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The first phase of PREDICT: Surveillance for emerging infectious zoonotic diseases of wildlife origin (2009-2014)

  • Canadian Wildlife Health Cooperative | Réseau Canadien pour la Santé de la Faune
Abstracts / International Journal of Infectious Diseases 53S (2016) 4–163 31
Ecology and environmental drivers of
antimicrobial resistance
U. Theuretzbacher
Center for Anti-Infective Agents, Vienna/AT
Given the multifaceted nature of the resistance problem, the
focus of attention has expanded from human and animal antibiotic
use to the human influence on resistance in the environment. The
link between the animal and human sector are well studied and
led to policy changes in some parts of the world. Such regulatory
initiatives are still missing in the environmental field which is usu-
ally not included in the One Health approach to tackle the global
resistance problem.
The direct release of multidrug resistant bacteria from health-
care settings and animal farms into the environment as well as the
pollution of the environment with high concentrations of antibi-
otics create a dangerous resistance reservoir. Recent metagenomics
studies highlighted the role of mobile genetic elements (mobilome)
as environmental pollutants and their role in co-assembling of
resistance determinants and horizontal transfer from environ-
mental bacteria to pathogens and vice-versa. Phages are widely
distributed in nature and may act as vehicles for such co-localized
resistance cluster genes resistance genes with significant implica-
tions for the horizontal spread of antibiotic resistance. They are
enriched in microbial genomes or independent of their bacte-
rial host in hospital wastewater systems, animal husbandry and
its wastes, aquaculture, and in wastewater treatment plants. This
resistance gene pool (environmental resistome) has been described
in natural waters, sediments near effluent pipe openings, and in
soil especially agricultural farm land due to irrigation with con-
taminated water. High levels of antibiotic residues in wastewater
plants, natural waters but also reclaimed water supply systems
due to unregulated pollution from antibiotic manufacturing plants
exert unprecedented selection pressure in nature. Addressing this
problem requires concerted policy actions and needs to be included
in the One Health approach of current global initiatives.
Estimating FluNearYou correlation to CDC’s
R. Arafata,, E. Bakotab, E. Santosb
aHouston Health Department, Office of Surveillance
and Public Health Preparedness, Houston, TEXAS/US
bHHD, HHD, Houston/US
Purpose: To provide evidence for the data quality of Flu Near
You (FNY) by evaluating the national and Houston datasets against
CDC influenza-like illness (ILI) data.
Methods & Materials: Eachweek, FNY users submitsurveys that
describe the symptoms experienced for the previous week. The sur-
vey tracks if and when a user has received a flu shot and experienced
ILI. This study used those survey responses. The data were deiden-
tified and provided by the Skoll Global Threats Fund to the Houston
Health Department (HHD). The FNY data were compared to ILINet’s
national summary of ILI and influenza positive tests by estimating
the correlation coefficient for the 2014-2015 influenza season. FNY
total ILI counts were correlated to total positive influenza tests, and
FNY percent ILI was compared to ILINet’s unweighted percent ILI.
Mean correlation coefficients for 1,000 bootstraps were estimated
for a sequence of weekly user counts of 10 to 10,435 in increments
of 10. Bootstrapped samples were stratified by ZIP code to account
for fluctuations in weekly participation for both FNY and ILINet, as
both datasets see an increase in user participation during influenza
season. R version 3.2 was used for all analyses; HHD received the
line-list dataset from FNY that contained nearly 400,000 entries.
Each entry corresponds to a single person.
Results: Correlation of the full FNY dataset against ILI & flu
tests are very high (r2= .94 and .92 respectively).
Weekly reports from < 200 weekly users have high variance
in their correlation to ILINet and a moderate correlation coefficient
(r2between 0.3 and 0.7).
At low participation counts, (< 400 per week) FNY correlates
better with positive influenza tests than percentage with ILI.
Overall, FNY data correlates well with national ILINet data,
even at limited participation levels.
Conclusion: Approximately two-thirds of the counties within
the United States have a population of < 50,000. As such, FNY
provides a simple, low-cost opportunity for public health officials
within those jurisdictions to obtain data that reasonably mirrors
ILINet. For larger jurisdictions, FNY is another tool available to track
and identify seasonal influenza and engage the public on preven-
The first phase of PREDICT: Surveillance for
emerging infectious zoonotic diseases of
wildlife origin (2009-2014)
D. Jolya,, C. Kreuder Johnsonb, T. Goldsteinc, S.J.
Anthonyd, W. Kareshe, P. Daszake, N. Wolfef,S.
Murrayg, J. Mazeth
aMetabiota, Nanaimo, BRITISH COLUMBIA/CA
bUniversity of California - Davis, Wildlife Health
Center, Davis, CA/US
cUniversity of California - Davis, Davis, CA/US
dColumbia University, Center for Infection and
Immunity, MSPH, New York, NY/US
eEcoHealth Alliance, New York, NY/US
fGlobal Viral Forecasting Initiative, San Francisco,
gSmithsonian, Washington, DC/US
hUC Davis, Davis, CA/US
Purpose: Based on the premise that the majority of emerg-
ing infectious zoonotic diseases originate in wildlife species, the
United States Agency for International Development created the
Emerging Pandemic Threats Program to increase capacity in the
developing world to detect and respond to emerging threats. A
coalition of organizations led by the University of California at
Davis, and including EcoHealth Alliance, Wildlife Conservation
Society, Metabiota Inc., and the Smithsonian Institution, imple-
mented the first phase of PREDICT (2009-2014), the component of
the program tasked with developing the capacity for early detection
of these emerging threats.
Methods & Materials: Based on an iterative process of field and
digital data collection and statistical computer modeling, PREDICT
identified geographic, taxonomic, and behavioural interfaces likely
to lead to disease emergence.
Results: Between 2009 and 2014, over 250,000 samples from
over 56,000 animals were collected from wildlife in close proxim-
ity to humans (46.6%), free-ranging wildlife and hunted wildlife
(31.8%), traded and market wildlife (14.6%), and other sampling
sources (7%). Family-level viral screening was conducted using
32 Abstracts / International Journal of Infectious Diseases 53S (2016) 4–163
consensus PCR, in 32 laboratories in 20 developing countries
around the world. Over 800 hundred novel viruses were found,
based on molecular characterisation and on the percentage
sequence identity between established species, in addition to over
a hundred known viruses.
Conclusion: Implementation of the PREDICT surveillance strat-
egy and prioritization process has improved the capacity in hotspot
countries to detect and respond to emerging disease threats.
Emerging and re-emerging infectious diseases
in displaced populations 1998 to 2016: An
analysis of ProMED-mail reports
J.W. Ramatowskia,, L. Madoffa, B. Lassmanna,N.
aInternational Society for Infectious Diseases,
Brookline, MA/US
bCDC, Atlanta, GA/US
Purpose: Understanding the occurrence of emerging and re-
emerging infectious disease outbreaks in displaced populations is
important to ensure adequate control measures.
Methods & Materials: The 1994–2015 ProMED-mail record
database was queried for records containing the term “refugee,”
“asylum seeker,” and “displaced.” For the purpose of this analysis,
together these groups are termed displaced populations (DPs). Of
the 52,247 records, 600 were returned. Records containing one of
the listed terms were then assessed for the following information:
reported disease outbreak location, reported disease, origin of DPs,
and number of people affected by the outbreak. Unique outbreak
events were then identified. One outbreak event possibly contained
multiple records. Rates of outbreak events, per total number of
ProMED-mail reports each year, were calculated to ensure that
any changes, over time, were not simply secondary to changes
in the total number of reports posted on ProMED and were com-
pared using a two-sided t-test; P <0.05 was considered statistically
Results: Of 600 records, 118 disease outbreaks spanning years
1998–2015 were identified for use by this review. The mean inci-
dence of reported outbreak events increased across three, 5-year
interval periods (Figure 1). Kenya, Uganda, and Sudan had seven or
more outbreak events between years 1998-2015. The number of
outbreak events in DPs per total ProMED-mail posts between the
first and third 5-year interval increased by 277% (P <0.01). In total,
>559,000 cases of emerging and re-emerging infectious diseases
were reported from the 118 events. Of these, >520,000 cases were
related to the cholera outbreak in internally displaced people after
the 2010 Haitian earthquake. Additionally, >14,000 vaccine pre-
ventable disease cases (measles, chickenpox, polio, and tetanus)
and >10,000 Hepatitis E cases were reported. Less common out-
breaks included malaria, dengue, hemorrhagic fevers, meningitis,
leishmaniasis, louse-borne relapsing fever, anthrax and typhoid
Time Period Sum of Reports Average Reports-Per-Year
2010-2015 65 11
2004-2009 33 6
1998-2003 17 3
Conclusion: As the number of displaced people grows, there
has been an associated rise in reports related to emerging and re-
emerging diseases in DPs. The results of this analysis underscore
the importance of adequate infrastructure, human resources, clean
water access, and ongoing support needed to prevent, diagnose,
and treat infectious diseases in DPs, with particular emphasis in
under-resourced countries.
Digital functions in a participatory One Health
surveillance initiative aiming for pandemic
P. Susampaoa,, K. Chanachaib, P. Petraa,T.
Yanoc, S. Pattamakaewd, E. Laiyad,L.
Srikitjakarne, A. Crawleyf, J. Olsenf,M.
aOpendream Co., Ltd., Bangkok/TH
bDepartment Of Livestock Development, Bangkok/TH
cFaculty of Veterinary Medicine, Chiang Mai
University, Department of Food Animal Clinics,
Chiang Mai/TH
dFaculty of Veterinary Medicine, Chiang Mai
University, Chiang Mai/TH
eFaculty of Veterinary Medicine, Chiang Mai
University, Veterinary Biosciences and Veterinary
Public Health, Chiang Mai/TH
fSkoll Global Threats Fund, San Francisco/US
gSkoll Global Threats Fund, San Francisco, CA/US
Purpose: A community-based Participatory One Health Dis-
ease Detection system (PODD) using smart phone technology was
piloted in Chiang Mai, Thailand. Volunteers from 300 villages and
74 community governmental agencies were selected purposively
to submit daily surveillance reports of poultry health and disease
in their communities The primary objective of PODD in pilot phase
was to detect abnormal deaths in backyard animal in order to
elicit rapid investigation and response. Abnormal numbers or types
of death can be a signal of zoonotic diseases which transmits to
human and causes pandemic as a subsequence, such as abnormal
death in poultry could be an early clinical sign of highly pathogenic
avian influenza (HPAI), a potential precursor of an AI pandemic in
humans. Use of smart phones and digital technology is one of the
key factors making the PODD system workable.
Methods & Materials: The daily reports of poultry health and
abnormal poultry death are automatically captured, filtered with
predefined case and outbreak definitions, and projected onto a GIS
mapping system. The real time analysis of incoming reports allows
rapid detection of outbreaks and the generation of automatic SMS
warning messages to activate community contingency plans. A dis-
ease investigation team is dispatched to confirm the outbreak by
clinical examination and, as necessary, laboratory confirmation.
The system follows up automatically until 3 weeks after the last
report of sick animals or death in the affected area. All stakeholders
are notified after complete recovery to normal.
Results: During the first 16 months of PODD system piloting 25
abnormal death outbreaks were detected. Eight of the outbreaks
were laboratory confirmed with devastating epizootic pathogens,
while 17 of the outbreaks unable to confirmed the causes. Within
eight laboratory confirmed outbreaks, two of which resulted in
almost all chickens in the villages dying. The other six outbreaks
could be timely and effectively controlled by the communities.
Conclusion: Those early outbreak detection and rapid response
demonstrated the potential to integrate this PODD surveillance sys-
tem under their one health operation centres to prevent pandemic
in their community.
... Organized under the One Health theme, this work has been characterized by diversified Emergency Operations Centers (EOCs) and broader dissemination of environmental, animal and human health data by practitioners and researchers in the field. Examples include the EMPRES (Emergency Prevention System) database, a joint Food and Agriculture (FAO) and World Health Organization (WHO) venture that has been implemented in the global surveillance of highly pathogenic avian influenza, as well as the Predict pandemic surveillance program piloted by the United States Agency for International Development (USAID) and the One Health Institute at the University of California, Davis [6,7]. ...
... Importantly, the district is situated in a global biodiversity hotspot, the Eastern Afromontane, as defined by Conservation International. This hotspot, a thin arc curving through East Africa, contains approximately 10,856 unique species comprised of the taxonomic groups amphibians (229); birds (1,299); fishes (893); mammals (490); plants (7,598); and reptiles (347) [41]. ...
One Health is an emerging concept in the health sciences that approaches human, animal and environmental health from a single framework. This policy approach is grounded in the knowledge that approximately 70 percent of emerging diseases in humans originate from other species, and that this species crossover is precipitated by stresses to environmental systems such as habitat change and biodiversity loss. Remote sensing tools apply well to this approach due to the multitude of variables that can be measured across borders in real-time. This paper explores the challenges and opportunities of using satellite remote sensing to monitor biodiversity loss in real time, with a goal of predictive surveillance for emerging disease events. Key findings include that (1)certain emerging disease events are preceded by biodiversity changes that can be observed from space; (2)refining quantitative assessments of biodiversity loss is a critical next step; and (3)biodiversity loss as observed from space merits inclusion in emerging disease surveillance programs as a complement to in situ and epidemiological surveillance data.
... Technological advances made in the intervening years have allowed us to generate large amounts of data about the presence of micro-and macro-parasites in any given site for relatively little money and in a short time (e.g., Dunnum et al., 2017;Colella et al., 2021;Wille et al., 2021). Over the past decade, this capability has contributed to a kind of "basic field biology on steroids" in our accelerating view of pathogens in the global biosphere (USAID, 2014(USAID, , 2016Joly et al., 2016;Cook et al., 2017). ...
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Globally, humanity is coming to recognize the magnitude of the interactive crisis for emerging infectious disease (EID). Strategies for coping with EID have been largely in the form of reactive measures for crisis response. The DAMA protocol (Document, Assess, Monitor, Act), the operational policy extension of the Stockholm paradigm, constitutes a preventive/proactive dimension to those efforts. DAMA is aimed at focusing and extending human and material resources devoted to coping with the accelerating wave of EID. DAMA is integrative, combining efforts to strategically document the distribution of complex pathogen and host assemblages in the biosphere in the context of dynamic environmental interfaces that provide the opportunities for pathogen exchange and emergence. Movement of habitats and animals (a breakdown in ecological isolation) catalyzed by climate change and broader anthropogenic trajectories of environmental disruption provide the landscape of opportunity for emergence. Evolutionarily and ecologically conserved capacities for exploitation of host-based resources allow pathogens to persist in one place or among a particular spectrum of hosts and provide insights to predict outcomes of persistence and emergence in novel conditions and across changing ecological interfaces. DAMA trajectories combine “boots on the ground” contributions of citizen scientists working with field biologists in development and application of sophisticated archival repositories, bioinformatics, molecular biology, and satellite surveillance. DAMA is a focus for anticipation, mitigation, and prevention of EID through knowledge of pathogens present in the environment and actions necessary to diminish risk space for their emergence. DAMA can be an effective strategy for buying time in the arena of accelerating environmental and socioeconomic disturbance and expanding EID linked to a future of climate change. Information + action = prediction and lives saved in a realm of EID. This article has been produced in support of and with appreciation for the efforts by Gábor Földvári of the Institute of Evolution, Centre for Ecological Research, and the Centre for Eco-Epidemiology, National Laboratory for Health Security (both located at 1121 Budapest, Konkoly-Thege Miklós út 29-33, Hungary). Through his untiring efforts, team building, and leadership, he has secured the first EU-wide team research grant. This work was supported by the National Research, Development and Innovation Office in Hungary (RRF-2.3.1-21-2022-00006) and the COST Action CA21170 “Prevention, anticipation and mitigation of tick-borne disease risk applying the DAMA protocol (PRAGMATICK),” which represent the first funded efforts to apply the principles of the DAMA protocol.
... has investigated viruses at the human-animal interface since 2009 and has worked to construct platforms for virus infection surveillance, creating a transdisciplinary collaborative team and developing analysis pipelines or databases. [18][19][20] From 2009 to 2020, by investigating more than 164,000 animal and human samples, this project succeeded in detecting over 1100 viruses, including filoviruses and coronaviruses that have repeatedly caused infectious diseases in humans ( programs-projects/predict-project). ...
Zoonotic diseases considerably impact public health and socioeconomics. RNA viruses reportedly cause approximately 94% of zoonotic diseases documented from 1990 to 2010, emphasizing the importance of investigating RNA viruses in animals. Furthermore, it has also been estimated that hundreds of thousands of animal viruses capable of infecting humans are yet to be discovered, warning against the inadequacy of our understanding of viral diversity. High-throughput sequencing (HTS) technology has enabled the identification of viral infections with relatively little bias. Viral searches using both symptomatic and asymptomatic animal samples by HTS technology have revealed hidden viral infections. This review introduces the history of viral searches using HTS technology, current analytical limitations, and future potentials. We primarily summarize recent research on large-scale investigations on viral infections reusing HTS data from public databases. Furthermore, considering the accumulation of uncultivated viruses, we also discuss current studies and challenges for connecting viral sequences to their phenotypes using various approaches: performing data analysis, developing predictive modeling, or implementing high-throughput platforms of virological experiments. We believe that this article provides a future direction in large-scale investigations of potential zoonotic viruses using HTS technology. This article is protected by copyright. All rights reserved.
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Introduction Previous novel COVID-19 pandemics, SARS and middle east respiratory syndrome observed an association of infection in pregnancy with preterm delivery, stillbirth and increased maternal mortality. COVID-19, caused by SARS-CoV-2 infection, is the largest pandemic in living memory. Rapid accrual of robust case data on women in pregnancy and their babies affected by suspected COVID-19 or confirmed SARS-CoV-2 infection will inform clinical management and preventative strategies in the current pandemic and future outbreaks. Methods and analysis The pregnancy and neonatal outcomes in COVID-19 (PAN-COVID) registry are an observational study collecting focused data on outcomes of pregnant mothers who have had suspected COVID-19 in pregnancy or confirmed SARS-CoV-2 infection and their neonates via a web-portal. Among the women recruited to the PAN-COVID registry, the study will evaluate the incidence of: (1) miscarriage and pregnancy loss, (2) fetal growth restriction and stillbirth, (3) preterm delivery, (4) vertical transmission (suspected or confirmed) and early onset neonatal SARS-CoV-2 infection. Data will be centre based and collected on individual women and their babies. Verbal consent will be obtained, to reduce face-to-face contact in the pandemic while allowing identifiable data collection for linkage. Statistical analysis of the data will be carried out on a pseudonymised data set by the study statistician. Regular reports will be distributed to collaborators on the study research questions. Ethics and dissemination This study has received research ethics approval in the UK. For international centres, evidence of appropriate local approval will be required to participate, prior to entry of data to the database. The reports will be published regularly. The outputs of the study will be regularly disseminated to participants and collaborators on the study website ( ) and social media channels as well as dissemination to scientific meetings and journals. Study registration number ISRCTN68026880 .
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Erwinia tracheiphila is a virulent phytopathogen that infects two genera of cucurbit crop plants, Cucurbita spp. (pumpkin and squash) and Cucumis spp. (muskmelon and cucumber). One of the unusual ecological traits of this pathogen is that it is limited to temperate eastern North America. Here, we complete the first large-scale sequencing of an E. tracheiphila isolate collection. From phylogenomic, comparative genomic, and empirical analyses, we find that introduced Cucumis spp. crop plants are driving the diversification of E. tracheiphila into multiple lineages. Together, the results from this study show that locally unique biotic (plant population) and abiotic (climate) conditions can drive the evolutionary trajectories of locally endemic pathogens in unexpected ways.
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