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

Baidu's mapping service is estimated to have a 30% market share in China and more data can be found 5,6 . The parameters from the model of epidemic intensity predicted by humidity, crowding and population 368 size (see Table 1 , Model 6) were used to estimate relative intensity in the 380 urban centers. Predicted 369 values of epidemic intensity that fell outside the original covariate space [0,1] (n=7) were set to 1. A full 370 list of predicted epidemic intensities can be found in the Supplementary Information. 371 . 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.