Contact tracing evaluation for COVID-19 transmission in the different movement levels of a rural college town in the USA

Sifat A. Moon, Caterina M. Scoglio

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Contact tracing can play a key role in controlling human-to-human transmission of a highly contagious disease such as COVID-19. We investigate the benefits and costs of contact tracing in the COVID-19 transmission. We estimate two unknown epidemic model parameters (basic reproductive number R and confirmed rate δ2) by using confirmed case data. We model contact tracing in a two-layer network model. The two-layer network is composed by the contact network in the first layer and the tracing network in the second layer. In terms of benefits, simulation results show that increasing the fraction of traced contacts decreases the size of the epidemic. For example, tracing 25 % of the contacts is enough for any reopening scenario to reduce the number of confirmed cases by half. Considering the act of quarantining susceptible households as the contact tracing cost, we have observed an interesting phenomenon. The number of quarantined susceptible people increases with the increase of tracing because each individual confirmed case is mentioning more contacts. However, after reaching a maximum point, the number of quarantined susceptible people starts to decrease with the increase of tracing because the increment of the mentioned contacts is balanced by a reduced number of confirmed cases. The goal of this research is to assess the effectiveness of contact tracing for the containment of COVID-19 spreading in the different movement levels of a rural college town in the USA. Our research model is designed to be flexible and therefore, can be used to other geographic locations.

Original languageEnglish
Article number4891
JournalScientific Reports
Volume11
Issue number1
DOIs
StatePublished - Dec 2021
Externally publishedYes

Funding

This work has been supported by the National Science Foundation under Grant Award IIS-2027336.

FundersFunder number
National Science FoundationIIS-2027336

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