4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The duration of travel impacts the spatial dynamics of infectious diseases

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Significance

          The spatial dynamics of infectious-disease spread are driven by the biology of the pathogen and the connectivity patterns among human populations. Models of disease spread often use mobile-phone calling records to calculate the number of trips made among locations in the population, which is used as a proxy for population connectivity. However, the amount of time people spend in a destination (trip duration) also impacts the probability of onward disease transmission among locations. Here, we developed models that incorporate trip duration into the mechanism of disease spread, which helps us understand how fast and how far a pathogen might spread in a human population.

          Abstract

          Humans can impact the spatial transmission dynamics of infectious diseases by introducing pathogens into susceptible environments. The rate at which this occurs depends in part on human-mobility patterns. Increasingly, mobile-phone usage data are used to quantify human mobility and investigate the impact on disease dynamics. Although the number of trips between locations and the duration of those trips could both affect infectious-disease dynamics, there has been limited work to quantify and model the duration of travel in the context of disease transmission. Using mobility data inferred from mobile-phone calling records in Namibia, we calculated both the number of trips between districts and the duration of these trips from 2010 to 2014. We fit hierarchical Bayesian models to these data to describe both the mean trip number and duration. Results indicate that trip duration is positively related to trip distance, but negatively related to the destination population density. The highest volume of trips and shortest trip durations were among high-density districts, whereas trips among low-density districts had lower volume with longer duration. We also analyzed the impact of including trip duration in spatial-transmission models for a range of pathogens and introduction locations. We found that inclusion of trip duration generally delays the rate of introduction, regardless of pathogen, and that the variance and uncertainty around spatial spread increases proportionally with pathogen-generation time. These results enhance our understanding of disease-dispersal dynamics driven by human mobility, which has potential to elucidate optimal spatial and temporal scales for epidemic interventions.

          Related collections

          Most cited references31

          • Record: found
          • Abstract: found
          • Article: not found

          A universal model for mobility and migration patterns.

          Introduced in its contemporary form in 1946 (ref. 1), but with roots that go back to the eighteenth century, the gravity law is the prevailing framework with which to predict population movement, cargo shipping volume and inter-city phone calls, as well as bilateral trade flows between nations. Despite its widespread use, it relies on adjustable parameters that vary from region to region and suffers from known analytic inconsistencies. Here we introduce a stochastic process capturing local mobility decisions that helps us analytically derive commuting and mobility fluxes that require as input only information on the population distribution. The resulting radiation model predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena, from long-term migration patterns to communication volume between different regions. Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The role of the airline transportation network in the prediction and predictability of global epidemics

            The systematic study of large-scale networks has unveiled the ubiquitous presence of connectivity patterns characterized by large-scale heterogeneities and unbounded statistical fluctuations. These features affect dramatically the behavior of the diffusion processes occurring on networks, determining the ensuing statistical properties of their evolution pattern and dynamics. In this article, we present a stochastic computational framework for the forecast of global epidemics that considers the complete worldwide air travel infrastructure complemented with census population data. We address two basic issues in global epidemic modeling: (i) we study the role of the large scale properties of the airline transportation network in determining the global diffusion pattern of emerging diseases; and (ii) we evaluate the reliability of forecasts and outbreak scenarios with respect to the intrinsic stochasticity of disease transmission and traffic flows. To address these issues we define a set of quantitative measures able to characterize the level of heterogeneity and predictability of the epidemic pattern. These measures may be used for the analysis of containment policies and epidemic risk assessment.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Human mobility: Models and applications

                Bookmark

                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                8 September 2020
                24 August 2020
                24 August 2020
                : 117
                : 36
                : 22572-22579
                Affiliations
                [1] aDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD 21205 ;
                [2] bDepartment of Geography and the Environment, University of Southampton , Southampton SO17 1BJ, United Kingdom;
                [3] cWorldPop, University of Southampton , Southampton SO17 1BJ, United Kingdom;
                [4] dDepartment of Civil and Systems Engineering, Johns Hopkins Whiting School of Engineering , Baltimore, MD 21218;
                [5] eDepartment of Entomology, Pennsylvania State University , University Park, PA 16802;
                [6] fDepartment of Ecology and Evolutionary Biology, Princeton University , Princeton, NJ 08544;
                [7] gPrinceton School of Public and International Affairs, Princeton University , Princeton, NJ 08544
                Author notes
                1To whom correspondence may be addressed. Email: giles@ 123456jhu.edu .

                Edited by Alan Hastings, University of California, Davis, CA, and approved July 22, 2020 (received for review January 6, 2020)

                Author contributions: J.R.G. and A.W. designed research; J.R.G. performed research; J.R.G. analyzed data; and J.R.G., E.z.E.-S., A.J.T., L.G., O.N.B., C.J.E.M., and A.W. wrote the paper.

                Author information
                http://orcid.org/0000-0002-0954-4093
                http://orcid.org/0000-0002-7270-941X
                http://orcid.org/0000-0002-1158-3753
                http://orcid.org/0000-0003-3166-7521
                http://orcid.org/0000-0001-6320-3575
                Article
                201922663
                10.1073/pnas.1922663117
                7486699
                32839329
                a1cbfe85-f3c6-4bc9-b020-bd6cad14bd10
                Copyright © 2020 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 8
                Categories
                Biological Sciences
                Population Biology
                From the Cover

                spatial disease dynamics,human mobility,call data records,trip duration,spatial tsir

                Comments

                Comment on this article