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Non-pharmacologic interventions (NPIs) promote protective actions to lessen exposure risk to COVID-19 by reducing mobility patterns. However, there is a limited understanding of the underlying mechanisms associated with reducing mobility patterns especially for socially vulnerable populations. The research examines two datasets at a granular scale for five urban locations. Through exploratory analysis of networks, statistics, and spatial clustering, the research extensively investigates the exposure risk reduction after the implementation of NPIs to socially vulnerable populations, specifically lower income and non-white populations. The mobility dataset tracks population movement across ZIP codes for an origin-destination (O-D) network analysis. The population activity dataset uses the visits from census block groups (cbg) to points-of-interest (POIs) for network analysis of population-facilities interactions. The mobility dataset originates from a collaboration with StreetLight Data, a company focusing on transportation analytics, whereas the population activity dataset originates from a collaboration with SafeGraph, a company focusing on POI data. Both datasets indicated that low-income and non-white populations faced higher exposure risk. These findings can assist emergency planners and public health officials in comprehending how different populations are able to implement protective actions and it can inform more equitable and data-driven NPI policies for future epidemics., (© 2022. The Author(s).)

Deriving effective mobility control measures is critical for the control of COVID-19 spreading. In response to the COVID-19 pandemic, many countries and regions implemented travel restrictions and quarantines to reduce human mobility and thus reduce virus transmission. But since human mobility decreased heterogeneously, we lack empirical evidence of the extent to which the reductions in mobility alter the way people from different regions of cities are connected, and what containment policies could complement mobility reductions to conquer the pandemic. Here, we examined individual movements in 21 of the most affected counties in the United States, showing that mobility reduction leads to a segregated place network and alters its relationship with pandemic spread. Our findings suggest localized area-specific policies, such as geo-fencing, as viable alternatives to city-wide lockdown for conquering the pandemic after mobility was reduced., (© 2021. The Author(s).)

The objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2787 counties in the United States using data-driven machine learning models. Existing mathematical models of disease spread usually focused on the case prediction with different infection rates without incorporating multiple heterogeneous features that could impact the spatial and temporal trajectory of COVID-19. Recognizing this, we trained a data-driven model using 23 features representing six key influencing factors affecting the pandemic spread: social demographics of counties, population activities, mobility within the counties, movement across counties, disease attributes, and social network structure. Also, we categorized counties into multiple groups according to their population densities, and we divided the trajectory of COVID-19 into three stages: the outbreak stage, the social distancing stage, and the reopening stage. The study aimed to answer two research questions: (1) The extent to which the importance of heterogeneous features evolved at different stages; (2) The extent to which the importance of heterogeneous features varied across counties with different characteristics. We fitted a set of random forest models to determine weekly feature importance. The results showed that: (1) Social demographic features, such as gross domestic product, population density, and minority status maintained high-importance features throughout stages of COVID-19 across 2787 studied counties; (2) Within-county mobility features had the highest importance in counties with higher population densities; (3) The feature reflecting the social network structure (Facebook, social connectedness index), had higher importance for counties with higher population densities. The results showed that the data-driven machine learning models could provide important insights to inform policymakers regarding feature importance for counties with various population densities and at different stages of a pandemic life cycle.

Background: The outcome of coronavirus disease 2019 (COVID-19) is complicated by various comorbidities; asthma, a common chronic disease, may be considered one of these conditions. This study aimed to investigate the effect of asthma as a potential comorbid condition on the COVID-19 prognosis. Materials and Methods: This retrospective study included all RT-PCR confirmed COVID-19 cases recorded on the Shiraz health department's electronic database from January to May 2020. A questionnaire was designed to collect information about patients' demographics, their history of asthma and other comorbidities, and the severity of COVID-19 by contacting them by phone. Results: Of 3163 COVID-19 patients, 109 (3.4%) had self-reported asthma with a mean age of 42.7 ± 19.1 years. Most patients (98%) had mild-to-moderate asthma, while 2% had severe disease. Among asthmatic patients, fourteen (12.8%) were admitted to the hospital, and five (4.6%) died. Univariate logistic regression results showed that asthma had no significant effect on hospitalization (OR 0.95, 95% CI: 0.54-1.63) and mortality (OR 1.18, 95% CI: 0.48-2.94) in patients with COVID-19. Compared living and deceased patients with COVID-19, the pooled OR was 18.2 (95% CI: 7.3–40.1) for cancer, 13.5 (95% CI: 8.2–22.5) for age 40-70 years, 3.1 (95% CI: 2–4.8) for hypertension, 3.1 (95% CI: 1.8–5.3) for cardiac disease and 2.1 (95% CI: 1.3–3.5) for diabetes mellitus. Conclusion: This study showed that asthma is not associated with an increased risk of hospitalization and mortality in patients with COVID-19. Further studies are needed to investigate the risk of different asthma phenotypes on the severity of COVID-19 disease. [ABSTRACT FROM AUTHOR]

The Covid-19 has presented an unprecedented challenge to public health worldwide. However, residents in different countries showed diverse levels of Covid-19 awareness during the outbreak and suffered from uneven health impacts. This study analyzed the global Twitter data from January 1st to June 30th, 2020, to answer two research questions. What are the linguistic and geographical disparities of public awareness in the Covid-19 outbreak period reflected on social media? Does significant association exist between the changing Covid-19 awareness and the pandemic outbreak? We established a Twitter data mining framework calculating the Ratio index to quantify and track awareness. The lag correlations between awareness and health impacts were examined at global and country levels. Results show that users presenting the highest Covid-19 awareness were mainly those tweeting in the official languages of India and Bangladesh. Asian countries showed more disparities in awareness than European countries, and awareness in Eastern Europe was higher than in central Europe. Finally, the Ratio index had high correlations with global mortality rate, global case fatality ratio, and country-level mortality rate, with 21-31, 35-42, and 13–18 leading days, respectively. This study yields timely insights into social media use in understanding human behaviors for public health research. [ABSTRACT FROM AUTHOR]