ABSTRACT
COVID-19 raises attention to epistemological risks related to everyday human activities. Our work quantifies infection transmission risks at different human activity places, including different types of settlements at macro-scale and establishments (restaurants, bars, etc.) at micro-scale, using evidences from COVID-19 in 906 urban areas across four continents. Relatively stable rules of how infection risks are distributed across human settlements and establishments are found. At micro-scale, the infection transmission risks at various establishments differ across countries, but generally, physical activity, entertainment and catering establishments lead to more infections than other activity places. At macro-scale, contrary to common beliefs, we find consistent pattern that transmission does not increase with settlement size and density. When considering interaction between the two scales, there is also consistent pattern that a smaller proportion of infections take place at specific establishments in larger settlements, suggesting that general public spaces such as streets play a greater role in transmission due to longer trips. Though with limitations, our work provides the first steps towards a system of knowledge on the linkage between places, human activities and disease transmission.
Subject(s)
COVID-19ABSTRACT
The efficacy of government interventions in epidemic has become a hot subject since the onset of COVID-19. There is however much variation in the results quantifying the effects of interventions, which is partly related to the varying modelling approaches employed by existing studies. This paper therefore aims to examine how the choice of modelling approach would affect the estimation results of intervention effects, by experimenting with different modelling approaches on a same data set composed of the 500 most affected U.S. counties. We compare the most frequently used methods from the two classes of modelling approaches, which are Bayesian hierarchical model from the class of computational approach and difference-in-difference from the class of natural experimental approach. We find that computational methods are likely to produce larger estimates of intervention effects due to simultaneous voluntary behavioral changes. In contrast, natural experimental methods are more likely to extract the true effect of interventions. Among different difference-in-difference estimators, the two-way fixed effect estimator seems to be an efficient one. Our work can inform the methodological choice of future research on this topic, as well as more robust re-interpretation of existing works, to facilitate both future epidemic response plans and the science of public health.
Subject(s)
COVID-19ABSTRACT
Places are fundamental factors in the spread of epidemics, as they are where people agglomerate and interact. This paper explores how different types of places--activity spaces at micro-level and human settlements at macro-level--impact the transmission of infections using evidences from COVID-19. We examine eleven types of activity spaces and find heterogeneous impacts across countries, yet we also find that non-essential activity spaces tend to have larger impacts than essential ones. Contrary to common beliefs, settlement size and density are not positively associated with reproduction numbers. Further, the impacts of closing activity spaces vary with settlement types and are consistently lower in larger settlements in all sample countries, suggesting more complex pattern of virus transmission in large settlements. This work takes first steps in systematically evaluating the epistemological risks of places at multiple scales, which contributes to knowledge in urban resilience, health and livability. TeaserActivity spaces and human settlement characteristics impact the spread of epidemics in multiple ways and should be considered in policy making.