Containment of COVID-19: Simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation

Abstract

Efficient contact tracing and testing are fundamental tools to contain the transmission of SARS-CoV-2. We used multi-agent simulations to estimate the daily testing capacity required to find and isolate a number of infected agents sufficient to break the chain of transmission of SARS-CoV-2, so decreasing the risk of new waves of infections. Depending on the non-pharmaceutical mitigation policies in place, the size of secondary infection clusters allowed or the percentage of asymptomatic and paucisymptomatic (i.e., subclinical) infections, we estimated that the daily testing capacity required to contain the disease varies between 0.7 and 9.1 tests per thousand agents in the population. However, we also found that if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of new daily infections did not always decrease and could even increase exponentially, irrespective of the testing capacity. Under these conditions, we show that population-level information about geographical distribution and travel behaviour could inform sampling policies to aid a successful containment, while avoiding concerns about government-controlled mass surveillance.

Assessing the Impact of Policy Measures in Reducing the COVID-19 Pandemic: A Case Study of South Asia

“…The coefficient of economic support index with a negative sign, and p-value of less than 5% explored how government financial support for poor families during the pandemic plays a significant role in reducing the spread of COVID-19 infection. These results are in line with the findings of [44,45]. The stringency coefficient index with negative sign and significant p value provides evidence that the spread of the COVID-19 epidemic can be significantly suppressed by stringency measures adopted by the South Asian countries in the long-run.…”

Depression Symptoms Mediate Mismatch Between Perceived Severity of the COVID-19 Pandemic and Preventive Motives

“…For example, government officials might need to minimize uncertainty about the current pandemic status by planning efficient contact tracing and testing methods (Fiore et al, 2021) so that citizens could establish correct beliefs. At the same time, to promote voluntary cooperation from the people, we stress the risk of premature relaxation of prevention policies or overly optimistic information because the unexpectedly disappointing outcome may set off public resistance.…”

Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus

“…In accordance with the Centers for Disease Control and Prevention (CDC), proactive testing for COVID-19 infection is a key factor in determining where and how the SARS-CoV-2 virus is spreading within a population. The early identification of infected people leads to more rapid treatment and isolation for them, as well as for those who were exposed to them [1][2][3]. This type of monitoring is essential to reduce the spread of the disease (CDC, 2020).…”

“…Almagor and Picascia (2020) evaluated the effectiveness of a COVID-19 contact tracing application using an agent-based model [34]. Fiore et al (2021) deployed multi-agent simulations to estimate the daily testing capacity required to find and isolate a number of infected agents sufficient to break the transmission chain of COVID-19 infections [1]. Ghaffarzadegan (2021) developed a simulation model for what-if analyses to further monitor and mitigate the spread of COVID-19 in universities [35,36].…”