SYNOPSISObjectives. The goals of this project were to assess the feasibility of conducting rapid human immunodeficiency virus (HIV) testing in outreach and community settings to increase knowledge of HIV serostatus among groups disproportionately affected by HIV and to identify effective nonclinical venues for recruiting people in the targeted populations.Methods. Community-based organizations (CBOs) in seven U.S. cities conducted rapid HIV testing in outreach and community settings, including public parks, homeless shelters, and bars. People with reactive preliminary positive test results received confirmatory testing, and people confirmed to be HIVpositive were referred to health-care and prevention services.Results. A total of 23,900 people received rapid HIV testing. Of the 267 people (1.1%) with newly diagnosed HIV infection, 75% received their confirmatory test results and 64% were referred to care. Seventy-six percent were from racial/ethnic minority groups, and 58% identified themselves as men who have sex with men, 72% of whom reported having multiple sex partners in the past year. Venues with the highest proportion of new HIV diagnoses were bathhouses, social service organizations, and needle-exchange programs. The acceptance rate for testing was 60% among sites collecting this information.Conclusions. Findings from this demonstration project indicate that offering rapid HIV testing in outreach and community settings is a feasible approach for reaching members of minority groups and people at high risk for HIV infection. The project identified venues that would be important to target and offered lessons that could be used by other CBOs to design and implement similar programs in the future.
The global coronavirus disease (COVID-19) outbreak forced a shift from face-to-face education to online learning in higher education settings around the world. From the outset, COVID-19 online learning (CoOL) has differed from conventional online learning due to the limited time that students, instructors, and institutions had to adapt to the online learning platform. Such a rapid transition of learning modes may have affected learning effectiveness, which is yet to be investigated. Thus, identifying the predictive factors of learning effectiveness is crucial for the improvement of CoOL. In this study, we assess the significance of university support, student–student dialogue, instructor–student dialogue, and course design for learning effectiveness, measured by perceived learning outcomes, student initiative, and satisfaction. A total of 409 university students completed our survey. Our findings indicated that student–student dialogue and course design were predictive factors of perceived learning outcomes whereas instructor–student dialogue was a determinant of student initiative. University support had no significant relationship with either perceived learning outcomes or student initiative. In terms of learning effectiveness, both perceived learning outcomes and student initiative determined student satisfaction. The results identified that student–student dialogue, course design, and instructor–student dialogue were the key predictive factors of CoOL learning effectiveness, which may determine the ultimate success of CoOL.
With the domestic and international spread of the coronavirus disease 2019 (COVID-19), much attention has been given to estimating pandemic risk. We propose the novel application of a well-established scientific approachthe network analysisto provide a direct visualization of the COVID-19 pandemic risk; infographics are provided in the figures. By showing visually the degree of connectedness between different regions based on reported confirmed cases of COVID-19, we demonstrate that network analysis provides a relatively simple yet powerful way to estimate the pandemic risk.
We analyze the COVID-19 pandemic development in Latin America by network analysis to demonstrate the effectiveness of air travel restriction in reducing pandemic risk and provide risk analysis for air travel reopening in Latin America. We reinforce the importance of restricting air travel before and during local transmission of COVID-19.
Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.
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