Modeling and control of epidemics through testing policies

Utilization of the Unlinked Case Proportion to Control COVID-19: A Focus on the Non-pharmaceutical Interventional Policies of the Korea and Japan

OBJECTIVES

Korea and Japan have managed the spread of coronavirus disease 2019 (COVID-19) using markedly different policies, referred to as the "3T" and "3C" strategies, respectively. This study examined these differences to assess the roles of active testing and contact tracing as non-pharmaceutical interventions (NPIs). We compared the proportion of unlinked cases (UCs) and test positivity rate (TPR) as indicators of tracing and testing capacities.

METHODS

We outlined the evolution of NPI policies and investigated temporal trends in their correlations with UCs, confirmed cases, and TPR prior to the Omicron peak. Spearman correlation coefficients were reported between the proportion of UCs, confirmed cases, and TPR. The Fisher r-to-z transformation was employed to examine the significance of differences between correlation coefficients.

RESULTS

The proportion of UCs was significantly correlated with confirmed cases (r=0.995, p<0.001) and TPR (r=0.659, p<0.001) in Korea and with confirmed cases (r=0.437, p<0.001) and TPR (r=0.429, p<0.001) in Japan. The Fisher r-to-z test revealed significant differences in correlation coefficients between the proportion of UCs and confirmed cases (z=16.07, p<0.001) and between the proportion of UCs and TPR (z=2.12, p=0.034) in Korea and Japan.

CONCLUSIONS

Higher UCs were associated with increases in confirmed cases and TPR, indicating the importance of combining testing and contact tracing in controlling COVID-19. The implementation of stricter policies led to stronger correlations between these indicators. The proportion of UCs and TPR effectively indicated the effectiveness of NPIs. If the proportion of UCs shows an upward trend, more testing and contact tracing may be required.

COVID-19 and other viruses: Holding back its spreading by massive testing

The experience of Singapore and South Korea makes it clear that under certain circumstances massive testing is an effective way for containing the advance of the COVID-19. In this paper, we propose a modified SEIR model which takes into account tracing and massive testing, proving theoretically that more tracing and testing implies a reduction of the total number of infected people in the long run. We apply this model to the spreading of the first wave of the disease in Spain, obtaining numerical results. After that, we introduce a heuristic approach in order to minimize the COVID-19 spreading by planning effective test distributions among the populations of a region over a period of time. As an application, the impact of distributing tests among the counties of New York according to this method is computed in terms of the number of saved infected individuals.

On the effect of the number of tests and their time of application in tracing policies against COVID-19

In this paper we explore the effect of the number of daily tests on an epidemics control policy purely based on testing and selective quarantine, and the impact of these actions depending on the time their application starts. We introduce a general model incorporating a stochastic disease evolution, a particular weighted graph representing the population, and an optimal contact tracing strategy to allocate available tests. Simulations on a community of 50'000 individuals show that the evolution of the epidemic produces a clear non-linear response to the variation of the number of tests used and to the starting time of their application. These results suggest that not only a minimum number of tests is necessary to obtain a positive outcome from the tracing strategy but also that there exists a saturation on the contribution of additional tests. The results also show that the timing in the application of the measures is as important as the measures themselves and that an excessive delay can be hardly overcome.