Dataset Information

Emerging dynamics from high-resolution spatial numerical epidemics.

ABSTRACT: Simulating nationwide realistic individual movements with a detailed geographical structure can help optimise public health policies. However, existing tools have limited resolution or can only account for a limited number of agents. We introduce Epidemap, a new framework that can capture the daily movement of more than 60 million people in a country at a building-level resolution in a realistic and computationally efficient way. By applying it to the case of an infectious disease spreading in France, we uncover hitherto neglected effects, such as the emergence of two distinct peaks in the daily number of cases or the importance of local density in the timing of arrival of the epidemic. Finally, we show that the importance of super-spreading events strongly varies over time.

SUBMITTER: Thomine O

PROVIDER: S-EPMC8568339 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Emerging dynamics from high-resolution spatial numerical epidemics.

Thomine Olivier O Alizon Samuel S Boennec Corentin C Barthelemy Marc M Sofonea Mircea M

eLife 20211015

Simulating nationwide realistic individual movements with a detailed geographical structure can help optimise public health policies. However, existing tools have limited resolution or can only account for a limited number of agents. We introduce Epidemap, a new framework that can capture the daily movement of more than 60 million people in a country at a building-level resolution in a realistic and computationally efficient way. By applying it to the case of an infectious disease spreading in F ...[more]

PMID: 34652271

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Project description:BackgroundBacterial meningitis causes a high burden of disease in the African meningitis belt, with regular seasonal hyperendemicity and sporadic short, but intense, localized epidemics during the late dry season occurring at a small spatial scale [i.e., below the district level, in individual health centers (HCs)]. In addition, epidemic waves with larger geographic extent occur every 7-10 y. Although atmospheric dust load is thought to be an essential factor for hyperendemicity, its role for localized epidemics remains hypothetic.ObjectivesOur goal was to evaluate the association of localized meningitis epidemics in HC catchment areas with the dust load and the occurrence of cases in the same population early in the dry season.MethodsWe compiled weekly reported cases of suspected bacterial meningitis at the HC resolution for 14 districts of Burkina Faso for the period 2004-2014. Using logistic regression, we evaluated the association of epidemic HC-weeks with atmospheric dust [approximated by the aerosol optical thickness (AOT) satellite product] and with the observation of early meningitis cases during October-December.ResultsAlthough AOT was strongly associated with epidemic HC-weeks in crude analyses across all HC-weeks during the meningitis season [odds ratio (OR) [Formula: see text]; 95% CI: 4.90, 9.50], the association was no longer apparent when controlling for calendar week (OR [Formula: see text]; 95% CI: 0.60, 1.50). The number of early meningitis cases reported during October-December was associated with epidemic HC-weeks in the same HC catchment area during January-May of the following year (OR for each additional early case [Formula: see text]; 95% CI: 1.06, 1.21).ConclusionsSpatial variations of atmospheric dust load do not seem to be a factor in the occurrence of localized meningitis epidemics, and the factor triggering them remains to be identified. The pathophysiological mechanism linking early cases to localized epidemics is not understood, but their occurrence and number of early cases could be an indicator for epidemic risk. https://doi.org/10.1289/EHP2752.

Project description:BackgroundChikungunya virus (CHIKV) continues to cause explosive epidemics in Brazil. We investigated its transmission dynamics in Salvador, Brazil, to understand the factors driving its reemergence and spread.MethodsIn this epidemiological study, we analyzed by census tracts the chikungunya cases reported in Salvador during the 2019-2020 epidemics. We used SaTScan software to identify spatiotemporal clusters and assessed how census tract characteristics (socioeconomic, environmental, and prior chikungunya occurrence) influenced chikungunya incidence through a Bayesian spatial model using Integrated Laplace Approximation (INLA).FindingsCitywide, 19,129 cases (mean age: 40.2, range: 0-112; male: 41.8%, female: 58.0%, non-binary: 0.2%) were reported between 2016 and 2020, with a significant increase in 2019 and 2020 (4549 and 13,071 cases, respectively). We found nine spatiotemporal clusters in 2019 and seven in 2020, with 17.2% (387 of 2252) overlap of census tracts between the two years. The chikungunya incidence by census tract was negatively correlated with income and vegetation but positively correlated with land surface temperature. The census tract level incidence in 2020 exhibited a non-linear correlation with the 2019 incidence; up to a certain level, the 2020 risk increased as the 2019 incidence increased, but when the 2019 incidence was extreme, the 2020 risk was reduced.InterpretationThese findings suggest that CHIKV transmission is localized, even during what appeared to be a citywide epidemic, creating high-risk pockets within the city. Socioeconomic factors, environmental conditions, and prior chikungunya incidence, probably reflecting herd immunity, all influence case incidence.FundingSecretary of Health of Salvador, Federal University of Bahia, Oswaldo Cruz Foundation, National Council for Scientific and Technological Development, Foundation for Research Support of the Bahia State, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES), Clinical and Applied Research Network in Chikungunya, Global Virus Network, Burroughs Wellcome Fund, Wellcome Trust, and the United States National Institutes of Health.

Dataset Information

Emerging dynamics from high-resolution spatial numerical epidemics.

Publications

Emerging dynamics from high-resolution spatial numerical epidemics.

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