ABSTRACT
Background: COVID-19 in Africa was less severe with fewer reported cases, hospitalizations and deaths compared to other continents. However, the lack of adequate surveillance systems in Africa makes estimating the burden of infection challenging. Serosurveillance can aid in determining the frequency of infection within this population. This study is aimed to estimate SARS-CoV-2 seroprevalence, describe the SARS-CoV-2 antibody (Ab) levels, and examine associations of seroreactivity among Ugandan blood donors. Method(s): Samples were obtained from the Mirasol Evaluation of Reduction in Infections Trial (MERIT), a randomized, double blind, controlled clinical trial evaluating transfusion transmitted infections. MERIT blood donor samples (n=3,517) were collected from Kampala, Uganda between October 2019 to April 2022. Additional blood donor samples (n=1,876) were collected from around the country between November-December 2021. Samples were tested for Ab to SARS-CoV-2 nucleocapsid (N) and spike (S) using an electrochemiluminescence immunoassay assay (Meso Scale Diagnostics, Gaithersburg, MD) per manufacturer's protocol. Samples seroreactive to both N and S Ab were considered Ab positive to SARS-CoV-2. Seroprevalence among MERIT donors were estimated within each quarter. Factors associated with seroreactivity from November-December 2021 were assessed by chi-square test. Result(s): SARS-CoV-2 seroprevalence increased from < 2.0% in October 2019- June 2020 to 82.5% in January-April 2022. Three distinct peaks in seroreactivity were seen in October-November 2020, July-August 2021, and January-April 2022 (see Figure). Among seroreactive donors, median N Ab levels increased 9-fold and median S Ab 19-fold over the study period. In November-December 2021, SARS-CoV-2 seroprevalence was higher among donors from Kampala (58.8%) compared to more rural regions of Hoima (47.7%), Jinja (47.9%), and Masaka (54.4%;p=0.007);S seroprevalence was lower among HIV+ donors (58.8% vs. 84.9%;p=0.009). Conclusion(s): Blood donors in Uganda showed high prevalence of Ab to SARSCoV- 2 by March of 2022, indicating that the infection levels were similar to many other regions of the globe. Higher seroprevalence was observed in the capital compared to more rural areas in Uganda. Further, increasingly high antibody levels among seropositive donors may indicate repeat infections. The lower COVID-19 morbidity and mortality was not due to a lack of exposure of the virus, but other factors yet to be determined.
ABSTRACT
When COVID-19 necessitated remote teaching, mechanics faculty needed to quickly convert hands-on teaching props into equally effective online equivalents. This constraint sparked a new innovation in a Mechanics of Materials course. Unable to pass around a foam beam to demonstrate concepts such as "plane sections remain plane," or an annotated wood cube to illustrate the sign convention for shear stress, dozens of interactive CAD models were developed with the open-source browser software SketchUp. The CAD models have been uploaded to SketchUp's 3D Warehouse and placed in the public domain. They are opened by students in browser windows and are manipulated in 3D space. Familiarity with the modeling software led to a second innovation: the presentation of exam problems in SketchUp. In an exam, students are provided with a hyperlink to a CAD model in the public domain. Students navigate the model in 3D space, note key dimensions, and perform requested calculations. Assessment of the impact of these innovations is ongoing in Fall 2020, as the 2D problems used on paper exams in prior years are now being presented to students in full 3D. This paper will explain how this approach is easily accessible to all faculty, including those with minimal CAD experience. Additionally, the public-domain 3D models will be demonstrated, and links shared, so that these visualizations may be used at other institutions and shared across the engineering education community. © American Society for Engineering Education, 2021
ABSTRACT
The disproportionate number of foreign-born workers employed in meat and poultry processing reflects structural, social, and economic inequities that likely contribute to an increased COVID-19 incidence in this population. In May 2020, the Maryland Department of Health and CDC investigated factors that might affect person-to-person SARS-CoV-2 transmission among persons who worked at two poultry processing facilities. A survey administered to 359 workers identified differences in risk factors for SARS-CoV-2 infection between workers born outside the United States and U.S.-born workers. Compared with U.S.-born workers, foreign-born workers had higher odds of working in fixed locations on the production floor (odds ratio [OR] for cutup and packaging jobs = 4.8), of having shared commutes (OR = 1.9), and of living with other poultry workers (OR = 6.0). They had lower odds of participating in social gatherings (OR for visits to family = 0.2;OR for visits to friends = 0.4), and they visited fewer businesses in the week before the survey than did their U.S.-born coworkers. Some workplace risk factors can be mitigated through engineering and administrative controls focused on the production floor, and this will be of particular benefit to the foreign-born workers concentrated in these areas. Employers and health departments can also partner with local organizations to disseminate culturally and linguistically tailored messages about risk reduction behaviors in community settings, including shared transportation section and household members dwelling in close quarters.
ABSTRACT
Background: The performance of serological antibody tests to SARS-CoV-2 infection varies widely and little is known about their performance in Africa. We assessed the performance of CoronaCHEK Lateral Flow Point of Care Tests on samples from Rakai, Uganda and Baltimore, Maryland, USA. Methods: Samples from subjects known to be SARS-CoV-2 PCR+ (Uganda: 50 samples from 50 individuals, and Baltimore: 266 samples from 38 individuals) and samples from pre-pandemic individuals collected prior to 2019 (Uganda: 1077 samples, Baltimore: 580 samples) were analyzed with the CoronaCHEK assay per manufacturers protocol. Sensitivity by duration of infection and specificity among pre-pandemic samples were assessed for the IgM and IgG bands separately and for any reactivity. Poisson regression models were used to calculate prevalence ratios (PR) for factors associated with a false-positive test among pre-pandemic samples. Results: In Baltimore samples, sensitivity for any reactivity increased with duration of infection with 39% (95% CI 30, 49) during 0-7 days since first positive PCR, 86% (95% CI 79, 92) for 8-14 days, and 100% (95% CI 89,100) after 15 days (See Figure). In Uganda, sensitivity was 100% (95% CI 61,100) during 0-7 days, 75% (95%CI 53, 89) for 8-14 days, and 87% (95%CI 55, 97) after 14 days since first positive PCR. Specificity results among pre-pandemic samples from Uganda was 96.5% (95% CI 97.5, 95.2), significantly lower than the 99.3% (95% CI 98.2, 99.8) observed in samples from Baltimore (p<0.01). In Ugandan samples, individuals with a false positive result were more likely to have had a fever more than a month prior to sample acquisition (PR 2.9, 95% CI 1.1, 7.0). Conclusion: Sensitivity of the CoronaCHEK appeared to be significantly higher in Ugandan samples from individuals within their first week of infection compared to their Baltimorean counterparts. By the second week of infection the sensitivity appeared the same between geographic areas. The specificity was significantly lower in Ugandan samples than those from Baltimore. False positive results from pre-pandemic Uganda appear to be correlated with the convalescent disease state, potentially indicative of a highly cross-reactive immune response in these individuals from East Africa.
ABSTRACT
Artificial intelligence (AI) algorithms hold promise to reduce inequalities across race and socioeconomic status. One of the most important domains of racial and economic inequalities is medical outcomes;Black and low-income people are more likely to die from many diseases. Algorithms can help reduce these inequalities because they are less likely than human doctors to make biased decisions. Unfortunately, people are generally averse to algorithms making important moral decisions—including in medicine—undermining the adoption of AI in healthcare. Here we use the COVID-19 pandemic to examine whether the threat of racial and economic inequality increases the preference for algorithm decision-making. Four studies (N = 2819) conducted in the United States and Singapore show that emphasizing inequality in medical outcomes increases the preference for algorithm decision-making for triage decisions. These studies suggest that one way to increase the acceptance of AI in healthcare is to emphasize the threat of inequality and its negative outcomes associated with human decision-making. © 2021 Elsevier Ltd