• Correlates of COVID-19 Vaccine Hesitancy among a Community Sample of African Americans Living in the Southern United States

      Moore, Justin Xavier; Gilbert, Keon L.; Lively, Katie L.; Laurent, Christian; Chawla, Rishab; Li, Cynthia; Johnson, Ryan; Petcu, Robert; Mehra, Mehul; Spooner, Antron; et al. (MDPI, 2021-08-08)
      In the United States, African Americans (AAs) have been disproportionately affected by COVID-19 mortality. However, AAs are more likely to be hesitant in receiving COVID-19 vaccinations when compared to non-Hispanic Whites. We examined factors associated with vaccine hesitancy among a predominant AA community sample. We performed a cross-sectional analysis on data collected from a convenience sample of 257 community-dwelling participants in the Central Savannah River Area from 5 December 2020, through 17 April 2021. Vaccine hesitancy was categorized as resistant, hesitant, and acceptant. We estimated relative odds of vaccine resistance and vaccine hesitancy using polytomous logistic regression models. Nearly one-third of the participants were either hesitant (n = 40, 15.6%) or resistant (n = 42, 16.3%) to receiving a COVID-19 vaccination. Vaccine-resistant participants were more likely to be younger and were more likely to have experienced housing insecurity due to COVID-19 when compared to both acceptant and hesitant participants, respectively. Age accounted for nearly 25% of the variation in vaccine resistance, with 21-fold increased odds (OR: 21.93, 95% CI: 8.97–5.26–91.43) of vaccine resistance in participants aged 18 to 29 compared to 50 and older adults. Housing insecurity accounted for 8% of the variation in vaccine resistance and was associated with 7-fold increased odds of vaccine resistance (AOR: 7.35, 95% CI: 1.99–27.10). In this sample, AAs under the age of 30 and those experiencing housing insecurity because of the COVID-19 pandemic were more likely to be resistant to receiving a free COVID-19 vaccination.
    • High-Throughput Next-Generation Sequencing Respiratory Viral Panel: A Diagnostic and Epidemiologic Tool for SARS-CoV-2 and Other Viruses

      Sahajpal, Nikhil S.; Mondal, Ashis K.; Njau, Allan; Petty, Zachary; Chen, Jiani; Ananth, Sudha; Ahluwalia, Pankaj; Williams, Colin; Ross, Ted M.; Chaubey, Alka; et al. (MDPI, 2021-10-14)
      Two serious public health challenges have emerged in the current COVID-19 pandemic namely, deficits in SARS-CoV-2 variant monitoring and neglect of other co-circulating respiratory viruses. Additionally, accurate assessment of the evolution, extent, and dynamics of the outbreak is required to understand the transmission of the virus. To address these challenges, we evaluated 533 samples using a high-throughput next-generation sequencing (NGS) respiratory viral panel (RVP) that includes 40 viral pathogens. The performance metrics revealed a PPA, NPA, and accuracy of 95.98%, 85.96%, and 94.4%, respectively. The clade for pangolin lineage B that contains certain distant variants, including P4715L in ORF1ab, Q57H in ORF3a, and S84L in ORF8 covarying with the D614G spike protein mutation, were the most prevalent early in the pandemic in Georgia, USA. The isolates from the same county formed paraphyletic groups, indicating virus transmission between counties. The study demonstrates the clinical and public health utility of the NGS-RVP to identify novel variants that can provide actionable information to prevent or mitigate emerging viral threats and models that provide insights into viral transmission patterns and predict transmission/resurgence of regional outbreaks as well as providing critical information on co-circulating respiratory viruses that might be independent factors contributing to the global disease burden.
    • Implementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept

      Awad, Susanne F.; Musuka, Godfrey; Mukandavire, Zindoga; Froass, Dillon; MacKinnon, Neil J.; Cuadros, Diego F.; Department of Population Health Sciences (MDPI AG, 2021-10-25)
      Geospatial vaccine uptake is a critical factor in designing strategies that maximize the population-level impact of a vaccination program. This study uses an innovative spatiotemporal model to assess the impact of vaccination distribution strategies based on disease geospatial attributes and population-level risk assessment. For proof of concept, we adapted a spatially explicit COVID-19 model to investigate a hypothetical geospatial targeting of COVID-19 vaccine rollout in Ohio, United States, at the early phase of COVID-19 pandemic. The population-level deterministic compartmental model, incorporating spatial-geographic components at the county level, was formulated using a set of differential equations stratifying the population according to vaccination status and disease epidemiological characteristics. Three different hypothetical scenarios focusing on geographical subpopulation targeting (areas with high versus low infection intensity) were investigated. Our results suggest that a vaccine program that distributes vaccines equally across the entire state effectively averts infections and hospitalizations (2954 and 165 cases, respectively). However, in a context with equitable vaccine allocation, the number of COVID-19 cases in high infection intensity areas will remain high; the cumulative number of cases remained >30,000 cases. A vaccine program that initially targets high infection intensity areas has the most significant impact in reducing new COVID-19 cases and infection-related hospitalizations (3756 and 213 infections, respectively). Our approach demonstrates the importance of factoring geospatial attributes to the design and implementation of vaccination programs in a context with limited resources during the early stage of the vaccine rollout.