We study the spatial-temporal pattern of the spread of the 2009 H1N1 influenza virus using a metapopulation model linked by commuting flow based on the reported influenza cases during the early stages of the epidemic in the Republic of Korea. The spatial heterogeneities, such as the local reproductive number and peak time, are investigated at province level. Furthermore, we discuss the effect of early intervention strategies, isolation and commuting restrictions, on the reduction of incidence at each province level. A major finding of this study is that early intervention at the source area of infection is more effective than interventions at the commuting-hub areas if the cost is limited.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jtbi.2018.06.016 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Demographic and historical processes influencing Cochliomyia hominivorax (Diptera: Calliphoridae) population structure across South America.
Parasit Vectors
January 2025
Department of Genetics and Evolutionary Biology. Institute of Biosciences, University of São Paulo, Rua Do Matão, 277, São Paulo, SP, 05508-090, Brazil.
Background: In this study, we investigated the genetic variability and population structure of the New World screwworm fly Cochliomyia hominivorax. We tested the hypothesis that the species exhibits a center-periphery distribution of genetic variability, with higher genetic diversity in central populations (e.g.
View Article and Find Full Text PDFProcess-based models for range dynamics are urgently needed due to increasing intensity of human-induced biodiversity change. Despite a few existing models that focus on demographic processes, their use remains limited compared to the widespread application of correlative approaches. This slow adoption is largely due to the challenges in calibrating biological parameters and the high computational demands for large-scale applications.
View Article and Find Full Text PDFClimate change could amplify weak synchrony in large marine ecosystems.
Proc Natl Acad Sci U S A
January 2025
Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS 66045.
Climate change is increasing the frequency of large-scale, extreme environmental events and flattening environmental gradients. Whether such changes will cause spatially synchronous, large-scale population declines depends on mechanisms that limit metapopulation synchrony, thereby promoting rescue effects and stability. Using long-term data and empirical dynamic models, we quantified spatial heterogeneity in density dependence, spatial heterogeneity in environmental responses, and environmental gradients to assess their role in inhibiting synchrony across 36 marine fish and invertebrate species.
View Article and Find Full Text PDFMulti-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model.
PLoS Comput Biol
January 2025
Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi, China.
Human mobility between different regions is a major factor in large-scale outbreaks of infectious diseases. Deep learning models incorporating infectious disease transmission dynamics for predicting the spread of multi-regional outbreaks due to human mobility have become a hot research topic. In this study, we incorporate the Graph Transformer Neural Network and graph learning mechanisms into a metapopulation SIR model to build a hybrid framework, Metapopulation Graph Transformer Neural Network (M-Graphormer), for high-dimensional parameter estimation and multi-regional epidemic prediction.
View Article and Find Full Text PDFBias in mobility datasets drives divergence in modeled outbreak dynamics.
Commun Med (Lond)
January 2025
Department of Demography, University of California, Berkeley, California, USA.
Background: Digital data sources such as mobile phone call detail records (CDRs) are increasingly being used to estimate population mobility fluxes and to predict the spatiotemporal dynamics of infectious disease outbreaks. Differences in mobile phone operators' geographic coverage, however, may result in biased mobility estimates.
Methods: We leverage a unique dataset consisting of CDRs from three mobile phone operators in Bangladesh and digital trace data from Meta's Data for Good program to compare mobility patterns across these sources.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!