for risk.
Conclusions: This framework can inform the strategic timing and spatial allocation of public health resources to
combat ZIKV throughout the US, and highlights the need to develop methods to obtain reliable estimates of key
epidemiological parameters.
Keywords: Zika, ZIKV, Importation risk, Autochthonous transmission risk
In February 2016, the World Health Organization (WHO)
declared Zika virus (ZIKV) a Public Health Emergency of
International Concern [1]. Though the Public Health
Emergency has been lifted, ZIKV still poses a great threat
for reemergence in susceptible regions in seasons to come
[2]. In the US, the 268 reported mosquito-borne autoch-
thonous (local) ZIKV cases occurred in Southern Florida
and Texas, with the potential range of a primary ZIKV
vector, Aedes aegypti, including over 30 states [35]. Of
the 2487 identified imported ZIKV cases in the US
through the end of August, 137 had occurred in Texas.
Given historic small, autochthonous outbreaks (ranging
from 4 to 25 confirmed cases) of another arbovirus
vectored by Ae. Aegyptidengue (DENV) [57], Texas
was known to be at risk for autochthonous arbovirus
transmission, and the recent outbreaks have highlighted
the need for increased surveillance and optimized re-
source allocation in the states and the rest of the vulner-
able regions of the Southern United States.
As additional ZIKV waves are possible in summer
2017, public health professionals will continue to face
considerable uncertainty in gauging the severity, geo-
graphic range of local outbreaks, and appropriate timing
of interventions, given the large fraction of undetected
ZIKV cases (asymptomatic) and economic tradeoffs of
disease prevention and response [811]. Depending on
the ZIKV symptomatic fraction, reliability and rapidity
of diagnostics, importation rate, and transmission rate,
the detection of five autochthonous cases in a Texas
county, for example, may indicate a small chain of cases
* Correspondence:
Equal contributors
Department of Integrative Biology, The University of Texas at Austin, Austin,
Full list of author information is available at the end of the article
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (, which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
( applies to the data made available in this article, unless otherwise stated.
Castro et al. BMC Infectious Diseases (2017) 17:284
DOI 10.1186/s12879-017-2394-9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
from a single importation, a self-limiting outbreak, or a
large, hidden epidemic underway (Fig. 1). These diver-
ging possibilities have precedents. In French Polynesia, a
handful of ZIKV cases were reported by October 2013; 2
months later an estimated 14,00029,000 individuals
had been infected [8, 9]. By contrast, Anguilla had 17
confirmed cases from late 2015 into 2016 without a sub-
sequent epidemic, despite large ZIKV epidemics in sur-
rounding countries [12]. To address the uncertainty, the
CDC issued guidelines for state and local agencies; they
recommend initiation of public health responses follow-
ing local reporting of two non-familial autochthonous
ZIKV cases [13].
Previous risk assessments of ZIKV have provided static
a priori assessments based on historical incidence and
vector suitability, but they do not provide dynamic risk
assessments as cases accumulate in a region. Here, we
present a framework to support real-time risk assess-
ment, and demonstrate its application in Texas. Our
framework accounts for the uncertainty regarding ZIKV
epidemiology, including importation rates, reporting
rates, local vector populations, and socioeconomic con-
ditions, and can be readily updated as our understanding
of ZIKV evolves. To estimate current and future epi-
demic risk from real-time ZIKV case reports, the model
incorporates a previously published method for estimat-
ing local ZIKV transmission risk and a new model for
estimating local importation risk. Across Texas254
counties, we find that the estimated risk of a locally sus-
tained ZIKV outbreak rises precipitously as autochthon-
ous cases accumulate, and that counties at the southern
tip of the Texas-Mexico border and in the Houston
Metropolitan Area are at the highest risk for ZIKV
transmission. This statewide variation in risk stems pri-
marily from mosquito suitability and socio-environmental
constraints on ZIKV transmission rather than heterogen-
eity in importation rates.
Our risk-assessment framework is divided into three sec-
tions: (1) county-level epidemiological estimates of ZIKV
importation and relative transmission rates, (2) county-
specific ZIKV outbreak simulations, and (3) ZIKV risk
analysis (Additional file 1: Figure S1). To demonstrate this
approach, we estimate county-level ZIKV risks throughout
the state of Texas for August 2016, given that, by May
2016, Texas experienced dozens of ZIKV importations
without subsequent vector-borne transmission.
Estimating importation rates
Our analysis assumes that any ZIKV outbreaks in Texas
originate with infected travelers returning from active
ZIKV regions. To estimate the ZIKV importation rate for
specific counties, we (1) estimated the Texas statewide im-
portation rate (expected number of imported cases per
day) for August 2016, (2) estimated the probability (import
risk) that the next Texas import will arrive in each county,
and (3) took the product of the state importation rate and
each county importation probability.
1. During the first quarter of 2016, 27 ZIKV travel-
associated cases were reported in Texas [5], yielding
a baseline first quarter estimate of 0.3 imported
cases/day throughout Texas. In 2014 and 2015,
arbovirus introductions into Texas increased
threefold over this same time period, perhaps
0 25 50 75 100
Time (days)
0 25 50 75 100
Time (days)
Fig. 1 ZIKV emergence scenarios. A ZIKV infection could spark (a) a self-limiting outbreak or (b) a growing epidemic. Cases are partitioned into
symptomatic (grey) and asymptomatic (black). Arrows indicate new ZIKV importations by infected travelers and vertical dashed lines indicate case
reporting events. On the 75th day, these divergent scenarios are almost indistinguishable to public health surveillance, as exactly three cases have
been detected in both. By the 100th day, the outbreak (a) has died out with 21 total infections while the epidemic (b) continues to grow with
already 67 total infections. Each scenario is a single stochastic realization of the model with R
= 1.1, reporting rate of 10%, and introduction rate
of 0.1 case/day
Castro et al. BMC Infectious Diseases (2017) 17:284 Page 2 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
driven by seasonal increases in arbovirus activity in
endemic regions and the approximately 40% increase
from quarter 1 to quarter 3 in international travelers
to the US [14]. Taking this as a baseline (lower
bound) scenario, we projected a corresponding
increase in ZIKV importations to 0.9 cases/day
(statewide) for the third quarter.
2. To build a predictive model for import risk, we fit a
probabilistic model (maximum entropy) [15]of
importation risk to 183 DENV, 38 CHIKV, and 31
ZIKV Texas county-level reported importations from
2002 to 2016 and 10 informative socioeconomic,
environmental, and travel variables (Additional file 1:
section 1.1). Given the geographic and biological
overlap between ZIKV, DENV and Chikungunya
(CHIKV), we used historical DENV and CHIKV
importation data to supplement ZIKV importations
in the importation risk model, while recognizing that
future ZIKV importations may be fueled by large
epidemic waves in neighboring regions and summer
travel, and thus far exceed recent DENV and CHIKV
importations [16]. Currently, DENV, CHIKV, and
ZIKV importation patterns differ most noticeably
along the Texas-Mexico border. Endemic DENV
transmission and sporadic CHIKV outbreaks in
Mexico historically have spilled over into neighboring
Texas counties. In contrast, ZIKV is not yet as
widespread in Mexico as it is in Central and
South America, with less than 10 reported ZIKV
importations along the border to date (October 2016).
We included DENV and CHIKV importation data in
the model fitting so as to consider potential future
importation pressure from Mexico, as ZIKV continues
its increasing trend since March 2016 [17]. To find
informative predictors for ZIKV importation risk,
we analyzed 72 socio-economic, environmental,
and travel variables, and removed near duplicate
variables and those that contributed least to model
performance, based on out-of-sample cross validation
of training and testing sets of data [18,19], reducing
the original set of 72 variables to 10 (Additional file 1:
Tables S3-S4). We validated our importation model
by comparing the predicted distribution of cases
across the state given a total number of imported
cases (September 2016) as a linear predictor of the
empirical distribution of cases across counties.
County transmission rates (R
The risk of ZIKV emergence following an imported case
will depend on the likelihood of mosquito-borne transmis-
sion. For emerging diseases like ZIKV, the public health
and research communities initially face considerable uncer-
tainty in the drivers and rates of transmission, given the
lack of field and experimental studies and epidemiological
data, and often derive insights through analogy to similar
diseases. For our case study, we estimated county-level
ZIKV transmission potential by Ae. aegypti using a recently
published model [20], that derives some of its key parame-
ters from DENV data. The utility of our framework de-
pends on the validity of such estimates and will increase as
our knowledge of ZIKV improves. However, we expect our
results to be robust to most sources of uncertainty regard-
ing ZIKV and DENV epidemiology, as they may influence
the absolute but not relative county-level risks.
We estimated the ZIKV reproduction number (R
the average number of secondary infections caused by a
single infectious individual in a fully susceptible popula-
tion, for each Texas county following the method de-
scribed in Perkins et al. [20]. The method calculates R
using a temperature-dependent formulation of the Ross-
Macdonald model, where mosquito mortality rate (μ)and
extrinsic incubation period of ZIKV (n) are temperature
dependent functions; the human-mosquito transmission
probability (b = 0.4), number of days of human infectious-
ness (c/r= 3.5), and the mosquito biting rate (a = 0.67)
are held constant at previously calculated values [2025];
and the economic-modulated mosquito-human contact
scaling factor (m) is a function of county mosquito abun-
dance and GDP data fit to historic ZIKV seroprevalence
data [20]. To account for uncertainty in the temperature-
dependent functions (the extrinsic incubation period (EIP)
and mosquito mortality rate) and in the relationship be-
tween economic index and the mosquito-to-human con-
tact rate, Perkins et al. generated functional distributions
via 1000 Monte Carlo samples from the underlying par-
ameter distributions. We assume DENV estimates for
these temperature-dependent functions, since we lack
such data for ZIKV and these Flaviviruses are likely to ex-
hibit similar relationships between temperature and EIP in
Ae. Aegypti [25]. We used the resulting distributions to es-
timate R
for each county, based on county estimates for
the average August temperature, mosquito abundance
from Kraemer et al. [24], and GDP [25]. Our R
were similar to those reported by Perkins et al. [20]
with 95% confidence intervals spanning from 0 to 3.1
(Additional file 1: Figure S3). Given this uncertainty, and
that our primary aim is to demonstrate the risk assessment
framework rather than provide accurate estimates of R
Texas, we use these estimates to estimate relative county-
level transmission risks (by scaling the county R
from 0 to 1). In each simulation, we assume that a countys
is the product of its relative risk and a chosen maximum
of 1.5 This is consistent with historical arbovirus activity
in Texas (which has never sustained a large arbovirus epi-
demic) and demonstrates the particular utility of the ap-
proach in distinguishing outbreaks from epidemics around
the epidemic threshold of R
Castro et al. BMC Infectious Diseases (2017) 17:284 Page 3 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
ZIKV outbreak simulation model
Assuming mosquito-borne transmission as the main
driver of epidemic dynamics, to transmit ZIKV, a mos-
quito must bite an infected human, the mosquito must
get infected with the virus, and then the infected mos-
quito must bite a susceptible human. Rather than expli-
citly model the full transmission cycle, we aggregated
the two-part cycle of ZIKV transmission (mosquito-to-
human and human-to-mosquito) into a single exposure
period where the individual has been infected by ZIKV,
but not yet infectious, and do not explicitly model
mosquitos. For the purposes of this study, we need only
ensure that the model produces a realistic human-to-
human generation time of ZIKV transmission, and the
simpler model is more flexible to disease transmission
pathways. We fit the generation time of the ZIKV model
to early ZIKV Epidemiological estimates, with further fit-
ting details described in Additional file 1: section 2.4.
The resultant model thus follows a Susceptible-Exposed-
Infectious-Recovered (SEIR) transmission process stem-
ming from a single ZIKV infection using a Markov
branching process model (Additional file 1: Figure S4). The
temporal evolution of the compartments is governed by
daily probabilities of infected individuals transitioning be-
tween disease states. New cases arise from importations or
autochthonous transmission (Additional file 1: Table S5).
We treat days as discrete time steps, and the next disease
state progression depends solely on the current state and
cases cause a Poisson distributed number of secondary
cases per day (via human to mosquito to human transmis-
sion), but this assumption can be relaxed as more informa-
tion regarding the distribution of secondary cases becomes
available. We also assume infectious individuals are intro-
duced daily according to a Poisson distributed number of
cases around the importation rate. Furthermore, Infectious
cases are categorized into reported and unreported
cases according to a reporting rate. We assume that
reporting rates approximately correspond to the per-
centage (~20%) of symptomatic ZIKV infections [10]
and occur at the same rate for imported and locally ac-
quired cases. Additionally, we make the simplifying as-
sumption that reported cases transmit ZIKV at the same
rate as unreported cases. We track imported and autoch-
thonous cases separately, and conduct risk analyses based
on reported autochthonous cases only, under the assump-
tion that public health officials will have immediate and
reliable travel histories for all reported cases [13].
For each county risk scenario, defined by an importation
rate, transmission rate, and reporting rate, we ran 10,000
stochastic simulations. Each simulation began with one
imported infectious case and terminated either when
there were no individuals in either the Exposed or Infec-
tious classes or the cumulative number of autochthon-
ous infections reached 2000. Thus the total outbreak
time may differ across simulations. We held R
throughout each simulation, as we sought to model early
outbreak dynamics over short periods (relative to the
seasonality of transmission) following introduction. We
classified simulations as either epidemics or self-limiting
outbreaks; epidemics were simulations that fulfilled two
criteria: reached 2000 cumulative autochthonous infec-
tions and had a maximum daily prevalence (defined as
the number of current infectious cases) exceeding 50 au-
tochthonous cases (Additional file 1: Figure S6). The sec-
ond criterion distinguishes simulations resulting in large
self-sustaining outbreaks (that achieve substantial peaks)
from those that accumulate infections through a series of
small, independent clusters (that fail to reach the daily
prevalence threshold). The latter occurs occasionally
under low R
s and high importation rates scenarios.
To verify that our simulations do not aggregate cases
from clear temporally separate clusters, we calculated
the distribution of times between sequential cases
(Additional file 1: Figure S7). In our simulated epi-
demics, almost all sequentially occurring cases occur
within 14 days of each other, consistent with the
CDCs threshold for identifying local transmission
events (based on the estimated maximum duration of
the ZIKV incubation period) [13].
Outbreak analysis
Our stochastic framework allows us to provide multiple
forms of real-time county-level risk assessments as re-
ported cases accumulate. For each county, we found the
probability that an outbreak will progress into an epi-
demic, as defined above, as a function of the number of
reported autochthonous cases. We call this epidemic
risk. To solve for epidemic risk in a county following the
xth reported autochthonous case, we first find all simu-
lations that experience at least xreported autochthonous
cases, and then calculate the proportion of those that
are ultimately classified as epidemics. For example, con-
sider a county in which 1000 of 10,000 simulated out-
breaks reach at least two reported autochthonous cases
and only 50 of the 1000 simulations ultimately fulfill the
two epidemic criteria; the probability of detecting two
cases in the county would be 10% and the estimated epi-
demic risk following two reported cases in that county
would be 5%. This simple epidemic classification scheme
rarely misclassifies a string of small outbreaks as an epi-
demic, with the probability of such an error increasing
with the importation rate. For example, epidemics should
not occur when R
= 0.9. If the importation rate is high,
overlapping series of moderate outbreaks occasionally
meet the two epidemic criteria. Under the highest
Castro et al. BMC Infectious Diseases (2017) 17:284 Page 4 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
importation rate we considered (0.3 cases/day), only 1% of
outbreaks were misclassified.
This method can be applied to evaluate universal trig-
gers (like the recommended two-case trigger) or derive ro-
bust triggers based on risk tolerance of public health
agencies. For example, if a policymaker would like to initi-
ate interventions as soon as the risk of an epidemic
reaches 30%, we would simulate local ZIKV transmission
and solve for the number of reported cases at which the
probability of an epidemic first exceeds 30%. Generally,
the recommended triggers decrease (fewer reported cases)
as the policymaker threshold for action decreases, (e.g.
10% versus 30% threshold) and as the local transmission
potential increases (e.g. R
= 1.5 versus R
ZIKV importation risk within Texas is predicted by vari-
ables reflecting urbanization, mobility patterns, and socio-
economic status (Additional file 1: Table S3), and is
concentrated in metropolitan counties of Texas (Fig. 2a). In
comparing the predictions of this model to out-of-sample
data from April to September 2016, the model underesti-
mated the statewide total number of importations (81 vs
151), but robustly predicted the relative importation rates
between counties (β= 0.97, R
= 0.74, p<0.001).Thetwo
highest risk countiesHarris, which includes Houston, and
Travis, which includes Austin, have an estimated 27% and
10% chance of receiving the next imported Texas case re-
spectively and contain international airports.
ZIKV transmission risk is concentrated in southeastern
Texas (Fig. 2b), partially overlapping with regions of high
range widely (from 0.8 to 3.1 for the highest-risk county),
reflecting the uncertainty in socioeconomic and environ-
mental drivers of ZIKV (Additional file 1: Figure S3). We
therefore analyzed the relative rather than absolute trans-
mission risks. For purposes of demonstration, we assumed
a plausible maximum county-level R
of 1.5, which closely
followed our median estimates, and scaled the transmission
risk for each county accordingly. The following risk ana-
lyses can be readily refined as we gain more precise and lo-
calized estimates of ZIKA transmission potential.
Wide ranges of outbreaks are possible under a single set
of epidemiological conditions (Fig. 3a). The relationship be-
tween what policymakers can observe (cumulative reported
cases) and what they wish to know (current underlying dis-
ease prevalence) can be obscured by such uncertainty, and
will depend critically on reporting rates (Fig. 3b). Under a
scenario estimated for Cameron County which experienced
the only autochthonous ZIKV transmission in Texas and
with a 20% reporting rate, ten linked and reported autoch-
thonous cases correspond to 6 currently circulating cases
with a 95% CI of 116 from inherent, early-stage outbreak
stochasticity. From this wide range of outbreak trajectories,
we can characterize time-varying epidemic risk as cases ac-
cumulate in a given county. We track the probability of
epidemic expansion following each additional reported case
in high and low reporting rate scenarios (Fig. 3c).
San Antonio
El Paso
Corpus Christi
0.002 0.02 0.2
Import Probability
0.25 0.50 0.75 1.00
Relative Transmission Risk
Fig. 2 ZIKV importation and transmission risk estimates across Texas for August 2016. aColor indicates the probability that the next ZIKV import
will occur in a given county for each of the 254 Texas counties. Probability is colored on a log scale. The 10 most populous cities in Texas are
labeled. Houstons Harris County has 2.7 times greater chance than Austins Travis County of receiving the next imported case. bEstimated
county-level transmission risk for ZIKV (See Additional file 1: Figure S7 for seasonal differences). Harris county and Dallas County rank among the
top 5 and top 10 for both importation and transmission risk respectively; counties in McAllen and Houston metropolitan area rank among the
top 20. Bolded county border indicates counties with recorded local ZIKV transmission
Castro et al. BMC Infectious Diseases (2017) 17:284 Page 5 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
These curves can support both real-time risk assess-
ment as cases accumulate and the identification of sur-
veillance triggers indicating when risk exceeds a
specified threshold. For example, suppose a policymaker
wanted to initiate an intervention upon two reported
cases, this would correspond with a 49% probability of
an epidemic if 10% of cases are reported, but only 25% if
the reporting rate is doubled. Alternatively suppose a
policy maker wishes to initiate an intervention when the
chance of an epidemic exceeds 50%. In the low reporting
rate scenario, they should act immediately following the
third autochthonous reported case, but could wait until
the eleventh case with the high reporting rate.
To evaluate a universal intervention trigger of two re-
ported autochthonous cases, we estimate both the prob-
ability of two reported cases in each county and the level
of epidemic risk at the moment the trigger event occurs
(second case reported). Assuming a baseline importation
rate extrapolated from importation levels in March 2016
to August 2016, county R
scaled from a maximum of
1.5, and a 20% reporting rate, only a minority of counties
are likely to experience a trigger event (Fig. 4a). While
247 of the 254 counties (97%) have non-zero probabil-
ities of experiencing two reported autochthonous cases,
only 86 counties have at least a 10% chance of such an
event (assuming they experience at least one import-
ation), with the remaining 168 counties having a median
probability of 0.0038 (range 0.0005 to 0.087). Assuming
that a second autochthonous case has indeed been re-
ported, we find that the underlying epidemic risk varies
widely among the 247 counties, with most counties hav-
ing near zero epidemic probabilities and a few counties
far exceeding a 50% chance of epidemic expansion. For
example, two reported autochthonous cases in Harris
County, correspond to a 99% chance of ongoing trans-
mission that would proceed to epidemic proportions
without intervention, with the rest of the Houston
metropolitan also at relatively high risk ranging from 0
(Galveston) to 90% (Waller) (Fig. 4b).
Given that a universal trigger may signal disparate
levels of ZIKV risk, policy makers might seek to adapt
their triggers to local conditions. Suppose a policymaker
wishes to design triggers that indicate a 50% chance of
an emerging epidemic (Fig. 4c). Under the baseline im-
portation and reporting rates, an estimated 31 of the 254
counties in Texas are expected to reach a 50% epidemic
probability, with triggers ranging from one (Harris
County) to 21 (Jefferson County) reported autochthon-
ous cases, with a median of two cases. Counties who de-
tect cases simply due to high importation rates do not
have triggers, and the magnitude of a trigger helps quan-
tify a countys absolute risk for an epidemic as a function
of the reported autochthonous cases.
Our framework provides a data-driven approach to esti-
mating ZIKA emergence risks from potentially sparse
and biased surveillance data [26, 27]. By mapping ob-
served cases to current and future risks, in the face of
considerable uncertainty, the approach can also be used
to design public health action plans and evaluate the
utility of local versus regional triggers. We demonstrate
its application across the 254 ecologically and demo-
graphically diverse counties of Texas, one of the two
0 50 100 150
Time (days)
Reported Autochthonous Cases
0 5 10 15
Reported Cases
Cases (log scale)
Reporting Rate
0 5 10 15
Reported Cases
Epidemic Probability
Fig. 3 Real-time risk-assessment for ZIKV transmission. All figures are based on transmission and importation risks estimated for Cameron County,
Texas. aTwo thousand simulated outbreaks. bTotal number of (current) autochthonous cases as a function of the cumulative reported autochthonous
cases, under a relatively high (dashed) or low (solid) reporting rate. Ribbons indicate 50% quantiles. cThe increasing probability of imminent epidemic
expansion as reported autochthonous cases accumulate for a low (solid) and high (dashed) reporting rate. Suppose a policy-maker plans to trigger a
public health response as soon as a second case is reported (vertical line). Under a 10% reporting rate, this trigger would correspond to a 49%
probability of an ensuing epidemic. Under a 20% reporting rate, the probability would be 25%
Castro et al. BMC Infectious Diseases (2017) 17:284 Page 6 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
states that has sustained autochthonous ZIKV outbreaks
[6, 7]. The approach requires local estimates of ZIKV
importation and transmission rates. For the Texas ana-
lysis, we developed a novel model for estimating county-
level ZIKV importation risk and applied published
methods to estimate relative county-level transmission
risks (Fig. 2). We expect that most Texas counties are
not at risk for a sustained ZIKV epidemic (Fig. 4), and
find that many of the highest risk counties lie in the
southeastern region surrounding the Houston metropol-
itan area and the lower Rio Grande valley. However, R
estimates are uncertain, leaving the possibility that the
could be as high as other high risk regions that sus-
tained epidemics [20, 28, 29]. Our analysis is consistent
with historic DENV and CHIKV outbreaks and correctly
identifies Cameron county, the only Texas county to
have reported local transmission, as a potential ZIKV
hot-spot, especially when November estimates are used
[30] (Additional file 1: Figure S9).
Surveillance triggersguidelines specifying situations
that warrant interventionare a key component of many
public health response plans. Given the urgency and un-
certainty surrounding ZIKV, universal recommendations
can be both pragmatic and judicious. To assist Texas
policymakers in interpreting the two-case trigger for
intervention guidelines issued by the CDC [13], we used
our framework to integrate importation and transmis-
sion risks and assess the likelihood and implication of a
two-case event for each of Texas254 counties, under a
scenario projected from recent ZIKV data to August
2016. Across counties, there is enormous variation in
both the chance of a trigger and the magnitude of the
public health threat if and when two cases are reported.
Given this variation, rather than implement a universal
trigger, which may correspond to different threats in dif-
ferent locations, one could design local surveillance trig-
gers that correspond to a universal risk threshold. Our
modeling framework can readily identify triggers (num-
bers of reported cases) for indicating any specified epi-
demic event (e.g., prevalence reaching a threshold or
imminent epidemic expansion) with any specified risk
tolerance (e.g., 10% or 50% chance of that the event has
occurred), given local epidemiological conditions. We
found close agreement between the recommended two-
case trigger and our epidemic derived triggers based on
a 50% probability of expansion. Of the 30 counties with
derived triggers, the median trigger was 2, ranging from
one to 21 reported autochthonous cases. These findings
apply only to the early, pre-epidemic phase of ZIKV in
Texas, when importations occur primarily via travel
from affected regions outside the contiguous US.
These analyses highlight critical gaps in our under-
standing of ZIKV biology and epidemiology. The relative
transmission risks among Texas counties appear fairly
robust to these uncertainties, allowing us to identify high
risk regions, including Cameron County in the Lower
Rio Grande Valley. Public health agencies might there-
fore prioritize such counties for surveillance and inter-
ventions resources. Given the minimal incursions of
DENV and CHIKV into Texas over that past eleven
years since the first DENV outbreak in Cameron County,
and the high number of importations into putative hot-
spot counties without autochthonous transmission, we
suspect that, if anything, we may be underestimating the
socioeconomic and behavioral impediments to ZIKV
transmission in the contiguous US. Our analysis also
San Antonio
0.00 0.25 0.50 0.75 1.00
Two−case Probability
San Antonio
0.00 0.25 0.50 0.75 1.00
Epidemic Probability
San Antonio
5 101520
Trigger (Reported Cases)
Fig. 4 Texas county ZIKV risk assessment. aProbability of an outbreak with at least two reported autochthonous ZIKV cases. bThe probability of
epidemic expansion at the moment the second autochthonous ZIKV case is reported in a county. White counties never reach two reported cases
across all 10,000 simulated outbreaks; light gray counties reach two cases, but never experience epidemics. cRecommended county-level surveillance
triggers (number of reported autochthonous cases) indicating that the probability of epidemic expansion has exceeded 50%. White counties indicate that
fewer than 1% of the 10,000 simulated outbreaks reached two reported cases.Allthreemapsassumea20%reportingrateandabaselineimportation
scenario for August 2016 (81 cases statewide per 90 days) projected from historical arbovirus data.
Castro et al. BMC Infectious Diseases (2017) 17:284 Page 7 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
reveals the significant impact of the reporting rate on the
timeliness and precision of detection. If only a small frac-
tion of cases are reported, the first few reported cases may
correspond to an isolated introduction or a growing epi-
demic. In contrast, if most cases are reported, policy-
makers can wait longer for cases to accumulate to trigger
interventions and have more confidence in their epi-
demiological assessments. ZIKV reporting rates are ex-
pected to remain low, because an estimated 80% of
infections are asymptomatic, and DENV reporting rates
have historically matched its asymptomatic proportion
[10, 31]. Obtaining a realistic estimate of the ZIKV report-
ing rate is arguably as important as increasing the rate it-
self, with respect to reliable situational awareness and
forecasting. An estimated 822% of ZIKV infections were
reported during the 20132014 outbreak in French
Polynesia [29]; however estimates ranging from 1 to 10%
have been reported during the ongoing epidemic in
Columbia [2, 28]. While these provide a baseline estimate
for the US, there are many factors that could increase (or
decrease) the reporting rate, such as ZIKV awareness
among both the public and health-care practitioners, or
active surveillance of regions with recent ZIKV cases. Our
analysis assumes that all counties have the same case de-
tection probabilities. However, only 40 of the 254 Texas
counties maintain active mosquito surveillance and con-
trol programs, potentially leading to differences in case
detection rates and surveillance efficacy throughout the
state [32]. Thus, rapid estimation of the reporting rate
using both traditional epidemiological data and new viral
sequenced based methods [33] should be a high priority
as they become available.
Our framework can support the development of re-
sponse plans, by forcing policymakers to be explicit
about risk tolerance, that is, the certainty needed before
sounding an alarm, and quantifying the consequences of
premature or delayed interventions. For example, should
ZIKV-related pregnancy advisories be issued when there
is only 5% chance of an impending epidemic? 10%
chance? 80%? A policymaker has to weigh the costs of
false positivesresulting in unnecessary fear and/or
interventionand false negativesresulting in suboptimal
disease control and preventioncomplicated by the diffi-
culty inherent in distinguishing a false positive from a
successful intervention. The more risk averse the policy-
maker (with respect to false negatives), the earlier the
trigger should be, which can be exacerbated by low
reporting rates, high importation rate, and inherent
ZIKV transmission potential. In ZIKV prone regions
with low reporting rates, even risk tolerant policymakers
should act quickly upon seeing initial cases; in lower risk
regions, longer waiting periods may be prudent.
Additional files
Additional file 1: Supplemental Information: A framework for assessing
real-time Zika risk in the United States. (PDF 10870 kb)
Additional file 2: Supplemental Data: Raw historic arbovirus Texas
county importation data. (XLS 9 kb)
Additional file 3: Supplemental Data: Texas county importation
predictor raw data. (XLS 81 kb)
CHIKV: Chikungunya Virus; DENV: Dengue Virus; SEIR model: Susceptible-
Exposed-Infectious-Recovered epidemiological model; WHO: World Health
Organization; ZIKV: Zika virus
We acknowledge the Texas Department of State Health Services (DSHS) for
providing historic arbovirus county importation data, and MUG Kraemer and
A Perkins for providing Texas mosquito abundance data and technical
guidance. We also thank M Johansson and M Meltzer of the Centers for
Disease Control and Prevention (CDC) and Dr. John Hellerstedt,
commissioner of the Texas DSHS, for critical conversations regarding the
modeling framework and its application to Texas and national ZIKV planning
efforts. We finally acknowledge the Texas Advanced Computing Center
(TACC) at The University of Texas at Austin for providing High performance
computing resources that have contributed to the research results reported
within this paper. URL:
LAC, SEB, LAM, and APG were supported by a National Institute of General
Medical Sciences MIDAS grant to LAM and APG (U01GM087719), and APG
received additional NIGMS support (U01GM105627). SJF, KL, and LAM were
funded through a grant from the Defense Threat Reduction Agency to LAM
(HDTRA-14-C-0114). SEB was additionally supported by the International
Clinics on Infectious Disease Dynamics and Data (ICI3D) program, which is
funded by a NIGMS grant (R25GM102149). NBD and XC were partially
supported by a contract with the Texas Department of State Health Services
(2015047259-001). The funders did not contribute to the design, of the
study, analysis, or interpretation of data.
Availability of data and materials
The datasets generated and analyzed during the current study are available
in the Additional files 2 and 3: supplemental materials and the R package,
rtZIKVrisk, which can be accessed here:
There was no individualized patient or medical data used in our study, and
all ZIKV case data is publicly available.
and study design. LAC, SJF, and XC collected the data used in the analysis. LAC,
KL, and SJF reviewed relevant literature. XC and ND developed and completed
the Texas importation risk analysis. LAM, LAC, and SJF interpreted all results. LAC
and SJF performed the analyses, created the figures, and wrote the first draft. All
authors contributed to the presentation of the results, and the writing and
approval of the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Not applicable.
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
Castro et al. BMC Infectious Diseases (2017) 17:284 Page 8 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Author details
Department of Integrative Biology, The University of Texas at Austin, Austin,
Graduate Program in Operations Research and Industrial
Engineering, The University of Texas at Austin, Austin, TX, USA.
Institute for
Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX,
Center for Ecology of Infectious Diseases, University of Georgia, Athens,
Department of Epidemiology and Biostatistics, University of
Georgia, Athens, Athens, GA, USA.
Center for Infectious Disease Modeling
and Analysis, Yale School of Public Health, New Haven, CT, USA.
Department of Ecology and Evolution, Yale University, New Haven, CT, USA.
The Santa Fe Institute, Santa Fe, NM, USA.
Received: 1 February 2017 Accepted: 11 April 2017
1. Gulland A. Zika virus is a global public health emergency, declares WHO.
BMJ Publishing Group Ltd. 2016;352:i657. doi:10.1136/bmj.i657.
2. Zhang Q, Sun K, Chinazzi M, Pastore-Piontti A, Dean NE, Rojas DP, et al.
Projected spread of Zika virus in the Americas. bioRxiv. 2016;
3. Estimated range of Aedes aegypti and Aedes albopictus in the United States,
2016. In: Atlanta: Centers for Disease Control and Prevention [Internet].
[cited 1 Jan 2016]. Available:
4. Florida Department of Health. Department of Health Daily Zika Update
[Internet]. Available:
5. Texas Department of State Health and Human Services. Zika in Texas
[Internet]. 2016. Available:
6. Texas Department of State Health and Human Services. Arbovirus activity in
Texas 2013 surveillance Report. 2014.
7. Texas Department of State Health and Human Services. Arbovirus activity in
Texas 2014 surveillance Report. 2014.
8. Lessler J, Ott CT, Carcelen AC, Konikoff JM, Williamson J, Bi Q, et al. Times to
key events in Zika virus infection and implications for blood donation: a
systematic review. Bull World Health Organ. 2016;94:8419. doi:10.2471/BLT.
9. Lloyd-Smith JO, Funk S, McLean AR, Riley S, Wood JLN. Nine challenges in
modelling the emergence of novel pathogens. Epidemics Elsevier BV. 2014;
10:359. doi:10.1016/j.epidem.2014.09.002.
10. Duffy MR, Chen T-H, Hancock WT, Powers AM, Kool JL, Lanciotti RS, et al.
Zika virus outbreak on Yap Island, Federated States of Micronesia. N Engl J
Med. 2009;360:253643. doi:10.1056/NEJMoa0805715.
11. Alfaro-Murillo JA, Parpia AS, Fitzpatrick MC, Tamagnan JA, Medlock J,
Ndeffo-Mbah ML, et al. A cost-effectiveness tool for informing policies on Zika
virus control. PLOS Negl Public Library of Science. 2016;10:e0004743. Available:
12. PAHO and WHO. Zika-Epidemiological Report - Anguilla; 2016. p. 12.
13. Interim CDC recommendations for Zika vector control in the continental
United States. In: Centers for Disease Control and Prevention [Internet]. 2016
[cited 5 Jan 2016]. Available:
14. Office of Travel & Tourism Industries. US Monthly Arrivals Trend Line
[Internet]. 2014 [cited 25 May 2016]. Available:
15. Jaynes ET. Information theory and statistical mechanics. Phys Rev American
Physical Society. 1957;106:62030. doi:10.1103/PhysRev.106.620.
16. Musso D, Cao-Lormeau VM, Gubler DJ. Zika virus: following the path of dengue
and chikungunya? Lancet (London, England). Elsevier; 2015;386: 243244.
17. PAHO and WHO. Zika-Epidemiological Report-Mexico. 2016.
18. Wolsey LA. Integer programming, vol. 42. New York: Wiley New York; 1998.
19. Merow C, Smith MJ, Silander JA. A practical guide to MaxEnt for modeling
speciesdistributions: what it does, and why inputs and settings matter.
Ecography (cop). Blackwell Publishing Ltd. 2013;36:105869. doi:10.1111/j.
20. Alex Perkins T, Siraj AS, Ruktanonchai CW, Kraemer MUG, Tatem AJ, Mlakar J,
et al. Model-based projections of Zika virus infections in childbearing
women in the Americas. Nat Microbiol Nature Publishing Group. 2016;1:
16126. doi:10.1038/nmicrobiol.2016.126.
21. Brady OJ, Johansson MA, Guerra CA, Bhatt S, Golding N, Pigott DM, et al.
Modelling adult Aedes aegypti and Aedes albopictus survival at different
temperatures in laboratory and field settings. Parasit Vectors. 2013;6:351.
22. Muir LE, Kay BH. Aedes aegypti Survival and dispersal estimated by mark-
release-recapture in northern Australia. AmJTrop Med Hyg. 1998;58:27782.
23. Nishiura H, Halstead SB. Natural history of dengue virus (DENV)-1 and DENV-4
infections: reanalysis of classic studies. J Infect Dis Oxford University Press. 2007;
195:100713. Available:
24. Chan M, Johansson MA. The incubation periods of dengue viruses. Vasilakis
N, editor. PLoS One Public Library of Science. 2012;7:e50972. doi:10.1371/
25. Black WC, Bennett KE, Gorrochótegui-Escalante N, Barillas-Mury CV,
Fernández-Salas I, de Lourdes MM, et al. Flavivirus susceptibility in Aedes
aegypti. Arch Med Res. 2002;33:37988. doi:10.1016/S0188-4409(02)00373-9.
26. Cauchemez S, Epperson S, Biggerstaff M, Swerdlow D, Finelli L, Ferguson
NM. Using routine surveillance data to estimate the epidemic potential of
emerging Zoonoses: application to the emergence of US swine origin
influenza a H3N2v virus. Peiris JSM, editor. PLoS Med. 2013;10:e1001399.
27. Blumberg S, Lloyd-Smith JO. Comparing methods for estimating R0 from the
size distribution of subcritical transmission chains. Epidemics. 2013;5:13145.
28. Rojas DP, Dean NE, Yang Y, Kenah E, Quintero J, Tomasi S, et al. The
epidemiology and transmissibility of Zika virus in Girardot and San Andres
island, Colombia, September 2015 to January 2016. Eur Secur. 2016;21:30283.
29. Kucharski AJ, Funk S, Eggo RM, Mallet H, Edmunds WJ, Nilles EJ.
Transmission dynamics of Zika virus in island populations : a modelling
analysis of the 201314 French Polynesia outbreak. bioRxiv. 2016; 115.
30. Texas Department of State Health and Human Services. Texas Announces
Local Zika Virus Case in Rio Grande Valley [Internet]. Available: http://www.
31. Dechant E, Rigau-Perez J. Hospitalizations for suspected dengue in Puerto
Rico, 1991-1995: estimation by capture-recapture methods. The Puerto Rico
Association of Epidemiologists. AmJTrop Med Hyg. 1999;61:5748. Available:
32. Sidwa TJ. Mosquito surveillance/control in Texas. 2016.
33. Scarpino SV, Iamarino A, Wells C, Yamin D, Ndeffo-Mbah M, Wenzel NS, et
al. Epidemiological and viral genomic sequence analysis of the 2014 ebola
outbreak reveals clustered transmission. Clin Infect Dis Oxford University
Press. 2015;60:107982. Available:
We accept pre-submission inquiries
Our selector tool helps you to find the most relevant journal
We provide round the clock customer support
Convenient online submission
Thorough peer review
Inclusion in PubMed and all major indexing services
Maximum visibility for your research
Submit your manuscript at
Submit your next manuscript to BioMed Central
and we will help you at every step:
Castro et al. BMC Infectious Diseases (2017) 17:284 Page 9 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
... Deterministic models use a set of input parameters, often from experimental findings or from the literature, to generate estimates on epidemiological characteristics that quantify spread, [9][10][11][12][13] whereas stochastic models predict or infer retrospectively the process (or parameters) of spread using methods based on statistical theory. [14][15][16][17][18][19][20][21][22][23][24] Deterministic models are effective for estimating mosquito-human interactions that facilitate infection [9][10][11]15,23,25,26 but they are unable to account for the inherent stochastic nature of disease transmission. 10,12,13 The majority of existing studies use stochastic models, which can flexibly integrate high-resolution information about environmental conditions and vector competence and thereby model finescale spatial heterogeneities in disease spread. ...
... Deterministic models use a set of input parameters, often from experimental findings or from the literature, to generate estimates on epidemiological characteristics that quantify spread, [9][10][11][12][13] whereas stochastic models predict or infer retrospectively the process (or parameters) of spread using methods based on statistical theory. [14][15][16][17][18][19][20][21][22][23][24] Deterministic models are effective for estimating mosquito-human interactions that facilitate infection [9][10][11]15,23,25,26 but they are unable to account for the inherent stochastic nature of disease transmission. 10,12,13 The majority of existing studies use stochastic models, which can flexibly integrate high-resolution information about environmental conditions and vector competence and thereby model finescale spatial heterogeneities in disease spread. ...
... 3,[30][31][32] Predictive studies can either be stochastic or deterministic and may aim to estimate Zika incidence, 14,24,36 importation of cases, 11,12,14 distribution of risk 18,21,37-39 and transmission potential. 15,23 Both predictive and causal-inference models can be applied retrospectively to assess disease spread, which is the main application identified among the studies we reviewed. Predictive models can also be used to track disease spread with potential real-time applications. ...
Full-text available
In recent years, Zika virus (ZIKV) has expanded its geographic range and in 2015-2016 caused a substantial epidemic linked to a surge in developmental and neurological complications in newborns. Mathematical models are powerful tools for assessing ZIKV spread and can reveal important information for preventing future outbreaks. We reviewed the literature and retrieved modelling studies that were developed to understand the spatial epidemiology of ZIKV spread and risk. We classified studies by type, scale, aim and applications and discussed their characteristics, strengths and limitations. We examined the main objectives of these models and evaluated the effectiveness of integrating epidemiological and phylogeographic data, along with socioenvironmental risk factors that are known to contribute to vector-human transmission. We also assessed the promising application of human mobility data as a real-time indicator of ZIKV spread. Lastly, we summarised model validation methods used in studies to ensure accuracy in models and modelled outcomes. Models are helpful for understanding ZIKV spread and their characteristics should be carefully considered when developing future modelling studies to improve arbovirus surveillance.
... Type 1 diabetes mellitus and glucose control [36] Lipid metabolism Reduction in triglycerides [37,38] Q fever A combination of HCQ and doxycycline has always been proposed and used for primary treatment for chronic Q-fever [39] Whipple's disease Whipple's disease is treated using a combination of HCQ and doxycycline [40] Fungal infections HCQ shows in vitro antifungal activity against intracellular fungi such as Histoplasma capsulatum and neoformans [41] Zika virus Zika virus, a member of the Flaviviridae family that is spread in humans by mosquitoes and ticks. HCQ prevents these mosquitoes and ticks from spreading the infection [42] Sjogren's syndrome Sjogren's syndrome is widely treated with HCQ [43] Chikungunya virus Chikungunya virus is an alphavirus that is mainly transmitted by Aedes aegypti and Aedes albopictus mosquitoes. The combination of methotrexate and sulfasalazine with HCQ showed potent effects. ...
Chloroquine (CQ) and its analog hydroxychloroquine (HCQ) are popular antimalarial drugs that also exhibit wide range of activities against other diseases such as cancer, diabetes, HIV, and microbial infections, among others. They are also reported to possess antioxidant properties. The popularity of these drugs skyrocketed with the emergence of coronavirus disease 2019 (COVID-19) that has caused the deaths of over 600,000,000 people worldwide just within 7 months. Due to the urgency of the time in discovering or repurposing new drugs that will be active against SARS-CoV-2, the causative agent of COVID-19, some initial in vitro studies found prospects in CQ and HCQ against SARS-CoV-2. HCQ instantly became a drug of choice over CQ for the treatment of COVID-19 patients because it is readily absorbed and less toxic. However, clinical studies found no positive indices to support the continued use of HCQ. This chapter looks into this by consulting current literatures in order to unravel the myth surrounding the approval and disapproval of the use of HCQ.
Background The lack of previous exposure to arbovirus and the ongoing geographical expansion of viable vector populations has fostered the implementation of preventive strategies in those areas more prone to disease importation. Catalonia receives a wealth of travelers from Southeast Asia, South America and the Caribbean and around 700 cases of imported arbovirosis (2012–2016, totaling dengue, chikungunya, and Zika), have been notified in primary care health centers, traveler advice public health services and main hospitals. With the large asymptomatic proportion of infections well-known for these diseases, the threat for autochthonous outbreaks increases in those areas that, for particular environmental and socio-demographic conditions, might be more susceptible. Operational early-warning systems are lacking in most places where these outbreaks pose a serious health treat. Methods Here we present the ARBOCAT platform for the prediction of autochthonous outbreaks of arboviruses emanating from imported cases, implemented for Catalonia at municipality resolution. Three sub-models provide estimations for importation rates and the basic reproduction number and their outcomes are used to fit a stochastic compartmental model that yields the generation time and the risk of local outbreaks for 948 municipalities. We used also ISIMIP-2b (The Inter-Sectoral Impact Model Intercomparison Project) temperature data to generate future outbreak risk maps for the RCP 2.6 and RCP 8.5 scenarios (where RCP is Representative Concentration Trajectories). Findings Substantial differences exist between the low and middle-risk scenarios but most Catalonia municipalities are not at risk for a sustained epidemic. Instead, high R0 are obtained for the maximum risk scenario, with the number of municipalities affected being over 150. In the RCP 8.5 scenario, many of the highest risk areas lie in the most populated cities in the coastal region, particularly in the south near to the Ebre’s river. Interpretation The current outbreak risk is low, both for the mean and minimum temperature scenarios and rises in the high-risk situation. Projections for 2050 are not so optimistic, leading to a significant increase in affected municipalities, over 100, mainly in the coastal area due to the temperature increase followed in RCP 8.5. Funding This study was partially supported by the Catalonia Department of Health and the Spanish Ministry of Science and Innovation.
Full-text available
The study sought to review the works of literature on agent-based modeling and the influence of climatic and environmental factors on disease outbreak, transmission, and surveillance. Thus, drawing the influence of environmental variables such as vegetation index, households, mosquito habitats, breeding sites, and climatic variables including precipitation or rainfall, temperature, wind speed, and relative humidity on dengue disease modeling using the agent-based model in an African context and globally was the aim of the study. A search strategy was developed and used to search for relevant articles from four databases, namely, PubMed, Scopus, Research4Life, and Google Scholar. Inclusion criteria were developed, and 20 articles met the criteria and have been included in the review. From the reviewed works of literature, the study observed that climatic and environmental factors may influence the arbovirus disease outbreak, transmission, and surveillance. Thus, there is a call for further research on the area. To benefit from arbovirus modeling, it is crucial to consider the influence of climatic and environmental factors, especially in Africa, where there are limited studies exploring this phenomenon.
Coronavirus Drug Discovery SARS-CoV-2 (COVID-19) Prevention, Diagnosis, and Treatment
Various individuals, community organizations and institutions must be involved in planning and developing a cure for the COVID-19 flu pandemic. In addition to governmental organizations, those who need to be involved in the process are responsible for implementing pandemic plans. There should be a balance between centralized national control and regional and local communities through the effective implementation of the guidelines. There is a need to introduce social distancing and to study and isolate cases to contain disease spread. Due to the amendment and tightening of the law "SARS-CoV-2" in many countries, special attention should be paid to respect for citizens, especially national minorities. That is why it is necessary to protect freedom statements and providing access to critical information; make sure that quarantines, locks and travel bans comply with legal standards; persons.
In August 2020 as Texas was coming down from a summer COVID-19 surge, forecasts suggested that Hurricane Laura was tracking towards 6M residents along the East Texas coastline threatening to spread COVID-19 across the state. To assist local authorities facing the dual threat, we developed a framework that integrates evacuation dynamics and local pandemic conditions to quantify COVID-19 importations due to hurricane evacuations. For Hurricane Laura, we estimate that 499,500 [90% Credible Interval (CI): 347,500, 624,000] people evacuated the Texan counties, and that there were 2,900 [90% CI: 1,700, 5,800] importations of COVID-19 across the state. To demonstrate the transferability of the framework, we apply it to a scenario with characteristics matching those of Hurricane Rita, where a much feared direct hit towards the highly populated Houston/Galveston area was forecasted. For this scenario we estimate 1,054,500 evacuations [90% CI: 832,500, 1,162,000], and 6,850 COVID-19 importations [90% CI: 4,100, 13,670]. Overall, we present a flexible and transferable framework that captures spatial heterogeneity and incorporates geographic components for anticipating potential epidemiological risks resulting from evacuation movement due to hurricane events.
In August 2020, as Texas was coming down from a large summer COVID-19 surge, forecasts suggested that Hurricane Laura was tracking towards 6M residents along the East Texas coastline, threatening to spread COVID-19 across the state and cause pandemic resurgences. To assist local authorities facing the dual-threat, we integrated survey expectations of coastal residents and observed hurricane evacuation rates in a statistical framework that combined with local pandemic conditions predicts how COVID-19 would spread in response to a hurricane. For Hurricane Laura, we estimate that 499,500 [90% Credible Interval (CI): 347,500, 624,000] people evacuated the Texan counties, that no single county accumulated more than 2.5% of hurricane evacuees, and that there were 2,900 [90% CI: 1,700, 5,800] exportations of Covid-19 across the state. In general, reception estimates were concentrated in regions with higher population densities. Nonetheless, higher importation risk is expected in small Districts, with a maximum number of importations of 10 per 10,000 residents in our case study. Overall, we present a flexible and transferable framework that captures spatial heterogeneity and incorporates geographic components for predicting population movement in the wake of a natural disaster. As hurricanes continue to increase in both frequency and strength, our framework can be deployed in response to anticipated hurricane paths to guide disaster preparedness and planning.
Vaccines are one of the most successful public health interventions in our history, after the development of the first vaccine more than 200 years ago, vaccinations have greatly decreased the burden of infectious diseases and protected from several pandemics worldwide (eradication of smallpox, near eradication of polio). A multitude of research efforts focuses on the improvement of established vaccines and the discovery of new vaccines during the last two decades. However, globalization including radical changes in the density, age distribution, and traveling habits of the population worldwide as well as the changing climate favor the emergence of old and new pathogens that bear the risk of becoming pandemic threats. In recent years, the rapid spread of severe infections such as Severe acute respiratory syndrome, Ebola, and COVID-19 have highlighted the dire need for global preparedness for pandemics, which necessitates the extremely rapid development and comprehensive distribution of vaccines against these pandemic pathogens. Hence, new vaccine technologies able to achieve rapid development as well as large-scale production are of pivotal importance. This review will briefly discuss some of the global historical pandemics and the new strategies for the development of vaccines as new approaches that might be able to tackle these challenges to global health.
Background/aims Over the last decade and following international trends, cases of mosquito-borne arboviral infections, notably dengue fever, chikungunya and Zika, have increased among travellers arriving in New Zealand, but no locally acquired cases have been identified. Imported cases are characterised and examined to identify trends and features that might assist in reducing transmission risk from travellers. Methods Information on traveller arrivals, notified cases and risk factors for disease acquisition were obtained from national sources. Trends in importation rates, seasonality are described and relationships of notifications with traveller arrivals were examined with a negative binomial regression model. Results There was a significant increase in dengue notifications combined with the emergence of Zika and chikungunya. Most notifications were from arrivals in Auckland from Pacific Islands during summer and early autumn. Conclusion/implications Overseas travel from New Zealand, particularly to the Pacific Islands and Southeast Asia, involves a risk of arboviral infection. The repeated introduction of arboviruses to New Zealand also increases the risk of local transmission in a country that has vector capable and vector potential mosquitoes, as well as an increasingly suitable climate for new vectors to establish.
Full-text available
Objective To estimate the timing of key events in the natural history of Zika virus infection. Methods In February 2016, we searched PubMed, Scopus and the Web of Science for publications containing the term Zika. By pooling data, we estimated the incubation period, the time to seroconversion and the duration of viral shedding. We estimated the risk of Zika virus contaminated blood donations. Findings We identified 20 articles on 25 patients with Zika virus infection. The median incubation period for the infection was estimated to be 5.9 days (95% credible interval, CrI: 4.4–7.6), with 95% of people who developed symptoms doing so within 11.2 days (95% CrI: 7.6–18.0) after infection. On average, seroconversion occurred 9.1 days (95% CrI: 7.0–11.6) after infection. The virus was detectable in blood for 9.9 days (95% CrI: 6.9–21.4) on average. Without screening, the estimated risk that a blood donation would come from an infected individual increased by approximately 1 in 10 000 for every 1 per 100 000 person–days increase in the incidence of Zika virus infection. Symptom-based screening may reduce this rate by 7% (relative risk, RR: 0.93; 95% CrI: 0.89–0.99) and antibody screening, by 29% (RR: 0.71; 95% CrI: 0.28–0.88). Conclusion Neither symptom- nor antibody-based screening for Zika virus infection substantially reduced the risk that blood donations would be contaminated by the virus. Polymerase chain reaction testing should be considered for identifying blood safe for use in pregnant women in high-incidence areas.
Full-text available
We use a data-driven global stochastic epidemic model to project past and future spread of the Zika virus (ZIKV) in the Americas. The model has high spatial and temporal resolution , and integrates real-world demographic, human mobility, socioeconomic, temperature, and vector density data. We estimate that the first introduction of ZIKV to Brazil likely occurred between August 2013 and April 2014. We provide simulated epidemic profiles of incident ZIKV infections for several countries in the Americas through December 2016. The ZIKV epidemic is characterized by slow growth and high spatial and seasonal heterogeneity, attributable to the dynamics of the mosquito vector and to the characteristics and mobility of the human populations. We project the expected timing and number of cases of micro-cephaly assuming three levels of risk associated with ZIKV infection during the first trimester of pregnancy. Our approach represents an early modeling effort aimed at projecting the potential magnitude and timing of the ZIKV epidemic that might be refined as new and more accurate data from the region will be available.
Full-text available
Zika virus is a mosquito-borne pathogen that is rapidly spreading across the Americas. Due to associations between Zika virus infection and a range of fetal maladies1,2, the epidemic trajectory of this viral infection poses a significant concern for the nearly 15 million children born in the Americas each year. Ascertaining the portion of this population that is truly at risk is an important priority. One recent estimate3 suggested that 5.42 million childbearing women live in areas of the Americas that are suitable for Zika occurrence. To improve on that estimate, which did not take into account the protective effects of herd immunity, we developed a new approach that combines classic results from epidemiological theory with seroprevalence data and highly spatially resolved data about drivers of transmission to make location-specific projections of epidemic attack rates. Our results suggest that 1.65 (1.45–2.06) million childbearing women and 93.4 (81.6–117.1) million people in total could become infected before the first wave of the epidemic concludes. Based on current estimates of rates of adverse fetal outcomes among infected women2,4,5, these results suggest that tens of thousands of pregnancies could be negatively impacted by the first wave of the epidemic. These projections constitute a revised upper limit of populations at risk in the current Zika epidemic, and our approach offers a new way to make rapid assessments of the threat posed by emerging infectious diseases more generally.
Full-text available
Transmission of Zika virus (ZIKV) was first detected in Colombia in September 2015. As of April 2016, Colombia had reported over 65,000 cases of Zika virus disease (ZVD). We analysed daily surveillance data of ZVD cases reported to the health authorities of San Andres and Girardot, Colombia, between September 2015 and January 2016. ZVD was laboratory-confirmed by reverse transcription-polymerase chain reaction (RT-PCR) in the serum of acute cases within five days of symptom onset. We use daily incidence data to estimate the basic reproductive number (R0) in each population. We identified 928 and 1,936 reported ZVD cases from San Andres and Girardot, respectively. The overall attack rate for reported ZVD was 12.13 cases per 1,000 residents of San Andres and 18.43 cases per 1,000 residents of Girardot. Attack rates were significantly higher in females in both municipalities (p < 0.001). Cases occurred in all age groups with highest rates in 20 to 49 year-olds. The estimated R0 for the Zika outbreak was 1.41 (95% confidence interval (CI): 1.15–1.74) in San Andres and 4.61 (95% CI: 4.11–5.16) in Girardot. Transmission of ZIKV is ongoing in the Americas. The estimated R0 from Colombia supports the observed rapid spread.
Full-text available
Background: As Zika virus continues to spread, decisions regarding resource allocations to control the outbreak underscore the need for a tool to weigh policies according to their cost and the health burden they could avert. For example, to combat the current Zika outbreak the US President requested the allocation of $1.8 billion from Congress in February 2016. Methodology/principal findings: Illustrated through an interactive tool, we evaluated how the number of Zika cases averted, the period during pregnancy in which Zika infection poses a risk of microcephaly, and probabilities of microcephaly and Guillain-Barré Syndrome (GBS) impact the cost at which an intervention is cost-effective. From Northeast Brazilian microcephaly incidence data, we estimated the probability of microcephaly in infants born to Zika-infected women (0.49% to 2.10%). We also estimated the probability of GBS arising from Zika infections in Brazil (0.02% to 0.06%) and Colombia (0.08%). We calculated that each microcephaly and GBS case incurs the loss of 29.95 DALYs and 1.25 DALYs per case, as well as direct medical costs for Latin America and the Caribbean of $91,102 and $28,818, respectively. We demonstrated the utility of our cost-effectiveness tool with examples evaluating funding commitments by Costa Rica and Brazil, the US presidential proposal, and the novel approach of genetically modified mosquitoes. Our analyses indicate that the commitments and the proposal are likely to be cost-effective, whereas the cost-effectiveness of genetically modified mosquitoes depends on the country of implementation. Conclusions/significance: Current estimates from our tool suggest that the health burden from microcephaly and GBS warrants substantial expenditures focused on Zika virus control. Our results justify the funding committed in Costa Rica and Brazil and many aspects of the budget outlined in the US president's proposal. As data continue to be collected, new parameter estimates can be customized in real-time within our user-friendly tool to provide updated estimates on cost-effectiveness of interventions and inform policy decisions in country-specific settings.
Full-text available
Between October 2013 and April 2014, more than 30,000 cases of Zika virus (ZIKV) disease were estimated to have attended healthcare facilities in French Polynesia. ZIKV has also been reported in Africa and Asia, and in 2015 the virus spread to South America and the Caribbean. Infection with ZIKV has been associated with neurological complications including Guillain-Barré Syndrome (GBS) and microcephaly, which led the World Health Organization to declare a Public Health Emergency of International Concern in February 2015. To better understand the transmission dynamics of ZIKV, we used a mathematical model to examine the 2013–14 outbreak on the six major archipelagos of French Polynesia. Our median estimates for the basic reproduction number ranged from 2.6–4.8, with an estimated 11.5% (95% CI: 7.32–17.9%) of total infections reported. As a result, we estimated that 94% (95% CI: 91–97%) of the total population of the six archipelagos were infected during the outbreak. Based on the demography of French Polynesia, our results imply that if ZIKV infection provides complete protection against future infection, it would take 12–20 years before there are a sufficient number of susceptible individuals for ZIKV to re-emerge, which is on the same timescale as the circulation of dengue virus serotypes in the region. Our analysis suggests that ZIKV may exhibit similar dynamics to dengue virus in island populations, with transmission characterized by large, sporadic outbreaks with a high proportion of asymptomatic or unreported cases.
The Zika virus, and its suspected link to an increase in the number of babies born with microcephaly and a spike in neurological conditions is a global public health emergency, the World Health Organization has declared.1 2 3 4 5 6 WHO’s public health emergency committee, which met on Monday 1 February, declared that the spread of the virus was an emergency of public health concern, triggering funding into research, vector control, and efforts to stop pregnant women becoming infected. WHO’s director general, Margaret Chan, told a press conference that the virus was an “extraordinary event and a public health threat to other parts of the world.” She said that evidence for a link between the virus and the increase in cases of microcephaly in babies and a spike in cases of …
Using Ebolavirus genomic and epidemiological data, we conduct the first joint analysis where both data types are used to fit dynamic transmission models for an ongoing outbreak. Our results indicate that transmission is clustered, highlighting a potential bias in medical demand forecasts, and provide the first empirical estimate of underreporting. © The Author 2014. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: