Skip to content
DraconiticResearch
Search Sources API
186,625 studies31 sourcesUpdated 26 min ago
Peer-reviewedCC BY-NC-NDBMC public health

A scenario projection framework for multi-scale epidemic modeling under uncertainty.

  • Shuting Yu1
  • Shunjiang Ni2,3
  • Shifei Shen1,4
4 affiliations
  1. 1School of Safety Science, Tsinghua University, Beijing, 100084, China.
  2. 2School of Safety Science, Tsinghua University, Beijing, 100084, China. [email protected].
  3. 3Institute of Public Safety Research, Tsinghua University, Beijing, 100084, China. [email protected].
  4. 4Institute of Public Safety Research, Tsinghua University, Beijing, 100084, China.
Read full textPDFDOICite

Abstract

Background: The profound uncertainty surrounding emerging infectious diseases, as exemplified by the cross-seasonal dynamics of COVID-19, poses significant challenges to conventional forecasting efforts. Traditional models often rely heavily on granular population flow data, limiting their applicability across city, national, and global scales. This study aims to shift the paradigm from deterministic prediction to multi-scale scenario projection.

Methods: We propose an extended SEQIHRS compartmental model integrating key epidemic drivers. The core innovation is the introduction of a space-induced immunity factor (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}k0\end{document}), which characterizes transmission barriers across geographical scales without requiring explicit mobility data. A periodic skewness function captures asymmetric seasonal effects, and a composite intervention framework simulates adaptive control measures. Three empirical datasets from Hong Kong, Mainland China, and the Global scale​ were utilized to calibrate and validate the model's structural plausibility.

Results: The model successfully reproduced the observed epidemic trends across diverse spatiotemporal settings. The close alignment between simulated curves and surveillance data demonstrates that the model can capture complex transmission patterns under various parameterizations, confirming its robustness as a scenario-generating engine.

Conclusions: Compared to traditional approaches, this framework offers three breakthroughs: (1) It achieves seamless multi-scale simulation​ (local to global) via the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}k0\end{document} factor, bypassing the need for complex population flow data; (2) It incorporates uncertainty through interpretable parameters, allowing users to explore a range of plausible outcomes rather than a single forecast; (3) It provides a practical tool for pandemic preparedness. By adjusting pathogen parameters, spatial scales, and intervention intensities, policymakers can conduct "what-if" analyses to support adaptive emergency responses.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12889-026-28716-z.

Related

  1. Government Healthcare Cost Trends and Fiscal Forecast in Kuwait: A Longitudinal Analysis and Scenario-Based Projection, 2020 - 2035
    A. M. AlSabah
    medRxivPreprintCC BYFull textEpidemiology & public healthmedRxiv
  2. Global, regional, and national impact of epidemic disasters on health workforce equality between 1990 and 2019: An ecological and modeling study
    Manman Chen, Wanzhou Wang, Feng Sha +7
    PLOS MedicinePeer-reviewedCC BY 4.0Full textEpidemiology & public healthPLOS
  3. A scenario-driven framework for reproducible in silico testing of physiological closed-loop control systems
    Pfannschmidt Valerie, Schael Michael, Olivier Lena +2
    Current Directions in Biomedical EngineeringPeer-reviewedCC BY-NC-NDFull textGeneral medicineDOAJ
  4. An optimization framework for healthcare facility fortification: A scenario-based defender–attacker model
    Isaline Baret, Yassine Ouazene, Nhan Quy Nguyen +1
    International journal of critical infrastructure protectionPeer-reviewedCC BYGeneral biologyGeneral medicineHAL
  5. An uncertainty-aware multimodal deep learning framework for Alzheimer’s disease classification using MRI and electronic health records
    Naif Almusallam, Ahmad Bacha, Maqsood Hayat +2
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textPsychiatry & mental healthCrossref

Cite

BibTeXRIS
BibTeX
@article{yu2026scenario,
  title = {{A scenario projection framework for multi-scale epidemic modeling under uncertainty.}},
  author = {Shuting Yu and Shunjiang Ni and Shifei Shen},
  journal = {BMC public health},
  year = {2026},
  month = aug,
  doi = {10.1186/s12889-026-28716-z},
  url = {https://europepmc.org/article/MED/42958205},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - A scenario projection framework for multi-scale epidemic modeling under uncertainty.
AU  - Shuting Yu
AU  - Shunjiang Ni
AU  - Shifei Shen
PY  - 2026
DA  - 2026/08/01
JO  - BMC public health
DO  - 10.1186/s12889-026-28716-z
UR  - https://europepmc.org/article/MED/42958205
AB  - Background: The profound uncertainty surrounding emerging infectious diseases, as exemplified by the cross-seasonal dynamics of COVID-19, poses significant challenges to conventional forecasting efforts. Traditional models often rely heavily on granular population flow data, limiting their applicability across city, national, and global scales. This study aims to shift the paradigm from deterministic prediction to multi-scale scenario projection.  Methods: We propose an extended SEQIHRS compartmental model integrating key epidemic drivers. The core innovation is the introduction of a space-induced immunity factor (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}k_0\end{document}), which characterizes transmission barriers across geographical scales without requiring explicit mobility data. A periodic skewness function captures asymmetric seasonal effects, and a composite intervention framework simulates adaptive control measures. Three empirical datasets from Hong Kong, Mainland China, and the Global scale​ were utilized to calibrate and validate the model's structural plausibility.  Results: The model successfully reproduced the observed epidemic trends across diverse spatiotemporal settings. The close alignment between simulated curves and surveillance data demonstrates that the model can capture complex transmission patterns under various parameterizations, confirming its robustness as a scenario-generating engine.  Conclusions: Compared to traditional approaches, this framework offers three breakthroughs: (1) It achieves seamless multi-scale simulation​ (local to global) via the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}k_0\end{document} factor, bypassing the need for complex population flow data; (2) It incorporates uncertainty through interpretable parameters, allowing users to explore a range of plausible outcomes rather than a single forecast; (3) It provides a practical tool for pandemic preparedness. By adjusting pathogen parameters, spatial scales, and intervention intensities, policymakers can conduct "what-if" analyses to support adaptive emergency responses.  Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12889-026-28716-z.
SN  - 1471-2458
ER  -