Mathematical assessment of Monkeypox with asymptomatic infection: Prediction and optimal control analysis with real data application
Shuo Li, Samreen Samreen, Saif Ullah, Salman A. AlQahtani, Sayed M. Tag, Ali Akgül
Abstract
The effeteness of control intervention against monkeypox infection is threatened after the emergence of new outbreaks in many endemic and non-endemic countries. The objective of the present study is to develop a new mathematical model examining the dynamics, future prediction, and effective control intervention of this emerging disease in Nigeria. The model is parameterized using the recent monkeypox outbreak from the start (end of February 2022) to January 07, 2023, in Nigeria. The model best fit to the actual cases is presented using a standard nonlinear least square minimizing residual method and the basic reproduction number is evaluated. Further, we present future predictions of the disease using our model simulation and disease dynamics have been illustrated with different controlling scenarios based on the real data set. In this study, we also conducted mathematical analysis of the model and used a normalized sensitivity analysis to identify the most critical parameters in the system. Moreover, an optimal control problem is developed using four time-dependent control interventions. Detailed simulations of constant controls, with and without time-dependent optimal controls are shown. It is concluded that to eradicate the infection the necessary control strategies are strict personal protection coupled with effective vaccination policy should be implemented. The findings of the present study provide valuable insights for health officials to implement effective and optimal control measures to curb the outbreak.