Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model

Balcan, Duygu; Gonçalves, Bruno; Hu, Hao; Ramasco, José J.; Colizza, Vittoria; Vespignani, Alessandro

doi:10.1016/j.jocs.2010.07.002

Cited by 484 publications

(479 citation statements)

References 38 publications

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“…To study spatiotemporal ZIKV spread, we use the Global Epidemic and Mobility Model (GLEAM), a previously described individualbased, stochastic, and spatial epidemic model (60)(61)(62)(63)(64)(65). This model integrates high-resolution demographic, human mobility, socioeconomic (gecon.yale.…”

Section: Methodsmentioning

confidence: 99%

Spread of Zika virus in the Americas

Zhang

Sun

Chinazzi

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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We use a data-driven global stochastic epidemic model to analyze the spread of the Zika virus (ZIKV) in the Americas. The model has high spatial and temporal resolution and integrates real-world demographic, human mobility, socioeconomic, temperature, and vector density data. We estimate that the first introduction of ZIKV to Brazil likely occurred between August 2013 and April 2014 (90% credible interval). We provide simulated epidemic profiles of incident ZIKV infections for several countries in the Americas through February 2017. The ZIKV epidemic is characterized by slow growth and high spatial and seasonal heterogeneity, attributable to the dynamics of the mosquito vector and to the characteristics and mobility of the human populations. We project the expected timing and number of pregnancies infected with ZIKV during the first trimester and provide estimates of microcephaly cases assuming different levels of risk as reported in empirical retrospective studies. Our approach represents a modeling effort aimed at understanding the potential magnitude and timing of the ZIKV epidemic and it can be potentially used as a template for the analysis of future mosquito-borne epidemics. Using a data-driven stochastic and spatial epidemic model, we present numerical results providing insight into the first introduction in the region and the epidemic dynamics across the Americas. We use the model to analyze the spatiotemporal spread and magnitude of the epidemic in the Americas through to February 2017, accounting for seasonal environmental factors and detailed population data. We also provide projections of the number of pregnancies infected with ZIKV during the first trimester, along with estimates for the number of microcephaly cases per country using three different levels of risk based on empirical retrospective studies (36, 37). ResultsIntroduction of ZIKV to the Americas. We identify 12 major transportation hubs in areas related to major events held in Brazil,

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Section: Methodsmentioning

confidence: 99%

Spread of Zika virus in the Americas

Zhang

Sun

Chinazzi

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

296

321

View full text Add to dashboard Cite

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“…A follow-up study [13] estimated that reductions in contact owing to the impacts of disease on behaviour caused a 71% reduction in the pathogen's basic reproductive number, R 0 , compared with what it would have been had sick people engaged in as many contacts as healthy people. Thus, it is clear that the pervasive effects of disease manifestations on host behaviour could have important epidemiological consequences, yet these effects are almost completely absent from transmission models of infectious diseases of humans (although see [14]). …”

Section: Introductionmentioning

confidence: 99%

Calling in sick: impacts of fever on intra-urban human mobility

et al. 2016

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Pathogens inflict a wide variety of disease manifestations on their hosts, yet the impacts of disease on the behaviour of infected hosts are rarely studied empirically and are seldom accounted for in mathematical models of transmission dynamics. We explored the potential impacts of one of the most common disease manifestations, fever, on a key determinant of pathogen transmission, host mobility, in residents of the Amazonian city of Iquitos, Peru. We did so by comparing two groups of febrile individuals (denguepositive and dengue-negative) with an afebrile control group. A retrospective, semi-structured interview allowed us to quantify multiple aspects of mobility during the two-week period preceding each interview. We fitted nested models of each aspect of mobility to data from interviews and compared models using likelihood ratio tests to determine whether there were statistically distinguishable differences in mobility attributable to fever or its aetiology. Compared with afebrile individuals, febrile study participants spent more time at home, visited fewer locations, and, in some cases, visited locations closer to home and spent less time at certain types of locations. These multifaceted impacts are consistent with the possibility that diseasemediated changes in host mobility generate dynamic and complex changes in host contact network structure.

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“…In general the deterministic models fail to consider the spatial aspects of spread of an epidemic, individual contact process and the individual behaviour. Global epidemic and mobility computational model [19] uses human mobility patterns at a global scale. The spatial spread of epidemic disease is modelled using the SEIR where the treated and death compartment is not analysed.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Modelling of Frequent Human Mobility Pattern to Analyse the Dynamics of Epidemic Disease

Parimala¹,

Lopez²

2016

IJIES

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Abstract:Spatial data mining is a rapidly growing field for analysing the data related to space and time. Nowadays most of the applications are based on these factors, so numerous data mining algorithms are developed for spatial characterization and to analyse the spatial trends. The spatial trend analysis determines the change in pattern of some non-spatial attributes on neighbourhood objects. In this paper, we identify spatio-temporal mobility pattern on the dynamics of Epidemic disease (H1N1) that plays a significant role in analysing the outbreak of an infectious disease. Modelling the transmission among the human population with respect to time and space leads to improved understanding of transmission mechanisms. A compartmental model is designed to characterize the disease dynamics of a random variable extracted from binomial and multinomial distribution. ArcGIS tool is used to visualize the mobility distribution of the infected host spatially and yields an output of frequent mobility locations with respect to different time slices. The results thus obtained would help the district administrative authorities to take strategic decisions and prevent the spread of the disease.

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Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model

Cited by 484 publications

References 38 publications

Spread of Zika virus in the Americas

Spread of Zika virus in the Americas

Calling in sick: impacts of fever on intra-urban human mobility

Spatio-Temporal Modelling of Frequent Human Mobility Pattern to Analyse the Dynamics of Epidemic Disease

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