| Author | Oliveira, Juliane Fonseca | |
| Author | Vasconcelos, Adriano O | |
| Author | Alencar, Andrêza L | |
| Author | Cunha, Maria Célia S L | |
| Author | Marcilio, Izabel | |
| Author | Barral-Netto, Manoel | |
| Author | Ramos, Pablo Ivan P | |
| Access date | 2025-05-19T19:25:04Z | |
| Available date | 2025-05-19T19:25:04Z | |
| Document date | 2025 | |
| Citation | OLIVEIRA, Juliane Fonseca et al. Balancing human mobility and health care coverage in sentinel surveillance of Brazilian indigenous areas: mathematical optimization approach. JMIR Public Health and Surveillance, v. 11, p. 1-7, 2025. | en_US |
| ISSN | 2369-2960 | en_US |
| URI | https://www.arca.fiocruz.br/handle/icict/70491 | |
| Sponsorship | Fundação Oswaldo Cruz (Fiocruz).
Universidade Federal do Rio de Janeiro (UFRJ).
Rockefeller Foundation's. | en_US |
| Language | eng | en_US |
| Publisher | JMIR Publications | en_US |
| Rights | open access | en_US |
| Title | Balancing Human Mobility and Health Care Coverage in Sentinel Surveillance of Brazilian Indigenous Areas: Mathematical Optimization Approach | en_US |
| Type | Article | en_US |
| DOI | 10.2196/69048 | |
| Abstract | Background: Optimizing sentinel surveillance site allocation for early pathogen detection remains a challenge, particularly in ensuring coverage of vulnerable and underserved populations. Objective: This study evaluates the current respiratory pathogen surveillance network in Brazil and proposes an optimized sentinel site distribution that balances Indigenous population coverage and national human mobility patterns. Methods: We compiled Indigenous Special Health District (Portuguese: Distrito Sanitário Especial Indígena [DSEI]) locations from the Brazilian Ministry of Health and estimated national mobility routes by using the Ford-Fulkerson algorithm, incorporating air, road, and water transportation data. To optimize sentinel site selection, we implemented a linear optimization algorithm that maximizes (1) Indigenous region representation and (2) human mobility coverage. We validated our approach by comparing results with Brazil’s current influenza sentinel network and analyzing the health attraction index from the Brazilian Institute of Geography and Statistics to assess the feasibility and potential benefits of our optimized surveillance network
Results: The current Brazilian network includes 199 municipalities, representing 3.6% (199/5570) of the country’s cities. The optimized sentinel site design, while keeping the same number of municipalities, ensures 100% coverage of all 34 DSEI regions while rearranging 108 (54.3%) of the 199 cities from the existing flu sentinel system. This would result in a more representative sentinel network, addressing gaps in 9 of 34 previously uncovered DSEI regions, which span 750,515 km² and have a population of 1.11 million. Mobility coverage would improve by 16.8 percentage points, from 52.4% (4,598,416 paths out of 8,780,046 total paths) to 69.2% (6,078,747 paths out of 8,780,046 total paths). Additionally, all newly selected cities serve as hubs for medium- or high-complexity health care, ensuring feasibility for pathogen surveillance. Conclusions: The proposed framework optimizes sentinel site allocation to enhance disease surveillance and early detection. By maximizing DSEI coverage and integrating human mobility patterns, this approach provides a more effective and equitable surveillance network, which would particularly benefit underserved Indigenous regions. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil. | en_US |
| Affilliation | Universidade Federal do Rio de Janeiro. Instituto Luiz Coimbra de Pós-Graduação e Pesquisa em Engenharia. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Universidade Federal Rural de Pernambuco. Departamento de Ciência da Computação. Recife, PE, Brasil. | en_US |
| Affilliation | Universidade Federal do Rio de Janeiro. Instituto Luiz Coimbra de Pós-Graduação e Pesquisa em Engenharia. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil / Escola Bahiana de Medicina e Saúde Pública (EBMSP). Salvador, BA, Brasil. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Medicina e Saúde Pública de Precisão. Salvador, BA, Brasil. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil. | en_US |
| Subject | Representative sentinel surveillance | en_US |
| Subject | Early pathogen detection | en_US |
| Subject | Indigenous health | en_US |
| Subject | Human mobility | en_US |
| Subject | Surveillance Network optimization | en_US |
| Subject | Infectious disease surveillance | en_US |
| Subject | Public health strategy | en_US |
| Subject | Brazil | en_US |
| DeCS | Vigilância de Evento Sentinela | en_US |
| DeCS | Saúde de Populações Indígenas | en_US |
| DeCS | Migração Humana | en_US |
| DeCS | Brasil | en_US |
