Early SARS-CoV-2 dynamics and immune responses in unvaccinated participants of an intensely sampled longitudinal surveillance study

Background A comprehensive understanding of the SARS-CoV-2 infection dynamics and the ensuing host immune responses is needed to explain the pathogenesis as it relates to viral transmission. Knowledge gaps exist surrounding SARS-CoV-2 in vivo kinetics, particularly in the earliest stages after exposure. Methods An ongoing, workplace clinical surveillance study was used to intensely sample a small cohort longitudinally. Nine study participants who developed COVID-19 between November, 2020 and March, 2021 were monitored at high temporal resolution for three months in terms of viral loads as well as associated inflammatory biomarker and antibody responses. CD8 + T cells targeting SARS-CoV-2 in blood samples from study participants were evaluated. Results Here we show that the resulting datasets, supported by Bayesian modeling, allowed the underlying kinetic processes to be described, yielding a number of unexpected findings. Early viral replication is rapid (median doubling time, 3.1 h), providing a narrow window between exposure and viral shedding, while the clearance phase is slow and heterogeneous. Host immune responses different widely across participants. Conclusions Results from our small study give a rare insight into the life-cycle of COVID-19 infection and hold a number of important biological, clinical, and public health implications. Plain language summary Managing the response to the COVID-19 pandemic requires information about how quickly the virus reproduces and the effect on the immune system of the person who is infected. We measured the speed at which SARS-CoV-2 reproduces in unvaccinated individuals at various timepoints between when they first became infected, and there was no longer any detectable virus present in their bodies. We also measured changes in their immune response. Our findings can be used to develop guidelines for the clinical management of COVID-19 patients and optimize testing procedures to determine whether people are infected with SARS-CoV-2. Gunawardana et al. monitor the viral load, inflammatory biomarkers and antibody response long-term in people who developed COVID-19. Early viral replication is rapid, providing a narrow window between exposure and viral shedding.

Ubiquitous Micro-Modular Homologies among Genomes from Viruses to Bacteria to Human Mitochondrial DNA: Platforms for Recombination during Evolution?

The emerging Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and its variants have raised tantalizing questions about evolutionary mechanisms that continue to shape biology today. We have compared the nucleotide sequence of SARS-CoV-2 RNA to that of genomes of many different viruses, of endosymbiotic proteobacterial and bacterial DNAs, and of human mitochondrial DNA. The entire 4,641,652 nt DNA sequence of Escherichia coli K12 has been computer-matched to SARS-CoV-2 RNA. Numerous, very similar micro-modular clusters of 3 to 13 nucleotides lengths were detected with sequence identities of 40 to >50% in specific genome segments between SARS-CoV-2 and the investigated genomes. These clusters were part of patch-type homologies. Control sequence comparisons between 1000 randomly computer-composed sequences of 29.9 kb and with the A, C, G, T base composition of SARS-CoV-2 genome versus the reference Wuhan SARS-CoV-2 sequence showed similar patterns of sequence homologies. The universal A, C, G, T genetic coding mode might have succeeded in evolution due in part to its built-in capacity to select for a substantial reservoir of micro-modular domains and employ them as platforms for integrative recombination. Their role in SARS-CoV-2 interspecies transition and the generation of variants appears likely, but their actual involvement will require detailed investigations.

Misperceptions of COVID-19 illness risk and preferences for business and school closures in the United States.

Misperceptions about COVID-19 health risks may be associated with preferences for school and business closures and fear of becoming seriously ill. We analyzed data from the Franklin Templeton-Gallup Economic of Recovery Study (July-December 2020, N = 35,068). Primary outcomes were whether a respondent favored closure of businesses or in-person schooling for elementary/secondary students. We also assessed respondents' fear of COVID-19 illness. We assessed risk misperceptions using respondents' estimates of the proportion of deaths from COVID-19 that occurred in persons under 55 years-old, the proportion of hospitalizations for COVID-19 that occurred in persons under 55 years-old, the mortality rate among patients hospitalized with COVID-19, and the rate of hospitalization for patients infected with COVID-19. The proportion of respondents who favored business closures ranged from 37% to 53%, and the proportion of respondents who favored school closures ranged from 38% to 44%. Most participants reported beliefs about COVID-19 health risks that were inaccurate, and overestimation of health risk was most common. For example, while deaths in persons younger than 55 years-old accounted for 7% of total U.S. deaths, respondents estimated that this population represented 43% of deaths. Overestimating COVID-19 health harms was associated with increased likelihood of fear of serious illness if infected, preferences for business closures, and preferences for school closures. U.S. survey respondents overestimated several COVID-19 risks, and overestimation was associated with increased fear of serious illness and stronger preferences for business/school lockdowns.

Association of COVID-19 Risk Misperceptions With Household Isolation in the United States: Survey Study.

BACKGROUND: Adverse mental and emotional health outcomes are increasingly recognized as a public health challenge associated with the COVID-19 pandemic. OBJECTIVE: The goal of this study was to examine the association of COVID-19 risk misperceptions with self-reported household isolation, a potential risk factor for social isolation and loneliness. METHODS: We analyzed data from the Franklin Templeton-Gallup Economics of Recovery Study (July to December 2020) of 24,649 US adults. We also analyzed data from the Gallup Panel (March 2020 to February 2021), which included 123,516 observations about loneliness. The primary outcome was self-reported household isolation, which we defined as a respondent having no contact or very little contact with people outside their household, analogous to quarantining. RESULTS: From July to December 2020, 53% to 57% of respondents reported living in household isolation. Most participants reported beliefs about COVID-19 health risks that were inaccurate, and overestimation of health risk was most common. For example, while deaths in persons younger than 55 years old accounted for 7% of total US deaths, respondents estimated that this population represented 43% of deaths. Overestimating COVID-19 health risks was associated with increased self-reported household isolation, with percentage differences ranging from 5.6 to 11.8 (P<.001 at each time point). Characteristics associated with self-reported household isolation from the July and August 2020 surveys and persisting in the December 2020 survey included younger age (18 to 39 years), having a serious medical condition, having a household member with a serious medical condition, and identifying as a Democrat. In the Gallup Panel, self-reported household isolation was associated with a higher prevalence of loneliness. CONCLUSIONS: Pandemic-related harms to emotional and mental well-being may be attenuated by reducing risk overestimation and household isolation preferences that exceed public health guidelines.

Male-Female Disparities in Years of Potential Life Lost Attributable to COVID-19 in the United States: A State-by-State Analysis

Males are at higher risk relative to females of severe outcomes following COVID-19 infection. Focusing on COVID-19-attributable mortality in the United States (U.S.), we quantified and contrasted years of potential life lost (YPLL) attributable to COVID-19 by sex based on data from the U.S. National Center for Health Statistics as of 31 March 2021, specifically by contrasting male and female percentages of total YPLL with their respective percent population shares and calculating age-adjusted male-to-female YPLL rate ratios, both nationally and for each of the 50 states and the District of Columbia. Using YPLL before age 75 to anchor comparisons between males and females and a novel Monte Carlo simulation procedure to perform estimation and uncertainty quantification, our results reveal a near-universal pattern across states of higher COVID-19-attributable YPLL among males compared to females. Furthermore, the disproportionately high COVID-19 mortality burden among males is generally more pronounced when measuring mortality burden in terms of YPLL compared to death counts, reflecting dual phenomena of males dying from COVID-19 at higher rates and at systematically younger ages relative to females. The U.S. COVID-19 epidemic also offers lessons underscoring the importance of cultivating a public health environment that recognizes sex-specific needs as well as different patterns in risk factors, health behaviors, and responses to interventions between men and women. Public health strategies incorporating focused efforts to increase COVID-19 vaccinations among men are particularly urged.

Longitudinal COVID-19 Surveillance and Characterization in the Workplace with Public Health and Diagnostic Endpoints.

Public health practices and high vaccination rates currently represent the primary interventions for managing the spread of coronavirus disease 2019 (COVID-19). We initiated a clinical study based on frequent, longitudinal workplace disease surveillance to control severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission among employees and their household members. We hypothesized that the study would reduce the economic burden and loss of productivity of both individuals and small businesses resulting from standard isolation methods, while providing new insights into virus-host dynamics. Study participants (27 employees and 27 household members) consented to provide frequent nasal or oral swab samples that were analyzed by reverse transcription-quantitative PCR (RT-qPCR) for SARS-CoV-2 RNA. Two study participants were found to be infected by SARS-CoV-2 during the study. One subject, a household member, was SARS-CoV-2 RNA positive for at least 71 days and had quantifiable serum virus-specific antibody concentrations for over 1 year. One unrelated employee became positive for SARS-CoV-2 RNA over the course of the study but remained asymptomatic, with low associated viral RNA copy numbers, no detectable serum IgM and IgG concentrations, and IgA concentrations that decayed rapidly (half-life: 1.3 days). A COVID-19 infection model was used to predict that without surveillance intervention, up to 7 employees (95% confidence interval [CI] = 3 to 10) would have become infected, with at most 1 of them requiring hospitalization. Our scalable and transferable surveillance plan met its primary objectives and represents a powerful example of an innovative public health initiative dovetailed with scientific discovery. IMPORTANCE The rapid spread of SARS-CoV-2 and the associated COVID-19 has precipitated a global pandemic heavily challenging our social behavior, economy, and health care infrastructure. In the absence of widespread, worldwide access to safe and effective vaccines and therapeutics, public health measures represent a key intervention for curbing the devastating impacts from the pandemic. We are conducting an ongoing clinical study based on frequent, longitudinal workplace disease surveillance to control SARS-CoV-2 transmission among employees and their household members. Our study was successful in surveying the viral and immune response dynamics in two participants with unusual infections: one remained positive for SARS-CoV-2 for 71 days, while the other was asymptomatic, with low associated viral RNA copy numbers. A COVID-19 infection model was used to predict that without surveillance intervention, up to 7 employees would have become infected, with at most 1 of them requiring hospitalization, underscoring the importance of our program.

SARS-CoV-2 worldwide replication drives rapid rise and selection of mutations across the viral genome: a time-course study - potential challenge for vaccines and therapies.

Scientists and the public were alarmed at the first large viral variant of SARS-CoV-2 reported in December 2020. We have followed the time course of emerging viral mutants and variants during the SARS-CoV-2 pandemic in ten countries on four continents. We examined > 383,500 complete SARS-CoV-2 nucleotide sequences in GISAID (Global Initiative of Sharing All Influenza Data) with sampling dates extending until April 05, 2021. These sequences originated from ten different countries: United Kingdom, South Africa, Brazil, United States, India, Russia, France, Spain, Germany, and China. Among the 77 to 100 novel mutations, some previously reported mutations waned and some of them increased in prevalence over time. VUI2012/01 (B.1.1.7) and 501Y.V2 (B.1.351), the so-called UK and South Africa variants, respectively, and two variants from Brazil, 484K.V2, now called P.1 and P.2, increased in prevalence. Despite lockdowns, worldwide active replication in genetically and socio-economically diverse populations facilitated selection of new mutations. The data on mutant and variant SARS-CoV-2 strains provided here comprise a global resource for easy access to the myriad mutations and variants detected to date globally. Rapidly evolving new variant and mutant strains might give rise to escape variants, capable of limiting the efficacy of vaccines, therapies, and diagnostic tests.

Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model.

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.

Racial and Ethnic Disparities in Years of Potential Life Lost Attributable to COVID-19 in the United States: An Analysis of 45 States and the District of Columbia.

The coronavirus disease 2019 (COVID-19) epidemic in the United States has disproportionately impacted communities of color across the country. Focusing on COVID-19-attributable mortality, we expand upon a national comparative analysis of years of potential life lost (YPLL) attributable to COVID-19 by race/ethnicity (Bassett et al., 2020), estimating percentages of total YPLL for non-Hispanic Whites, non-Hispanic Blacks, Hispanics, non-Hispanic Asians, and non-Hispanic American Indian or Alaska Natives, contrasting them with their respective percent population shares, as well as age-adjusted YPLL rate ratios-anchoring comparisons to non-Hispanic Whites-in each of 45 states and the District of Columbia using data from the National Center for Health Statistics as of 30 December 2020. Using a novel Monte Carlo simulation procedure to perform estimation, our results reveal substantial racial/ethnic disparities in COVID-19-attributable YPLL across states, with a prevailing pattern of non-Hispanic Blacks and Hispanics experiencing disproportionately high and non-Hispanic Whites experiencing disproportionately low COVID-19-attributable YPLL. Furthermore, estimated disparities are generally more pronounced when measuring mortality in terms of YPLL compared to death counts, reflecting the greater intensity of the disparities at younger ages. We also find substantial state-to-state variability in the magnitudes of the estimated racial/ethnic disparities, suggesting that they are driven in large part by social determinants of health whose degree of association with race/ethnicity varies by state.

Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California.

In emerging epidemics, early estimates of key epidemiological characteristics of the disease are critical for guiding public policy. In particular, identifying high-risk population subgroups aids policymakers and health officials in combating the epidemic. This has been challenging during the coronavirus disease 2019 (COVID-19) pandemic because governmental agencies typically release aggregate COVID-19 data as summary statistics of patient demographics. These data may identify disparities in COVID-19 outcomes between broad population subgroups, but do not provide comparisons between more granular population subgroups defined by combinations of multiple demographics. We introduce a method that helps to overcome the limitations of aggregated summary statistics and yields estimates of COVID-19 infection and case fatality rates - key quantities for guiding public policy related to the control and prevention of COVID-19 - for population subgroups across combinations of demographic characteristics. Our approach uses pseudo-likelihood based logistic regression to combine aggregate COVID-19 case and fatality data with population-level demographic survey data to estimate infection and case fatality rates for population subgroups across combinations of demographic characteristics. We illustrate our method on California COVID-19 data to estimate test-based infection and case fatality rates for population subgroups defined by gender, age, and race/ethnicity. Our analysis indicates that in California, males have higher test-based infection rates and test-based case fatality rates across age and race/ethnicity groups, with the gender gap widening with increasing age. Although elderly infected with COVID-19 are at an elevated risk of mortality, the test-based infection rates do not increase monotonically with age. The workforce population, especially, has a higher test-based infection rate than children, adolescents, and other elderly people in their 60-80. LatinX and African Americans have higher test-based infection rates than other race/ethnicity groups. The subgroups with the highest 5 test-based case fatality rates are all-male groups with race as African American, Asian, Multi-race, LatinX, and White, followed by African American females, indicating that African Americans are an especially vulnerable California subpopulation.

An evidence review of face masks against COVID-19.

The science around the use of masks by the public to impede COVID-19 transmission is advancing rapidly. In this narrative review, we develop an analytical framework to examine mask usage, synthesizing the relevant literature to inform multiple areas: population impact, transmission characteristics, source control, wearer protection, sociological considerations, and implementation considerations. A primary route of transmission of COVID-19 is via respiratory particles, and it is known to be transmissible from presymptomatic, paucisymptomatic, and asymptomatic individuals. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. Given the current shortages of medical masks, we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory particles become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask wearing by infectious people ("source control") with benefits at the population level, rather than only mask wearing by susceptible people, such as health care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.