Identifiability and Model Misspecification for Modelling Recurrent Infections Using Routine Health Care Data

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ID: 317618
2026
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Abstract
For infectious diseases where people can experience multiple infections during their lifetime, the time between observed infections in individuals (or "time to recurrence") can provide valuable information on infection and transmission dynamics. Routinely collected data, such as electronic health records, are a potential source of time to recurrence data. However, they are challenging to analyse because patients can drop out of the data set in a way which is not visible to the data collection process. Standard epidemiological approaches, such as parametric survival analysis with imputation, cannot be applied to such data. In this study, we explored the feasibility of interrogating routinely collected time to recurrence data by calibrating mechanistic transmission models with explicit dropout mechanisms. We identified model structures and parameter regimes where the method could precisely and accurately estimate important epidemiological quantities. Application of our method to real data of malaria infections routinely collected in Papua, Indonesia, was able to estimate the forces of infection for different malaria species, the rate of dropout and recrudescence for P. falciparum, and the probability of treatment success. Our method has the potential to increase the value of existing and new data sets for informing public health research.
Reference Key
openalex_W7165043742 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ada W C Yan, Jennifer A Flegg, Jeanne Rini Poespoprodjo, Nicholas M. Douglas, Ric N Price, Angela Devine, David J Price, Rebecca H. Chisholm
Journal american journal of epidemiology
Year 2026
DOI
10.1093/aje/kwag138
URL
https://doi.org/10.1093/aje/kwag138
Keywords Keywords not found

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