Mathematical Contact Tracing Models for the COVID-19 Pandemic: A Systematic Review of the Literature

TL;DR

This paper reviews mathematical models used during the COVID-19 pandemic that include contact tracing as a strategy to control the spread of the virus.

Contribution

The study provides a systematic review of how contact tracing was parameterized in mathematical models to mitigate the pandemic.

Findings

Most studies used compartmental models to simulate transmission, with contact tracing often in a separate compartment.

A total of 53 articles were included, showing significant heterogeneity in model assumptions and parameters.

Non-pharmaceutical interventions like quarantine and reproduction numbers were commonly considered in the models.

Abstract

Background: Contact tracing (CT) is a primary means of controlling infectious diseases, such as coronavirus disease 2019 (COVID-19), especially in the early months of the pandemic. Objectives: This work is a systematic review of mathematical models used during the COVID-19 pandemic that explicitly parameterise CT as a potential mitigator of the effects of the pandemic. Methods: This review is registered in PROSPERO. A comprehensive literature search was conducted using the PubMed, EMBASE, Cochrane Library, CINAHL, and Scopus databases. Two reviewers independently selected the title/abstract, full text, data extraction, and risk of bias. Disagreements were resolved through discussion. The characteristics of the studies and mathematical models were collected from each study. Results: A total of 53 articles out of 2101 were included. The modelling of the COVID-19 pandemic was the main objective of 23 studies, while the remaining articles evaluated the forecast transmission of COVID-19. Most studies used compartmental models to simulate COVID-19 transmission (26, 49.1%), while others used agent-based (16, 34%), branching processes (5, 9.4%), or other mathematical models (6). Most studies applying compartmental models consider CT in a separate compartment. Quarantine and basic reproduction numbers were also considered in the models. The quality assessment scores ranged from 13 to 26 of 28. Conclusions: Despite the significant heterogeneity in the models and the assumptions on the relevant model parameters, this systematic review provides a comprehensive overview of the models proposed to evaluate the COVID-19 pandemic, including non-pharmaceutical public health interventions such as CT. Prospero Registration: CRD42022359060.

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