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

Within each prefecture, we calculate Lloyd's index of mean crowding 9,21 treating the population count of 87 each pixel as an individual unit (Methods, Figure 1B and C). The term 'mean crowding' used here is a 88 specific metric that summarizes both, population density and how density is distributed across a 89 prefecture (patchiness). Values on the resulting index above the mean pixel population count within each 90 prefecture suggest a spatially-aggregated population structure (Methods). For example, Guangzhou has 91 high values of crowding whilst Quzhou which has a more evenly distributed population in its prefecture 92 ( Figure 1B and C) . Using the centroid of each prefecture we calculate daily mean temperature and 93 specific humidity; these values are subsequently averaged over each prefecture's reporting period 94 (Methods). We performed log-linear regression modeling to determine the association between epidemic 95 intensity with the socio-economic and environmental variables (Methods). 96 . CC-BY 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.