Effectiveness of Testing and Contact-Tracing to Counter COVID-19 Pandemic: Designed Experiments of Agent-Based Simulation

Abstract

The widespread outbreak of the novel coronavirus disease COVID-19 has posed an enormous threat to global public health. A different set of policy interventions has been implemented to mitigate the spread in most countries. While the use of personal protective equipment and social distancing has been specifically emphasized, South Korea has deployed massive testing and contact-tracing program from the early stage of the outbreak. This study aims at investigating the effectiveness of testing and contact-tracing to counter the spread of infectious diseases. Based on the SEICR (susceptible-exposed-infectious-confirmed-recovered) model, an agent-based simulation model is developed to represent the behavior of disease spreading with the consideration of testing and contact-tracing in place. Designed experiments are conducted to verify the effects of testing and contact tracing on the peak number of infections. It has been observed that testing combined with contact tracing may lower the peak infections to a great extent, and it can thus be avoided for the hospital bed capacity to be overwhelmed by infected patients. It is implied that an adequate capability of testing and contact-tracing may enable us to become better prepared for an impending risk of infectious diseases.

Contact tracing as a measure to combat COVID-19 and other infectious diseases

“…It is worth noting that the virus spread reduction due to contact tracing in the present small-world network model is much more significant than that in the continuous deterministic models. For example, results 25 , 26 from continuous deterministic models with comparable input parameters show a much less profound reduction of infection cases. A specific example 26 is with a 40% testing coverage, 1 day turnaround time of test results, and 60% contact tracing ratio, the peak infection number is reduced by only 50%, a level much lower than Figs 2 and 4 imply.…”

“…For example, results 25 , 26 from continuous deterministic models with comparable input parameters show a much less profound reduction of infection cases. A specific example 26 is with a 40% testing coverage, 1 day turnaround time of test results, and 60% contact tracing ratio, the peak infection number is reduced by only 50%, a level much lower than Figs 2 and 4 imply.…”

Internet of Things Based Contact Tracing Systems

“…However, such a model only considers a few parameters in addition to the SEIR model, such as the CT rate, success rate of CT, and average daily contacts. The susceptible-exposed-infectious-confirmed-recovered (SEICR) model is adopted in [14] to the agent-based modeling (ABM) as an alternative to the theoretical modeling to deal with the complexity of the related factors. However, the agent-based modeling or simulation for applicable sensing mechanisms arising from COVID-19 with an IoT architecture has not been studied in the literature.…”

A mathematical model to assess the impact of testing and isolation compliance on the transmission of COVID-19

“…Additionally, a review of the types and usefulness of various compartmental models in predicting COVID-19 spread was conducted by Padmanabhan et al ( Padmanabhan et al, 2021 ). The effects of testing, isolation, and compliance have also been examined in the context of ABMs, as in ( Almagor & Picascia, 2020 ; Kim & Koo, 2021 ; Moghadas et al, 2020 ; Álvarez & Rojas-Galeano, 2020 ). In particular, Moghadas et al ( Moghadas et al, 2020 ) suggested that mass testing of the asymptomatic individuals is necessary to mitigate the spread of COVID-19; this helps slow the disease spread and thus bring the pandemic under control.…”