Crowding and the epidemic intensity of COVID-19 transmission

Abstract

34The COVID-19 pandemic is straining public health systems worldwide and major non-35 pharmaceutical interventions have been implemented to slow its spread [1][2][3][4] . During the initial phase 36 of the outbreak the spread was primarily determined by human mobility 5,6 . Yet empirical evidence 37 on the effect of key geographic factors on local epidemic spread is lacking 7 . We analyse highly-38 resolved spatial variables for cities in China together with case count data in order to investigate 39 the role of climate, urbanization, and variation in interventions across China. Here we show that 40 the epidemic intensity of COVID-19 is strongly shaped by crowding, such that epidemics in dense 41cities are more spread out through time, and denser cities have larger total incidence. Observed 42 differences in epidemic intensity are well captured by a metapopulation model of COVID-19 that 43 explicitly accounts for spatial hierarchies. Densely-populated cities worldwide may experience more 44 prolonged epidemics. Whilst stringent interventions can shorten the time length of these local 45 epidemics, although these may be difficult to implement in many affected settings. 46 47 Main text identify drivers of local transmission in Chinese cities, with a focus on epidemic intensity among 68 provinces in China. 69 70To explore the impact of urbanization, temperature, and humidity, we used daily incidence data of 71 confirmed COVID-19 cases (date of onset) aggregated at the prefectural level (n = 293) in China. 72Prefectures are administrative units that typically have one urban center (Figure 1). We aggregate 73 individual level data that were collected from official government reports 18 . Epidemiological data in each 74 prefecture were truncated to exclude dates before the first and after the last day of reported cases. The 75 shape of epidemic curves varied between prefectures with some showing rapid rises and declines in cases 76 and others showing more prolonged epidemics (Figure 1A). We estimate epidemic intensity for each 77 prefecture from these data by calculating the inverse Shannon entropy of the distribution of incident 78 cases 9 . We define the incidence distribution "# for a given city to be the proportion of COVID-19 cases 79

State-level variation of initial COVID-19 dynamics in the United States

“…This is in line with other work on COVID-19 that found population demography important at the US county level [ 37 ] and in other countries. Interestingly, while the correlation between the intensity of a contagious respiratory infection and population density is intuitive, our findings are actually contrary to patterns observed during Influenza seasons [ 38 ] and for COVID-19 in China [ 39 ]. The different behavior observed here could be due to COVID-19 being less reliant on environmental factors and mostly driven by social behaviours.…”

Mask Wearing and Control of SARS-CoV-2 Transmission in the United States

“…We fit multivariate logistic regression models using R (version 3.6.2) to predict the community transmission control outcome (binary R t ) using state- and week-specific estimates of mask wearing (crude and survey-weighted) and social distancing (relative residential time). State population density was included as a potential confounder given the association between population structure and SARS-CoV-2 transmission 31 , 32 as well as the association between urban versus rural regions and face mask usage 8 . Percent non-white was included as a confounder due to the relationship with epidemiological indicators of SARS-CoV-2 33 and uptake of non-pharmaceutical interventions 34 .…”

The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology

“…When two Bluetooth-enabled devices are within range, the date, time, distance and duration of interaction can be recorded. The frequency or number of these interactions (analyzed anonymously to form, broadly, measures of clustering or proximal interaction rates over time) may be important given the role of sustained interaction or overcrowding of individuals [32][33][34] and contact structure in SARS-CoV-2 transmission 35 . Furthermore, Bluetooth data in combination with GPS data or a network of Bluetooth sensors can be used to quantify the amount of time people spend at home or other identified locations when lockdown measures are in place to determine if policies are effective.…”