ArticleScientific reports2020
How the individual human mobility spatio-temporally shapes the disease transmission dynamics.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Machine learning reveals temperature as a key predictor of dengue risk across Thailand's provinces: A 20-year analysis.PLoS neglected tropical diseases · 2026Article
- Age-specific chikungunya outbreak response immunisation strategies in Brazil: a modelling study.EClinicalMedicine · 2025Article
- Wastewater Surveillance for Group APathogens (Basel, Switzerland) · 2025Article
- Assessing the spatio-temporal risk of Aedes-borne arboviral diseases in non-endemic regions: The case of Northern Spain.PLoS neglected tropical diseases · 2025Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- Critical mobility in policy making for epidemic containment.Scientific reports · 2025Article
- Model-inferred timing and infectious period of the chickenpox outbreak source.BMC infectious diseases · 2024Article
- The impact of COVID-19 mobility restrictions on dengue transmission in urban areas.PLoS neglected tropical diseases · 2024Article
- Online bilateral matching methodology for anti-epidemic resources based on spatial transmission risk.Scientific reports · 2024Article
- Vaccination strategies impact the probability of outbreak extinction: A case study of COVID-19 transmission.Heliyon · 2024Article
- Exploring indoor and outdoor dust as a potential tool for detection and monitoring of COVID-19 transmission.iScience · 2024Article
- Building a pathway to One Health surveillance and response in Asian countries.Science in One Health · 2024Review
- Addressing the Knowledge Deficit in Hospital Bed Planning and Defining an Optimum Region for the Number of Different Types of Hospital Beds in an Effective Health Care System.International journal of environmental research and public health · 2023Review
- Population genomics of diarrheagenic Escherichia coli uncovers high connectivity between urban and rural communities in Ecuador.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2023Article
- Latent sub-structural resilience mechanisms in temporal human mobility networks during urban flooding.Scientific reports · 2023Article
- The impact of national and international travel on spatio-temporal transmission of SARS-CoV-2 in Belgium in 2021.BMC infectious diseases · 2023Article
- Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico.Entropy (Basel, Switzerland) · 2023Article
- Convergence in Mobility Data Sets From Apple, Google, and Meta.JMIR public health and surveillance · 2023Article
- Mobility and the spatial spread of sars-cov-2 in Belgium.Mathematical biosciences · 2023Article
- Cross-municipality migration and spread of tuberculosis in South Africa.Scientific reports · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human mobility plays a crucial role in the temporal and spatial spreading of infectious diseases. During the past few decades, researchers have been extensively investigating how human mobility affects the propagation of diseases. However, the mechanism of human mobility shaping the spread of epidemics is still elusive. Here we examined the impact of human mobility on the infectious disease spread by developing the individual-based SEIR model that incorporates a model of human mobility. We considered the spread of human influenza in two contrasting countries, namely, Belgium and Martinique, as case studies, to assess the specific roles of human mobility on infection propagation. We found that our model can provide a geo-temporal spreading pattern of the epidemics that cannot be captured by a traditional homogenous epidemic model. The disease has a tendency to jump to high populated urban areas before spreading to more rural areas and then subsequently spread to all neighboring locations. This heterogeneous spread of the infection can be captured by the time of the first arrival of the infection [Formula: see text], which relates to the landscape of the human mobility characterized by the relative attractiveness. These findings can provide insights to better understand and forecast the disease spreading.
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