Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Estimating the distribution of time to extinction of infectious diseases in mean-field approaches

Tools
- Tools
+ Tools

Aliee, Maryam, Rock, Kat S. and Keeling, Matthew James (2020) Estimating the distribution of time to extinction of infectious diseases in mean-field approaches. Journal of The Royal Society Interface, 17 (173). 20200540. doi:10.1098/rsif.2020.0540 ISSN 1742-5689.

[img]
Preview
PDF
WRAP-Estimating-distribution-time-extinction-infectious-diseases-mean-field-Keeling-2020.pdf - Published Version - Requires a PDF viewer.
Available under License Creative Commons Attribution 4.0.

Download (811Kb) | Preview
Official URL: http://dx.doi.org/10.1098/rsif.2020.0540

Request Changes to record.

Abstract

A key challenge for many infectious diseases is to predict the time to extinction under specific interventions. In general, this question requires the use of stochastic models which recognize the inherent individual-based, chance-driven nature of the dynamics; yet stochastic models are inherently computationally expensive, especially when parameter uncertainty also needs to be incorporated. Deterministic models are often used for prediction as they are more tractable; however, their inability to precisely reach zero infections makes forecasting extinction times problematic. Here, we study the extinction problem in deterministic models with the help of an effective ‘birth–death’ description of infection and recovery processes. We present a practical method to estimate the distribution, and therefore robust means and prediction intervals, of extinction times by calculating their different moments within the birth–death framework. We show that these predictions agree very well with the results of stochastic models by analysing the simplified susceptible–infected–susceptible (SIS) dynamics as well as studying an example of more complex and realistic dynamics accounting for the infection and control of African sleeping sickness (Trypanosoma brucei gambiense).

Item Type: Journal Article
Subjects: R Medicine > RC Internal medicine
Divisions: Faculty of Science, Engineering and Medicine > Science > Mathematics
Library of Congress Subject Headings (LCSH): African trypanosomiasis -- Mathematical models, Stochastic analysis, Biomathematics -- Research
Journal or Publication Title: Journal of The Royal Society Interface
Publisher: The Royal Society Publishing
ISSN: 1742-5689
Official Date: 23 December 2020
Dates:
DateEvent
23 December 2020Published
9 December 2020Available
12 November 2020Accepted
Volume: 17
Number: 173
Article Number: 20200540
DOI: 10.1098/rsif.2020.0540
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 10 December 2020
Date of first compliant Open Access: 11 December 2020
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
OPP1184344Bill and Melinda Gates Foundationhttp://dx.doi.org/10.13039/100000865
OPP1177824Bill and Melinda Gates Foundationhttp://dx.doi.org/10.13039/100000865

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us