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

Predicting the epidemiology of the COVID-19 pandemic is a central priority for guiding epidemic 49 responses around the world. China has undergone its first epidemic wave and, remarkably, cities across 50 the country are now reporting few or no locally-acquired cases 8 . Analyses have indicated that that the 51 spread of COVID-19 from Hubei to the rest of China was driven primarily by human mobility 6 and the 52 stringent measures to restrict human movement and public gatherings within and among cities in China 53 have been associated with bringing local epidemics under control 5 . Key uncertainties remain as to which 54 geographic factors drive local transmission dynamics and affect the intensity of transmission of COVID-55 19. For respiratory pathogens, "epidemic intensity" (i.e., the peakedness of the number of cases through 56 time, or the shortest period during which the majority of cases are observed) varies with increased indoor 57 crowding, and socio-economic and climatic factors 9-14 . Epidemic intensity is minimized when incidence 58 is spread evenly across weeks and increases as incidence becomes more focused in particular days 59 ( Figure 1C , see a detailed description of how epidemic intensity is defined in Ref. 9 ). In any given 60 location, higher epidemic intensity requires a larger surge capacity in the public health system 15 , 61 especially for an emerging respiratory pathogen such as COVID-19 16 . 62 during epidemic wave that occurred on day . The inverse Shannon entropy of incidence for a given 80