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is the (which was not peer-reviewed) The copyright holder for this preprint .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 We found that epidemic intensity is significantly negatively correlated with mean population crowding 106 and varies widely across the country (Figure 2 , Extended Data Table 1 , p-value < 0.001). Our 107 observation contrasts those expected from simple and classical epidemiological models where it would be 108 expected to see more intensity in crowded areas 22,23 . We hypothesize that the mechanism that underlies 109 the more crowded cities experience less intense outbreaks because crowding enables more widespread 110 and sustained transmission between households leading incidence to be more widely distributed in time 111 (see section below for detailed simulation, Methods). Population size, mean temperature, and mean 112 specific humidity were all significant but their correlation coefficients were much smaller (Extended 113 Data Table 1) . A multivariate-model was able to explain a large fraction of the variation in epidemic 114 intensity across Chinese cities (R 2 = 0.54). We perform sensitivity analysis to account for potential noise 115 in the city level incidence distribution (Extended Data Fig. 1) . 116 117 . 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. One key uncertainty in previous applications of models of epidemic intensity was the contribution of 127 disease importation(s) on the shape of the epidemic 9 . Due to the unprecedented scale of human mobility 128 restrictions imposed in China, the fact that the early epidemic was effectively from a single source, 129 coupled with the availability of real-time data on mobility, we can evaluate the impact of these 130 restrictions on the epidemic intensity relative to the local dynamics. To do so, we performed a univariate 131 analysis (Extended Data Table 1 ) and found that human mobility explained 14% of the variation in 132 epidemic intensity. This further supports earlier findings that COVID-19 had already spread throughout 133 much of China prior to the cordon sanitaire of Hubei province and that the pattern of seeding potentially 134 modulates epidemic intensity 6,24 . These findings are also in agreement with previous work on other 135 pathogens (measles, influenza) which showed that once local epidemics are established case importation 136 becomes less important in determining epidemic intensity 25 . 137 138 . 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.