Effect of specific non-pharmaceutical intervention policies on SARS-CoV-2 transmission in the counties of the United States.
Ontology highlight
ABSTRACT:
SUBMITTER: Yang B
PROVIDER: S-EPMC8195990 | biostudies-literature | 2021 Jun
REPOSITORIES: biostudies-literature
Publications
Effect of specific non-pharmaceutical intervention policies on SARS-CoV-2 transmission in the counties of the United States.
Yang Bingyi B Huang Angkana T AT Garcia-Carreras Bernardo B Hart William E WE Staid Andrea A Hitchings Matt D T MDT Lee Elizabeth C EC Howe Chanelle J CJ Grantz Kyra H KH Wesolowksi Amy A Lemaitre Joseph Chadi JC Rattigan Susan S Moreno Carlos C Borgert Brooke A BA Dale Celeste C Quigley Nicole N Cummings Andrew A McLorg Alizée A LoMonaco Kaelene K Schlossberg Sarah S Barron-Kraus Drew D Shrock Harrison H Lessler Justin J Laird Carl D CD Cummings Derek A T DAT
Nature communications 20210611 1
Non-pharmaceutical interventions (NPIs) remain the only widely available tool for controlling the ongoing SARS-CoV-2 pandemic. We estimated weekly values of the effective basic reproductive number (R<sub>eff</sub>) using a mechanistic metapopulation model and associated these with county-level characteristics and NPIs in the United States (US). Interventions that included school and leisure activities closure and nursing home visiting bans were all associated with a median R<sub>eff</sub> below ...[more]
Similar Datasets
Project description:In Spring 2021, SARS-CoV-2 B.1.1.7 (Alpha) became the predominant variant in the U.S. Research suggests that Alpha has increased transmissibility compared to non-Alpha lineages. We estimated household secondary infection risk (SIR), assessed characteristics associated with transmission, and compared symptoms of persons with Alpha and non-Alpha infections. We followed households with SARS-CoV-2 infection for two weeks in San Diego County and metropolitan Denver, January to April 2021. We collected epidemiologic information and biospecimens for serology, RT-PCR, and whole genome sequencing. We stratified SIR and symptoms by lineage, and identified characteristics associated with transmission using Generalized Estimating Equations. We investigated 127 households with 322 household contacts; 72 households (56.7%) had member(s) with secondary infections. SIRs were not significantly higher for Alpha (61.0% [95% confidence interval (CI) 52.4-69.0%]) than non-Alpha (55.6% [CI 44.7-65.9%], P = 0.49). In households with Alpha, persons who identified as Asian or Hispanic/Latino had significantly higher SIRs than those who identified as White (P = 0.01 and 0.03, respectively). Close contact (e.g., kissing, hugging) with primary cases was associated with increased transmission for all lineages. Persons with Alpha infection were more likely to report constitutional symptoms than persons with non-Alpha (86.9% vs. 76.8%, P = 0.05). Household SIRs were similar for Alpha and non-Alpha. Comparable SIRs may be due to saturation of transmission risk in households owing to extensive close contact, or true lack of difference in transmission rates. Avoiding close contact within households may reduce SARS-CoV-2 transmission for all lineages among household members.
| S-EPMC9047162 | biostudies-literature
Project description:BACKGROUND:There have been outbreaks of SARS-CoV-2 in long term care facilities and recent reports of disproportionate death rates among the vulnerable population. The goal of this study was to better understand the impact of SARS-CoV-2 infection on the non-institutionalized disabled population in the United States using data from the most affected states as of April 9th, 2020. METHODS:This was an ecological study of county-level factors associated with the infection and mortality rate of SARS-CoV-2 in the non-institutionalized disabled population. We analyzed data from 369 counties from the most affected states (Michigan, New York, New Jersey, Pennsylvania, California, Louisiana, Massachusetts) in the United States using data available by April 9th, 2020. The variables include changes in mobility reported by Google, race/ethnicity, median income, education level, health insurance, and disability information from the United States Census Bureau. Bivariate regression analysis adjusted for state and median income was used to analyze the association between death rate and infection rate. RESULTS:The independent sample t-test of two groups (group 1: Death rate? 3.4% [median] and group 2: Death rate < 3.4%) indicates that counties with a higher total population, a lower percentage of Black males and females, higher median income, higher education, and lower percentage of disabled population have a lower rate (< 3.4%) of SARS-CoV-2 related mortality (all p-values<4.3E-02). The results of the bivariate regression when controlled for median income and state show counties with a higher White disabled population (est: 0.19, 95% CI: 0.01-0.37; p-value:3.7E-02), and higher population with independent living difficulty (est: 0.15, 95% CI: -0.01-0.30; p-value: 6.0E-02) have a higher rate of SARS-CoV-2 related mortality. Also, the regression analysis indicates that counties with higher White disabled population (est: -0.22, 95% CI: -0.43-(-0.02); p-value: 3.3E-02), higher population with hearing disability (est: -0.26, 95% CI: -0.42- (-0.11); p-value:1.2E-03), and higher population with disability in the 18-34 years age group (est: -0.25, 95% CI: -0.41-(-0.09); p-value:2.4E-03) show a lower rate of SARS-CoV-2 infection. CONCLUSION:Our results indicate that while counties with a higher percentage of non-institutionalized disabled population, especially White disabled population, show a lower infection rate, they have a higher rate of SARS-CoV-2 related mortality.
| S-EPMC7325182 | biostudies-literature
Project description:Cloth face coverings and surgical masks have become commonplace across the United States in response to the SARS-CoV-2 epidemic. While evidence suggests masks help curb the spread of respiratory pathogens, population level, empirical research remains limited. Face masks have quickly become a topic of public debate as government mandates have started requiring their use. Here we investigate the association between self-reported mask wearing, social distancing and community SARS-CoV-2 transmission in the United States, as well as the effect of statewide mandates on mask uptake. Serial cross-sectional surveys were administered June 3 through July 27, 2020 via a web platform. Surveys queried individuals' likelihood to wear a face mask to the grocery store or with family and friends. Responses (N = 378,207) were aggregated by week and state and combined with measures of the instantaneous reproductive number (Rt ), social distancing proxies, respondent demographics and other potential sources of confounding. We fit multivariate logistic regression models to estimate the association between mask wearing and community transmission control (Rt <1) for each state and week. Multiple sensitivity analyses were considered to corroborate findings across mask wearing definitions, Rt estimators and data sources. Additionally, mask wearing in 12 states was evaluated two weeks before and after statewide mandates. We find an increasing trend in mask usage across the U.S., although uptake varies by geography and demographic groups. A multivariate logistic model controlling for social distancing and other variables found a 10% increase in mask wearing was associated with a 3.53 (95% CI: 2.03, 6.43) odds of transmission control (Rt <1). We also find that communities with high mask wearing and social distancing have the highest predicted probability of a controlled epidemic. These positive associations were maintained across sensitivity analyses. Following state mandates, mask wearing did not show significant statistical changes in uptake, however the positive trend of increased mask wearing over time was preserved. Widespread utilization of face masks combined with social distancing increases the odds of SARS-CoV-2 transmission control. Mask wearing rose separately from government mask mandates, suggesting supplemental public health interventions are needed to maximize mask adoption and disrupt the spread of SARS-CoV-2, especially as social distancing measures are relaxed.
| S-EPMC7457618 | biostudies-literature
Project description:ObjectivesThroughout the COVID-19 pandemic, masking has been a widely used mitigation practice in kindergarten through 12th grade (K-12) school districts to limit within-school transmission. Prior studies attempting to quantify the impact of masking have assessed total cases within schools; however, the metric that more optimally defines effectiveness of mitigation practices is within-school transmission, or secondary cases. We estimated the impact of various masking practices on secondary transmission in a cohort of K-12 schools.MethodsWe performed a multistate, prospective, observational, open cohort study from July 26, 2021 to December 13, 2021. Districts reported mitigation practices and weekly infection data. Districts that were able to perform contact tracing and adjudicate primary and secondary infections were eligible for inclusion. To estimate the impact of masking on secondary transmission, we used a quasi-Poisson regression model.ResultsA total of 1 112 899 students and 157 069 staff attended 61 K-12 districts across 9 states that met inclusion criteria. The districts reported 40 601 primary and 3085 secondary infections. Six districts had optional masking policies, 9 had partial masking policies, and 46 had universal masking. In unadjusted analysis, districts that optionally masked throughout the study period had 3.6 times the rate of secondary transmission as universally masked districts; and for every 100 community-acquired cases, universally masked districts had 7.3 predicted secondary infections, whereas optionally masked districts had 26.4.ConclusionsSecondary transmission across the cohort was modest (<10% of total infections) and universal masking was associated with reduced secondary transmission compared with optional masking.
| S-EPMC9647584 | biostudies-literature
Project description:PurposeThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began proliferating widely throughout the world in late 2019/early 2020, creating a global pandemic and health crisis. Although vaccines became available to the public approximately 1 year after the onset of the pandemic, there still remains much hesitancy surrounding vaccination. One key concern comes from reports of breakthrough infections among the vaccinated that show comparable levels of peak viral load as the unvaccinated, calling into question the ability of vaccines to prevent transmission. Therefore young, healthy individuals who are at low risk of serious complications themselves have little incentive to receive a vaccine that they are not convinced will protect others around them. To address this important concern, this study aimed to evaluate the extent to which vaccination rates are associated with reduced SARS-CoV-2 transmission among the unvaccinated population.MethodsAn observational study was conducted in the United States of America throughout the months of June through September, 2021. Vaccination rate and incidence of coronavirus disease 2019 (COVID-19) were obtained for each state, along with a number of important control variables. Panel data regression was used to predict incidence among the unvaccinated based on each state's vaccination rate.ResultsStates with a higher proportion of fully vaccinated individuals reported fewer new cases among the remaining unvaccinated population.ConclusionThese data add to accumulating evidence that COVID-19 vaccinations can indeed slow the spread of SARS-CoV-2, and are an important tool in society's arsenal to put this pandemic behind us.
| S-EPMC8938221 | biostudies-literature
Project description: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.
| S-EPMC9294068 | biostudies-literature
Project description:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began proliferating widely throughout the world in late 2019/early 2020, creating a global pandemic and health crisis. Although vaccines became available to the public approximately one year after the onset of the pandemic, there still remains much hesitancy surrounding vaccination even two years into the pandemic. One key concern comes from reports of breakthrough infections among the vaccinated that show comparable levels of peak viral load as the unvaccinated, calling into question the ability of vaccines to slow or prevent transmission. Therefore young, healthy individuals who are at low risk of serious complications themselves have little incentive to receive a vaccine that they are not convinced will protect others around them. To address this important concern, this article analyzes COVID-19 incidence in the United States as a function of each state's vaccination rate. Results show that states with higher percentages of fully vaccinated individuals report fewer new cases among the remaining unvaccinated population. These data add to accumulating evidence that COVID-19 vaccinations can indeed slow the spread of SARS-CoV-2, and are an important tool in society's arsenal to put this pandemic behind us.
| S-EPMC8609904 | biostudies-literature
Project description:The highly transmissible B.1.1.7 variant of SARS-CoV-2, first identified in the United Kingdom, has gained a foothold across the world. Using S gene target failure (SGTF) and SARS-CoV-2 genomic sequencing, we investigated the prevalence and dynamics of this variant in the United States (US), tracking it back to its early emergence. We found that, while the fraction of B.1.1.7 varied by state, the variant increased at a logistic rate with a roughly weekly doubling rate and an increased transmission of 40%-50%. We revealed several independent introductions of B.1.1.7 into the US as early as late November 2020, with community transmission spreading it to most states within months. We show that the US is on a similar trajectory as other countries where B.1.1.7 became dominant, requiring immediate and decisive action to minimize COVID-19 morbidity and mortality.
| S-EPMC8009040 | biostudies-literature
Project description:SARS-CoV-2 is a zoonotic virus with documented bi-directional transmission between people and animals. Transmission of SARS-CoV-2 from humans to free-ranging white-tailed deer (Odocoileus virginianus) poses a unique public health risk due to the potential for reservoir establishment where variants may persist and evolve. We collected 8,830 respiratory samples from free-ranging white-tailed deer across Washington, D.C. and 26 states in the United States between November 2021 and April 2022. We obtained 391 sequences and identified 34 Pango lineages including the Alpha, Gamma, Delta, and Omicron variants. Evolutionary analyses showed these white-tailed deer viruses originated from at least 109 independent spillovers from humans, which resulted in 39 cases of subsequent local deer-to-deer transmission and three cases of potential spillover from white-tailed deer back to humans. Viruses repeatedly adapted to white-tailed deer with recurring amino acid substitutions across spike and other proteins. Overall, our findings suggest that multiple SARS-CoV-2 lineages were introduced, became enzootic, and co-circulated in white-tailed deer.
| S-EPMC10333304 | biostudies-literature
Project description:To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March-December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time.
| S-EPMC10659176 | biostudies-literature