Overall burden and characteristics of COVID-19 in the United States during 2020

The COVID-19 pandemic disrupted health systems and economies throughout the world during 2020 and was particularly devastating for the United States. Many of epidemiological features that produced observed rates of morbidity and mortality have not been thoroughly assessed. Here we use a data-driven model-inference approach to simulate the pandemic at county-scale in the United States during 2020 and estimate critical, time-varying epidemiological properties underpinning the dynamics of the virus. The pandemic in the US during 2020 was characterized by an overall ascertainment rate of 21.6% (95% credible interval (CI):18.9 - 25.5%). Population susceptibility at years end was 68.8% (63.4 - 75.3%), indicating roughly one third of the US population had been infected. Community infectious rates, the percentage of people harboring a contagious infection, rose above 0.8% (0.6 - 1.0%) before the end of the year, and were as high as 2.4% in some major metropolitan areas. In contrast, the infection fatality rate fell to 0.3% by years end; however, community control of transmission, estimated from trends of the time-varying reproduction number, Rt, slackened during successive pandemic waves. In the coming months, as vaccines are distributed and administered and new more transmissible virus variants emerge and spread, greater use of non-pharmaceutical interventions will be needed.

Association between COVID-19 mortality and population level health and socioeconomic indicators

With the availability of multiple COVID-19 vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes. To date, vaccination strategies in the US have focused on individual characteristics such as age and occupation. In this study, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines. Using spatial autoregressive models, we demonstrate that 43% of the variability in COVID-19 mortality in US counties can be explained by health/socioeconomic factors, adjusting for case rates. Of the indicators considered, prevalence of chronic kidney disease and proportion of population living in nursing homes were found to have the strongest association. In the context of vaccine rollout globally, our findings indicate that national and subnational estimates of burden of disease could be useful for minimizing COVID-19 mortality.

Simulating the impact of different vaccination policies on the COVID-19 pandemic in New York City

PurposeTo analyze potential COVID-19 epidemic outcomes in New York City under different SARS-CoV-2 virus circulation scenarios and vaccine rollout policies from early Jan 2021 to end of June 2021. Key findingsIn anticipation of the potential arrival and dominance of the more infectious SARS-CoV-2 variant: O_LIMass-vaccination would be critical to mitigating epidemic severity (26-52% reduction in infections, hospitalizations, and deaths, compared to no vaccination, provided the new UK variant supplants currently circulating variants). C_LIO_LIPrioritizing key risk groups for earlier vaccination would lead to greater reductions in hospitalizations and deaths than infections. Thus, in general this would be a good strategy. C_LIO_LICurrent vaccination prioritization policy is suboptimal. To avert more hospitalizations and deaths, mass-vaccination of all individuals 65 years or older should be done as soon as possible. For groups listed in the same phase, 65+ year-olds should be given first priority ahead of others. C_LIO_LIAvailable vaccine doses should be given to the next priority groups as soon as possible without awaiting hesitant up-stream groups. C_LIO_LIWhile efficacy of vaccination off-protocol is unknown, provided immune response following a first vaccine dose persists, delaying the 2nd vaccine dose by [~]1 month (i.e. administer the two doses 8 weeks apart) can substantially reduce infections, hospitalizations, and deaths compared to the 3-week apart regimen. Across all scenarios tested here, delaying the 2nd vaccine dose leads to the largest reduction in severe epidemic outcomes (e.g. hospitalizations and deaths). Therefore, to protect as many people as possible, this strategy should be considered if rapid increases in infections, hospitalization or deaths and/or shortages in vaccines were to occur. C_LI

The Association between Early Country-level Testing Capacity and Later COVID-19 Mortality Outcomes

BackgroundThe COVID-19 pandemic has overrun hospital systems while exacerbating economic hardship and food insecurity on a global scale. In an effort to understand how early action to find and control the virus is associated with cumulative outcomes, we explored how country-level testing capacity affects later COVID-19 mortality. MethodsWe used the Our World in Data database to explore testing and mortality records in 27 countries from December 31, 2019 to September 30, 2020; we applied ordinary-least squares regression with clustering on country to determine the association between early COVID-19 testing capacity (cumulative tests per case) and later COVID-19 mortality (time to specified mortality thresholds), adjusting for country-level confounders, including median age, GDP, hospital bed capacity, population density, and non-pharmaceutical interventions. ResultsHigher early testing implementation, as indicated by more cumulative tests per case when mortality was still low, was associated with longer accrual time for higher per capita deaths. For instance, a higher cumulative number of tests administered per case at the time of 6 deaths per million persons was positively predictive of a longer time to reach 15 deaths per million, after adjustment for all confounders ({beta}=0.659; P=0.001). ConclusionsCountries that developed stronger COVID-19 testing capacity at early timepoints, as measured by tests administered per case identified, experienced a slower increase of deaths per capita. Thus, this study operationalizes the value of testing and provides empirical evidence that stronger testing capacity at early timepoints is associated with reduced mortality and better pandemic control.

Differential COVID-19 case positivity in New York City neighborhoods: socioeconomic factors and mobility

New York City has been one of the hotspots of the COVID-19 pandemic and during the first two months of the outbreak considerable variability in case positivity was observed across the citys ZIP codes. In this study, we examined: a) the extent to which the variability in ZIP code level cases can be explained by aggregate markers of socioeconomic status and daily change in mobility; and b) the extent to which daily change in mobility independently predicts case positivity. Our analysis indicates that the markers considered together explained 56% of the variability in case positivity through April 1 and their explanatory power decreased to 18% by April 30. Our analysis also indicates that changes in mobility during this time period are not likely to be acting as a mediator of the relationship between ZIP-level SES and case positivity. During the middle of April, increases in mobility were independently associated with decreased case positivity. Together, these findings present evidence that heterogeneity in COVID-19 case positivity during the New York City spring outbreak was largely driven by residents socioeconomic status.

Estimating the infection fatality risk of COVID-19 in New York City, March 1-May 16, 2020

During March 1-May 16, 2020, 191,392 laboratory-confirmed COVID-19 cases were diagnosed and reported and 20,141 confirmed and probable COVID-19 deaths occurred among New York City (NYC) residents. We applied a network model-inference system developed to support the Citys pandemic response to estimate underlying SARS-CoV-2 infection rates. Based on these estimates, we further estimated the infection fatality risk (IFR) for 5 age groups (i.e. <25, 25-44, 45-64, 65-74, and 75+ years) and all ages overall, during March 1-May 16, 2020. We estimated an overall IFR of 1.45% (95% Credible Interval: 1.09-1.87%) in NYC. In particular, weekly IFR was estimated as high as 6.1% for 65-74 year-olds and 17.0% for 75+ year-olds. These results are based on more complete ascertainment of COVID-19-related deaths in NYC and thus likely more accurately reflect the true, higher burden of death due to COVID-19 than previously reported elsewhere. It is thus crucial that officials account for and closely monitor the infection rate and population health outcomes and enact prompt public health responses accordingly as the pandemic unfolds.

Differential Effects of Intervention Timing on COVID-19 Spread in the United States

Assessing the effects of early non-pharmaceutical interventions1-5 on COVID-19 spread in the United States is crucial for understanding and planning future control measures to combat the ongoing pandemic6-10. Here we use county-level observations of reported infections and deaths11, in conjunction with human mobility data12 and a metapopulation transmission model13,14, to quantify changes of disease transmission rates in US counties from March 15, 2020 to May 3, 2020. We find significant reductions of the basic reproductive numbers in major metropolitan areas in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same control measures been implemented just 1-2 weeks earlier, a substantial number of cases and deaths could have been averted. Specifically, nationwide, 56.5% [95% CI: 48.1%-65.9%] of reported infections and 54.0% [95% CI: 43.6%-63.8%] of reported deaths as of May 3, 2020 could have been avoided if the same control measures had been implemented just one week earlier. We also examine the effects of delays in re-implementing social distancing following a relaxation of control measures. A longer response time results in a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive response in controlling the COVID-19 pandemic.

Projection of COVID-19 Cases and Deaths in the US as Individual States Re-open May 4,2020

In March and April 2020, control measures enforcing social distancing and restricting individual movement and contact were adopted across the United States in an effort to slow the spread and growth of COVID-19. However, a number of states have now begun to ease these restrictions. Here, we evaluate the effects of loosening stay-at-home orders on COVID-19 incidence and related outcomes. We use a metapopulation model applied at county resolution to simulate the spread and growth of COVID-19 incidence in the United States. We calibrate the model against county-level daily case and death data collected from February 21, 2020 to May 2, 2020, and project the outbreak in 3,142 US counties for 6 weeks into the future. Projections for daily reported cases, daily new infections (both reported and unreported), new and cumulative hospital bed demand, ICU bed and ventilator demand, as well as daily mortality, are generated. We observe a rebound in COVID-19 incidence and deaths beginning in late May, approximately 2 to 4 weeks after states begin to reopen. Importantly, the lag between infection acquisition and case confirmation, coupled with insufficient broader testing and contact tracing, will mask any rebound and exponential growth of the COVID-19 until it is well underway.