ArticleJournal of the Royal Society, Interface2020
Estimating the distribution of time to extinction of infectious diseases in mean-field approaches.
Article in Journal of the Royal Society, Interface, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Transmission and cost-effectiveness modelling to estimate the progress towards elimination and future strategy optimisation for gambiense human African trypanosomiasis in Uganda.BMC infectious diseases · 2026Article
- Modelling timelines to elimination of sleeping sickness in the Democratic Republic of Congo, accounting for possible cryptic human and animal transmission.Parasites & vectors · 2024Article
- Comparison of stochastic and deterministic models for gambiense sleeping sickness at different spatial scales: A health area analysis in the DRC.PLoS computational biology · 2024Article
- Health economic evaluation of strategies to eliminate gambiense human African trypanosomiasis in the Mandoul disease focus of Chad.PLoS neglected tropical diseases · 2023Article
- Does a One Health approach to human African trypanosomiasis control hasten elimination? A stochastic compartmental modeling approach.Acta tropica · 2023Article
- Modelling to infer the role of animals in gambiense human African trypanosomiasis transmission and elimination in the DRC.PLoS neglected tropical diseases · 2022Article
- Update of transmission modelling and projections of gambiense human African trypanosomiasis in the Mandoul focus, Chad.Infectious diseases of poverty · 2022Article
- ModellingJournal of the Royal Society, Interface · 2021Article
- Modelling to explore the potential impact of asymptomatic human infections on transmission and dynamics of African sleeping sickness.PLoS computational biology · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed 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 (
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