Mammal assemblage composition predicts global patterns in emerging infectious disease risk.

As a source of emerging infectious diseases, wildlife assemblages (and related spatial patterns) must be quantitatively assessed to help identify high-risk locations. Previous assessments have largely focussed on the distributions of individual species; however, transmission dynamics are expected to depend on assemblage composition. Moreover, disease-diversity relationships have mainly been studied in the context of species loss, but assemblage composition and disease risk (e.g. infection prevalence in wildlife assemblages) can change without extinction. Based on the predicted distributions and abundances of 4466 mammal species, we estimated global patterns of disease risk through the calculation of the community-level basic reproductive ratio R0, an index of invasion potential, persistence, and maximum prevalence of a pathogen in a wildlife assemblage. For density-dependent diseases, we found that, in addition to tropical areas which are commonly viewed as infectious disease hotspots, northern temperate latitudes included high-risk areas. We also forecasted the effects of climate change and habitat loss from 2015 to 2035. Over this period, many local assemblages showed no net loss of species richness, but the assemblage composition (i.e. the mix of species and their abundances) changed considerably. Simultaneously, most areas experienced a decreased risk of density-dependent diseases but an increased risk of frequency-dependent diseases. We further explored the factors driving these changes in disease risk. Our results suggest that biodiversity and changes therein jointly influence disease risk. Understanding these changes and their drivers and ultimately identifying emerging infectious disease hotspots can help health officials prioritize resource distribution.

The Economic Case for a Pandemic Fund.

The rapid urban spread of Ebola virus in West Africa in 2014 and consequent breakdown of control measures led to a significant economic impact as well as the burden on public health and wellbeing. The US government appropriated $5.4 Billion for FY2015 and WHO proposed a $100 Million emergency fund largely to curtail the threat of future outbreaks. Using epidemiological analyses and economic modeling, we propose that the best use of these and similar funds would be to serve as global insurance against the continued threat of emerging infectious diseases. An effective strategy would involve the initial investment in strengthening mobile and adaptable capacity to deal with the threat and reality of disease emergence, coupled with repeated investment to maintain what is effectively a 'national guard' for pandemic prevention and response. This investment would create a capital stock that could also provide access to safe treatment during and between crises in developing countries, lowering risk to developed countries.

Global hotspots and correlates of emerging zoonotic diseases.

Zoonoses originating from wildlife represent a significant threat to global health, security and economic growth, and combatting their emergence is a public health priority. However, our understanding of the mechanisms underlying their emergence remains rudimentary. Here we update a global database of emerging infectious disease (EID) events, create a novel measure of reporting effort, and fit boosted regression tree models to analyze the demographic, environmental and biological correlates of their occurrence. After accounting for reporting effort, we show that zoonotic EID risk is elevated in forested tropical regions experiencing land-use changes and where wildlife biodiversity (mammal species richness) is high. We present a new global hotspot map of spatial variation in our zoonotic EID risk index, and partial dependence plots illustrating relationships between events and predictors. Our results may help to improve surveillance and long-term EID monitoring programs, and design field experiments to test underlying mechanisms of zoonotic disease emergence.

Hotspots of canine leptospirosis in the United States of America.

Leptospirosis is a widespread zoonotic disease that causes hepatic and renal disease in dogs and human beings. The incidence of leptospirosis in dogs in the USA appears to be increasing. This study used 14 years of canine leptospirosis testing data across 3109 counties in the USA to analyze environmental and socio-economic correlates with rates of infection and to produce a map of locations of increased risk for canine leptospirosis. Boosted regression trees were used to identify the probability of a dog testing positive for leptospirosis based on microscopic agglutination test (MAT) results, and environmental and socio-economic data. The Midwest, East and Southwest were more likely to yield positive tests for leptospirosis, although specific counties in Appalachia had some of the highest predicted probabilities. Location (suburban areas or areas with deciduous forest) and climate (precipitation and temperature) were predictors for positive MAT results for leptospirosis, although the precise direction and strength of the effects was difficult to interpret. Wide geographic variation in predicted risk was identified. This risk mapping approach may provide opportunities for improved diagnosis, control and prevention of leptospirosis in dogs.

Evaluation and Verification of the Global Rapid Identification of Threats System for Infectious Diseases in Textual Data Sources.

The Global Rapid Identification of Threats System (GRITS) is a biosurveillance application that enables infectious disease analysts to monitor nontraditional information sources (e.g., social media, online news outlets, ProMED-mail reports, and blogs) for infectious disease threats. GRITS analyzes these textual data sources by identifying, extracting, and succinctly visualizing epidemiologic information and suggests potentially associated infectious diseases. This manuscript evaluates and verifies the diagnoses that GRITS performs and discusses novel aspects of the software package. Via GRITS' web interface, infectious disease analysts can examine dynamic visualizations of GRITS' analyses and explore historical infectious disease emergence events. The GRITS API can be used to continuously analyze information feeds, and the API enables GRITS technology to be easily incorporated into other biosurveillance systems. GRITS is a flexible tool that can be modified to conduct sophisticated medical report triaging, expanded to include customized alert systems, and tailored to address other biosurveillance needs.

FLIRT-ing with Zika: A Web Application to Predict the Movement of Infected Travelers Validated Against the Current Zika Virus Epidemic.

INTRODUCTION:  Beginning in 2015, Zika virus rapidly spread throughout the Americas and has been linked to neurological and autoimmune diseases in adults and babies. Developing accurate tools to anticipate Zika spread is one of the first steps to mitigate further spread of the disease. When combined, air traffic data and network simulations can be used to create tools to predict where infectious disease may spread to and aid in the prevention of infectious diseases. Specific goals were to: 1) predict where travelers infected with the Zika Virus would arrive in the U.S.; and, 2) analyze and validate the open access web application's (i.e., FLIRT) predictions using data collected after the prediction was made. METHOD: FLIRT was built to predict the flow and likely destinations of infected travelers through the air travel network. FLIRT uses a database of flight schedules from over 800 airlines, and can display direct flight traffic and perform passenger simulations between selected airports. FLIRT was used to analyze flights departing from five selected airports in locations where sustained Zika Virus transmission was occurring. FLIRT's predictions were validated against Zika cases arriving in the U.S. from selected airports during the selected time periods.  Kendall's τ and Generalized Linear Models were computed for all permutations of FLIRT and case data to test the accuracy of FLIRT's predictions. RESULTS: FLIRT was found to be predictive of the final destinations of infected travelers in the U.S. from areas with ongoing transmission of Zika in the Americas from 01 February 2016 - 01 to April 2016, and 11 January 2016 to 11 March 2016 time periods. MIA-FLL, JFK-EWR-LGA, and IAH were top ranked at-risk metro areas, and Florida, Texas and New York were top ranked states at-risk for the future time period analyzed (11 March 2016 - 11 June 2016). For the 11 January 2016 to 11 March 2016 time period, the region-aggregated model indicated 7.24 (95% CI 6.85 - 7.62) imported Zika cases per 100,000 passengers, and the state-aggregated model suggested 11.33 (95% CI 10.80 - 11.90) imported Zika cases per 100,000 passengers. DISCUSSION: The results from 01 February 2016 to 01 April 2016 and 11 January 2016 to 11 March 2016 time periods support that modeling air travel and passenger movement can be a powerful tool in predicting where infectious diseases will spread next. As FLIRT was shown to significantly predict distribution of Zika Virus cases in the past, there should be heightened biosurveillance and educational campaigns to medical service providers and the general public in these states, especially in the large metropolitan areas.

Global biogeography of human infectious diseases.

The distributions of most infectious agents causing disease in humans are poorly resolved or unknown. However, poorly known and unknown agents contribute to the global burden of disease and will underlie many future disease risks. Existing patterns of infectious disease co-occurrence could thus play a critical role in resolving or anticipating current and future disease threats. We analyzed the global occurrence patterns of 187 human infectious diseases across 225 countries and seven epidemiological classes (human-specific, zoonotic, vector-borne, non-vector-borne, bacterial, viral, and parasitic) to show that human infectious diseases exhibit distinct spatial grouping patterns at a global scale. We demonstrate, using outbreaks of Ebola virus as a test case, that this spatial structuring provides an untapped source of prior information that could be used to tighten the focus of a range of health-related research and management activities at early stages or in data-poor settings, including disease surveillance, outbreak responses, or optimizing pathogen discovery. In examining the correlates of these spatial patterns, among a range of geographic, epidemiological, environmental, and social factors, mammalian biodiversity was the strongest predictor of infectious disease co-occurrence overall and for six of the seven disease classes examined, giving rise to a striking congruence between global pathogeographic and "Wallacean" zoogeographic patterns. This clear biogeographic signal suggests that infectious disease assemblages remain fundamentally constrained in their distributions by ecological barriers to dispersal or establishment, despite the homogenizing forces of globalization. Pathogeography thus provides an overarching context in which other factors promoting infectious disease emergence and spread are set.