Assessing the Burden of COVID-19 in Developing Countries: Systematic Review, Meta-Analysis, and Public Policy Implications

IntroductionThe infection-fatality rate (IFR) of COVID-19 has been carefully measured and analyzed in high-income countries, whereas there has been no systematic analysis of age-specific seroprevalence or IFR for developing countries. MethodsWe systematically reviewed the literature to identify all COVID-19 serology studies in developing countries that were conducted using population representative samples collected by early 2021. For each of the antibody assays used in these serology studies, we identified data on assay characteristics, including the extent of seroreversion over time. We analyzed the serology data using a Bayesian model that incorporates conventional sampling uncertainty as well as uncertainties about assay sensitivity and specificity. We then calculated IFRs using individual case reports or aggregated public health updates, including age-specific estimates whenever feasible. ResultsSeroprevalence in many developing country locations was markedly higher than in high-income countries. In most locations, seroprevalence among older adults was similar to that of younger age cohorts, underscoring the limited capacity that these nations have to protect older age groups. Age-specific IFRs were roughly 2x higher than in high-income countries. The median value of the population IFR was about 0.5%, similar to that of high-income countries, because disparities in healthcare access were roughly offset by differences in population age structure. ConclusionThe burden of COVID-19 is far higher in developing countries than in high-income countries, reflecting a combination of elevated transmission to middle-aged and older adults as well as limited access to adequate healthcare. These results underscore the critical need to accelerate the provision of vaccine doses to populations in developing countries. Key Points- Age-stratified infection fatality rates (IFRs) of COVID-19 in developing countries are about twice those of high-income countries. - Seroprevalence (as measured by antibodies against SARS-CoV-2) is broadly similar across age cohorts, underscoring the challenges of protecting older age groups in developing countries. - Population IFR in developing countries is similar to that of high-income countries, because differences in population age structure are roughly offset by disparities in healthcare access as well as elevated infection rates among older age cohorts. - These results underscore the urgency of disseminating vaccines throughout the developing world.

Model-based Evaluation of Continued COVID-19 Risk at Long Term Care Facilities

The COVID-19 pandemic severely impacted long-term care facilities resulting in the death of approximately 8% of residents nationwide. As COVID-19 case rates decline and state and county restrictions are lifted, facility managers, local and state health agencies are challenged with defining their own policies moving forward to appropriately mitigate disease transmission. The continued emergence of variants of concern has highlighted the need for a readily available tool that can be employed at the facility-level to determine best practices for mitigation and ensure resident and staff safety. To assist leadership in determining the impact of various infection surveillance and response strategies, we developed an agent-based model and an online dashboard interface that simulates COVID-19 infection within congregate care settings under various mitigation measures. In this paper, we demonstrate how this dashboard can be used to quantify the continued risk for COVID-19 infections within a facility given a designated testing schedule and vaccine requirements. Our results highlight the critical nature of testing cadence, test sensitivity and specificity, and the impact of removing asymptomatic infected individuals from the workplace. We also show that monthly surveillance testing at long-term care facilities is unlikely to successfully mitigate SARS-CoV-2 outbreaks in congregate care settings. DisclosuresThis work was supported by Colorado State Universitys Center for Healthy Aging, the Center for Vector-Bourne Infectious Disease, the Office of the Vice President for Research, the College of Health and Human Sciences, the Collage of Natural Sciences, the College of Veterinary Medicine and Biomedical Sciences, and the Walter Scott Jr College of Engineering.

Disparities in SARS-CoV-2 seroprevalence among individuals presenting for care in central North Carolina over a six-month period

BackgroundRobust community-level SARS-CoV-2 prevalence estimates have been difficult to obtain in the American South and outside of major metropolitan areas. Furthermore, though some previous studies have investigated the association of demographic factors such as race with SARS-CoV-2 exposure risk, fewer have correlated exposure risk to surrogates for socioeconomic status such as health insurance coverage. MethodsWe used a highly specific serological assay utilizing the receptor binding domain of the SARS-CoV-2 spike-protein to identify SARS-CoV-2 antibodies in remnant blood samples collected by the University of North Carolina Health system. We estimated the prevalence of SARS-CoV-2 in this cohort with Bayesian regression, as well as the association of critical demographic factors with higher prevalence odds. FindingsBetween April 21st and October 3rd of 2020, a total of 9,624 unique samples were collected from clinical sites in central NC and we observed a seroprevalence increase from 2{middle dot}9 (1{middle dot}7, 4{middle dot}3) to 9{middle dot}1 (7{middle dot}2, 11{middle dot}1) over the study period. Individuals who identified as Latinx were associated with the highest odds ratio of SARS-CoV-2 exposure at 7{middle dot}77 overall (5{middle dot}20, 12{middle dot}10). Increased odds were also observed among Black individuals and individuals without public or private health insurance. InterpretationOur data suggests that for this care-accessing cohort, SARS-CoV-2 seroprevalence was significantly higher than cumulative total cases reported for the study geographical area six months into the COVID-19 pandemic in North Carolina. The increased odds of seropositivity by ethnoracial grouping as well as health insurance highlights the urgent and ongoing need to address underlying health and social disparities in these populations. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for studies published through March 21st, 2021. We used search terms that included "COVID-19", "SARS-CoV-2", "prevalence" and "seroprevalence". Our search resulted in 399 papers, from which we identified 58 relevant studies describing SARS-CoV-2 seroprevalence at sites around the United States from March 1 to December 9, 2020, 12 of which utilized remnant clinical samples and three of which overlapped with our study area. Most notably, one study of 4,422 asymptomatic inpatients and outpatients in central NC from April 28-June 19, 2020 found an estimated seroprevalence of 0{middle dot}7 -0{middle dot}8%, and another study of 177,919 inpatients and outpatients (3,817 from NC) from July 27-September 24, 2020 found an estimated seroprevalence of 2{middle dot}5 -6{middle dot}8%. Added value of this studyThis is the largest SARS-CoV-2 seroprevalence cohort published to date in NC. Importantly, we used a Bayesian framework to account for uncertainty in antibody assay sensitivity and specificity and investigated seropositivity by important demographic variables that have not yet been studied in this context in NC. This study corroborates other reports that specific demographic factors including race, ethnicity and the lack of public or private insurance are associated with elevated risk of SARS-CoV-2 infection. Furthermore, in a subset of serum samples, we identify other SARS-CoV-2 antibodies elicited by these individuals, including functionally neutralizing antibodies. Implications of all the available evidenceIt is difficult to say the exact seroprevalence in the central North Carolina area, but a greater proportion of the population accessing healthcare has been infected by SARS-CoV-2 than is reflected by infection cases confirmed by molecular testing. Furthermore, local governments need to prioritize addressing the many forms of systemic racism and socioeconomic disadvantage that drive SARS-CoV-2 exposure risk, such as residential and occupational risk, and an urgent need to provide access to SARS-CoV-2 testing and vaccination to these groups.

Estimating COVID-19 Cases on University Campuses Prior To Semester

For many institutions of higher learning, the beginning of each semester is marked by a significant migration of young adults into the area. In the midst of the COVID19 pandemic, this presents an opportunity for active cases to be introduced into a community. Prior to the Fall 2020 semester, Colorado State University researchers combined student home locations with recent case counts compiled by the New York Times to assign a probability to each individual of arriving with COVID19. These probabilities were combined to estimate that there would be 7.8 new cases among the on-campus population. Comprehensive testing of arriving students revealed 7 new cases, which validated the approach. The procedure was repeated to explore what could happen if students had returned to campus after Fall break. The estimate of 48 cases corroborated the Universitys early decision to transition to fully remote learning after break.

Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys

Establishing how many people have already been infected by SARS-CoV-2 is an urgent priority for controlling the COVID-19 pandemic. Patchy virological testing has hampered interpretation of confirmed case counts, and unknown rates of asymptomatic and mild infections make it challenging to develop evidence-based public health policies. Serological tests that identify past infection can be used to estimate cumulative incidence, but the relative accuracy and robustness of various sampling strategies has been unclear. Here, we used a flexible framework that integrates uncertainty from test characteristics, sample size, and heterogeneity in seroprevalence across tested subpopulations to compare estimates from sampling schemes. Using the same framework and making the assumption that serological positivity indicates immune protection, we propagated these estimates and uncertainty through dynamical models to assess the uncertainty in the epidemiological parameters needed to evaluate public health interventions. We examined the relative accuracy of convenience samples versus structured surveys to estimate population seroprevalence and found that sampling schemes informed by demographics and contact networks outperform uniform sampling. The framework can be adapted to optimize the design of serological surveys given particular test characteristics and capacity, population demography, sampling strategy, and modeling approach, and can be tailored to support decision-making around introducing or removing interventions.