Dataset Information

Infectiousness of places - Impact of multiscale human activity places in the transmission of COVID-19.

ABSTRACT: COVID-19 raises attention to epidemic transmission in various places. This study analyzes the transmission risks associated with human activity places at multiple scales, including different types of settlements and eleven types of specific establishments (restaurants, bars, etc.), using COVID-19 data in 906 urban areas across four continents. Through a difference-in-difference approach, we identify the causal effects of activities at various places on epidemic transmission. We find that at the micro-scale, though the transmission risks at different establishments differ across countries, sports, entertainment, and catering establishments are generally more infectious. At the macro-scale, contradicting common beliefs, it is consistent across countries that transmission does not increase with settlement size and density. It is also consistent that specific establishments play a lesser role in transmission in larger settlements, suggesting more transmission happening elsewhere. These findings contribute to building a system of knowledge on the linkage between places, human activities, and disease transmission.

SUBMITTER: Liu L

PROVIDER: S-EPMC9630073 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Infectiousness of places - Impact of multiscale human activity places in the transmission of COVID-19.

Liu Lun L Wang Hui H Zhang Zhu Z Zhang Weiyi W Zhuang Shengsheng S Wang Shenhao S Silva Elisabete A EA Lv Tingmiao T Chio Chi On CO Wang Yifan Y Dao Rina R Tang Chuchang C Ao-Ieong On Ieng OI

npj urban sustainability 20221103 1

COVID-19 raises attention to epidemic transmission in various places. This study analyzes the transmission risks associated with human activity places at multiple scales, including different types of settlements and eleven types of specific establishments (restaurants, bars, etc.), using COVID-19 data in 906 urban areas across four continents. Through a difference-in-difference approach, we identify the causal effects of activities at various places on epidemic transmission. We find that at the ...[more]

PMID: 37521773

Similar Datasets

Project description:ImportanceIt is unknown how well cell phone location data portray social distancing strategies or if they are associated with the incidence of coronavirus disease 2019 (COVID-19) cases in a particular geographical area.ObjectiveTo determine if cell phone location data are associated with the rate of change in new COVID-19 cases by county across the US.Design, setting, and participantsThis cohort study incorporated publicly available county-level daily COVID-19 case data from January 22, 2020, to May 11, 2020, and county-level daily cell phone location data made publicly available by Google. It examined the daily cases of COVID-19 per capita and daily estimates of cell phone activity compared with the baseline (where baseline was defined as the median value for that day of the week from a 5-week period between January 3 and February 6, 2020). All days and counties with available data after the initiation of stay-at-home orders for each state were included.ExposuresThe primary exposure was cell phone activity compared with baseline for each day and each county in different categories of place.Main outcomes and measuresThe primary outcome was the percentage change in COVID-19 cases 5 days from the exposure date.ResultsBetween 949 and 2740 US counties and between 22 124 and 83 745 daily observations were studied depending on the availability of cell phone data for that county and day. Marked changes in cell phone activity occurred around the time stay-at-home orders were issued by various states. Counties with higher per-capita cases (per 100 000 population) showed greater reductions in cell phone activity at the workplace (β, -0.002; 95% CI, -0.003 to -0.001; P < 0.001), areas classified as retail (β, -0.008; 95% CI, -0.011 to -0.005; P < 0.001) and grocery stores (β, -0.006; 95% CI, -0.007 to -0.004; P < 0.001), and transit stations (β, -0.003, 95% CI, -0.005 to -0.002; P < 0.001), and greater increase in activity at the place of residence (β, 0.002; 95% CI, 0.001-0.002; P < 0.001). Adjusting for county-level and state-level characteristics, counties with the greatest decline in workplace activity, transit stations, and retail activity and the greatest increases in time spent at residential places had lower percentage growth in cases at 5, 10, and 15 days. For example, counties in the lowest quartile of retail activity had a 45.5% lower growth in cases at 15 days compared with the highest quartile (SD, 37.4%-53.5%; P < .001).Conclusions and relevanceOur findings support the hypothesis that greater reductions in cell phone activity in the workplace and retail locations, and greater increases in activity at the residence, are associated with lesser growth in COVID-19 cases. These data provide support for the value of monitoring cell phone location data to anticipate future trends of the pandemic.

Dataset Information

Infectiousness of places - Impact of multiscale human activity places in the transmission of COVID-19.

Publications

Infectiousness of places - Impact of multiscale human activity places in the transmission of COVID-19.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets