Research group: TheSiMBioSys

Human diseases that originate from non-human reservoirs, zoonoses, constitute 75% of emerging infectious diseases and pose a significant threat to public health. In the particular case of Ebola, the 2014 epidemic in West Africa has been the largest registered ever, affecting tens of thousands individuals with mortality rates close to 75%. In addition, Ebola virus (EV) decimates the great ape population, thus posing a conservation hazard, represents a major threat worldwide through the importation of infections and its possible misuse as biological weapon, and has dramatic economic and humanitarian consequences. The proposed studies hypothesize that (a) understanding the ecology of the main EV reservoir, i.e. bats, due to the environmental pressure/changes and the consideration of socioeconomic, cultural and demographic (SCD) factors is required to establish accurately the risk of outbreaks and spillovers (of Ebola and of other zoonotic diseases such as SARS-CoV-2), and (b) risk assessment demands to quantify rigorously the large uncertainty involved with data. Specific aims of this proposal are (1) to understand the migratory pattern of the Ebola reservoir due environmental pressure/changes and (2) to assess the effect of SCD factors in the probability of hemorrhagic fever outbreaks. The proposed methodology to address these questions combines tools from computational epidemiology, engineering, data science, and uncertainty quantification. The ultimate goal of this proposal is to shift the current research paradigm in the context of Ebola to better understand the interplay between the climate- and resources-driven ecology of the Ebola zoonotic reservoir and the risk of hemorrhagic fever spreading in humans when considering SCD factors.

Javier Buceta

National Institutes of Health (NIH)