"Stable Estimation of Time Dependent Transmission rate: A retrospective look at the Covid 19 Epidemic in Ivory Coast West Africa."
Stable estimation of system parameters for infectious disease outbreaks is important for the design of an adequate forecasting algorithm. Stable estimation of disease parameters is also paramount in studying epidemics after the fact. In particular, for compartmental epidemic models, the transmission rate is important in evaluating one’s response to an outbreak.
The Coronavirus disease 2019 (COVID-19) pandemic triggered a global response as countries and organizations mobilized to combat the epidemic. The World Health Organization provided guidance and recommendations including lockdowns, quarantine, travel restrictions, and social distancing. Local governments, enacted responses based on their specific socio-economic contexts as the pandemic exposed many systemic vulnerabilities in many countries’ health systems, disaster preparedness, and adequate response capabilities.
In this study, we offer a retrospective look at the Pandemic in Côte D’Ivoire through the stable estimation of the time-dependent transmission rate of the disease throughout the epidemic from 2019 to 2022.
As a first approach, we use a Suceptible-Exposed-Infectious-Recovered compartmental model and pre-estimated disease parameters to fit the number of reported cases with respect to the time-dependent transmission rate comparing different functions to find the best-suited model. We estimate the transmission rate as a function of time using both parametric and non-parametric functions to capture the evolution of the transmission of the disease along with the control measures put in place by the local government and draw conclusions and lessons for the future.
Additional authors: Akossi Aurelie