Crowding and the epidemic intensity of COVID-19 transmission

  1. Benjamin Rader
  2. Anjalika Nande
  3. Ben Adlam
  4. Alison L. Hill
  5. Robert C. Reiner
  6. David M. Pigott
  7. Bernardo Gutierrez
  8. COVID-19 data working group
  9. John S. Brownstein
  10. Marcia C. Castro
  11. Huaiyu Tian
  12. Oliver G. Pybus
  13. Samuel V. Scarpino
  14. Moritz UG Kraemer

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Abstract

The COVID-19 pandemic is straining public health systems worldwide and major non-pharmaceutical interventions have been implemented to slow its spread 1-4 . During the initial phase of the outbreak the spread was primarily determined by human mobility 5,6 . Yet empirical evidence on the effect of key geographic factors on local epidemic spread is lacking 7 . We analyse highly-resolved spatial variables for cities in China together with case count data in order to investigate the role of climate, urbanization, and variation in interventions across China. Here we show that the epidemic intensity of COVID-19 is strongly shaped by crowding, such that epidemics in dense cities are more spread out through time, and denser cities have larger total incidence. Observed differences in epidemic intensity are well captured by a metapopulation model of COVID-19 that explicitly accounts for spatial hierarchies. Densely-populated cities worldwide may experience more prolonged epidemics. Whilst stringent interventions can shorten the time length of these local epidemics, although these may be difficult to implement in many affected settings.

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  2. Version published to 10.1101/2020.04.15.20064980v2 on medRxiv
  3. Version published to 10.1101/2020.04.15.20064980v1 on medRxiv