Title : Mathematical modeling of COVID-19 dynamics in a West African context
Abstract:
The novel Coronavirus Disease 2019 (covid-19), which emerged in Wuhan, China is a highly infectious disease caused by (SARS-CoV-2) and has significantly affected public health and socio-economic well-being worldwide. Its transmission highlights the potentially important role of transmission heterogeneities, requiring better modeling approaches to determine their role in dynamics and control. This study aims to develop a method for detecting heterogeneities in susceptibility or connectivity in covid-19 transmission by fitting a modified Susceptible, Infected, Recovered model to incidence data. The parameters of the model are estimated using Markov Chain Monte Carlo techniques. The proposed method is tested on simulated data to ascertain its effectiveness before applying it to real-world incidence time-series from different Nigerian States supplied by their Centre for Disease Control. The best performing models including different sources of heterogeneity, is determined using the Watanabe-Akaike Information Criteria (WAIC) and Leave-one-out-cross-validation (LOO) and used to make recommendations on possible interventions. By identifying and detecting heterogeneities that act to lower the Herd Immunity Threshold and the effective reproduction number, findings from this study will be useful in providing an improved understanding of disease spread, reducing the epidemic size and the burden of disease to make better-informed decisions for managing emerging and re-emerging infectious diseases like covid-19.
