Mathematical Modeling of Covid-19 and Dengue Co-Infection Dynamics in Bangladesh: Optimal Control and Data-Driven Analysis

Abstract

This paper aims to explore the transmission dynamics of COVID-19 with dengue co-infection using mathematical modeling. In this study, SIR model is developed that explains the trajectory of the epidemic to boost a plan for an effective control strategy for COVID-19 in Bangladesh. The model is extended to optimal control strategies. Pontryagin’s Principle is used to establish the appropriate conditions for the existence of optimal control and the optimality system for the co-infection model. Coinfected cases were reduced with control greater than without control. Using Omicron incidence cases from 1st January – 13th April 2022, the maximum likelihood estimate of R0 with a 95% confidence interval is1.89 [ 95% CI: 1.88, 1.91]. The R0 estimated from the exponential growth method is 2.08 [95% CI: 2.07,2.09]and time-dependent estimate is 2.10[95% CI: 1.72,2.58]. The generalized logistic growth model predicted 19, 52,131 cumulative cases on day 103 (April 13, 2022), and a relatively flat curve of cumulative growth of COVID-19 cases implies a declining trend of new cases. The study also found from sensitivity analysis that, R0 is proportional to the mean generation time. This paper attempted to focus on suppressing the COVID-19 co-infections by preventing dengue and COVID-19. The results of the study show that by implementing optimal control spread of dengue and COVID-19 could be minimized. The logistic growth model suggests that the infection rate of COVID-19 is decreasing.