Published February 22, 2022 | Version v1
Journal article Open

Fine-scale heterogeneity in population density predicts wave dynamics in dengue epidemics

  • 1. University of Chicago
  • 2. Escola Nacional de Saúde Pública Sergio Arouca
  • 3. Fundação Oswaldo Cruz
  • 4. University of Michigan

Description

The spread of dengue and other arboviruses constitutes an expanding global health threat. The extensive heterogeneity in population distribution and potential complexity of movement in megacities of low and middle-income countries challenges predictive modeling, even as its importance to disease spread is clearer than ever. Using surveillance data at fine resolution following the emergence of the DENV4 dengue serotype in Rio de Janeiro, we document a pattern in the size of successive epidemics that is invariant to the scale of spatial aggregation. This pattern emerges from the combined effect of herd immunity and seasonal transmission, and is strongly driven by variation in population density at sub-kilometer scales. It is apparent only when the landscape is stratified by population density and not by spatial proximity as has been common practice. Models that exploit this emergent simplicity should afford improved predictions of the local size of successive epidemic waves.

Data availability

The data for the population and the time series of presence and absence of infections in each unit, as well as the aggregated time series of cases by administrative region and population density group, are available at: https://github.com/vromeoaznar/DengueRio_peakRatio. Requests concerning the epidemiological raw data should be made to the Secretariat of Health of Rio de Janeiro city.

The code to produce the figures and to simulate the model is also available at: https://github.com/vromeoaznar/DengueRio_peakRatio.

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Additional details

Identifiers

DOI
10.1038/s41467-022-28231-w
Other
oai:uchicago.tind.io:5328

Funding

National Science Foundation
National Institutes of Health
1761612
National Institutes of Health
1R01AI143852
National Institutes of Health
1U54GM111274

UChicago Information

Division(s)
Biological Sciences Division
Department(s)
Ecology and Evolution
Center(s) or Institute(s)
Mansueto Institute for Urban Innovation