Revealing geographic transmission pattern of COVID-19 using neighborhood-level simulation with human mobility data and SEIR model: A Case Study of South Carolina (preprint)

Direct human physical contact accelerates COVID-19 transmission. Smartphone mobility data has been an emerging data source to reveal fine-grained human mobility, which can be used to estimate the intensity of physical contact surrounding different locations. Our study applied smartphone mobility data to simulate the second wave spreading of COVID-19 in January 2021 in three major metropolitan statistical areas (Columbia, Greenville, and Charleston) in South Carolina, United States. Based on the simulation, the number of historical county-level COVID-19 cases was allocated to neighborhoods (Census blockgroups) and points of interest (POIs), and the transmission rate of each allocated place was estimated. The result reveals that the COVID-19 infections during the study period mainly occurred in neighborhoods (86%), and the number is approximately proportional to the neighborhoods population. Restaurants and elementary and secondary schools contributed more COVID-19 infections than other POI categories. The simulation results for the coastal tourism Charleston area show high transmission rates in POIs related to travel and leisure activities. The results suggest that the neighborhood-level infectious controlling measures are critical in reducing COVID-19 infections. We also found that the households of lower socioeconomic status may be an umbrella against infection due to fewer visits to places such as malls and restaurants associated with their low financial status. Control measures should be tailored to different geographic locations since transmission rates and infection counts of POI categories vary among metropolitan areas.

Does place connectivity moderate the association between concentrated disadvantage and COVID-19 fatality in the United States? (preprint)

Concentrated disadvantaged areas have been disproportionately affected by COVID-19 outbreak in the United States (US). Meanwhile, highly connected areas may contribute to higher human movement, leading to higher COVID-19 cases and deaths. This study examined whether place connectivity moderated the association between concentrated disadvantage and COVID-19 fatality. Using COVID-19 fatality over four time periods, we performed mixed-effect negative binomial regressions to examine the association between concentrated disadvantage, Twitter-based place connectivity, and county-level COVID-19 fatality, considering potential state-level variations. Results revealed that concentrated disadvantage was significantly associated with an increased COVID-19 fatality. More importantly, moderation analysis suggested that place connectivity significantly exacerbated the harmful effect of concentrated disadvantage on COVID-19 fatality, and this significant moderation effect increased over time. In response to COVID-19 and other future infectious disease outbreaks, policymakers are encouraged to focus on the disadvantaged areas that are highly connected to provide additional pharmacological and non-pharmacological intervention policies.

Social capital, urbanization level, and COVID-19 vaccination uptake in the United States: A national level analysis (preprint)

Vaccination remains the most promising mitigation strategy for the COVID-19 pandemic. However, existing literature shows significant disparities in vaccination uptake in the United States. Using publicly available national-level data, we aimed to explore if county-level social capital can further explain disparities in vaccination uptake rate adjusting for demographic and social determinants of health (SDOH) variables; and if association between social capital and vaccination uptake may vary by urbanization level. Bivariate analyses and hierarchical multivariable quasi-binomial regression analysis were conducted, then the regression analysis was stratified by urban-rural status. The current study suggests that social capital contributes significantly to the disparities of vaccination uptake in the US. The results of stratification analysis show common predictors of vaccine uptake but also suggest various patterns based on urbanization level regarding the associations of COVID-19 vaccination uptake with SDOH and social capital factors. The study provides a new perspective to address disparities in vaccination uptake through fostering social capital within communities, which may inform tailored public health intervention efforts in enhancing social capital and promoting vaccination uptake.

How different pre-existing mental disorders and their co-occurrence affects clinical outcomes of COVID-19? A study based on real-world data in the Southern United States (preprint)

Importance A growing body of research focuses on the impact of pre-existing mental disorders on clinical outcomes of COVID-19 illness. Although a psychiatric history might be an independent risk factor for COVID-19 infection and mortality, no studies have systematically investigated how different clusters of pre-existing mental disorders may affect COVID-19 clinical outcomes or showed how the coexistence of mental disorder clusters is related to COVID-19 clinical outcomes. Objective To explore how different pre-existing mental disorders and their co-occurrence affects COVID-19-related clinical outcomes based on real-world data. Design, Setting, and Participants Using a retrospective cohort study design, a total of 476,775 adult patients with lab-confirmed and probable COVID-19 between March 06, 2020 and April 14, 2021 in South Carolina, United States were included in the current study. The electronic health record data of COVID-19 patients were linked to all payer-based claims data through the SC Revenue and Fiscal Affairs Office. Main Outcomes and Measures Key COVID-19 clinical outcomes included severity, hospitalization, and death. COVID-19 severity was defined as asymptomatic, mild, and moderate/severe. Pre-existing mental disorder diagnoses from Jan 2, 2019 to Jan 14, 2021 were extracted from the patients’ healthcare utilization data via ICD-10 codes. Mental disorders were categorized into internalizing disorders, externalizing disorders, and thought disorders. Results Of the 476,775 COVID-19 patients, 55,300 had pre-existing mental disorders. There is an elevated risk of COVID-19-related hospitalization and death among participants with pre-existing mental disorders adjusting for key socio-demographic covariates (i.e., age, gender, race, ethnicity, residence, smoking). Co-occurrence of any two clusters was positively associated with COVID-19-related hospitalization and death. The odds ratio of being hospitalized was 2.50 (95%CI 2.284, 2.728) for patients with internalizing and externalizing disorders, 3.34 (95%CI 2.637, 4.228) for internalizing and thought disorders, 3.29 (95%CI 2.288, 4.733) for externalizing and thought disorders, and 3.35 (95%CI 2.604, 4.310) for three clusters of mental disorders. Conclusions and Relevance Pre-existing internalizing disorders, externalizing disorders, and thought disorders are positively related to COVID-19 hospitalization and death. Co-occurrence of any two clusters of mental disorders have elevated risk of COVID-19-related hospitalization and death compared to those with a single cluster.

The role of HIV infection in the clinical spectrum of COVID-19: a population-based cohort analysis based on US National COVID Cohort Collaborative (N3C) Enclave data (preprint)

The authors have withdrawn this manuscript because of the accidental low cell size in the supplementary materials. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author .

Demographic disparities in clinical outcomes of COVID-19: data from a statewide cohort in South Carolina (preprint)

Background: Current literature examining the clinical characteristics of COVID-19 patients underrepresent COVID-19 cases who were either asymptomatic or had a mild illness. Objective: To generate a state level description and examine the demographic disparities of clinical outcomes of COVID-19. Design: Statewide population-based cohort study Setting: COVID-19 surveillance facilities in South Carolina Patients: Adults COVID-19 cases reported to the SC DHEC by Case Report Form from March 04 to December 31, 2020 Measurements: The primary predictors were socio-demographic characteristics. The outcomes were COVID-19 disease severity, hospitalization, and mortality, which collected from the standardized CRF. Results: Among a total of 280,177 COVID-19 cases, 5.2% (14,451) were hospitalized and 1.9% (5,308) died. Individuals who were older, male gender, Blacks, Hispanic or Latino, and residing in small towns had higher odds for hospitalization and death from COVID-19 (Ps<0.0001). Regarding disease severity, 144,157 (51.5%) were asymptomatic, while 34.4% and 14.2% had mild and moderate/severe symptoms, respectively. Older individuals (OR: 1.14, 95%CI: 1.11, 1.18), Hispanic or Latino (OR: 2.07; 95%CI: 1.96, 2.18), and people residing in small towns (OR: 1.15; 95%CI: 1.08, 1.23) had higher odds of experiencing moderate/severe symptoms, while male and Asian (vs Whites) patients had lower odds of experiencing moderate/severe symptoms. Limitations: Potential misclassification of outcomes due to missing data; other variables were not evaluated, such as comorbidities. Conclusion: As the first statewide population-based study using data from multiple healthcare systems with a long follow up period in the US, we provide a more generalizable picture of COVID-19 symptoms and clinical outcomes. The findings from this study reinforce the fact that rural residence, racial and ethnic social determinants of health, unfortunately, remain predictors of poor health outcomes for COVID-19 patients.

Spatial-temporal relationship between population mobility and COVID-19 outbreaks in South Carolina: A time series forecasting analysis (preprint)

BackgroundPopulation mobility is closely associated with coronavirus 2019 (COVID-19) transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive non-pharmaceutical interventions for disease control. South Carolina (SC) is one of the states which reopened early and then suffered from a sharp increase of COVID-19. ObjectiveTo examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility to predict daily new cases at both state- and county- levels in SC. MethodsThis longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020 in SC and its top five counties with the largest number of cumulative confirmed cases. Daily new case was calculated by subtracting the cumulative confirmed cases of previous day from the total cases. Population mobility was assessed using the number of users with travel distance larger than 0.5 mile which was calculated based on their geotagged twitters. Poisson count time series model was employed to carry out the research goals. ResultsPopulation mobility was positively associated with state-level daily COVID-19 incidence and those of the top five counties (i.e., Charleston, Greenville, Horry, Spartanburg, Richland). At the state-level, final model with time window within the last 7-day had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3-, 7-, 14- days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9-, 14-, 28-, 20-, and 9- days, respectively. The 14-day prediction accuracy ranged from 60.3% to 74.5%. ConclusionsPopulation mobility was positively associated with COVID-19 incidences at both state- and county- levels in SC. Using Twitter-based mobility data could provide acceptable prediction for COVID-19 daily new cases. Population mobility measured via social media platform could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic (preprint)

This study reveals the human mobility from various sources and the luxury nature of social distancing in the U.S during the COVID-19 pandemic by highlighting the disparities in mobility dynamics from lower-income and upper-income counties. We collect, process, and compute mobility data from four sources: 1) Apple mobility trend reports, 2) Google community mobility reports, 3) mobility data from Descartes Labs, and 4) Twitter mobility calculated via weighted distance. We further design a Responsive Index (RI) based on the time series of mobility change percentages to quantify the general degree of mobility-based responsiveness to COVID-19 at the U.S. county level. We find statistically significant positive correlations in the RI between either two data sources, revealing their general similarity, albeit with varying Pearsons r coefficients. Despite the similarity, however, mobility from each source presents unique and even contrasting characteristics, in part demonstrating the multifaceted nature of human mobility. The positive correlation between RI and income at the county level is significant in all mobility datasets, suggesting that counties with higher income tend to react more aggressively in terms of reducing more mobility in response to the COVID-19 pandemic. Most states present a positive difference in RI between their upper-income and lower-income counties, where diverging patterns in time series of mobility changes percentages can be found. To our best knowledge, this is the first study that cross-compares multi-source mobility datasets. The findings shed light on not only the characteristics of multi-source mobility data but also the mobility patterns in tandem with the economic disparity. HighlightsO_LIHuman mobility data provide valuable insight into how we adjust our travel behaviors during the COVID-19 pandemic. C_LIO_LIHuman mobility records from Descartes Labs, Apple, Google, and Twitter are compared. C_LIO_LIMulti-source mobility datasets well capture the general impact of COVID-19 pandemic on mobility in the U.S. but present unique and even contrasting characteristics C_LIO_LIThe proposed responsive index quantifies the level of mobility-based reaction in response to the COVID-19 pandemic C_LIO_LIAll selected mobility datasets suggest a statistically significant positive correlation between the responsive index and median income at the U.S. county level. C_LI