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Peer-reviewedCC BY 4.0Journal of the Royal Society Interface

Local dynamical survival analysis and human sensor networks for epidemic tracking and forecasting

  • Micaela Richter1
  • Prescott Alexander2
  • Grzegorz Rempala1
  • Eben Kenah1
  1. 1Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
  2. 2Information Systems and Modeling, Los Alamos National Laboratory, Los Alamos, NM, USA
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Abstract

Predictive modelling for infectious diseases remains an important problem in epidemiology. Current approaches are often limited by their dependence on time-series data, which can lead to biased estimates of key epidemiological parameters. Here, we present an extension of the dynamical survival analysis approach that can accommodate epidemic data from a large-scale, complex, agent-based model. We use localized predictions that allow for flexibility with time-dependent parameters to account for human behaviour and environmental factors during the epidemic. Our results demonstrate that it is possible to evaluate the quality of predictions and highlight the potential benefits of surveillance via longitudinal follow-up of individuals sampled from the population, which we call a human sensor network.

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Cite

BibTeXRIS
BibTeX
@article{richter2026local,
  title = {{Local dynamical survival analysis and human sensor networks for epidemic tracking and forecasting}},
  author = {Micaela Richter and Prescott Alexander and Grzegorz Rempala and Eben Kenah},
  journal = {Journal of the Royal Society Interface},
  year = {2026},
  month = oct,
  doi = {10.1098/rsif.2026.0111},
  url = {https://doi.org/10.1098/rsif.2026.0111},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Local dynamical survival analysis and human sensor networks for epidemic tracking and forecasting
AU  - Micaela Richter
AU  - Prescott Alexander
AU  - Grzegorz Rempala
AU  - Eben Kenah
PY  - 2026
DA  - 2026/10/07
JO  - Journal of the Royal Society Interface
DO  - 10.1098/rsif.2026.0111
UR  - https://doi.org/10.1098/rsif.2026.0111
AB  - Predictive modelling for infectious diseases remains an important problem in epidemiology. Current approaches are often limited by their dependence on time-series data, which can lead to biased estimates of key epidemiological parameters. Here, we present an extension of the dynamical survival analysis approach that can accommodate epidemic data from a large-scale, complex, agent-based model. We use localized predictions that allow for flexibility with time-dependent parameters to account for human behaviour and environmental factors during the epidemic. Our results demonstrate that it is possible to evaluate the quality of predictions and highlight the potential benefits of surveillance via longitudinal follow-up of individuals sampled from the population, which we call a human sensor network.
SN  - 1742-5689
SN  - 1742-5662
ER  -