US COVID-19 Research Articles

Impacts of COVID-19-related social distancing measures on personal environmental sound exposures

The COVID-19 pandemic has created substantial and dynamic disruptions in society, personal behavior, and potentially chronic sound exposures, which are associated with hearing loss, cardiovascular disease, and other health impacts. Leveraging preliminary data from our unique nationwide Apple Hearing Study, we explored changes in personal sound exposures resulting from COVID-19-related social distancing. Volunteer participants opted to share environmental sound data from their Apple Watch and headphone sound data from their iPhone. Participants for this analysis were chosen from four states which exhibited diverse responses to COVID-19. Equivalent continuous average sound exposures (in A-weighted decibels, dBA) were computed per person-day and normalized to 8 hour LEX8h exposures. Daily mean LEX8h exposures across two time periods, a baseline period (before the first known US COVID-19 death at the time of analysis) and an intervention period (starting with each state’s first COVID-related public health social distancing announcement and ending on April 22, 2020) were defined to assess changes in sound exposure. We modeled sound levels across 5,894 participants and 516,729 monitored days using a linear mixed-effects model with random effects for participant. The overall reduction in LEX8h between baseline and intervention was 2.6 ± 0.05 dBA (mean ± SE). There was a significant day-of-week effect during the baseline period, with the lowest exposures on Monday and the highest on Saturday. This effect was not noted during the intervention period. COVID-19 social distancing measures were associated with an approximately 3 dBA reduction in personal environmental sound exposures; this represents a substantial and meaningful reduction in this harmful exposure. Our analysis demonstrates the utility of everyday use devices in detecting behavior and exposure changes associated with the COVID-19 pandemic, and the usefulness of longitudinal, large-scale characterization of personal exposures and health impacts using wearable technology.

White Counties Stand Apart: The Primacy of Residential Segregation in COVID-19 and HIV Diagnoses

Emerging epidemiological data suggest that white Americans have a lower risk of acquiring COVID-19. Although many studies have pointed to the role of systemic racism in COVID-19 racial/ethnic disparities, few studies have examined the contribution of racial segregation. Residential segregation is associated with differing health outcomes by race/ethnicity for various diseases, including HIV. This commentary documents differing HIV and COVID-19 outcomes and service delivery by race/ethnicity and the crucial role of racial segregation. Using publicly available Census data, we divide US counties into quintiles by percentage of non-Hispanic white residents and examine HIV diagnoses and COVID-19 per 100,000 population. HIV diagnoses decrease as the proportion of white residents increase across US counties. COVID-19 diagnoses follow a similar pattern: Counties with the highest proportion of white residents have the fewest cases of COVID-19 irrespective of geographic region or state political party inclination (i.e., red or blue states). Moreover, comparatively fewer COVID-19 diagnoses have occurred in primarily white counties throughout the duration of the US COVID-19 pandemic. Systemic drivers place racial minorities at greater risk for COVID-19 and HIV. Individual-level characteristics (e.g., underlying health conditions for COVID-19 or risk behavior for HIV) do not fully explain excess disease burden in racial minority communities. Corresponding interventions must use structural- and policy-level solutions to address racial and ethnic health disparities.

Sex in the Time of COVID-19: Results of an Online Survey of Gay, Bisexual and Other Men Who Have Sex with Men's Experience of Sex and HIV Prevention During the US COVID-19 Epidemic.

This paper presents data from a recent cross-sectional survey of gay, bisexual and other men who have sex with men (GBMSM) in the US, to understand changes in sexual behavior and access to HIV prevention options (i.e. condoms and pre-exposure prophylaxis (PrEP)) during the COVID-19 lockdown period. The Love and Sex in the Time of COVID-19 survey was conducted online from April to May, 2020. GBMSM were recruited through advertisements featured on social networking platforms, recruiting a sample size of 518 GBMSM. Analysis considers changes three in self-reported measures of sexual behavior: number of sex partners, number of anal sex partners and number of anal sex partners not protected by pre-exposure prophylaxis (PrEP) or condoms. Approximately two-thirds of the sample reported that they believed it was possible to contract COVID-19 through sex, with anal sex reported as the least risky sex act. Men did not generally feel it was important to reduce their number of sex partners during COVID-19, but reported a moderate willingness to have sex during COVID-19. For the period between February and April–May 20,202, participants reported a mean increase of 2.3 sex partners during COVID-19, a mean increase of 2.1 anal sex partners (range − 40 to 70), but a very small increase in the number of unprotected anal sex partners. Increases in sexual behavior during COVID-19 were associated with increases in substance use during the same period. High levels of sexual activity continue to be reported during the COVID-19 lockdown period and these high levels of sexual activity are often paralleled by increases in substance use and binge drinking. There is a clear need to continue to provide comprehensive HIV prevention and care services during COVID-19, and telehealth and other eHealth platforms provide a safe, flexible mechanism for providing services.

Dynamics and Development of the COVID-19 Epidemic in the United States: A Compartmental Model Enhanced With Deep Learning Techniques.

BackgroundCompartmental models dominate epidemic modeling. Transmission parameters between compartments are typically estimated through stochastic parameterization processes that depends on detailed statistics of transmission characteristics, which are economically and resource-wise expensive to collect.ObjectiveWe aim to apply deep learning techniques as a lower data dependency alternative to estimate transmission parameters of a customized compartmental model, for the purpose of simulating the dynamics of the US coronavirus disease (COVID-19) epidemic and projecting its further development.MethodsWe constructed a compartmental model and developed a multistep deep learning methodology to estimate the model’s transmission parameters. We then fed the estimated transmission parameters to the model to predict development of the US COVID-19 epidemic for 35 and 42 days. Epidemics are considered suppressed when the basic reproduction number (R0) is less than 1.ResultsThe deep learning–enhanced compartmental model predicts that R0 will fall to <1 around August 17-19, 2020, at which point the epidemic will effectively start to die out, and that the US “infected” population will peak around August 16-18, 2020, at 3,228,574 to 3,308,911 individual cases. The model also predicted that the number of accumulative confirmed cases will cross the 5 million mark around August 7, 2020.ConclusionsCurrent compartmental models require stochastic parameterization to estimate the transmission parameters. These models’ effectiveness depends upon detailed statistics on transmission characteristics. As an alternative, deep learning techniques are effective in estimating these stochastic parameters with greatly reduced dependency on data particularity.

Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study

BackgroundWithin 4 months of COVID-19 first being reported in the USA, it spread to every state and to more than 90% of all counties. During this period, the US COVID-19 response was highly decentralised, with stay-at-home directives issued by state and local officials, subject to varying levels of enforcement. The absence of a centralised policy and timeline combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the USA a challenge.MethodsWe used daily mobility data derived from aggregated and anonymised cell (mobile) phone data, provided by Teralytics (Zürich, Switzerland) from Jan 1 to April 20, 2020, to capture real-time trends in movement patterns for each US county, and used these data to generate a social distancing metric. We used epidemiological data to compute the COVID-19 growth rate ratio for a given county on a given day. Using these metrics, we evaluated how social distancing, measured by the relative change in mobility, affected the rate of new infections in the 25 counties in the USA with the highest number of confirmed cases on April 16, 2020, by fitting a statistical model for each county.FindingsOur analysis revealed that mobility patterns are strongly correlated with decreased COVID-19 case growth rates for the most affected counties in the USA, with Pearson correlation coefficients above 0·7 for 20 of the 25 counties evaluated. Additionally, the effect of changes in mobility patterns, which dropped by 35–63% relative to the normal conditions, on COVID-19 transmission are not likely to be perceptible for 9–12 days, and potentially up to 3 weeks, which is consistent with the incubation time of severe acute respiratory syndrome coronavirus 2 plus additional time for reporting. We also show evidence that behavioural changes were already underway in many US counties days to weeks before state-level or local-level stay-at-home policies were implemented, implying that individuals anticipated public health directives where social distancing was adopted, despite a mixed political message.InterpretationThis study strongly supports a role of social distancing as an effective way to mitigate COVID-19 transmission in the USA. Until a COVID-19 vaccine is widely available, social distancing will remain one of the primary measures to combat disease spread, and these findings should serve to support more timely policy making around social distancing in the USA in the future.FundingNone.

Modeling, state estimation, and optimal control for the US COVID-19 outbreak

The novel coronavirus SARS-CoV-2 and resulting COVID-19 disease have had an unprecedented spread and continue to cause an increasing number of fatalities worldwide. While vaccines are still under development, social distancing, extensive testing, and quarantining of confirmed infected subjects remain the most effective measures to contain the pandemic. These measures carry a significant socioeconomic cost. In this work, we introduce a novel optimization-based decision-making framework for managing the COVID-19 outbreak in the US. This includes modeling the dynamics of affected populations, estimating the model parameters and hidden states from data, and an optimal control strategy for sequencing social distancing and testing events such that the number of infections is minimized. The analysis of our extensive computational efforts reveals that social distancing and quarantining are most effective when implemented early, with quarantining of confirmed infected subjects having a much higher impact. Further, we find that “on-off” policies alternating between strict social distancing and relaxing such restrictions can be effective at “flattening” the curve while likely minimizing social and economic cost.

Experiences and Insights from the Early US COVID-19 Epicenter: A Rapid Assessment Procedure Informed Clinical Ethnography Case Series

Objective The Coronavirus disease (COVID-19) outbreak has evolved into a pandemic crisis, with King County in Washington State emerging as the early US epicenter. A literature review revealed few reports providing front-line clinical and research teams guidance related to multilevel, rapidly evolving COVID-19 directives. Method The Rapid Assessment Procedure Informed Clinical Ethnography (RAPICE) method was used to develop a clinical case series and conduct participant observation during an ongoing comparative effectiveness trial of peer-integrated, patient-centered interventions after traumatic injury. Participants were patients enrolled in the intervention arm of the ongoing trial, as well as front-line clinicians, patient peer interventionists, and clinical research team members implementing the trial. All participants were exposed to the Washington State COVID-19 outbreak. Results Primary and secondary COVID-19 prevention strategies were feasibly integrated into ongoing care coordination and behavioral interventions for at-risk patients. Beyond the compilation of case studies, as an iterative method, RAPICE data collection naturalistically evolved to include observations of intervention team activity occurring within the larger pandemic epicenter context. A daily clinical research team huddle that flexibly accommodated virtual participation was also feasibly implemented. Conclusions Primary and secondary COVID-19 prevention strategies can be feasibly integrated into ongoing clinical interventions during the pandemic. Routine, proactive clinical and research team communication that transparently addresses ethical tensions and health-sustaining activities may promote well-being for providers grappling with rapidly evolving pandemic directives. Proactive assessments of individual provider vulnerabilities for severe COVID-19 related respiratory illness may also be a crucial element of the health care system pandemic responses.

The Race against COVID-19 in the US

The Race against COVID-19 in the US

Question of US Hegemony and COVID-19 Pandemic

A key feature of contemporary international politics is the changing nature of American presence on the world stage. Before the COVID-19 pandemic emerged, power of the United States (U.S.) as a unipolar superpower was experiencing change, giving way to multipolarity. For the past three decades, America was the predominant global power, leading the international response to every man-made or natural crisis and calamity. This time, however, the complexity of the COVID-19 pandemic has undermined the American ability to lead the international community in managing the impact of COVID-19 pandemic. By its very nature, COVID-19 is challenging prevailing international norms of hard and soft power. This paper critically evaluates the role of American power, its central position in the international political economy and global governance to highlight how deeply it is embedded in the international order, and suited to mount a global response to an international challenge.

US COVID-19 State Mortality and Case Progression - A Midterm Report Card

The US is leading the world in terms of the number of deaths and cases due to COVID-19. After the end of the national lockdown, there has been a significant rise in both new cases and deaths associated with COVID-19—especially in the southern and western states. This study presents an interim report of each state (50 states, DC, and Puerto Rico), in terms of mortality rate (MR, the number of cumulative deaths per 100,000 population due to COVID-19 by July 15, 2020) and Case Rate Progression (CRP, the percent change in the number of cumulative cases per 100,000 population increased from May 31 to July 15, 2020). The study provided an A - F grade report card for each state when compared to one another. The results showed that the states that received F’s in MR are the following (starting from the worst): New Jersey, New York, Massachusetts, Connecticut, Washington DC, Rhode Island, and Louisiana. The following states received F’s in CRP (starting from the worst): Arizona, Florida, South Carolina, Texas, Idaho, Arkansas, Montana, Alaska, Nevada, and Oklahoma. A poor CRP grade reflects rampant increases in new COVID-19 cases, followed by resulting increases in hospitalization, health resource utilization, and mortality. Some prior high casualty states with MR grades of F (e.g. New York, New Jersey, and Connecticut) have maintained low new case counts and received A grades in CRP. They are exemplary in terms of COVID-19 state/region-wide response and public health efforts.

Dynamics and Prediction of the COVID-19 Epidemics in the U.S. – A Compartmental Model with Deep Learning Enhancement

Compartmental models dominate epidemic modeling. Estimations of transmission parameters between compartments are typically done through stochastic parameterization processes that depend upon detailed statistics on transmission characteristics, which are economically and resource-wide expensive to collect. We apply deep learning techniques as a lower data dependency alternative to estimating transmission parameters of a customized compartmental model, for the purposes of projecting further development of the US COVID-19 epidemics. The deep learning-enhanced compartment model predicts that the basic reproduction rate (R0 ) will become less than one around June 22–27, 2020, and that the infection transmission parameter will drop to virtually zero around July 14, 2020, implying that the total number of confirmed cases will likely become stabilized around that timeframe (projected at 2·4 million). Funding Statement: None. Declaration of Interests: Authors declare no competing interests.

Learning from LMICs: best practices for leveraging sentinel surveillance systems to track the US COVID-19 pandemic

Learning from LMICs: best practices for leveraging sentinel surveillance systems to track the US COVID-19 pandemic

The Predictable Inequities of COVID-19 in the US: Fundamental Causes and Broken Institutions

The Predictable Inequities of COVID-19 in the US: Fundamental Causes and Broken Institutions