A contact tracing SIR model for randomly mixed populations

• Published in: Journal of biological dynamics Vol. 16; no. 1; pp. 859 - 879
• Main Authors: Bednarski, Sam, Cowen, Laura L.E., Ma, Junling, Philippsen, Tanya, van den Driessche, P., Wang, Manting
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 01.12.2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Asymptomatic; Communicable Diseases - epidemiology; Compartmental disease model; Contact tracing; Contact Tracing - methods; control reproduction number; Coronaviruses; COVID-19; Disease transmission; Epidemic models; Epidemics; Epidemiological Models; Humans; Infections; Infectious diseases; Laboratories; Mathematical models; Models, Biological; pair dynamics; Pathogens; Pharmaceuticals; Public health; Quarantine; Severe acute respiratory syndrome coronavirus 2; Statistical analysis; Stochastic models; tracing capacity; tracing coverage; control reproduction number; tracing coverage; Compartmental disease model; pair dynamics; tracing capacity
• ISSN: 1751-3758; 1751-3766
• DOI: 10.1080/17513758.2022.2153938

Contact tracing is an important intervention measure to control infectious diseases. We present a new approach that borrows the edge dynamics idea from network models to track contacts included in a compartmental SIR model for an epidemic spreading in a randomly mixed population. Unlike network models, our approach does not require statistical information of the contact network, data that are usually not readily available. The model resulting from this new approach allows us to study the effect of contact tracing and isolation of diagnosed patients on the control reproduction number and number of infected individuals. We estimate the effects of tracing coverage and capacity on the effectiveness of contact tracing. Our approach can be extended to more realistic models that incorporate latent and asymptomatic compartments.