Human mobility patterns to inform sampling sites for early pathogen detection and routes of spread: a network modeling and validation study

Background: 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. Methods: We compiled a comprehensive dataset on intercity mobility spanning air, road, and waterway transport, and constructed a graph-based representation of Brazil's mobility network. The Ford-Fulkerson algorithm, coupled with centrality measures, were employed to rank cities according to their suitability as sentinel hubs. Findings: Our results disentangle the complex transportation network of Brazil, with flights alone transporting 79.9 million (CI 58.3 to 10.1 million) passengers annually during 2017-22, seasonal peaks occurring in late spring and summer, and roadways with a maximum capacity of 78.3 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. Interpretation: 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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by Rockefeller Foundation grant 2023-PPI-007 awarded to MB-N. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript [https://github.com/andrezaleite/reproducibility\_transportation\_hubs-early\_warning\_surveillance_systems.git][1] [1]: https://github.com/andrezaleite/reproducibility_transportation_hubs-early_warning_surveillance_systems.git