COMPUTATIONAL FRAMEWORK to EXPLORE IMPACT of ENVIRONMENTAL STRESS on EPIDEMICS

In the field of epidemiology, not only the disease and the carriers, but also the surrounding environment and the associated stresses play a vital role. Environmental stresses in a novel habitat may facilitate adaptive shifts. Organisms living under environmental stresses often experience higher mutation rates and display greater phenotypic and genetic variation. There is controversial evidence available in the literature about the impact of environmental stresses on the organisms and the resulting variation in mutation rates and the immune responses. In nature, "selection"and the high energetic costs of stress usually reduce this variation. The prior knowledge of the interaction between the stress and disease epidemics may help to control the disease spread at an early stage. A mathematical model of epidemiology, specifically focusing on the vector borne diseases, with environmental stress is reported in this paper. The model is validated with the aid of stability analysis. During this research, a set of parametric values is obtained using reverse engineering. For this purpose, the parametric evaluation is reported with the help of Monte Carlo Markov Chain (MCMC) reverse engineering. Among other factors, the environmental stresses are also responsible for different dynamics of the same disease, in different continents of the world. The proposed research methodology will help in forecasting the epidemiological problems such as the current threat of coronavirus.