A renewal equation model for disease transmission dynamics with contact tracing

Datum / Date: 15.2.21
Čas / Time: 15:00 — 16:00
Predavalnica / Location: ZOOM (See link below)
Predavatelj / Lecturer: Francesca Scarabel (York University, Canada)
Naslov / Title: A renewal equation model for disease transmission dynamics with contact tracing
Povzetek / Abstract:

I will present a deterministic model for disease transmission dynamics including diagnosis of symptomatic individuals and contact tracing. The model is structured by time since infection and formulated in terms of the individual disease rates and the parameters characterising diagnosis and contact tracing processes. By incorporating a mechanistic formulation of the processes at the individual level, we obtain an integral equation (delayed in calendar time and advanced in the time since infection) for the probability that an infected individual is detected and isolated at any point in time. This is then coupled with a renewal equation for the total incidence to form a closed system describing the transmission dynamics involving contact tracing. After presenting the derivation of the model, I will conclude with some applications of public health relevance, especially in the context of the ongoing COVID-19 pandemic.

Joint work with Lorenzo Pellis (University of Manchester), Nicholas H Ogden (PHAC, Public Health Agency of Canada) and Jianhong Wu (York University).

Reference: Scarabel F, Pellis L, Ogden NH, Wu J. A renewal equation model to assess roles and limitations of contact tracing for disease outbreak control, available on medRxiv: https://doi.org/10.1101/2020.12.27.20232934

We are looking forward to meeting at the video-conference. Join Zoom Meeting HERE!

Everyone is welcome and encouraged to attend.

For more info visit our YouTube Channel.

Share with others

Accessibility Toolbar

Privacy Overview

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.