Previously submitted to: JMIR Public Health and Surveillance (no longer under consideration since Mar 01, 2024)
Date Submitted: Aug 22, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Mathematical contact tracing models for the COVID-19 pandemic: A systematic review of the literature
ABSTRACT
Background:
Background Contact tracing (CT) is a primary means of controlling infectious diseases such as Coronavirus disease 2019, especially in the first months of the pandemic.
Objective:
This work is a systematic review of mathematical models used during the COVID-19 pandemic that explicitly parameterize CT as potential mitigator of the effects of pandemic.
Methods:
This review is registered in Prospero. A comprehensive literature research was conducted on PubMed, EMBASE, Cochrane library through CINAHL, and Scopus databases. Two reviewers independently screened the title/abstract, full text, data extraction, and risk of bias. Disagreements were solved through discussion. Characteristics of the study and mathematical models were collected from each study.
Results:
A total of 53 articles out of 2101 were included. The modeling of the COVID-19 pandemic was the main objective of 23 studies; the remaining articles evaluate 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 separate compartments. Quarantine and basic reproduction numbers were also considered in the models. The quality assessment scores ranged from 13 to 26 out of 28.
Conclusions:
Conclusions Despite the significant heterogeneity in models and the assumptions on relevant model parameters, this systematic review provides a comprehensive overview of the models proposed to evaluate the COVID-19 pandemic, including nonpharmaceutical public health interventions such as CT. Clinical Trial: Prospero registration: CRD42022359060
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