Modelling Impact of High-Rise, High-Density Built Environment on COVID-19 Risks: Empirical Results from a Case Study of Two Chinese Cities.

Characteristics of the urban environment (e.g., building density and road network) can influence the spread and transmission of coronavirus disease 2019 (COVID-19) within cities, especially in high-density high-rise built environments. Therefore, it is necessary to identify the key attributes of high-density high-rise built environments to enhance modelling of the spread of COVID-19. To this end, case studies for testing attributes for modelling development were performed in two densely populated Chinese cities with high-rise, high-density built environments (Hong Kong and Shanghai).The investigated urban environmental features included 2D and 3D urban morphological indices (e.g., sky view factor, floor area ratio, frontal area density, height to width ratio, and building coverage ratio), socioeconomic and demographic attributes (e.g., population), and public service points-of-interest (e.g., bus stations and clinics). The modelling effects of 3D urban morphological features on the infection rate are notable in urban communities. As the spatial scale becomes larger, the modelling effect of 2D built environment factors (e.g., building coverage ratio) on the infection rate becomes more notable. The influence of several key factors (e.g., the building coverage ratio and population density) at different scales can be considered when modelling the infection risk in urban communities. The findings of this study clarify how attributes of built environments can be applied to predict the spread of infectious diseases. This knowledge can be used to develop effective planning strategies to prevent and control epidemics and ensure healthy cities.

Protective effect of pneumococcal conjugate vaccination on the short-term association between low temperatures and childhood pneumonia hospitalizations: Interrupted time-series and case-crossover analyses in Matlab, Bangladesh.

Studies have shown that ambient extreme temperatures (heat and cold) were associated with an increased risk of childhood pneumonia, but the evidence is very limited in low-middle-income countries. It also remains unknown whether pneumococcal conjugate vaccine (PCV) could prevent temperature-related childhood pneumonia. This study collected data on ambient temperature and hospitalizations for childhood pneumonia in Matlab, Bangladesh from 2012 to 2016. Interrupted time series (ITS) analysis was employed to assess the impact of PCV (10-valent) intervention on childhood pneumonia hospitalizations. A time-stratified case-crossover analysis with a conditional logistic regression was performed to examine the association of childhood pneumonia hospitalizations with extreme temperatures and heatwaves before and after PCV10 intervention. Subgroup analyses were conducted to explore the modification effects of seasons, age, gender, and socioeconomic levels on temperature-related childhood pneumonia hospitalizations. We found that after PCV10 intervention, there was a sharp decrease in hospitalizations for childhood pneumonia (relative risk (RR): 0.59, 95% confidence interval (CI): 0.43-0.83). During the study period, heat effects on childhood pneumonia appeared immediately on the current day (odds ratio (OR): 1.28; 95% CI: 1.02-1.60, lag 0), while cold effects appeared 4 weeks later (OR: 1.53, 95% CI: 1.06-2.22, lag 28). Importantly, cold effects decreased significantly after PCV10 (p-value＜0.05), but heat and heatwave effects increased after PCV10 (p-value＜0.05). Particularly, children from families with a middle or low socioeconomic level, boys, and infants were more susceptible to heat-related pneumonia. This study suggests that PCV10 intervention in Bangladesh may help decrease cold-related not heat-related childhood pneumonia.