Abstract
Taking Beijing as a case, this paper conducted a survey to collect the characteristics of residents’ daily activities, including the mode of frequency and duration of travel, the type and environment of activities, and the duration and frequency of activities. We calculated the COVID-19 exposure risk of residents in different activities based on the exposure risk formula; the influencing factors of residents’ exposure risk were analyzed by regression analysis. The variance of residents’ COVID-19 exposure risk was calculated by coefficient of variation. The main conclusions are as follows: (1) There are differences in activity types of COVID-19 exposure risk, which are survival activity, daily activity and leisure activity from high to low. (2) There are differences in populations of COVID-19 exposure risk. Education level, occupation and income are the main factors affecting residents’ COVID-19 exposure risk. (3) There is internal inequity in the risk of COVID-19 exposure. The exposure risk was higher on work days than on rest days. Health inequities at work are highest on both work days and rest days. Among the different population characteristics, male, 31–40 years old, married, with a high school education, income level of 20,001–25,000 yuan, with a non-local rural hukou, rental housing, farmers, three generations or more living together have a greater degree of COVID-19 exposure risk.
Highlights
In China, COVID-19 has been under control overall, sporadic cases are still occurring, and the risk of localized outbreaks is of great concerns [2]
In the above formula, the first half of the summation formula calculates the risk of exposure on work days, and the second half represents the risk of exposure on rest days, both of which consist of activity exposure and travel exposure
U: activity venue/coefficient of the environment during travel process, namely indoor/outdoor; θ: activity venue/coefficient of the number of surrounding people in the travel environment; μ: coefficient of the mobility of people around the activity venue; tix represents the length of time residents stay at the activity venue; ωix represents the frequency with which residents engage in a particular activity during work days or rest days; v: The mode residents travel y to engage in a certain activity; ti represents the amount of time that residents spend on a y certain mode of travel; μi represents the coefficient of mobility of people in a certain mode y of travel; ωi represents the travel frequency, which is the same as the activity frequency in the research
Summary
Since December 2019, the COVID-19 epidemic has become a world public health event, threatening the safety of human lives and bringing an unprecedented impact on the economic and social development of various countries [1]. In China, COVID-19 has been under control overall, sporadic cases are still occurring, and the risk of localized outbreaks is of great concerns [2]. As the capital of China, Beijing is the center of national transportation. With more than 20 million permanent residents and a large scale of floating population, Beijing is under great pressure in the epidemic prevention. High density of population allocation makes the control situation more serious and complicated
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