{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:32:39Z","timestamp":1768566759377,"version":"3.49.0"},"posted":{"date-parts":[[2024,1,13]]},"group-title":"Public and Global Health","reference-count":31,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2024,1,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Detecting and foreseeing pathogen dispersion is crucial in preventing widespread disease transmission. Human mobility is a critical issue in human transmission of infectious agents. Through a mobility data-driven approach, we determined municipalities in Brazil that could make up an advanced sentinel network, allowing for early detection of circulating pathogens and their associated transmission routes.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We compiled a comprehensive dataset on intercity mobility spanning air, road, and waterway transport, and constructed a graph-based representation of Brazil\u2019s mobility network. The Ford-Fulkerson algorithm, coupled with centrality measures, were employed to rank cities according to their suitability as sentinel hubs.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>Our results disentangle the complex transportation network of Brazil, with flights alone transporting 79\u00b79 million (CI 58\u00b73 to 10\u00b71 million) passengers annually during 2017-22, seasonal peaks occurring in late spring and summer, and roadways with a maximum capacity of 78\u00b73 million passengers weekly. We ranked the 5,570 Brazilian cities to offer flexibility in prioritizing locations for early pathogen detection through clinical sample collection. Our findings are validated by epidemiological and genetic data independently collected during the SARS-CoV-2 pandemic period. The mobility-based spread model defined here was able to recapitulate the actual dissemination patterns observed during the pandemic. By providing essential clues for effective pathogen surveillance, our results have the potential to inform public health policy and improve future pandemic response efforts.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Interpretation<\/jats:title>\n                  <jats:p>Our results unlock the potential of designing country-wide clinical sample collection networks using data-informed approaches, an innovative practice that can improve current surveillance systems.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Funding<\/jats:title>\n                  <jats:p>Rockefeller Foundation grant 2023-PPI-007 awarded to MB-N.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Research in context<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Evidence before this study<\/jats:title>\n                    <jats:p>\n                      We searched PubMed on Jun 1, 2023, without language or date restrictions, for the following query: (\u201cmobility network*\u201d OR \u201ctransport* network*\u201d OR \u201csentinel network*\u201d OR \u201csurveillance network*\u201d) AND \u201cmodel*\u201d AND \u201csurveillance\u201d. The 469 search results were systematically evaluated, and we identified seven original research studies that applied modeling-based approaches to inform the placement, design, or layout of surveillance\/sentinel networks. Of these seven studies, four aimed at optimizing the layout of networks for the monitoring of influenza-like illnesses (ILI), while the others aimed at detecting problems arising from the use of medicines based on pharmacy surveillance; detecting the reporting of common acute conditions through a sentinel network of general practitioners; and optimizing the surveillance strategy for plant pests (\n                      <jats:italic>S. noctilio<\/jats:italic>\n                      ). Most studies employed maximum coverage algorithms that aim to maximize the protected population. Only a single study incorporated mobility patterns to inform the planning of site placement. Studies that involved ILI sentinel networks were geographically restricted to two United States states (Iowa and Texas), and only one study performed a comprehensive whole of United States modeling.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Added value of this study<\/jats:title>\n                    <jats:p>Despite the urgent need to improve the capacity and timeliness of clinical sample collection for public health surveillance, very few studies have tackled the design problem for optimal placement of these sampling sites, and even fewer have used large-scale mobility data to inform these design choices in an epidemiologically-relevant way. Our work contributes to this challenge by leveraging airline\/roadway\/fluvial mobility data for Brazil that, converted into a graph-based representation and using network metrics, allowed us to pinpoint an optimal layout strategy that could improve the current flu surveillance network of this country. Using data collected during the COVID-19 pandemic, we validated the transmission routes and pathways of SARS-CoV-2 spread, confirming that the mobility data-informed spread scenarios recapitulated the actual dissemination of the virus.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Implications of all the available evidence<\/jats:title>\n                    <jats:p>Mobility data, coupled with network-centered approaches, can complement the identification of strategic locations for early pathogen detection and spread routes.<\/jats:p>\n                  <\/jats:sec>\n                <\/jats:sec>","DOI":"10.1101\/2024.01.12.24301207","type":"posted-content","created":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T11:20:13Z","timestamp":1705144813000},"source":"Crossref","is-referenced-by-count":1,"title":["Human mobility patterns to inform sampling sites for early pathogen detection and routes of spread: a network modeling and validation study"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7083-0646","authenticated-orcid":false,"given":"Andr\u00eaza L.","family":"Alencar","sequence":"first","affiliation":[]},{"given":"Maria C\u00e9lia L. S.","family":"Cunha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7167-8754","authenticated-orcid":false,"given":"Juliane F.","family":"Oliveira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0271-8599","authenticated-orcid":false,"given":"Adriano O.","family":"Vasconcelos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7752-9090","authenticated-orcid":false,"given":"Gerson G.","family":"Cunha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5001-1873","authenticated-orcid":false,"given":"Ray B.","family":"Miranda","sequence":"additional","affiliation":[]},{"given":"F\u00e1bio M. H. S.","family":"Filho","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2237-6431","authenticated-orcid":false,"given":"Corbiniano","family":"Silva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5664-4436","authenticated-orcid":false,"given":"Ricardo","family":"Khouri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4534-2509","authenticated-orcid":false,"given":"Thiago","family":"Cerqueira-Silva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7857-9946","authenticated-orcid":false,"given":"Luiz","family":"Landau","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5823-7903","authenticated-orcid":false,"given":"Manoel","family":"Barral-Netto","sequence":"additional","affiliation":[]},{"given":"Pablo Ivan P.","family":"Ramos","sequence":"additional","affiliation":[]}],"member":"54368","reference":[{"key":"2024011610251857000_2024.01.12.24301207v1.1","doi-asserted-by":"crossref","first-page":"e185","DOI":"10.1016\/S1473-3099(22)00723-X","article-title":"Advancing detection and response capacities for emerging and re-emerging pathogens in Africa","volume":"23","year":"2023","journal-title":"Lancet Infect Dis"},{"key":"2024011610251857000_2024.01.12.24301207v1.2","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1126\/science.abn1900","article-title":"Stopping pandemics before they start: Lessons learned from SARS-CoV-2","volume":"375","year":"2022","journal-title":"Science"},{"key":"2024011610251857000_2024.01.12.24301207v1.3","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.aau5485"},{"key":"2024011610251857000_2024.01.12.24301207v1.4","doi-asserted-by":"publisher","DOI":"10.1038\/nature06536"},{"key":"2024011610251857000_2024.01.12.24301207v1.5","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2562-8"},{"key":"2024011610251857000_2024.01.12.24301207v1.6","first-page":"e34","article-title":"Reconstruction and prediction of viral disease epidemics","volume":"147","year":"2018","journal-title":"Epidemiol Infect"},{"key":"2024011610251857000_2024.01.12.24301207v1.7","doi-asserted-by":"crossref","first-page":"e47673","DOI":"10.2196\/47673","article-title":"Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential","volume":"10","year":"2024","journal-title":"JMIR Public Health Surveill"},{"key":"2024011610251857000_2024.01.12.24301207v1.8","first-page":"1955","article-title":"The interplay of spatial spread of COVID-19 and human mobility in the urban system of China during the Chinese New Year","volume":"48","year":"2021","journal-title":"Environment and Planning B: Urban Analytics and City Science"},{"key":"2024011610251857000_2024.01.12.24301207v1.9","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0016591"},{"key":"2024011610251857000_2024.01.12.24301207v1.10","doi-asserted-by":"publisher","DOI":"10.1093\/jtm\/taaa021"},{"key":"2024011610251857000_2024.01.12.24301207v1.11","first-page":"43","article-title":"Plungis J","volume":"15","year":"2013","journal-title":"Statista. charleston adv"},{"key":"2024011610251857000_2024.01.12.24301207v1.12","unstructured":"Minist\u00e9rio da Infraestrutura. Ministry of Infrastructure, Brazil. http:\/\/portal.infraestrutura.gov.br\/ (accessed Dec 20, 2023)."},{"key":"2024011610251857000_2024.01.12.24301207v1.13","doi-asserted-by":"publisher","DOI":"10.1038\/518477a"},{"key":"2024011610251857000_2024.01.12.24301207v1.14","doi-asserted-by":"publisher","DOI":"10.1038\/nature16996"},{"key":"2024011610251857000_2024.01.12.24301207v1.15","doi-asserted-by":"publisher","DOI":"10.1038\/nature22400"},{"key":"2024011610251857000_2024.01.12.24301207v1.16","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwp270"},{"key":"2024011610251857000_2024.01.12.24301207v1.17","doi-asserted-by":"crossref","first-page":"e1008477","DOI":"10.1371\/journal.pcbi.1008477","article-title":"The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures","volume":"16","year":"2020","journal-title":"PLoS Comput Biol"},{"key":"2024011610251857000_2024.01.12.24301207v1.18","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0009835"},{"key":"2024011610251857000_2024.01.12.24301207v1.19","doi-asserted-by":"publisher","DOI":"10.1126\/science.aba9757"},{"key":"2024011610251857000_2024.01.12.24301207v1.20","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2021.100465"},{"key":"2024011610251857000_2024.01.12.24301207v1.21","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/1476-072X-12-56","article-title":"How many suffice? A computational framework for sizing sentinel surveillance networks","volume":"12","year":"2013","journal-title":"Int J Health Geogr"},{"key":"2024011610251857000_2024.01.12.24301207v1.22","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-20399-3"},{"key":"2024011610251857000_2024.01.12.24301207v1.23","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1038\/s41591-021-01378-7","article-title":"COVID-19 in Amazonas, Brazil, was driven by the persistence of endemic lineages and P.1 emergence","volume":"27","year":"2021","journal-title":"Nat Med"},{"key":"2024011610251857000_2024.01.12.24301207v1.24","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1038\/s41564-022-01191-z","article-title":"Genomic epidemiology of the SARS-CoV-2 epidemic in Brazil","volume":"7","year":"2022","journal-title":"Nat Microbiol"},{"key":"2024011610251857000_2024.01.12.24301207v1.25","doi-asserted-by":"crossref","first-page":"104156","DOI":"10.1016\/j.isci.2022.104156","article-title":"Phylogenetic-based inference reveals distinct transmission dynamics of SARS-CoV-2 lineages Gamma and P.2 in Brazil","volume":"25","year":"2022","journal-title":"iScience"},{"key":"2024011610251857000_2024.01.12.24301207v1.26","unstructured":"Brazilian National Civil Aviation Agency (ANAC). https:\/\/www.anac.gov.br (accessed Dec 20, 2023)."},{"key":"2024011610251857000_2024.01.12.24301207v1.27","unstructured":"Instituto Brasileiro de Geografia e Estat\u00edstica. Coordenac\u25a1J\u00e3o de Geografia,. Ligac\u25a1J\u00f5es Rodovi\u00e1rias E Hidrovi\u00e1rias, 2016. Rio de Janeiro: Rio de Janeiro\u25a1J: IBGE, Instituto Brasileiro de Geografia e Estat\u00edstica, 2017."},{"key":"2024011610251857000_2024.01.12.24301207v1.28","unstructured":"Brazilian National Transport Confederation. https:\/\/anuariodotransporte.cnt.org.br\/2022\/Inicial (accessed Dec 20, 2023)."},{"key":"2024011610251857000_2024.01.12.24301207v1.29","unstructured":"Ministry of Health, Brazil. Informatics Department Open Data SUS System. https:\/\/covid.saude.gov.br\/ (accessed Dec 20, 2023)."},{"key":"2024011610251857000_2024.01.12.24301207v1.30","doi-asserted-by":"publisher","DOI":"10.4153\/CJM-1956-045-5"},{"key":"2024011610251857000_2024.01.12.24301207v1.31","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.tranpol.2020.06.018","article-title":"Airport risk of importation and exportation of the COVID-19 pandemic","volume":"96","year":"2020","journal-title":"Transport Policy"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2024.01.12.24301207","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T19:48:21Z","timestamp":1768506501000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.1101\/2024.01.12.24301207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,13]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1101\/2024.01.12.24301207","relation":{},"subject":[],"published":{"date-parts":[[2024,1,13]]},"subtype":"preprint"}}