Crowding and the epidemic intensity of COVID-19 transmission

Benjamin Rader; Samuel Scarpino; Anjalika Nande; Alison Hill; Benjamin Dalziel; Robert Reiner; David Pigott; Bernardo Gutierrez; Munik Shrestha; John Brownstein; Marcia Castro; Huaiyu Tian; Bryan Grenfell; Oliver Pybus; Jessica Metcalf; Moritz U.G. Kraemer

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.15.20064980 doi: medRxiv preprint influenza is likely driven by the accumulation of immunity rather than the specific network structure of 207 individuals. More generally, our work provides empirical support for the role of spatial organization in 208 determining infectious disease dynamics and the limited capacity of cordon sanitaires to control local 209 epidemics 28,33 . We were unable to test more specific hypotheses about which interventions may have 210 impacted the intensity of transmission within and between cities. Further, even though humidity was 211 negatively associated with epidemic intensity it did not explain the majority of the variation and more 212 work will be needed to find causal evidence for the effect of humidity on transmission dynamics of 213 COVID-19. Therefore, maps showing epidemic intensity in cities outside China (Figure 3) infections (influenza) 9 . The Shannon entropy of incidence for a given prefecture and year is then given by 320 # = )− ∑ "# log "# " / -1 . Because # is a function of incidence distribution in each location rather than 321 raw incidence it is invariant under differences in overall reporting rates between cities or attack rates. We Real-time measures of human mobility were extracted from the Baidu Qianxi web platform to estimate 326 the proportion of daily movement between the city of Wuhan to Hubei and 30 other Chinese provinces. 327