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.
Highlights Four-way pooling correctly identified SARS-CoV-2 in 94 % of positive samples (n = 30/32) tested. Low viral loads, corresponding with late C T s, may be missed by the pooling process. 1:4 pooling is associated with expected 2 C T loss in analytical sensitivity. All individually negative samples (n = 128) were also negative by 4-way pooling.
Open Data Kit (ODK) is an open-source, modular toolkit that enables organizations to build application-specific information services for use in resource-constrained environments. ODK is one of the leading data collection solutions available and has been deployed by a wide variety of organizations in dozens of countries around the world. This paper discusses how recent feedback from users and developers led us to redesign the ODK system architecture. Specifically, the design principles for ODK 2.0 focus on: 1) favoring runtime languages over compile time languages to make customizations easier for individuals with limited programming experience; 2) implementing basic data structures as single rows within a table of data; 3) storing that data in a database that is accessible across applications and client devices; and 4) increasing the diversity of input types by enabling new data input methods from sensors. We discuss how these principles have led to the refinement of the existing ODK tools, and the creation of several new tools that aim to improve the toolkit, expand its range of applications, and make it more customizable by users.
In low-resource settings in developing countries, most records are still captured and maintained using paper forms.Despite a recent proliferation of digital data collection systems, paper forms remain a trusted, low-cost and ubiquitous medium that will continue to be utilized in these communities for years to come. However, it can be challenging to aggregate, share, and analyze the data collected using paper forms. This paper presents mScan, a mobile smartphone application that uses computer vision to capture data from paper forms that use a multiple choice or bubble format. The initial mScan implementation targets the task of digitizing paper forms used to record vaccine statistics in rural health centers in Mozambique. We have evaluated the accuracy and performance of mScan under a variety of different environmental conditions, and our results show that mScan is a robust tool that is capable of accurately capturing and digitizing data from paper forms.
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.
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