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This paper simulates a hypothetical pan-coronavirus vaccine that confers immediate sterilizing immunity against all SARS-CoV-2 variants. Simulations used a SEIIS (susceptible, exposed, infective, immune, susceptible) spreadsheet model that ran two parallel subpopulations: one that accepted vaccination and another that refused it. The two subpopulations could transmit infections to one another. Using data from the United States (US), the simulated vaccine was tested against limiting factors such as vaccine hesitancy, slow vaccination distribution, and the development of high-transmission variants. The vaccine was often successful at reducing cases, but high-transmission variants and discontinuation of non-pharmaceutical interventions (NPIs) such as masking greatly elevated cases. A puzzling outcome was that if NPIs were discontinued and high-transmission variants became common, the model predicted consistently higher rates of disease than are actually observed in the US in 2024. However, if cumulative exposure to virus antigens increased the duration of immunity or decreased the infectivity of the virus, the model predictions were brought back into a more realistic range. The major finding was that even when a COVID-19 vaccine always produces sterilizing immunity against every SARS-CoV-2 variant, its ability to control the epidemic can be compromised by multiple common conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a new epidemic prediction model that hybridizes several models, such as the autoregressive integrated moving average model (ARIMA), random forest (RF), and response surface method (RSM). The modeling process based on ensemble empirical mode decomposition (EEMD) is particularly suitable for dealing with non-stationary and nonlinear data. ARIMA's timeliness and difference have strong deterministic information extraction ability. RF is robust and stable, with fast speed, and strong generalization ability. Under the adjustability and correspondence of the response surface, the comprehensiveness of the model is well demonstrated. Taking the United States as an example, the proposed ARIMA-RF-RSM model is used to explore the development mechanism of the early epidemic according to the data of the early epidemic of coronavirus disease 2019 (COVID-19). The proposed model has high prediction accuracy (mean absolute percentage error (MAPE) is 1.97% and root mean square error (RSME) is 7.24%). It helps to take effective prevention and control measures in time. In addition, the model has universal applicability to the analysis of disease transmission in relevant areas. [ABSTRACT FROM AUTHOR]

The transmission of coronavirus disease‐2019 (COVID‐19) epidemic is a global emergency, which is worsened by the genetic mutations of SARS‐CoV‐2. However, till date, few statistical studies have researched the COVID‐19 spread patterns in terms of the variant cases. Hence, this paper aims to explore the associated risk factors of Delta variant, the most contagious strain of COVID‐19. The study collected the state‐level COVID‐19 Delta variant cases in the United States during a 12‐week period and included potential environmental, socioeconomic, and public prevention factors as independent variables. Instead of regarding the covariate effects as constant, this paper proposes a flexible Bayesian hierarchical model with spatio‐temporally varying coefficients to account for data heterogeneity. The method enables us to cluster the states into distinctive groups based on the temporal trends of the coefficients and simultaneously identify significant risk factors for each cluster. The findings contribute novel insight into the dynamics of covariate effects on the COVID‐19 Delta variant over space and time, which could help the government develop targeted prevention measures for vulnerable regions based on the selected risk factors. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 virus has, up to the time of this article, resulted in >770 million cases of COVID-19 illness worldwide, and approximately 7 million deaths, including >1.1 million in the United States. Although defined as a respiratory virus, early in the pandemic, it became apparent that considerable numbers of people recovering from COVID-19 illness experienced persistence or new onset of multi-system health problems, including neurologic and cognitive and behavioral health concerns. Persistent multi-system health problems are defined as Post-COVID-19 Condition (PCC), Post-Acute Sequelae of COVID-19, or Long COVID. A significant number of those with PCC report cognitive problems. This paper reviews the current state of scientific knowledge on persisting cognitive symptoms in adults following COVID-19 illness. A brief history is provided of the emergence of concerns about persisting cognitive problems following COVID-19 illness and the definition of PCC. Methodologic factors that complicate clear understanding of PCC are reviewed. The review then examines research on patterns of cognitive impairment that have been found, factors that may contribute to increased risk, behavioral health variables, and interventions being used to ameliorate persisting symptoms. Finally, recommendations are made about ways neuropsychologists can improve the quality of existing research. [ABSTRACT FROM AUTHOR]

Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2's propensity for asymptomatic transmission, raise the question "how reliable was contact tracing for COVID-19 in the United States"? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62-1.68%) of transmission events with PCR testing and 1.00% (95% uncertainty interval 0.98-1.02%) with rapid antigen testing. When considering a more robust contact tracing scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6-62.8%). We did not assume presence of asymptomatic transmission or superspreading, making our estimates upper bounds on the actual percentages traced. These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic resulted in profound changes in mobility patterns and altered travel behaviors locally and globally. As a result, movement metrics have widely been used by researchers and policy makers as indicators to study, model, and mitigate the impacts of the COVID-19 pandemic. However, the veracity and variability of these mobility metrics have not been studied. This paper provides a systematic review of mobility and social distancing metrics available to researchers during the pandemic in 2020 in the United States. Twenty-six indices across nine different sources are analyzed and assessed with respect to their spatial and temporal coverage as well as sample representativeness at the county-level. Finally global and local indicators of spatial association are computed to explore spatial and temporal heterogeneity in mobility patterns. The structure of underlying changes in mobility and social distancing is examined in different US counties and across different data sets. We argue that a single measure might not describe all aspects of mobility perfectly. [ABSTRACT FROM AUTHOR]

This paper uses the Value of Statistical Life (VSL) literature to weigh the costs and benefits of non-pharmaceutical interventions of the U.S. COVID-19 stay-at-home orders that affected 92 percent of the U.S. workforce at their peak in April 2020. We calculate the prevaccine COVID-19 infection fatality rate to have been 0.85 percent. We find that the stay-at-home orders saved most likely about 71,000 lives and led to a net benefit to the United States of 1.7 percent of GDP after accounting for lives saved and drops in workforce participation. Through October 31, 2021, the VSL of U.S. lives lost to COVID-19 was over $8.4 trillion. [ABSTRACT FROM AUTHOR]

• Vaccine-derived herd immunity against COVID-19 can be achieved if at least 60% of the population is fully-vaccinated. • COVID-19 elimination is feasible by June 2022 if the current baseline full vaccination coverage is increased by about 20%. • Elimination prospects are improved significantly if vaccination is combined with the use of moderate to high-quality masks. • Having more people wear surgical masks is more beneficial than having fewer people wear the highly-effective N95 masks. • Treatment of symptomatic individuals has a significant impact on reducing daily hospitalizations. The effectiveness of control interventions against COVID-19 is threatened by the emergence of SARS-CoV-2 variants of concern. We present a mathematical model for studying the transmission dynamics of two of these variants (Delta and Omicron) in the United States, in the presence of vaccination, treatment of individuals with clinical symptoms of the disease and the use of face masks. The model is parameterized and cross-validated using observed daily case data for COVID-19 in the United States for the period from November 2021 (when Omicron first emerged) to March 2022. Rigorous qualitative analysis of the model shows that the disease-free equilibrium of the model is locally-asymptotically stable when the control reproduction number of the model (denoted by R c) is less than one. This equilibrium is shown to be globally-asymptotically stable for a special case of the model, where disease-induced mortality is negligible and both vaccine-derived immunity in fully-vaccinated individuals and natural immunity do not wane, when the associated reproduction number is less than one. The epidemiological implication of the latter result is that the combined vaccination-boosting strategy can lead to the elimination of the pandemic if its implementation can bring (and maintain) the associated reproduction number to a value less than one. An analytical expression for the vaccine-derived herd immunity threshold is derived. Using this expression, together with the baseline values of the parameters of the parameterized model, we showed that the vaccine-derived herd immunity can be achieved in the United States (so that the pandemic will be eliminated) if at least 68 % of the population is fully-vaccinated with two of the three vaccines approved for use in the United States (Pfizer or Moderna vaccine). Furthermore, this study showed (as of the time of writing in March 2022) that the control reproduction number of the Omicron variant was approximately 3.5 times that of the Delta variant (the reproduction of the latter is computed to be ≈ 0.2782), indicating that Delta had practically died out and that Omicron has competitively-excluded Delta (to become the predominant variant in the United States). Based on our analysis and parameterization at the time of writing of this paper (March 2022), our study suggests that SARS-CoV-2 elimination is feasible by June 2022 if the current baseline level of the coverage of fully-vaccinated individuals is increased by about 20 %. The prospect of pandemic elimination is significantly improved if vaccination is combined with a face mask strategy that prioritizes moderately effective and high-quality masks. Having a high percentage of the populace wearing the moderately-effective surgical mask is more beneficial to the community than having low percentage of the populace wearing the highly-effective N95 masks. We showed that waning natural and vaccine-derived immunity (if considered individually) offer marginal impact on disease burden, except for the case when they wane at a much faster rate (e.g., within three months), in comparison to the baseline (estimated to be within 9 months to a year). Treatment of symptomatic individuals has marginal effect in reducing daily cases of SARS-CoV-2, in comparison to the baseline, but it has significant impact in reducing daily hospitalizations. Furthermore, while treatment significantly reduces daily hospitalizations (and, consequently, deaths), the prospects of COVID-19 elimination in the United States are significantly enhanced if investments in control resources are focused on mask usage and vaccination rather than on treatment. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, caused by SARS-CoV-2, disproportionately affected certain segments of society, particularly the elderly population (which suffered the brunt of the burden of the pandemic in terms of severity of the disease, hospitalization, and death). This study presents a generalized multigroup model, with m heterogeneous subpopulations, to assess the population-level impact of age heterogeneity and vaccination on the transmission dynamics and control of the SARS-CoV-2 pandemic in the United States. Rigorous analysis of the model for the homogeneous case (i.e., the model with m = 1) reveal that its disease-free equilibrium is globally-asymptotically stable for two special cases (with perfect vaccine efficacy or negligible disease-induced mortality) whenever the associated reproduction number is less than one. The model has a unique and globally-asymptotically stable endemic equilibrium, for special a case, when the associated reproduction threshold exceeds one. The homogeneous model was fitted using the observed cumulative mortality data for the United States during three distinct waves (Waves A (October 17, 2020 to April 5, 2021), B (July 9, 2021 to November 7, 2021) and C (January 1, 2022 to May 7, 2022)) chosen to align with time periods when the Alpha, Delta and Omicron were, respectively, the predominant variants in the United States. The calibrated model was used to derive a theoretical expression for achieving vaccine-derived herd immunity (needed to eliminate the disease in the United States). It was shown that, using the one-group homogeneous model, vaccine-derived herd immunity is not attainable during Wave C of the pandemic in the United States, regardless of the coverage level of the fully-vaccinated individuals. Global sensitivity analysis was carried out to determine the parameters of the model that have the most influence on the disease dynamics and burden. These analyses reveal that control and mitigation strategies that may be very effective during one wave may not be so very effective during the other wave or waves. However, strategies that target asymptomatic and pre-symptomatic infectious individuals are shown to be consistently effective across all waves. To study the impact of the disproportionate effect of COVID-19 on the elderly population, we considered the heterogeneous model for the case where the total population is subdivided into the subpopulations of individuals under 65 years of age and those that are 65 and older. The resulting two-group heterogeneous model, which was also fitted using the cumulative mortality data for wave C, was also rigorously analysed. Unlike for the case of the onegroup model, it was shown, for the two-group model, that vaccine-derived herd immunity can indeed be achieved during Wave C of the pandemic if at least 61% of the populace is fully vaccinated. Thus, this study shows that adding age heterogeneity into a SARS-CoV-2 vaccination model with homogeneous mixing significantly reduces the level of vaccination coverage needed to achieve vaccine-derived herd immunity (specifically, for the heterogeneous model, herd-immunity can be attained duringWave C if a moderate proportion of susceptible individuals are fully vaccinated). The consequence of this result is that vaccination models for SARS-CoV-2 that do not explicitly account for age heterogeneity may be overestimating the level of vaccine-derived herd immunity threshold needed to eliminate the SARS-CoV-2 pandemic. [ABSTRACT FROM AUTHOR]

The rapid spread of COVID-19, caused by the SARS-CoV-2 virus, has had and continues to pose a significant threat to global health. We propose a predictive model based on the gated recurrent unit (GRU) that investigates the influence of non-pharmaceutical interventions (NPIs) on the progression of COVID-19. The proposed model is validated by case studies for multiple states in the United States. It should be noted that the proposed model can be generalized to other regions of interest. The results show that the predictive model can achieve accurate forecasts across the US. The forecast is then utilized to identify the optimal mitigation policies. The goal is to identify the best stringency level for each policy that can minimize the total number of new COVID-19 cases while minimizing the mitigation costs. A meta-heuristics method, named multi-population evolutionary algorithm with differential evolution (MPEA-DE), has been developed to identify optimal mitigation strategies that minimize COVID-19 infection cases while reducing economic and other negative implications. We compared the optimal mitigation strategies identified by the MPEA-DE model with three baseline search strategies. The results show that MPEA-DE performs better than other baseline models based on prescription dominance. [ABSTRACT FROM AUTHOR]

While the early effects of the COVID-19 pandemic on the United States labor market are well-established, less is known about the long-term impact of SARS-CoV-2 infection and Long COVID on employment. To address this gap, we analyzed self-reported data from a prospective, national cohort study to estimate the effects of SARS-CoV-2 symptoms at three months post-infection on missed workdays and return to work. The analysis included 2,939 adults in the Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE) study who tested positive for their initial SARS-CoV-2 infection at the time of enrollment, were employed before the pandemic, and completed a baseline and three-month electronic survey. At three months post-infection, 40.8% of participants reported at least one SARS-CoV-2 symptom and 9.6% of participants reported five or more SARS-CoV-2 symptoms. When asked about missed work due to their SARS-CoV-2 infection at three months, 7.2% of participants reported missing ≥10 workdays and 13.9% of participants reported not returning to work since their infection. At three months, participants with ≥5 symptoms had a higher adjusted odds ratio of missing ≥10 workdays (2.96, 95% CI 1.81–4.83) and not returning to work (2.44, 95% CI 1.58–3.76) compared to those with no symptoms. Prolonged SARS-CoV-2 symptoms were common, affecting 4-in-10 participants at three-months post-infection, and were associated with increased odds of work loss, most pronounced among adults with ≥5 symptoms at three months. Despite the end of the federal Public Health Emergency for COVID-19 and efforts to "return to normal", policymakers must consider the clinical and economic implications of the COVID-19 pandemic on people's employment status and work absenteeism, particularly as data characterizing the numerous health and well-being impacts of Long COVID continue to emerge. Improved understanding of risk factors for lost work time may guide efforts to support people in returning to work. [ABSTRACT FROM AUTHOR]

Robust testing capacity was necessary for public health agencies to respond to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 19 (COVID-19) pandemic. As the nation faced the need for robust testing capacity, it became necessary to use all possible resources. In many cases, veterinary diagnostic laboratories rose to meet this demand because these facilities routinely perform high throughput diagnostic testing of large animal populations and are typically familiar with pathogens of high pandemic concern. In this study, we evaluated the impact of veterinary diagnostic laboratories in the United States on SARS-CoV-2 testing. Results of surveys, semi-structured interviews, and analysis of publicly available information showed that veterinary diagnostic laboratories had a substantial impact on human health through population-level testing in the COVID-19 response, supporting timely and informed public health interventions. This success was not without significant hurdles, as many participating veterinary diagnostic laboratories experienced restriction in their response due to difficulties obtaining the Clinical Laboratory Improvement Amendments (CLIA) certification required to conduct human diagnostic testing. Our results point out the importance of reducing hurdles before the next major public health emergency to enhance access to testing resources overall and to ultimately improve population health. [ABSTRACT FROM AUTHOR]

Background We described the oral nirmatrelvir/ritonavir (NMV/r) and molnupiravir (MOV) uptake among a subgroup of highly vaccinated adults in a US national prospective cohort who were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between 12/2021 and 10/2022. Methods We estimate antiviral uptake within 5 days of SARS-CoV-2 infection, as well as age- and gender-adjusted antiviral uptake prevalence ratios by antiviral eligibility (based on age and comorbidities), sociodemographic characteristics, and clinical characteristics including vaccination status and history of long coronavirus disease 2019 (COVID). Results NMV/r uptake was 13.6% (95% CI, 11.9%–15.2%) among 1594 participants, and MOV uptake was 1.4% (95% CI, 0.8%–2.1%) among 1398 participants. NMV/r uptake increased over time (1.9%; 95% CI, 1.0%–2.9%; between 12/2021 and 3/2022; 16.5%; 95% CI, 13.0%–20.0%; between 4/2022 and 7/2022; and 25.3%; 95% CI, 21.6%–29.0%; between 8/2022 and 10/2022). Participants age ≥65 and those who had comorbidities for severe COVID-19 had higher NMV/r uptake. There was lower NMV/r uptake among non-Hispanic Black participants (7.2%; 95% CI, 2.4%–12.0%; relative to other racial/ethnic groups) and among individuals in the lowest income groups (10.6%; 95% CI, 7.3%–13.8%; relative to higher income groups). Among a subset of 278 participants with SARS-CoV-2 infection after 12/2021 who also had a history of prior SARS-CoV-2 infection, those with (vs without) a history of long COVID reported greater NMV/r uptake (22.0% vs 7.9%; P =.001). Among those prescribed NMV/r (n = 216), 137 (63%; 95% CI, 57%–70%) reported that NMV/r was helpful for reducing COVID-19 symptoms. Conclusions Despite proven effectiveness against severe outcomes, COVID-19 antiviral uptake remains low among those with SARS-CoV-2 infection in the United States. Further outreach to providers and patients to improve awareness of COVID-19 oral antivirals and indications is needed. [ABSTRACT FROM AUTHOR]

Disparities in vaccination coverage for coronavirus disease 2019 (COVID-19) in the United States (U.S.) are consistent barriers limiting our ability to control the spread of disease, particularly those by age and race/ethnicity. This study examines the association between previous vaccination for common adult infectious diseases and vaccination for SARS-CoV-2 among a cohort of veterans in the U.S. Sociodemographic and clinical data were utilized from three databases within the Veterans Health Administration included in the electronic health record. We examined the association of previous vaccination for common adult vaccinations through six separate multivariable logistic regression analyses, one for each previous vaccine exposure, adjusting for demographic and clinical variables. We also examined the association of receiving any one of the six common adult vaccinations and vaccination against SARS-CoV-2. Adjusted models indicate higher odds of vaccination for SARS-CoV-2 among those who received each of the previous vaccinations. Significant differences were also noted by race/ethnicity and age. Veterans who recorded receiving any one of the previous vaccinations for common adult infections had significantly greater odds of receiving any vaccination against SARS-CoV-2. Understanding veterans' previous vaccination status can assist researchers and clinicians in impacting the uptake of novel vaccines, such as vaccination against SARS-CoV-2. [ABSTRACT FROM AUTHOR]

COVID-19 infection adversely impacts patients' wellbeing and daily lives. This survey-based study examined differences in patient-reported COVID-19 symptoms, Health-Related Quality of Life (HRQoL) and Work Productivity and Activity Impairment (WPAI) among groups of patients defined based on age and symptom-based long COVID status. Symptomatic, COVID-19-positive US outpatients were recruited from 31 January–30 April 2022. Outcomes were collected via validated instruments at pre-COVID, Day 3, Week 1, Week 4, Month 3 and Month 6 following infection, with changes assessed from pre-COVID and between groups, adjusting for covariates. EQ-5D-5L HRQoL and WPAI scores declined in all groups, especially during the first week. Long COVID patients reported significantly higher symptoms burden and larger drops in HRQoL and WPAI scores than patients without long COVID. Their HRQoL and WPAI scores did not return to levels comparable to pre-COVID through Month 6, except for absenteeism. Patients without long COVID generally recovered between Week 4 and Month 3. Older (>50) and younger adults generally reported comparable symptoms burden and drops in HRQoL and WPAI scores. During the first week of infection, COVID-19-related health issues caused loss of 14 to 26 work hours across the groups. These data further knowledge regarding the differential impacts of COVID-19 on clinically relevant patient groups. [ABSTRACT FROM AUTHOR]

According to the parasite-stress theory, collectivism serves as a trait of ingroup assortative sociality, providing defense against infectious diseases. This study investigated the association between cultural collectivism and COVID-19 severity at the state (Study 1: N = 51), county (Study 2: N = 3,133), and daily (Study 3: N = 52,806) levels from the beginning of 2020 to the end of 2022. State-level collectivism was assessed using two distinct measures: the U.S. collectivism index, focusing on social interconnectedness and interdependence, and the subjective-culture individualism-collectivism index (reversed), capturing attitudes and beliefs related to religion, abortion, and same-sex marriage. By employing random-intercept multilevel models, the results demonstrated significant and negative effects of state-level collectivism, as measured by the U.S collectivism index, on COVID-19 cases per million, COVID-19 deaths per million, and composite COVID-19 severity index, after controlling for confounding factors, such as socioeconomic development, ecological threats, disease protective behaviors, cultural norms, and political influences. A mini meta-analysis (Study 4: N = 9) confirmed the significance of these effects across studies. These findings supported the proactive role of collectivism in defending against the novel coronavirus in the United States, aligning with the parasite-stress theory of sociality. However, the subjective-culture individualism-collectivism index (reversed) did not exhibit a significant relationship with COVID-19 severity when confounding factors were considered. The high correlation between the subjective-culture individualism-collectivism index (reversed) and the controlled variables suggested shared variance that could diminish its impact on COVID-19 outcomes. Accordingly, the present findings underscore the significance of accounting for confounding factors when examining the association between collectivism and COVID-19 severity at population level. By considering relevant confounding factors, researchers could gain a comprehensive understanding of the complex interplay between cultural collectivism and its influence on COVID-19 severity. Overall, this research contributes to our understanding of how cultural collectivism shapes the COVID-19 pandemic in the United States, emphasizing the importance of adjusting for confounding effects in population level studies. [ABSTRACT FROM AUTHOR]

Does geographic variation in personality across the United States relate to COVID‐19 vaccination rates? To answer this question, we combined multiple state‐level datasets: (a) Big Five personality averages (i.e., extraversion, agreeableness, conscientiousness, neuroticism, and openness; Rentfrow et al., 2008), (b) COVID‐19 full‐vaccination rates (CDC, 2021a), (c) health‐relevant demographic covariates (population density, per capita gross domestic product, and racial/ethnic data; Webster et al., 2021), and (d) political and religiosity data. Analyses showed openness as the strongest correlate of full‐vaccination rates (r = 0.51). Controlling for other traits, demographic covariates, and spatial dependence, openness remained significantly related to full‐vaccination rates (rp = 0.55). Adding political and religiosity data to this model diminished openness effects for full‐vaccination rates to non‐significance (rp = 0.26); however, extraversion emerged as a significant correlate of full‐vaccination rates (rp = 0.37). Although politics are paramount, we suspect that states with higher average openness scores are more conducive to novel thinking and behavior—dispositions that may be crucial in motivating people to take newly‐developed vaccines based on new technologies to confront a novel coronavirus. [ABSTRACT FROM AUTHOR]

The continuing transmission of coronavirus disease 2019 (COVID-19) remains a world-wide 21st-century public health emergency of concern. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused greater than 600 million cases of COVID-19 and over 6 million deaths globally. COVID-19 continues to be a highly transmissible disease despite efforts by public health officials and healthcare providers to manage and control the disease. Variants identified in selected worldwide epicenters add to the complexity of vaccine efficacy, overage, and antibody titer maintenance and bioactivity. The identification of the SARS-CoV-2 variants is described with respect to evading protective efficacy of COVID-19 vaccines and breakthrough infections. Vaccines and other therapeutics have prevented millions of SARS-CoV-2 infections and thousands of deaths in the United States. We explore aspects of the immune response in a condensed discussion to understand B and T cell lymphocyte regulatory mechanisms and antibody effectiveness and senescence. Finally, COVID-19 therapies including Paxlovid, Remdisivir, Molnupiravir and convalescent plasma in non-hospitalized patients are presented with limitations for identification, collection, and distribution to infected patients. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) vaccines are highly efficacious at preventing symptomatic infection, severe disease, and death. Most of the evidence that COVID-19 vaccines also reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is based on retrospective, observational studies. Specifically, an increasing number of studies are evaluating vaccine effectiveness against the secondary attack rate of SARS-CoV-2 using data available in existing health-care databases or contact-tracing databases. Since these types of databases were designed for clinical diagnosis or management of COVID-19, they are limited in their ability to provide accurate information on infection, infection timing, and transmission events. We highlight challenges with using existing databases to identify transmission units and confirm potential SARS-CoV-2 transmission events. We discuss the impact of common diagnostic testing strategies, including event-prompted and infrequent testing, and illustrate their potential biases in estimating vaccine effectiveness against the secondary attack rate of SARS-CoV-2. We articulate the need for prospective observational studies of vaccine effectiveness against the SARS-CoV-2 secondary attack rate, and we provide design and reporting considerations for studies using retrospective databases. [ABSTRACT FROM AUTHOR]

Primary immunodeficiencies (PIs) are a group of diseases that increase susceptibility to infectious diseases. Few studies have examined the relationship between PI and COVID-19 outcomes. In this study, we used Premier Healthcare Database, which contains information on inpatient discharges, to analyze COVID-19 outcomes among 853 adult PI and 1,197,430 non-PI patients who visited the emergency department. Hospitalization, intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), and death had higher odds in PI patients than in non-PI patients (hospitalization aOR: 2.36, 95% CI: 1.87–2.98; ICU admission aOR: 1.53, 95% CI: 1.19–1.96; IMV aOR: 1.41, 95% CI: 1.15–1.72; death aOR: 1.37, 95% CI: 1.08–1.74), and PI patients spent on average 1.91 more days in the hospital than non-PI patients when adjusted for age, sex, race/ethnicity, and chronic conditions associated with severe COVID-19. Of the largest four PI groups, selective deficiency of the immunoglobulin G subclass had the highest hospitalization frequency (75.2%). This large study of United States PI patients provides real-world evidence that PI is a risk factor for adverse COVID-19 outcomes. [ABSTRACT FROM AUTHOR]

Reconstructing the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.S. CDC. They employed three assays, with different sensitivities and specificities, potentially introducing biases in seroprevalence estimates. Using models, we show that accounting for assays explains some of the observed state-to-state variation in seroprevalence, and when integrating case and death surveillance data, we show that when using the Abbott assay, estimates of proportions infected can differ substantially from seroprevalence estimates. We also found that states with higher proportions infected (before or after vaccination) had lower vaccination coverages, a pattern corroborated using a separate dataset. Finally, to understand vaccination rates relative to the increase in cases, we estimated the proportions of the population that received a vaccine prior to infection. SARS-CoV-2 seroprevalence surveys aim to estimate the proportion of the population that has been infected, but their accuracy depends on the characteristics of the test assay used. Here, the authors use statistical models to assess the impact of the use of different assays on estimates of seroprevalence in the United States. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

The United States of America (USA) was afflicted by extreme heat in the summer of 2021 and some states experienced a record‐hot or top‐10 hottest summer. Meanwhile, the United States was also one of the countries impacted most by the coronavirus disease 2019 (COVID‐19) pandemic. Growing numbers of studies have revealed that meteorological factors such as temperature may influence the number of confirmed COVID‐19 cases and deaths. However, the associations between temperature and COVID‐19 severity differ in various study areas and periods, especially in periods of high temperatures. Here we choose 119 US counties with large counts of COVID‐19 deaths during the summer of 2021 to examine the relationship between COVID‐19 deaths and temperature by applying a two‐stage epidemiological analytical approach. We also calculate the years of life lost (YLL) owing to COVID‐19 and the corresponding values attributable to high temperature exposure. The daily mean temperature is approximately positively correlated with COVID‐19 deaths nationwide, with a relative risk of 1.108 (95% confidence interval: 1.046, 1.173) in the 90th percentile of the mean temperature distribution compared with the median temperature. In addition, 0.02 YLL per COVID‐19 death attributable to high temperature are estimated at the national level, and distinct spatial variability from −0.10 to 0.08 years is observed in different states. Our results provide new evidence on the relationship between high temperature and COVID‐19 deaths, which might help us to understand the underlying modulation of the COVID‐19 pandemic by meteorological variables and to develop epidemic policy response strategies. Plain Language Summary: The spread of coronavirus disease 2019 (COVID‐19) pandemic has become of unparalleled public health concern from the beginning of 2020, especially in the United States of America (USA). In the summer of 2021, extreme heat swept the United States and some states even underwent the hottest or top‐10 hottest summer on record. The relationships between high temperature and COVID‐19 severity are less discussed compared with other environmental exposures and existing studies show different results. In this research, we focus on COVID‐19 deaths and use epidemiological approach to explore the effects of high temperature during the summer of 2021 in the USA. We find an approximately positive association between daily mean temperature and COVID‐19 mortality nationally. When temperature get to the 90th percentile from the local median, about 11% increase in the next 7‐day cumulative number of COVID‐19 deaths is noticed. Different temperature–mortality responses are observed at the region‐specific level. The findings of our study suggest that with spatial variations, high temperature could be an important factor influencing the COVID‐19 pandemic and might increase the risk of COVID‐19 mortality, which would be a useful supplement to assist policymakers to design COVID‐19 control measures. Key Points: This study explores the relationship between COVID‐19 deaths and temperature by applying a two‐stage epidemiological analytical approach during the summer of 2021 in the Unites StatesThe daily mean temperature is approximately positively correlated with COVID‐19 deaths nationwide, with different shapes of temperature–mortality curves noted at the regional levelEvaluation of temperature‐mortality relationship is important in controlling COVID‐19 severity [ABSTRACT FROM AUTHOR]

Protest against containment policies in U.S. states is fueled by two drivers: the stringency of containment policies and the partisan control over the governorship and legislatures in each state. In our analysis of the period from March 2020 to March 2022, we find more protest events in states fully controlled by Democrats than in Republican controlled states both in a sample consisting of all states and a balanced sample in which we constrain observations to those red and blue states with on average similarly stringent containment policies. Protest was therefore politicized, and we find that partisanship exerts a roughly equal substantive effect on the number of protest events as the stringency of containment policies. If we assume no direct effect of partisanship on protest but allow for causal heterogeneity along partisan lines in the effect of containment policies, we find that the same increase in the stringency of policies evokes a stronger protest response in blue states than in red states. [ABSTRACT FROM AUTHOR]

In 2019, a new variant of coronavirus, SARS-CoV-2 (COVID-19) created a global pandemic that has highlighted and exacerbated health disparities. Educating the general public about COVID-19 is one of the primary mitigation strategies amongst health professionals. English is not the preferred language for an estimated 22% of the United States population making effective mass communication efforts difficult to achieve. This study seeks to understand and compare several topics surrounding COVID-19 health communication and healthcare disparities between individuals with English language preference (ELP) and non-English language preference (NELP) within the United States. A survey available in seven languages asking about knowledge and opinions on COVID-19, vaccines, preferred sources of health information, and other questions, was administered February-April 2021 to patients at an urban federally qualified health center that also serves global refugees and immigrants. Descriptive statistics and comparative analysis were performed to identify differences between ELP and NELP individuals. Analysis of 144 surveys, 33 of which were NELP, showed 90.97% of all patients agreed that COVID-19 was a serious disease and 66.67% would receive the COVID-19 vaccine. There were numerous differences between ELP and NELP individuals, including trust in government, symptom identification, preferred source of health information, and feelings that cultural needs had been met. This study has identified several significant differences in patient perceptions relating to the COVID-19 pandemic when comparing NELP to ELP and highlighted areas where improvement can occur. Applying this information, easily utilized targeted resources can be created to quickly intervene and address health disparities among patients seeking care at an urban community health center. [ABSTRACT FROM AUTHOR]

Background The efficacy of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) convalescent plasma (CCP) for preventing infection in exposed, uninfected individuals is unknown. CCP might prevent infection when administered before symptoms or laboratory evidence of infection. Methods This double-blinded, phase 2 randomized, controlled trial (RCT) compared the efficacy and safety of prophylactic high titer (≥1:320 by Euroimmun ELISA) CCP with standard plasma. Asymptomatic participants aged ≥18 years with close contact exposure to a person with confirmed coronavirus disease 2019 (COVID-19) in the previous 120 hours and negative SARS-CoV-2 test within 24 hours before transfusion were eligible. The primary outcome was new SARS-CoV-2 infection. Results In total, 180 participants were enrolled; 87 were assigned to CCP and 93 to control plasma, and 170 transfused at 19 sites across the United States from June 2020 to March 2021. Two were excluded for screening SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) positivity. Of the remaining 168 participants, 12/81 (14.8%) CCP and 13/87 (14.9%) control recipients developed SARS-CoV-2 infection; 6 (7.4%) CCP and 7 (8%) control recipients developed COVID-19 (infection with symptoms). There were no COVID-19-related hospitalizations in CCP and 2 in control recipients. Efficacy by restricted mean infection free time (RMIFT) by 28 days for all SARS-CoV-2 infections (25.3 vs 25.2 days; P =.49) and COVID-19 (26.3 vs 25.9 days; P =.35) was similar for both groups. Conclusions Administration of high-titer CCP as post-exposure prophylaxis, although appearing safe, did not prevent SARS-CoV-2 infection. Clinical Trials Registration NCT04323800. [ABSTRACT FROM AUTHOR]

Background: Around the world, policymakers have clearly communicated that COVID-19 vaccination programs need to be accepted by a large proportion of the population to allow life return to normal. However, according to the Center for Disease Control, about 31% of the United States population had not completed the primary vaccination series as of November 2022. Aims: The primary aim of this work is to identify the factors associated by American citizens with the decision to be vaccinated against COVID-19. In addition, the proportion of fatal events from COVID-19 vaccinations was estimated and compared with the data in the VAERS database. Methods: An online survey of COVID-19 health experiences was conducted. Information was collected regarding reasons for and against COVID-19 inoculations, experiences with COVID-19 illness and COVID-19 inoculations by survey respondents and their social circles. Logit regression analyses were carried out to identify factors influencing the likelihood of being vaccinated. Results: A total of 2840 participants completed the survey between December 18 and 23, 2021. 51% (1383 of 2840) of the participants were female and the mean age was 47 (95% CI 46.36–47.64) years. Those who knew someone who experienced a health problem from COVID-19 were more likely to be vaccinated (OR: 1.309, 95% CI 1.094–1.566), while those who knew someone who experienced a health problem following vaccination were less likely to be vaccinated (OR: 0.567, 95% CI 0.461–0.698). 34% (959 of 2840) reported that they knew at least one person who had experienced a significant health problem due to the COVID-19 illness. Similarly, 22% (612 of 2840) of respondents indicated that they knew at least one person who had experienced a severe health problem following COVID-19 vaccination. With these survey data, the total number of fatalities due to COVID-19 inoculation may be as high as 278,000 (95% CI 217,330–332,608) when fatalities that may have occurred regardless of inoculation are removed. Conclusion: Knowing someone who reported serious health issues either from COVID-19 or from COVID-19 vaccination are important factors for the decision to get vaccinated. The large difference in the possible number of fatalities due to COVID-19 vaccination that emerges from this survey and the available governmental data should be further investigated. [ABSTRACT FROM AUTHOR]

Background: More than two years into the coronavirus disease (COVID-19) pandemic, it remains unclear whether financial incentives can reduce vaccine hesitancy and improve uptake among key unvaccinated populations. This study estimated the willingness of racial/ethnic minority adults in the United States to accept financial incentives for COVID-19 vaccination and the minimum amount needed to vaccinate a sufficiently high percentage of this population. Methods: From August through September 2021, we conducted an online survey of 367 Black/African American and Hispanic patients, age ≥ 18 years, from 8 community health centers in Rhode Island. Contingent valuation questions assessed respondents' willingness-to-accept (WTA) incentives for COVID-19 vaccination using random-starting-points and iterative incentive offers of $5 to $50 per dose. Ordered logistic regression models examined associations between respondent characteristics and WTA. Predictive probabilities were modeled using both within-survey range and out-of-survey range incentive offer amounts and compared against vaccination thresholds needed to reach herd immunity. Results: Less than 30% of unvaccinated survey respondents were WTA an incentive of $50/dose for vaccination. Models using out-of-survey incentive offer amounts greater than $50 suggested that 85% of respondents would agree $140/dose (95% CI: $43-$236) could convince other people to accept vaccination, while $209/dose (95% CI: -$91-$509) would be needed for 85% of respondents to accept vaccination themselves. Conclusions: Findings from this analysis may inform the design of incentive schemes aiming to reduce racial/ethnic disparities in vaccine and booster uptake, which will continue to be important as new variants of SARS-CoV-2 emerge. [ABSTRACT FROM AUTHOR]

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The sudden onset of the COVID-19 pandemic posed significant challenges for forecasting professionals worldwide. This article examines the early forecasts of COVID-19 transmission, using the context of the United States, one of the early epicenters of the crisis. The article compares the relative accuracy of selected models from two forecasters who informed government policy in the first three months of the pandemic, the Institute of Health Metrics and Evaluation (IHME) and Columbia University. Furthermore, we examine whether the forecasts improved as more data became available in the subsequent months of the pandemic, using the forecasts from Los Alamos National Laboratory and the University of Texas, Austin. The analysis focuses on mortality estimates and compares forecasts using epidemiological and curve-fitting models during the first wave of the pandemic from March 2020 to October 2020. As health agencies worldwide struggled with uncertainty in models and projections of COVID-19 caseload and mortality, this article provides important insights that can be useful for crafting policy responses to the ongoing pandemic and future outbreaks. [ABSTRACT FROM AUTHOR]

Since influenza and coronaviruses are currently deadly and emerging threats worldwide, better treatment, remediation and prevention options are needed. In that regard, a basic understanding of severe acute respiratory syndrome (SARS)-CoV-2/COVID-19 (Betacoronaviridae) and other viral pathogen mechanisms of transmission are expected. Unfortunately, unprecedented, and growing distrust of vaccines and even masks or personal protective equipment (PPE) in the United States and elsewhere presents itself as an added challenge. We postulate that development of improved and highly effective prophylactic measures, together with new life-saving therapies that do inhibit or otherwise treat infection of SARS-CoV-2, influenza and other viral pathogens, could be an adjunct measure to globally protect vulnerable individuals from pandemic threats. In this review, we share what we learned from the past COVID experience to offer a multifactorial and improved approach to current and future pandemic infections or threats using low-cost means. [ABSTRACT FROM AUTHOR]

The sudden spread of COVID-19 infections in a region can catch its healthcare system by surprise. Can one anticipate such a spread and allow healthcare administrators to prepare for a surge a priori? We posit that the answer lies in distinguishing between two types of waves in epidemic dynamics. The first kind resembles a spatio-temporal diffusion pattern. Its gradual spread allows administrators to marshal resources to combat the epidemic. The second kind is caused by super-spreader events, which provide shocks to the disease propagation dynamics. Such shocks simultaneously affect a large geographical region and leave little time for the healthcare system to respond. We use time-series analysis and epidemiological model estimation to detect and react to such simultaneous waves using COVID-19 data from the time when the B.1.617.2 (Delta) variant of the SARS-CoV-2 virus dominated the spread. We first analyze India's second wave from April to May 2021 that overwhelmed the Indian healthcare system. Then, we analyze data of COVID-19 infections in the United States (US) and countries with a high and low Indian diaspora. We identify the Kumbh Mela festival as the likely super-spreader event, the exogenous shock, behind India's second wave. We show that a multi-area compartmental epidemiological model does not fit such shock-induced disease dynamics well, in contrast to its performance with diffusion-type spread. The insufficient fit to infection data can be detected in the early stages of a shock-wave propagation and can be used as an early warning sign, providing valuable time for a planned healthcare response. Our analysis of COVID-19 infections in the US reveals that simultaneous waves due to super-spreader events in one country (India) can lead to simultaneous waves in other places. The US wave in the summer of 2021 does not fit a diffusion pattern either. We postulate that international travels from India may have caused this wave. To support that hypothesis, we demonstrate that countries with a high Indian diaspora exhibit infection growth soon after India's second wave, compared to countries with a low Indian diaspora. Based on our data analysis, we provide concrete policy recommendations at various stages of a simultaneous wave, including how to avoid it, how to detect it quickly after a potential super-spreader event occurs, and how to proactively contain its spread. [ABSTRACT FROM AUTHOR]

The spatial and temporal patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases and COVID-19 deaths in the United States are poorly understood. We show that variations in the cumulative reported cases and deaths by county, state, and date exemplify Taylor's law of fluctuation scaling. Specifically, on day 1 of each month from April 2020 through June 2021, each state's variance (across its counties) of cases is nearly proportional to its squared mean of cases. COVID-19 deaths behave similarly. The lower 99% of counts of cases and deaths across all counties are approximately lognormally distributed. Unexpectedly, the largest 1% of counts are approximately Pareto distributed, with a tail index that implies a finite mean and an infinite variance. We explain why the counts across the entire distribution conform to Taylor's law with exponent two using models and mathematics. The finding of infinite variance has practical consequences. Local jurisdictions (counties, states, and countries) that are planning for prevention and care of largely unvaccinated populations should anticipate the rare but extremely high counts of cases and deaths that occur in distributions with infinite variance. Jurisdictions should prepare collaborative responses across boundaries, because extremely high local counts of cases and deaths may vary beyond the resources of any local jurisdiction. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is a viral pathogen that quickly became a global pandemic in the winter of 2020–2021. In response, governments issued social distancing orders to minimize transmission by reducing community contacts. We tested the efficacy of this social distancing at the state level during the first 2 months of the pandemic in the United States. We utilized data on daily SARS‐CoV‐2 case numbers and human community mobility (anonymized, aggregated cell phone location data stratified into six categories used as an index of social distancing), the date of government‐issued social distancing orders, demographics, urbanization and public transportation. We implemented cross‐correlation to identify lag times between declines in mobility and SARS‐CoV‐2 cases. Incorporating state‐specific lag times, we tested for associations between case counts and mobility metrics using Bayesian multilevel models. Decreased mobility around grocery stores/pharmacies, retail/recreation locations, transit stations and workplaces was correlated with decreases in SARS‐CoV‐2 cases with significant lag times of ≥21 days. Social distancing orders were associated with fewer cumulative SARS‐CoV‐2 cases when they were put in place earlier. Community mobility had already started declining prior to most social distancing orders, especially the more restrictive orders implemented later in the pandemic. Social distancing is an important tool that has been implemented throughout the pandemic to decrease SARS‐CoV‐2 transmission, although with significant social and economic impacts. Our results suggest that declines in cases were observed several weeks subsequent to implementation of social distancing measures, and that implementing social distancing earlier could potentially minimize the duration of time these policies need to be in effect. Our findings can inform ongoing management of this pandemic and other emerging infectious disease outbreaks by identifying areas where reductions in mobility are associated with reduced disease transmission, and the expected time frame between behavioural changes and measurable population outcomes. [ABSTRACT FROM AUTHOR]

Detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) needs human samples, which inevitably contain trace human DNA and RNA. Sequence similarity may cause invalid detection results; however, there is still a lack of gene similarity analysis of SARS-CoV-2 and humans. All publicly reported complete genome assemblies in the Entrez genome database were collected for multiple sequence alignment, similarity and phylogenetic analysis. The complete genomes showed high similarity (>99.88% sequence identity). Phylogenetic analysis divided these viruses into three major clades with significant geographic group effects. Viruses from the United States showed considerable variability. Sequence similarity analysis revealed that SARS-CoV-2 has 612 similar sequences with the human genome and 100 similar sequences with the human transcriptome. The sequence characteristics and genome distribution of these similar sequences were confirmed. The sequence similarity and evolutionary mutations provide indispensable references for dynamic updates of SARS-CoV-2 detection primers and methods. [ABSTRACT FROM AUTHOR]

Aims and Objectives: This systematic review and meta‐analysis reports the seroprevalence of SARS‐CoV‐2 antibodies among nurses. Background: With a growing body of literature reporting the positive serology for SARS‐CoV‐2 antibodies among healthcare workers, it remains unclear whether staff at the point of direct patient care are more prone to developing and transmitting the virus. Given nurses make up the majority of the global health workforce, outbreaks among these workers could severely undermine a health system's capability to manage the pandemic. We aimed to summarise and report the seroprevalence of SARS‐CoV‐2 antibodies among nurses globally. Design: Systematic review and meta‐analyses. Methods: This systematic review was developed, undertaken and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guideline. We searched the electronic medical literature databases: MEDLINE; CINAHL; and EMBASE for studies reporting the seroprevalence of SARS‐CoV‐2 antibodies among nursing staff. Studies that reported nursing specific data were included in this review. Study quality was evaluated using the Joanna Briggs Institute checklist for studies reporting prevalence data. Studies were stratified according to the World Health Organisation region classifications, and results were presented using forest plots and summary prevalence and variance was estimated using a random effects model. Results: Our electronic search identified 1687 potential studies, of which 1148 were screened for eligibility after duplicates were removed, and 51 of the studies were included in our meta‐analysis. The overall seroprevalence of SARS‐CoV‐2 antibodies among nurses was estimated to be 8.1% (95% CI 6.9%–9.4%) among the 60,571 participants included in the studies. Seropositivity was highest in the African region (48.2%, 95% CI 39.2%–57.3%), followed by the European region (10.3%, 95% CI 8.0%–12.5%), the Region of the Americas (8.4%, 95% CI 6.0%–10.7%), the South‐East Asia region (3.0%, 95% CI 0.00%–6.5%) and the Western Pacific region (0.5%, 95% CI 0.0%–1.0%). Pooled estimates were unable to be calculated in the Eastern Mediterranean region due to insufficient studies. Conclusion: The seroprevalence of SARS‐CoV‐2 antibodies among nurses is comparable to other healthcare workers, and possibly similar to the general population. Early adoption and adherence to personal protective equipment and social distancing measures could explain these similarities, meaning the majority of staff contracted the virus through community transmission and not in a healthcare setting. Relevance to clinical practice: Fear and uncertainty have been features of this pandemic, including among nurses. This meta‐analysis should provide some comfort to nurses that risks are similar to community exposure when adequate PPE is available and there is an adherence to infection control measures. [ABSTRACT FROM AUTHOR]

We expanded a published mathematical model of SARS-CoV-2 transmission with complex, age-structured transmission and with laboratory-derived source and wearer protection efficacy estimates for a variety of face masks to estimate their impact on COVID-19 incidence and related mortality in the United States. The model was also improved to allow realistic age-structured transmission with a pre-specified R0 of transmission, and to include more compartments and parameters, e.g. for groups such as detected and undetected asymptomatic infectious cases who mask up at different rates. When masks are used at typically-observed population rates of 80% for those ≥ 65 years and 60% for those < 65 years, face masks are associated with 69% (cloth) to 78% (medical procedure mask) reductions in cumulative COVID-19 infections and 82% (cloth) to 87% (medical procedure mask) reductions in related deaths over a 6-month timeline in the model, assuming a basic reproductive number of 2.5. If cloth or medical procedure masks' source control and wearer protection efficacies are boosted about 30% each to 84% and 60% by cloth over medical procedure masking, fitters, or braces, the COVID-19 basic reproductive number of 2.5 could be reduced to an effective reproductive number ≤ 1.0, and from 6.0 to 2.3 for a variant of concern similar to delta (B.1.617.2). For variants of concern similar to omicron (B.1.1.529) or the sub-lineage BA.2, modeled reductions in effective reproduction number due to similar high quality, high prevalence mask wearing is more modest (to 3.9 and 5.0 from an R0 = 10.0 and 13.0, respectively). None-the-less, the ratio of incident risk for masked vs. non-masked populations still shows a benefit of wearing masks even with the higher R0 variants. [ABSTRACT FROM AUTHOR]

We investigate the climatic influence on COVID-19 transmission risks in 228 cities globally across three climatic zones. The results, based on the application of a Boosted Regression Tree algorithm method, show that average temperature and average relative humidity explain significant variations in COVID-19 transmission across temperate and subtropical regions, whereas in the tropical region, the average diurnal temperature range and temperature seasonality significantly predict the infection outbreak. The number of positive cases showed a decrease sharply above an average temperature of 10°C in the cities of France, Turkey, the US, the UK, and Germany. Among the tropical countries, COVID-19 in Indian cities is most affected by mean diurnal temperature, and those in Brazil by temperature seasonality. The findings have implications on public health interventions, and contribute to the ongoing scientific and policy discourse on the complex interplay of climatic factors determining the risks of COVID-19 transmission. [ABSTRACT FROM AUTHOR]

Background: The outbreak of a novel coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, raised worldwide concern. The present study investigates the association between anti-contagion policies and the spread of COVID-19 across the United States. Design and methods: We selected the most frequently implemented COVID-19 anti-contagion policies in all the U.S. states issued from 29 February 2020. Accordingly, we modified an epidemiological model and combined it with a comprehensive statistical analysis to evaluate the policies' individual and overall likely impact. Results: For the first time, a novel index, evaluates the associations between policy implementation and COVID-19 spread at both statewide and national levels. Our results indicate that governmental policies requiring mask use, businesses social distancing, and quarantining travelers may be most effective for controlling COVID-19 spread. Simultaneously, widespread orders like school closure and safer-at-home that can be particularly disruptive to the economy and social fabric of society may be unnecessary given their lack of association with reducing infection. Conclusions: The absence of any COVID-19 vaccines during the first several months of its pandemic necessitated using governmental policies to help stop the spread of this disease. Our index showed the association between implemented policies and COVID-19 spread, highlighting the specific policies with the greatest association - mandatory quarantine upon entering a state, businesses implementing social distancing, and mandatory mask use - and those with less association like school closure and saferat-home orders. This study provided evidence to inform policy choices for the current global crisis and future pandemics. [ABSTRACT FROM AUTHOR]

Each year there are nearly 57 million deaths worldwide, with over 2.7 million in the United States. Timely, accurate and complete death reporting is critical for public health, especially during the COVID-19 pandemic, as institutions and government agencies rely on death reports to formulate responses to communicable diseases. Unfortunately, determining the causes of death is challenging even for experienced physicians. The novel coronavirus and its variants may further complicate the task, as physicians and experts are still investigating COVID-related complications. To assist physicians in accurately reporting causes of death, an advanced Artificial Intelligence (AI) approach is presented to determine a chronically ordered sequence of conditions that lead to death (named as the causal sequence of death), based on decedent's last hospital discharge record. The key design is to learn the causal relationship among clinical codes and to identify death-related conditions. There exist three challenges: different clinical coding systems, medical domain knowledge constraint, and data interoperability. First, we apply neural machine translation models with various attention mechanisms to generate sequences of causes of death. We use the BLEU (BiLingual Evaluation Understudy) score with three accuracy metrics to evaluate the quality of generated sequences. Second, we incorporate expert-verified medical domain knowledge as constraints when generating the causal sequences of death. Lastly, we develop a Fast Healthcare Interoperability Resources (FHIR) interface that demonstrates the usability of this work in clinical practice. Our results match the state-of-art reporting and can assist physicians and experts in public health crisis such as the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Background: The San Francisco Bay Area was the first region in the United States to enact school closures to mitigate SARS-CoV-2 transmission. The effects of closures on contact patterns for schoolchildren and their household members remain poorly understood.Methods: We conducted serial cross-sectional surveys (May 2020, September 2020, February 2021) of Bay Area households with children to estimate age-structured daily contact rates for children and their adult household members. We examined changes in contact rates over the course of the COVID-19 pandemic, including after vaccination of household members, and compared contact patterns by household demographics using generalized estimating equations clustered by household.Results: We captured contact histories for 1,967 households on behalf of 2,674 children, comprising 15,087 non-household contacts over the three waves of data collection. Shortly after the start of shelter-in-place orders in May 2020, daily contact rates were higher among children from Hispanic families (1.52 more contacts per child per day; [95% CI: 1.14-2.04]), households whose parents were unable to work from home (1.82; [1.40-2.40]), and households with income < $150,000 (1.75; [1.33-2.33]), after adjusting for other demographic characteristics and household clustering. Between May and August 2020, non-household contacts of children increased by 145% (ages 5-12) and 172% (ages 13-17), despite few children returning to in-person instruction. Non-household contact rates among children were higher-by 1.75 [1.28-2.40] and 1.42 [0.89-2.24] contacts per child per day in 5-12 and 13-17 age groups, respectively, in households where at least one adult was vaccinated against COVID-19, compared to children's contact rates in unvaccinated households.Conclusions: Child contact rates rebounded despite schools remaining closed, as parents obtained childcare, children engaged in contact in non-school settings, and family members were vaccinated. The waning reductions observed in non-household contact rates of schoolchildren and their family members during a prolonged school closure suggests the strategy may be ineffective for long-term SARS-CoV-2 transmission mitigation. Reductions in age-assortative contacts were not as apparent amongst children from lower income households or households where adults could not work from home. Heterogeneous reductions in contact patterns raise concerning racial, ethnic and income-based inequities associated with long-term school closures as a COVID-19 mitigation strategy. [ABSTRACT FROM AUTHOR]

Aim: A comprehensive scientometric analysis produced in hematology and oncology on coronavirus disease-2019 (COVID-19) research is lacking. This study presents a detailed analysis of COVID-19 related hematology and oncology literature. Methods: The Web of Science (WoS) Core Collection was used for data collection. All published documents between 2020 and 2021 were included. The data exported from WoS enabled the extensive details of COVID-19 related literature in the hematology and oncology categories, including countries, institutions, authors, citations, and keywords. Scientometric interaction visualization of keywords and countries, and published journal co-authorships were created with free software. Worldwide participation of the countries in COVID-19 related hematology and oncology literature were shown by a graphic. Results: The search question displayed 4761 documents. The leading type of document was original articles (34.4%). The United States of America was the number one country, publishing 32.6% of all documents on COVID-19 related hematology and oncology research, followed by Italy, the United Kingdom, China, and France. Huazhong University of Science and Technology was the most contributing institution in the literature (2.8%), followed by Harvard Medical School and Memorial Sloan Kettering Cancer Center. The journal Blood has published the most documents about this field. The average citations per item was 7.2. The most used keywords over this period were "COVID-19," "SARS-CoV-2," "coronavirus," and "cancer". Conclusion: The results of the present study may assist health professionals interested in this field to better figure out the current trends in COVID-19 related hematology and oncology research worldwide, and it can provide them to reach a more accurate information in a shorter time. [ABSTRACT FROM AUTHOR]

By the end of 2021, the COVID-19 pandemic resulted in over 54 million cases and more than 800,000 deaths in the United States, and over 350 million cases and more than 5 million deaths worldwide. The uniqueness and gravity of this pandemic have been reflected in the public health guidelines poorly received by a growing subset of the United States population. These poorly received guidelines, including vaccine receipt, are a highly complex psychosocial issue, and have impacted the successful prevention of disease spread. Given the intricate nature of this important barrier, any single statistical analysis methodologically fails to address all convolutions. Therefore, this study utilized different analytical approaches to understand vaccine motivations and population-level trends. With 12,975 surveys from a state-wide year-long surveillance initiative, we performed three robust statistical analyses to evaluate COVID-19 vaccine hesitancy: principal component analysis, survival analysis and spatial time series analysis. The analytic goal was to utilize complementary mathematical approaches to identify overlapping themes of vaccine hesitancy and vaccine trust in a highly conservative US state. The results indicate that vaccine receipt is influenced by the source of information and the population's trust in the science and approval process behind the vaccines. This multifaceted statistical approach allowed for methodologically rigorous results that public health professionals and policy makers can directly use to improve vaccine interventions. [ABSTRACT FROM AUTHOR]

Background: To investigate the temporal characteristics of clinical variables of hospital-acquired acute kidney injury (AKI) in COVID-19 patients and to longitudinally predict AKI onset. Methods: There were 308 hospital-acquired AKI and 721 non-AKI (NAKI) COVID-19 patients from Stony Brook Hospital (New York, USA) data, and 72 hospital-acquired AKI and 303 NAKI COVID-19 patients from Tongji Hospital (Wuhan, China). Demographic, comorbidities, and longitudinal (3 days before and 3 days after AKI onset) clinical variables were used to compute odds ratios for and longitudinally predict hospital-acquired AKI onset. Results: COVID-19 patients with AKI were more likely to die than NAKI patients (31.5% vs 6.9%, adjusted p < 0.001, OR = 4.67 [95% CI 3.1, 7.0], Stony Brook data). AKI developed on average 3.3 days after hospitalization. Procalcitonin was elevated prior to AKI onset (p < 0.05), peaked, and remained elevated (p < 0.05). Alanine aminotransferase, aspartate transaminase, ferritin, and lactate dehydrogenase peaked the same time as creatinine, whereas d-dimer and brain natriuretic peptide peaked a day later. C-reactive protein, white blood cell and lymphocyte showed group differences − 2 days prior (p < 0.05). Top predictors were creatinine, procalcitonin, white blood cells, lactate dehydrogenase, and lymphocytes. They predicted AKI onset with areas under curves (AUCs) of 0.78, 0.66, and 0.56 at 0, − 1, and − 2 days prior, respectively. When tested on the Tongji Hospital data, the AUCs were 0.80, 0.79, and 0.77, respectively. Conclusions: Time-locked longitudinal data provide insight into AKI progression. Commonly clinical variables reasonably predict AKI onset a few days prior. This work may lead to earlier recognition of AKI and treatment to improve clinical outcomes. [ABSTRACT FROM AUTHOR]

Background We aimed to describe trends in adverse outcomes among patients who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between February and September 2020 within a national healthcare system. Methods We identified enrollees in the national United States Veterans Affairs healthcare system who tested positive for SARS-CoV-2 between 28 February 2020 and 30 September 2020 (n = 55 952), with follow-up extending to 19 November 2020. We determined trends over time in incidence of the following outcomes that occurred within 30 days of testing positive: hospitalization, intensive care unit (ICU) admission, mechanical ventilation, and death. Results Between February and July 2020, there were marked downward trends in the 30-day incidence of hospitalization (44.2% to 15.8%), ICU admission (20.3% to 5.3%), mechanical ventilation (12.7% to 2.2%), and death (12.5% to 4.4%), which subsequently plateaued between July and September 2020. These trends persisted after adjustment for sociodemographic characteristics, comorbid conditions, documented symptoms, and laboratory tests, including among subgroups of patients hospitalized, admitted to the ICU, or treated with mechanical ventilation. From February to September, there were decreases in the use of hydroxychloroquine (56.5% to 0%), azithromycin (48.3% to 16.6%), vasopressors (20.6% to 8.7%), and dialysis (11.6% to 3.8%) and increases in the use of dexamethasone (3.4% to 53.1%), other corticosteroids (4.9% to 29.0%), and remdesivir (1.7% to 45.4%) among hospitalized patients. Conclusions The risk of adverse outcomes in SARS-CoV-2–positive patients decreased markedly between February and July, with subsequent stabilization from July to September. These trends were not explained by changes in measured baseline patient characteristics and may reflect changing treatment practices or viral pathogenicity. [ABSTRACT FROM AUTHOR]

Background Both severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection and persistent infection have been reported, but sequence characteristics in these scenarios have not been described. We assessed published cases of SARS-CoV-2 reinfection and persistence, characterizing the hallmarks of reinfecting sequences and the rate of viral evolution in persistent infection. Methods A systematic review of PubMed was conducted to identify cases of SARS-CoV-2 reinfection and persistence with available sequences. Nucleotide and amino acid changes in the reinfecting sequence were compared with both the initial and contemporaneous community variants. Time-measured phylogenetic reconstruction was performed to compare intrahost viral evolution in persistent SARS-CoV-2 to community-driven evolution. Results Twenty reinfection and 9 persistent infection cases were identified. Reports of reinfection cases spanned a broad distribution of ages, baseline health status, reinfection severity, and occurred as early as 1.5 months or >8 months after the initial infection. The reinfecting viral sequences had a median of 17.5 nucleotide changes with enrichment in the ORF8 and N genes. The number of changes did not differ by the severity of reinfection and reinfecting variants were similar to the contemporaneous sequences circulating in the community. Patients with persistent coronavirus disease 2019 (COVID-19) demonstrated more rapid accumulation of sequence changes than seen with community-driven evolution with continued evolution during convalescent plasma or monoclonal antibody treatment. Conclusions Reinfecting SARS-CoV-2 viral genomes largely mirror contemporaneous circulating sequences in that geographic region, while persistent COVID-19 has been largely described in immunosuppressed individuals and is associated with accelerated viral evolution. [ABSTRACT FROM AUTHOR]

In 2020, the novel coronavirus (COVID-19) disrupted life around the globe. In the United States, governors issued state of emergency orders and mandated shelter-in-place and social distancing measures. While these measures are important, they ignore the nuances of risk for vulnerable groups, such as older adults. Moreover, social distancing measures made more visible the reality that many patients in care homes often die in isolation. In this commentary, we argue that a rethinking of later-life care is necessary and to understand this need, that critical geographers should expand on how we evaluate care. Here we start from a space of radical care ethics to examine the emotional experience of place and the role it should play in how we think about later-life care. Reflecting on state-mandated social distancing, we show that the current system of geriatric care in the United States does not promote dignified living for older adults and how older adults’ complex emotions are ignored in current later-life care. We conclude by recommending that the emotional experiences of place must be taken into consideration for scholars examining place-based later-life care of older adults. [ABSTRACT FROM AUTHOR]

Background: People infected with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) experience a wide range of clinical manifestations, from asymptomatic and mild illness to severe illness and death, influenced by age and a variety of comorbidities. Neutralizing antibodies (nAbs) are thought to be a primary immune defense against the virus. Large, diverse, well-characterized cohorts of convalescent individuals provide standardized values to benchmark nAb responses to past SARS-CoV-2 infection and define potentially protective levels of immunity.Methods and Findings: This analysis comprises an observational cohort of 329 HIV-seronegative adults in the United States (n = 167) and Peru (n = 162) convalescing from SARS-CoV-2 infection from May through October 2020. The mean age was 48 years (range 18 to 86), 54% of the cohort overall was Hispanic, and 34% identified as White. nAb titers were measured in serum by SARS-CoV-2.D614G Spike-pseudotyped virus infection of 293T/ACE2 cells. Multiple linear regression was applied to define associations between nAb titers and demographic variables, disease severity and time from infection or disease onset, and comorbidities within and across US and Peruvian cohorts over time. nAb titers peaked 28 to 42 days post-diagnosis and were higher in participants with a history of severe Coronavirus Disease 2019 (COVID-19) (p < 0.001). Diabetes, age >55 years, male sex assigned at birth, and, in some cases, body mass index were also independently associated with higher nAb titers, whereas hypertension was independently associated with lower nAb titers. nAb titers did not differ by race, underlying pulmonary disease or smoking. Two months post-enrollment, nAb ID50 (ID80) titers declined 3.5 (2.8)-fold overall. Study limitations in this observational, convalescent cohort include survivorship bias and missing early viral loads and acute immune responses to correlate with the convalescent responses we observed.Conclusions: In summary, in our cohort, nAb titers after SARS-CoV-2 infection peaked approximately 1 month post-diagnosis and varied by age, sex assigned at birth, disease severity, and underlying comorbidities. Our data show great heterogeneity in nAb responses among people with recent COVID-19, highlighting the challenges of interpreting natural history studies and gauging responses to vaccines and therapeutics among people with recent infection. Our observations illuminate potential correlations of demographic and clinical characteristics with nAb responses, a key element for protection from COVID-19, thus informing development and implementation of preventative and therapeutic strategies globally.Trial Registration: ClinicalTrials.gov NCT04403880. [ABSTRACT FROM AUTHOR]

While the trade press has started to report on consumer responses to brand messaging about the ongoing novel coronavirus (COVID-19) pandemic, empirical results have yet to emerge. In this study, we investigated how consumers in the United States responded to Instagram ads containing COVID-19 claims based on the perceived brand–social issue fit, which is determined by whether the brand's product was considered essential or nonessential. Results showed perceived brand–social issue fit to be of dual nature, where stronger perceptions of fit (i.e., essential products advertised with COVID-19 claims) resulted in more positive ad evaluations, brand attitudes, and consumer engagement intentions. Lower perceptions of fit (i.e., nonessential products with COVID-19 claims) led to perceptions of brand opportunism, which we conceptualized as a negative evaluative outcome that negatively impacted ad/brand attitude and consumer engagement intentions. The findings provide insights for brands interested in placing ads addressing the changing environmental conditions resulting from COVID-19 and advances the literature on pandemic messaging. [ABSTRACT FROM AUTHOR]

Restricting in-person interactions is an important technique for limiting the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Although early research found strong associations between cell phone mobility and infection spread during the initial outbreaks in the United States, it is unclear whether this relationship persists across locations and time. We propose an interpretable statistical model to identify spatiotemporal variation in the association between mobility and infection rates. Using 1 year of US county-level data, we found that sharp drops in mobility often coincided with declining infection rates in the most populous counties in spring 2020. However, the association varied considerably in other locations and across time. Our findings are sensitive to model flexibility, as more restrictive models average over local effects and mask much of the spatiotemporal variation. We conclude that mobility does not appear to be a reliable leading indicator of infection rates, which may have important policy implications. [ABSTRACT FROM AUTHOR]