Forecasting of infectious disease epidemics in sexual networks using attenuated renewal processes
- 1Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
- 2Modelling Division, Analysis and Intelligence Assessment, Chief Data Officer Group, United Kingdom Health Security Agency, London, United Kingdom.
- 3Department of Mathematical Sciences, University of Liverpool, Liverpool, United Kingdom.
Abstract
Forecasting emerging infectious disease outbreaks is particularly challenging for sexual networks of men who have sex with men (MSM), where transmission can be shaped by heterogeneous and overdispersed contact structures, mixing assortativity, and both one-time and recurrent partnerships. Using stochastic epidemic simulations on synthetic MSM networks and empirical mpox case data from major cities, we systematically evaluated five classes of short-term forecasting models, from random walk approaches to semi-mechanistic attenuated renewal processes. Model performance was assessed using the continuous ranked probability score (CRPS). Across both simulated and empirical epidemics in heterogeneous networks, renewal models attenuated by a reverse-sigmoid curve, to account for early depletion of highly-connected nodes, consistently outperformed alternatives, achieving more balanced and reliable forecasts. Our findings suggest a practical approach for robust forecasting in highly overdispersed networks, where distinguishing network effects from other factors remains challenging and usually requires additional data. Author summary: Forecasting infectious disease outbreaks is particularly difficult in networks of men who have sex with men (MSM). These networks are structurally complex: contact rates are highly skewed, partnerships are shaped by degree- and role-based assortativity, and transmission can occur through both one-time and recurrent partnerships. Together, these features produce epidemic dynamics that are hard to predict with standard models, which typically assume contacts are distributed more evenly across a population.
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BibTeX
@article{green2026forecasting,
title = {{Forecasting of infectious disease epidemics in sexual networks using attenuated renewal processes}},
author = {William D Green and Christopher E Overton and Thomas Ward and Ciara V McCarthy and Sebastian Funk},
journal = {PLOS Computational Biology},
year = {2026},
month = oct,
doi = {10.1371/journal.pcbi.1013987},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013987},
note = {CC BY},
}
RIS
TY - JOUR
TI - Forecasting of infectious disease epidemics in sexual networks using attenuated renewal processes
AU - William D Green
AU - Christopher E Overton
AU - Thomas Ward
AU - Ciara V McCarthy
AU - Sebastian Funk
PY - 2026
DA - 2026/10/13
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1013987
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013987
AB - Forecasting emerging infectious disease outbreaks is particularly challenging for sexual networks of men who have sex with men (MSM), where transmission can be shaped by heterogeneous and overdispersed contact structures, mixing assortativity, and both one-time and recurrent partnerships. Using stochastic epidemic simulations on synthetic MSM networks and empirical mpox case data from major cities, we systematically evaluated five classes of short-term forecasting models, from random walk approaches to semi-mechanistic attenuated renewal processes. Model performance was assessed using the continuous ranked probability score (CRPS). Across both simulated and empirical epidemics in heterogeneous networks, renewal models attenuated by a reverse-sigmoid curve, to account for early depletion of highly-connected nodes, consistently outperformed alternatives, achieving more balanced and reliable forecasts. Our findings suggest a practical approach for robust forecasting in highly overdispersed networks, where distinguishing network effects from other factors remains challenging and usually requires additional data. Author summary: Forecasting infectious disease outbreaks is particularly difficult in networks of men who have sex with men (MSM). These networks are structurally complex: contact rates are highly skewed, partnerships are shaped by degree- and role-based assortativity, and transmission can occur through both one-time and recurrent partnerships. Together, these features produce epidemic dynamics that are hard to predict with standard models, which typically assume contacts are distributed more evenly across a population.
SN - 1553-7358
SN - 1553-734X
ER -