As a measurement of the residential population, the Census population ignores the mobility of the people. This weakness may be alleviated by the use of ambient population, derived from social media data such as tweets. This research aims to examine the degree in which geotagged tweets, in contrast to the Census population, can explain crime. In addition, the mobility of Twitter users suggests that tweets as the ambient population may have a spillover effect on the neighboring areas. Based on a yearlong geotagged tweets dataset, negative binomial regression models are used to test the impact of tweets derived ambient population, as well as its possible spillover effect on theft crimes. Results show: (1) Tweets count is a viable replacement of the Census population for spatial theft pattern analysis; (2) tweets count as a measure of the ambient population shows a significant spillover effect on thefts, while such spillover effect does not exist for the Census population; (3) the combination of tweets and its spatial lag outperforms the Census population in theft crime analyses. Therefore, the spillover effect of the tweets derived ambient population should be considered in future crime analyses. This finding may be applicable to other social media data as well.

Background: The role of health-related disparities including sociodemographic, environmental, and critical care capacity in the COVID-19 pandemic are poorly understood. In the present study, we characterized vulnerable populations located in areas at higher risk of COVID-19 related mortality and low critical healthcare capacity in the U.S. Methods: Using Bayesian multilevel analysis and small area disease risk mapping, we assessed the spatial variation of COVID-19 related mortality risk for the U.S. in relation with healthcare disparities including race, ethnicity, poverty, air quality, and critical healthcare capacity. Results: Overall, highly populated, regional air hub areas, and minorities had an increased risk of COVID-19 related mortality. We found that with an increase of only 1 ug/m3 in long term PM2.5 exposure, the COVID-19 mortality rate increased by 13%. Counties with major air hubs had 18% increase in COVID-19 related death compared to counties with no airport connectivity. Sixty-eight percent of the counties with high COVID-19 related mortality risk were also counties with lower critical care capacity than national average. These counties were primary located at the North- and South-Eastern regions of the country. Conclusion: The existing disparity in health and environmental risk factors that exacerbate the COVID-19 related mortality, along with the regional healthcare capacity, determine the vulnerability of populations to COVID-19 related mortality. The results from this study can be used to guide the development of strategies for the identification and targeting preventive strategies in vulnerable populations with a higher proportion of minority groups living in areas with poor air quality and low healthcare capacity. KEY POINTS: Question: What are the sociodemographic and environmental drivers of the heterogeneous distribution of the COVID-19 related mortality in the U.S., and what are the vulnerable areas at higher risk of COVID-19 related mortality and low critical healthcare capacity? Findings: Higher proportions of African American and Latino populations, as well as high levels of air pollution and airport connectivity were linked to higher risk of COVID-19 related mortality. Over 68% of the counties with high COVID-19 related mortality risk were also counties with lower critical care capacity than national average. Meaning: In a time-limited response, the identification and targeting prevention efforts should focus in vulnerable populations located in high risk areas in which sociodemographic and environmental factors are exacerbating the burden of COVID-19 related deaths.

Background: The novel coronavirus SARS-CoV-2 (COVID-19) emerged in December 2019 in Wuhan, China and has spread since then to around 210 countries and territories by April 2020. Consequently, countries have adopted physical distance measures in an attempt to mitigate the uncontrolled spread of the virus. A critical question for policymakers to inform evidence-based practice is if and how physical distance measures slowed the propagation of COVID-19 in the early phase of the pandemic. Methods: This study aims to quantify the effects of physical distance mitigation measures on the propagation of the COVID-19 pandemic. Data from John Hopkins University on confirmed cases and testing data from the Our World in Data were used in an interrupted time series analysis to estimate the effects of physical distance measures on the growth rates of the pandemic in 12 countries of Asia, Africa, and Europe. Findings: We found that physical distance measures produced a significant decrease in the growth rates of the COVID-19 pandemic in five countries (Austria, Belgium, Italy, Malaysia, and South Korea). The test-positivity rate was significant in understanding the slowing growth rate of COVID-19 cases caused by the mitigation measures, as it provides important context that is missing from analysis based only on confirmed case data. Interpretation: Physical distance interventions effectively slowed the progression of the COVID-19 pandemic. The results of this study could inform infectious disease mitigation policies based on physical distance measures by quantifying the differential health outcomes of a pandemic with and without physical distance interventions. RESEARCH IN CONTEXT: Evidence before this study: The SARS-CoV-2 is a new virus identified in December 2019 in the province of Wuhan, China and as never before, a remarkable number of studies and reports have been released since the start of the pandemic. Several studies have used confirmed COVID-19 cases to estimate the growth rate of the pandemic. However, many studies have discussed limitations of including only confirmed cases attributable to the lack of information about testing protocols and testing rates among different countries. Finally, some researchers proposed the analysis of reported deaths by COVID-19 as a potential solution. However, this metric results in biased estimates because deaths by COVID-19 are known to be underreported. Added value of this study: We designed and implemented analytic methods based on our previous research applied to different infectious disease epidemics, to add evidence related to the impact of non-pharmaceutical containment strategies on the temporal progression of the COVID-19 pandemic. Specifically, this study adds quantitative evidence about the effects of physical distance measures on limiting the propagation of COVID-19 pandemics in different countries. Additionally, we included testing data in the analysis to assess intra- and inter-country variation in testing growth rates. We hypothesized that the test-positivity rate is an approximation to the incidence of the COVID-19 pandemics in countries with high testing rates. Additionally, we hypothesize that a significant decrease in the pandemic over time could be identified by a significant decrease in the confirmed cases along with a significant decrease in the test-positivity rate. Our results quantified the potential effects of physical distance interventions on the COVID-19 pandemic progression under different levels of testing and enforcement of mitigation policies. Implications of all the available evidence: Our analysis could lead to better approaches for estimating the effects of physical distance measures on the time course of infectious diseases. In addition, our analysis highlights the potential bias of estimated COVID-19 growth rates based only on confirmed cases. The results from our study could inform strategies for mitigating the COVID-19 or other future pandemics, especially in countries in an earlier stage of a pandemic.