RESUMO
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.
RESUMO
BackgroundThe healthcare system in China was largely overwhelmed during the unprecedented pandemic of coronavirus disease (COVID-19). HIV-related services have been unavoidably interrupted and impacted. However, the nature and scope of HIV service interruptions due to COVID-19 has rarely been characterized in China and how HIV service challenges affect the service interruptions are also unclear. The current study aimed at characterize HIV service interruption levels and analyzed its associated factors related to service challenges and institutional response from HIV healthcare providers viewpoint. MethodsA cross-sectional online survey was conducted among 1,029 HIV healthcare providers in Guangxi, China, from April to May 2020. Latent class analysis (LCA) was first used to identify HIV service interruption levels. Then hierarchical multinomial logistic regression was conducted to analyze the relationships of HIV care service challenges and institutional response with HIV service interruption levels. Simple slope analysis was employed to examine interaction effects between HIV service challenges and institutional response to COVID-19. ResultsFour classes of HIV service interruption were identified using LCA, with 22.0% complete interruption (class 1), 15.4% moderate interruption (class 2), 21.9% minor interruption (class 3) and 40.7% almost no interruption (class 4). Using class 4 as a reference group, HIV care service challenges were positively associated with the probabilities of service interruptions (Class 1: AOR=1.23, 95%CI: 1.19[~]1.26; Class 2: AOR= 1.10, 95%CI: 1.08[~]1.13; Class 3: AOR= 1.10, 95%CI: 1.08[~]1.12). Institutional response to HIV healthcare delivery was negatively associated with the probabilities of being classified into Class 1 ("Complete interruption") (AOR=0.97, 95%CI: 0.93[~]1.00) and Class 3 ("minor interruption [Outreach service]") (AOR=0.96, 95%CI: 0.93[~]0.99) as compared to Class 4 ("almost no interruption"). Institutional response to HIV healthcare delivery moderated the association of HIV service challenges with complete interruption, but not with the moderate or minor interruption when comparing with no interruption group. ConclusionsA substantial HIV service interruptions occurs due to the COVID-19 pandemic, particularly services that require face-to-face interactions, such as VCT counselling, follow up and outreach services. HIV service challenges largely hinder the HIV service delivery. Institutional response to HIV healthcare delivery could marginally buffer the negative effect of service challenges on complete HIV service interruptions. To maintain continuity of core HIV services in face of a pandemic, build a resilient health care system with adequate preparedness is necessary.
RESUMO
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.
