Suppressing the impact of the COVID-19 pandemic using controlled testing and isolation

Abstract

The Corona virus disease has significantly affected lives of people around the world. Existing quarantine policies led to large-scale lock-downs because of the slow tracking of the infection paths, and indeed we see new waves of the disease. This can be solved by contact tracing combined with efficient testing policies. Since the number of daily tests is limited, it is crucial to exploit them efficiently to improve the outcome of contact tracing (technological or human-based epidemiological investigations). We develop a controlled testing framework to achieve this goal. The key is to test individuals with high probability of being infected to identify them before symptoms appear. These probabilities are updated based on contact tracing and test results. We demonstrate that the proposed method could reduce the quarantine and morbidity rates compared to existing methods by up to a 50%. The results clearly demonstrate the necessity of accelerating the epidemiological investigations by using technological contact tracing. Furthermore, proper use of the testing capacity using the proposed controlled testing methodology leads to significantly improved results under both small and large testing capacities. We also show that for small new outbreaks controlled testing can prevent the large spread of new waves. Author contributions statement: The authors contributed equally to this work, including conceptualization, analysis, methodology, software, and drafting the work.

Figure 1

An illustration of the ATI algorithm proposed in this paper.

Figure 2

Simulation results for a COVID-19 outbreak in a population of 100,000 people, and testing capacity of 0.3% per day.

Figure 3

Simulation results for a COVID-19 outbreak in a population of 1 million people. Daily testing capacity ($0.3,5\%$).

Figure 4

Simulation results for a COVID-19 outbreak in a population of 100,000 people. Daily testing capacity is set to 1%.

Figure 5

Illustrations for model settings as described in the “Methods” section.