Optimal Control for a COVID-19 Model Accounting for Symptomatic and Asymptomatic

Abstract

Building on an SEIR-type model of COVID-19 where the infecteds are further divided into symptomatic and asymptomatic, a system incorporating the various possible interventions is formulated. Interventions, also referred to as controls, include transmission reduction (e.g., lockdown, social distancing, barrier gestures); testing/isolation on the exposed, symptomatic and asymptomatic compartments; and medical controls such as enhancing patients’ medical care and increasing bed capacity. By considering the government’s capacity, the best strategies for implementing the controls were obtained using optimal control theory. Results show that, if all the controls are to be used, the more able the government is, the more it should implement transmission reduction, testing, and enhancing patients’ medical care without increasing hospital beds. However, if the government finds it very difficult to implement the controls for economic reasons, the best approach is to increase the hospital beds. Moreover, among the testing/isolation controls, testing/isolation in the exposed compartment is the least needed when there is significant transmission reduction control. Surprisingly, when there is no transmission reduction control, testing/isolation in the exposed should be optimal. Testing/isolation in the exposed could seemingly replace the transmission reduction control to yield a comparable result to that when the transmission reduction control is being implemented.

Optimal vaccination control for COVID-19 in a metapopulation model: a case of the Philippines

“…where G Si , G Ii , G Ti , G Ri , and G Z are the right hand sides of Equations ( 5), ( 6), ( 7), (8), and (11), respectively, for i = 1, 2, ..., n. Applying Pontryagin's maximum principle, there exist adjoint variables λ Si , λ Ii , λ Ti , λ Ri , λ Z , i = 1, 2, ..., n, which satisfy the following system of ordinary differential equations:…”

“…Since the early stages of the pandemic, mathematical modeling has assisted the Philippines in its response, and a number of studies [6][7][8], including regional ones [9,10], have been published. In addition, it has been used extensively in many countries to evaluate pandemic control strategies such as lockdown, social distancing, testing, face mask use, and public awareness [11][12][13][14][15][16][17][18][19][20][21].…”

Vaccination and variants: A COVID-19 multi-strain model evolution for the Philippines

“…However, managing the number of susceptible does, which containment and vaccines have a significant impact role to play. Later, the same model was used to investigate the implemented non-pharmaceutical interventions (NPI) in the country [6]. Here, NPIs include lockdown, social distancing, mass testing, and strengthening the healthcare system.…”

Maximum Principle for Optimal Control of COVID-19 Spread

“…Optimal strategy in scenarios that involve administration of vaccines, increased vaccine supply, and limited combinations of interventions [19][20][21][22][23]. Furthermore, the mathematical model is extended to the optimal control problem by including control variables in the model as surgical face masks, social distancing, selfisolation, sensitization and awareness of disease transmission, as well as control including reducing the cost infrastructure such as testing facilities, which can lead to detection fast cases infected with Covid [24][25][26][27][28][29].…”