Human diseases that originate from non-human reservoirs, zoonoses, constitute 75% of emerging infectious diseases and pose a signi?cant threat to public health. In the particular case of Ebola, the recent 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, and (b) risk assessment demands to quantify rigorously the large uncertainty involved with data. Speci?c 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 ques- tions combines tools from computational epidemiology, engineering, data science, and uncertainty quanti?cation. In order to understand the ecology of the zoonotic niche, a compartmental epidemiology model that includes resources dynamics/variability, climate, and bat mobility (a system of non-homogeneous partial differential equa- tions) will be implemented and characterized. The model will be calibrated with factual satellite data by means of different regression models. A novel sampling technique (Functional Quantization) and extensive numerical simu- lations on a High Performance Computing platform will be used to evaluate the uncertainty of data/parameters. To assess the role played by SCD factors in modulating EV infection rates to have implemented and tested different regression models. All the results will be integrated using probability theory in order to quantify the risk of Ebola outbreaks using a case study: Nigeria. A deliverable of this proposal will be a tool to predict Ebola outbreaks and the dynamics of the zoonotic niche: PAREO (Predictive Analysis of the Risk of Ebola Outbreaks). Altogether, 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.
The need of forecasting tools able to integrate coherently a broad set of factors for estimating the risk of Ebola outbreaks has been pointed out as a priority by clinicians and researchers. On one hand, this proposal aims at developing such predictive framework by combining computer models and factual data about the ecology of the Ebola virus reservoir (bats), environmental effects, and socioeconomic factors. On the other hand, the proposed studies aim at assessing the risk of Ebola outbreaks by evaluating possible scenarios systematically.
| Fiorillo, Graziano; Bocchini, Paolo; Buceta, Javier (2018) A Predictive Spatial Distribution Framework for Filovirus-Infected Bats. Sci Rep 8:7970 |
