A theoretical framework for transitioning from patient-level to population-scale epidemiological dynamics: influenza A as a case study

W. S. Hart, P. K. Maini, C. A. Yates, R. N. Thompson

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Abstract

Multi-scale epidemic forecasting models have been used to inform population-scale predictions with within-host models and/or infection data collected in longitudinal cohort studies. However, most multi-scale models are complex and require significant modelling expertise to run. We formulate an alternative multi-scale modelling framework using a compartmental model with multiple infected stages. In the large-compartment limit, our easy-to-use framework generates identical results compared to previous more complicated approaches. We apply our framework to the case study of influenza A in humans. By using a viral dynamics model to generate synthetic patient-level data, we explore the effects of limited and inaccurate patient data on the accuracy of population-scale forecasts. If infection data are collected daily, we find that a cohort of at least 40 patients is required for a mean population-scale forecasting error below 10%. Forecasting errors may be reduced by including more patients in future cohort studies or by increasing the frequency of observations for each patient. Our work, therefore, provides not only an accessible epidemiological modelling framework but also an insight into the data required for accurate forecasting using multi-scale models.

Original languageEnglish
Article number20200230
Number of pages1
JournalJournal of the Royal Society, Interface
Volume17
Issue number166
Early online date13 May 2020
DOIs
Publication statusPublished - 27 May 2020

Keywords

  • cohort study
  • epidemiological model
  • infectious disease outbreak forecasting
  • influenza
  • multi-scale model
  • nested model

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Bioengineering
  • Biomaterials
  • Biochemistry
  • Biomedical Engineering

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