Optimal control and comprehensive cost-effectiveness analysis for COVID-19

Abstract

Cost-effectiveness analysis is a mode of determining both the cost and economic health outcomes of one or more control interventions. In this work, we have formulated a non-autonomous nonlinear deterministic model to study the control of COVID-19 to unravel the cost and economic health outcomes for the autonomous nonlinear model proposed for the Kingdom of Saudi Arabia. We calculated the strength number and noticed the strength number is less than zero, meaning the proposed model does not capture multiple waves, hence to capture multiple wave new compartmental model may require for the Kingdom of Saudi Arabia. We proposed an optimal control problem based on a previously studied model and proved the existence of the proposed optimal control model. The optimality system associated with the non-autonomous epidemic model is derived using Pontryagin’s maximum principle. The optimal control model captures four time-dependent control functions, thus, -practising physical or social distancing protocols; -practising personal hygiene by cleaning contaminated surfaces with alcohol-based detergents; -practising proper and safety measures by exposed, asymptomatic and symptomatic infected individuals; -fumigating schools in all levels of education, sports facilities, commercial areas and religious worship centres. We have performed numerical simulations to investigate extensive cost-effectiveness analysis for fourteen optimal control strategies. Comparing the control strategies, we noticed that; Strategy 1 (practising physical or social distancing protocols) is the most cost-saving and most effective control intervention in Saudi Arabia in the absence of vaccination. But, in terms of the infection averted, we saw that strategy 6, strategy 11, strategy 12, and strategy 14 are just as good in controlling COVID-19.

HIV and COVID-19 co-infection: A mathematical model and optimal control

“…A lot of mathematical models have been formulated to understand the dynamics of COVID-19 [47] , [48] , [49] , [50] , [51] . Geographical overlap of COVID-19 with other commodities such as HIV, malaria and TB is of global public health concern.…”

Computing R0 of dynamic models by a definition-based method

“…And as transmissibility evolves, herd immunity thresholds can be estimated by modified R 0 ( Gomes et al., 2022 ; Montalbán et al., 2020 ). Furthermore, following the results of the basic reproduction number, optimal control analyses were developed to reduce disease transmission, including co-infection ( Tchoumi et al., 2021 ), as well as the most cost-efficient measures ( Asamoah et al., 2022 ).…”

The impact of lockdown in Wuhan on residents confidence in controlling COVID-19 outbreak at the destination cities

“…And some studies have revealed that the Wuhan lockdown could benefit many people and communities, including the locals and the others (12, [20][21][22][23], and substantially suspends the national and global outbreak of COVID-19 pandemic (7,(24)(25)(26). Moreover, dynamical modeling is one of the useful tools to reveal the transmission dynamics of COVID-19 (27)(28)(29)(30). Sun et al (31) employed the dynamical model to investigate the effects of lockdown on the COVID-19 transmission in Wuhan, and found that although a later adoption of lockdown measures would reduce the scale of the epidemic in this city, there would be uncontrollable effects on other Chinese provinces and even the world.…”