The importance of quarantine: modelling the COVID-19 testing process

Abstract

We incorporate the disease state and testing state into the formulation of a COVID-19 epidemic model. For this model, the basic reproduction number is identified and its dependence on model parameters related to the testing process and isolation efficacy is discussed. The relations between the basic reproduction number, the final epidemic and peak sizes, and the model parameters are further explored numerically. We find that fast test reporting does not always benefit the control of the COVID-19 epidemic if good quarantine while awaiting test results is implemented. Moreover, the final epidemic and peak sizes do not always increase along with the basic reproduction number. Under some circumstances, lowering the basic reproduction number increases the final epidemic and peak sizes. Our findings suggest that properly implementing isolation for individuals who are waiting for their testing results would lower the basic reproduction number as well as the final epidemic and peak sizes.

Optimal Control for an Epidemic Model of COVID-19 with Time-Varying Parameters

“…Since the outbreak of the COVID-19 epidemic, a large number of models have been proposed to describe, simulate, and forecast its transmission rule. Some models are individualbased [9] or multi-scale [10], some models are based on cells and viruses [11] or stochastic processes [12,13], and others are developments of classical SIR and SEIR models [14][15][16][17][18][19][20]. On the other hand, there are a lot of studies about control measures.…”