Analysis of data management capacity in regions with high infectious disease spillover risk

This research is the subject of the NIEHS Intramural Targeted Climate Change & Health Program grant, awarded February 2023, which is conducted in collaboration with the Georgetown University Center for Global Health Science & Security, the Verena Institute, and the Janne E. Nolan Center on Strategic Weapons at the Council on Strategic Risks. Research by project collaborators has found that global limitations to data management and sharing may be strong enough to significantly distort most secondary analyses of emerging disease hotspots and drivers. Previously, using a new database of emerging infectious disease case and outbreak data created by the collaboratorsthe largest compiled to date, with over 63,000 distinct geospatial occurrences of 25 diseasescoauthors used spatial regression analyses to show that for 19 of 25 diseases, spillover incidents were predicted by travel time to healthcare, indicating that this variable acts as a proxy for disease surveillance and reporting capacity. On the other hand, a much smaller number of diseases showed any relationship to climate change (11 of 25), biodiversity loss (10), agricultural expansion (6), hunting pressure (4), or similar drivers. These patterns reflect the global state of emerging infectious disease surveillance: currently, the capacity for data collection, integration, and release is a stronger factor in observed patterns (reported infections) than any causal socioecological mechanisms. These data form the basis of my analysis of how national data management capacity determines the observed patterns of zoonotic and vector-borne disease emergence. Improvements in global infectious disease data systems are needed, but characterization efforts for data management capacity are still early in development and do not target the issues of infectious diseases specifically. This research evaluates data management capacity in spillover hotspots and how data are used to predict and validate risk metrics to determine how improvements can lead to better pandemic preparedness and response. The first step of this research is an evaluation of data management capacity in regions with high predicted outbreak potential, currently in preparation. Quality data is paramount for detecting and responding to disease outbreaks; therefore, data management capacity at national and global levels warrants independent investigation. We are assessing capacity both for early warning and for outbreak response by 1) explicitly defining data management capacity for zoonotic and vector-borne diseases, with a focus on pandemic preparedness and response and climate-sensitive infectious diseases; 2) quantifying gaps in data management and identifying whether they are from an explicit lack of in-country capacity or are due to political, security, financial, or other reasons; and 3) modeling the impact this has on outbreak potential. Using reports like the Open Data Barometer, Global Data Barometer, WHO SCORE, and World Bank Statistical Performance Indicators to catalogue currently available indicators, we are evaluating data management capacity specifically through the lens of preparedness and response. Funding for this project was not received until late Spring 2023. Postdoctoral fellow was onboarded in September 2023.