Optimal COVID-19 testing strategy on limited resources

The last three years have been spent combating COVID-19, and governments have been seeking optimal solutions to minimize the negative impacts on societies. Although two types of testing have been performed for this-follow-up testing for those who had close contact with infected individuals and mass-testing of those with symptoms-the allocation of resources has been controversial. Mathematical models such as the susceptible, infectious, exposed, recovered, and dead (SEIRD) model have been developed to predict the spread of infection. However, these models do not consider the effects of testing characteristics and resource limitations. To determine the optimal testing strategy, we developed a testing-SEIRD model that depends on testing characteristics and limited resources. In this model, people who test positive are admitted to the hospital based on capacity and medical resources. Using this model, we examined the infection spread depending on the ratio of follow-up and mass-testing. The simulations demonstrated that the infection dynamics exhibit an all-or-none response as infection expands or extinguishes. Optimal and worst follow-up and mass-testing combinations were determined depending on the total resources and cost ratio of the two types of testing. Furthermore, we demonstrated that the cumulative deaths varied significantly by hundreds to thousands of times depending on the testing strategy, which is encouraging for policymakers. Therefore, our model might provide guidelines for testing strategies in the cases of recently emerging infectious diseases.

Automated summary

The past three years have been dedicated to combating COVID-19 and finding optimal solutions to minimize its negative impacts. Different types of testing have been performed, but resource allocation has been controversial. To determine the best testing strategy, a testing-SEIRD model considering testing characteristics and limited resources was developed. Simulations revealed that the infection dynamics show an all-or-none response, and the optimal and worst testing combinations were determined based on resource availability and cost ratio. It was also demonstrated that the testing strategy significantly influenced the cumulative death toll.