An integrated framework for modelling respiratory disease transmission and designing surveillance networks using a sentinel index
Defining epidemiologically relevant placements for sentinel units is critical for establishing effective health surveillance systems. We propose a novel methodology to identify optimal sentinel unit locations using network approaches and metapopulation modelling. Disease transmission dynamics were modelled using syndromic data on respiratory diseases, integrated with road mobility data. A generalizable sentinel index is introduced as a metric that evaluates the suitability of a site to host a sentinel unit, based on topological metrics and metapopulation dynamics. A case study using syndromic data from primary health care attendances in Bahia, Brazil, validated the relevance of existing sentinel units while identifying opportunities for local re-designs to improve disease surveillance coverage.
Authors
- Marcos Barreto (ORCID: https://orcid.org/0000-0002-7818-1855)
- Luiz Landau (ORCID: https://orcid.org/0000-0001-7857-9946)
- Álvaro L. G. A. Coutinho (ORCID: https://orcid.org/0000-0002-4764-1142)
- Roberto F. S. Andrade (ORCID: https://orcid.org/0000-0002-9323-1400)
- Suani T. R. Pinho (ORCID: https://orcid.org/0000-0003-1404-3485)
- Manoel Barral‐Netto (ORCID: https://orcid.org/0000-0002-5823-7903)
- Eluã Ramos Coutinho (ORCID: https://orcid.org/0000-0003-2350-4319)
- Pablo Ivan Pereira Ramos (ORCID: https://orcid.org/0000-0002-9075-7861)
- Daniel C. P. Jorge (ORCID: https://orcid.org/0000-0003-4707-3234)
- Dérick G. F. Borges (ORCID: https://orcid.org/0000-0001-7627-4452)
Institutions
- Universidade Federal do Rio de Janeiro (BR)
- Universidade Federal da Bahia (BR)
- Instituto Federal da Bahia (BR)
- Universidade Federal Fluminense (BR)
- Princeton University (US)
- Instituto Federal Fluminense (BR)
- Knowledge Integration (United Kingdom) (GB)
- Fundação Oswaldo Cruz (BR)
- London School of Economics and Political Science (GB)
Publication Details
- Journal
- Royal Society Open Science
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1098/rsos.251195
- Primary Topic
- Data-Driven Disease Surveillance
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Rockefeller Foundation
- Royal Society
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
- Fundação Oswaldo Cruz
- Instituto Nacional de Ciência e Tecnologia de Sistemas Complexos