How to incorporate social vulnerability into epidemic mathematical modelling: recommendations from an international Delphi
Issued Date
2025-10-01
Resource Type
ISSN
02779536
eISSN
18735347
Scopus ID
2-s2.0-105011295873
Journal Title
Social Science and Medicine
Volume
383
Rights Holder(s)
SCOPUS
Bibliographic Citation
Social Science and Medicine Vol.383 (2025)
Suggested Citation
Naidoo M., Shephard W., Mtshali N., Kambewe I., Muthien B., Abuelezam N.N., Ponce-de-Leon M., Villela D.A.M., Paes-Sousa R., Pan-ngum W., Dowdy D., Morse S.S., Pena D., Barberia L.G., Houben R.M.G.J., Arcos González P., Robertson J.E., Muleia R., Lawal O., Rasella D. How to incorporate social vulnerability into epidemic mathematical modelling: recommendations from an international Delphi. Social Science and Medicine Vol.383 (2025). doi:10.1016/j.socscimed.2025.118352 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/111431
Title
How to incorporate social vulnerability into epidemic mathematical modelling: recommendations from an international Delphi
Author's Affiliation
Universidade de São Paulo
Universitat de Barcelona
University of the Witwatersrand, Johannesburg
Johns Hopkins Bloomberg School of Public Health
London School of Hygiene & Tropical Medicine
Fundacao Oswaldo Cruz
Universidad de Oviedo
Mailman School of Public Health
University of the Free State
MSU College of Human Medicine
Instituto de Salud Global de Barcelona
University of Port Harcourt
Centro Nacional de Supercomputación
Mahidol Oxford Tropical Medicine Research Unit
Instituto Nacional de Saude Maputo
Child Mind Institute, Inc.
Engender
Tiko Africa
Universitat de Barcelona
University of the Witwatersrand, Johannesburg
Johns Hopkins Bloomberg School of Public Health
London School of Hygiene & Tropical Medicine
Fundacao Oswaldo Cruz
Universidad de Oviedo
Mailman School of Public Health
University of the Free State
MSU College of Human Medicine
Instituto de Salud Global de Barcelona
University of Port Harcourt
Centro Nacional de Supercomputación
Mahidol Oxford Tropical Medicine Research Unit
Instituto Nacional de Saude Maputo
Child Mind Institute, Inc.
Engender
Tiko Africa
Corresponding Author(s)
Other Contributor(s)
Abstract
Epidemic mathematical modelling plays a crucial role in understanding and responding to infectious disease epidemics. However, these models often neglect social vulnerability (SV): the social, economic, political, and health system inequalities that inform disease dynamics. Despite its importance in health outcomes, SV is not routinely included in epidemic modelling. Given the critical need to include SV but limited direction, this paper aimed to develop research recommendations to incorporate SV in epidemic mathematical modelling. Using the Delphi technique, 22 interdisciplinary experts from 12 countries were surveyed to reach consensus on research recommendations. Three rounds of online surveys were completed, consisting of free-text and seven-point Likert scale questions. Descriptive statistics and inductive qualitative analyses were conducted. Consensus was reached on 27 recommendations across seven themes: collaboration, design, data selection, data sources, relationship dynamics, reporting, and calibration and sensitivity. Experts also identified 92 indicators of SV with access to sanitation (n = 14, 6.1 %), access to healthcare (n = 12, 5.3 %), and household density and composition (n = 12, 5.3 %) as the most frequently cited. Given the recent focus on the social determinants of pandemic resilience, this study provides both process and technical recommendations to incorporate SV into epidemic modelling. SV's inclusion provides a more holistic view of the real world and calls attention to communities at risk. This supports forecasting accuracy and the success of policy and programmatic interventions.