Sensible Long-lead Forecast of COVID-19 Epidemic Outcomes

Coronavirus disease 2019 (COVID-19) will likely remain a major public health burden; accurate forecast of COVID-19 epidemic outcomes several months into the future is needed to support more proactive planning. Here, we propose strategies to address three major forecast challenges, i.e., error growth, the emergence of new variants, and infection seasonality. Using these strategies in combination we generate retrospective predictions of COVID-19 cases and mortality 6 months in the future for 10 representative US states during July 2020 to September 2022. The optimized forecast approach using all three strategies consistently outperformed a baseline forecast approach across different variant waves and locations, for all forecast targets; overall, probabilistic forecast accuracy improved by 64% and 38% and point prediction accuracy by 133% and 87% for cases and deaths, respectively. Given this superior forecast skill, the strategies could support sensible long-lead forecast of COVID-19 and possibly other infectious diseases. One sentence summaryTo support more proactive planning, we propose approaches that substantially improve long-lead COVID-19 forecast accuracy.

The impact of COVID-19 vaccination in the US: averted burden of SARS-COV-2-related cases, hospitalizations and deaths

By August 1, 2022, the SARS-CoV-2 virus had caused over 90 million cases of COVID-19 and one million deaths in the United States. Since December 2020, SARS-CoV-2 vaccines have been a key component of US pandemic response; however, the impacts of vaccination are not easily quantified. Here, we use a dynamic county-scale metapopulation model to estimate the number of cases, hospitalizations, and deaths averted due to vaccination during the first six months of vaccine availability. We estimate that COVID-19 vaccination was associated with over 8 million fewer confirmed cases, over 120 thousand fewer deaths, and 700 thousand fewer hospitalizations during the first six months of the campaign.

Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: a multi-model study

BackgroundSARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains. MethodsNine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches. FindingsAbsent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts. ConclusionsResults from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.

SARS-CoV-2 transmission dynamics in South Africa and epidemiological characteristics of the Omicron variant

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) have been key drivers of new coronavirus disease 2019 (COVID-19) pandemic waves. To better understand variant epidemiologic characteristics, here we apply a model-inference system to reconstruct SARS-CoV-2 transmission dynamics in South Africa, a country that has experienced three VOC pandemic waves (i.e. Beta, Delta, and Omicron). We estimate key epidemiologic quantities in each of the nine South African provinces during March 2020 - Feb 2022, while accounting for changing detection rates, infection seasonality, nonpharmaceutical interventions, and vaccination. Model validation shows that estimated underlying infection rates and key parameters (e.g., infection-detection rate and infection-fatality risk) are in line with independent epidemiological data and investigations. In addition, retrospective predictions capture pandemic trajectories beyond the model training period. These detailed, validated model-inference estimates thus enable quantification of both the immune erosion potential and transmissibility of three major SARS-CoV-2 VOCs, i.e., Beta, Delta, and Omicron. These findings help elucidate changing COVID-19 dynamics and inform future public health planning.

Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination

What is already known about this topic?The highly transmissible SARS-CoV-2 Delta variant has begun to cause increases in cases, hospitalizations, and deaths in parts of the United States. With slowed vaccination uptake, this novel variant is expected to increase the risk of pandemic resurgence in the US in July--December 2021. What is added by this report?Data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant. These resurgences, which have now been observed in most states, were projected to occur across most of the US, coinciding with school and business reopening. Reaching higher vaccine coverage in July--December 2021 reduces the size and duration of the projected resurgence substantially. The expected impact of the outbreak is largely concentrated in a subset of states with lower vaccination coverage. What are the implications for public health practice?Renewed efforts to increase vaccination uptake are critical to limiting transmission and disease, particularly in states with lower current vaccination coverage. Reaching higher vaccination goals in the coming months can potentially avert 1.5 million cases and 21,000 deaths and improve the ability to safely resume social contacts, and educational and business activities. Continued or renewed non-pharmaceutical interventions, including masking, can also help limit transmission, particularly as schools and businesses reopen.

COVID-19 pandemic dynamics in India and impact of the SARS-CoV-2 Delta (B.1.617.2) variant

BackgroundThe COVID-19 Delta pandemic wave in India surged and declined within 3 months; cases then remained low despite the continued spread of Delta elsewhere. Here we aim to estimate key epidemiological characteristics of the Delta variant based on data from India and examine the underpinnings of its dynamics. MethodsWe utilize multiple datasets and model-inference methods to reconstruct COVID-19 pandemic dynamics in India during March 2020 - June 2021. We further use model estimates to retrospectively predict cases and deaths during July - mid-Oct 2021, under various vaccination and vaccine effectiveness (VE) settings to estimate the impact of vaccination and VE for non-Delta-infection recoverees. FindingsWe estimate that Delta escaped immunity in 34.6% (95% CI: 0 - 64.2%) of individuals with prior wildtype infection and was 57.0% (95% CI: 37.9 - 75.6%) more infectious than wildtype SARS-CoV-2. Models assuming higher VE among those with prior non-Delta infection, particularly after the 1st dose, generated more accurate predictions than those assuming no such increases (best-performing VE setting: 90/95% vs. 30/67% baseline for the 1st/2nd dose). Counterfactual modeling indicates that high vaccination coverage for 1st vaccine-dose in India ([~]50% by mid-Oct 2021) combined with the boosting of VE among recoverees averted around 60% of infections during July - mid-Oct 2021. InterpretationNon-pharmaceutical interventions, infection seasonality, and high coverage of 1-dose vaccination likely all contributed to pandemic dynamics in India during 2021. Given the shortage of COVID-19 vaccines globally and boosting of VE, for populations with high prior infection rates, prioritizing the first vaccine-dose may protect more people. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for studies published through Nov 3, 2021 on the Delta (B.1.617.2) SARS-CoV-2 variant that focused on three areas: 1) transmissibility [search terms: ("Delta variant" OR "B.1.617") AND ("transmission rate" OR "growth rate" OR "secondary attack rate" OR "transmissibility")]; 2) immune response ([search terms: ("Delta variant" OR "B.1.617") AND ("immune evas" OR "immune escape")]; and 3) vaccine effectiveness ([search terms: ("Delta variant" OR "B.1.617") AND ("vaccine effectiveness" OR "vaccine efficacy" OR "vaccination")]. Our search returned 256 papers, from which we read the abstracts and identified 54 relevant studies. Forty-two studies addressed immune evasion and/or vaccine effectiveness. Around half (n=19) of these studies measured the neutralizing ability of convalescent sera and/or vaccine sera against Delta and most reported some reduction (around 2-to 8-fold) compared to ancestral variants. The remainder (n=23) used field observations (often with a test-negative or cohort-design) and reported lower VE against infection but similar VE against hospitalization or death. Together, these laboratory and field observations consistently indicate that Delta can evade preexisting immunity. In addition, five studies reported higher B-cell and/or T-cell vaccine-induced immune response among recovered vaccinees than naive vaccinees, suggesting potential boosting of pre-existing immunity; however, all studies were based on small samples (n = 10 to 198 individuals). Sixteen studies examined transmissibility, including 1) laboratory experiments (n=6) showing that Delta has higher affinity to the cell receptor, fuses membranes more efficiently, and/or replicates faster than other SARS-CoV-2 variants, providing biological mechanisms for its higher transmissibility; 2) field studies (n=5) showing higher rates of breakthrough infections by Delta and/or higher viral load among Delta infections than other variants; and 3) modeling/mixed studies (n=5) using genomic or case data to estimate the growth rate or reproduction number, reporting a 60-120% increase. Only one study jointly estimated the increase in transmissibility (1.3-1.7-fold, 50% CI) and immune evasion (10-50%, 50% CI); this study also reported a 27.5% (25/91) reinfection rate by Delta. Added value of this studyWe utilize observed pandemic dynamics and the differential vaccination coverage for two vaccine doses in India, where the Delta variant was first identified, to estimate the epidemiological properties of Delta and examine the impact of prior non-Delta infection on immune boosting at the population level. We estimate that Delta variant can escape immunity from prior wildtype infection roughly one-third of the time and is around 60% more infectious than wildtype SARS-CoV-2. In addition, our analysis suggests the large increase in population receiving their first vaccine dose ([~]50% by end of Oct 2021) combined with the boosting effect of vaccination for non-Delta infection recoverees likely mitigated epidemic intensity in India during July - Oct 2021. Implications of all the available evidenceOur analysis reconstructs the interplay and effects of non-pharmaceutical interventions, infection seasonality, Delta variant emergence, and vaccination on COVID-19 pandemic dynamics in India. Modeling findings support prioritizing the first vaccine dose in populations with high prior infection rates, given vaccine shortages.

Epidemiological characteristics of three SARS-CoV-2 variants of concern and implications for future COVID-19 pandemic outcomes

Three SARS-CoV-2 variants classified as variants of concern - B.1.1.7, B.1.351, and P.1 - have spread globally. To characterize their viral and epidemiological properties in support of public health planning, we develop and apply a model-inference system to estimate the changes in transmissibility and immune escape for each variant, based on case and mortality data from the country where each variant emerged. Accounting for under-detection of infection, disease seasonality, concurrent non-pharmaceutical interventions, and mass-vaccination, we estimate that B.1.1.7 has a 46.6% (95% CI: 32.3 - 54.6%) increase in transmissibility but nominal immune escape from protection induced by prior wild-type infection; B.1.351 has a 32.4% (95% CI: 14.6 - 48.0%) increase in transmissibility and 61.3% (95% CI: 42.6 - 85.8%) immune escape; and P.1 has a 43.3% (95% CI: 30.3 - 65.3%) increase in transmissibility and 52.5% (95% CI: 0 - 75.8%) immune escape. Model simulations indicate that B.1.351 and P.1 could supplant B.1.1.7 dominance and lead to increased infections. Our findings highlight the importance of preventing the spread of B.1.351 and P.1, in addition to B.1.1.7, via continued preventive measures, prompt mass-vaccination of all populations, continued monitoring of vaccine efficacy, and possible updating of vaccine formulations to ensure high efficacy.

Non-pharmaceutical interventions and inoculation rate shape SARS-COV-2 vaccination campaign success

Nearly one year into the COVID-19 pandemic, the first SARS-COV-2 vaccines received emergency use authorization and vaccination campaigns began. A number of factors can reduce the averted burden of cases and deaths due to vaccination. Here, we use a dynamic model, parametrized with Bayesian inference methods, to assess the effects of non-pharmaceutical interventions, and vaccine administration and uptake rates on infections and deaths averted in the United States. We estimate that high compliance with non-pharmaceutical interventions could avert more than 60% of infections and 70% of deaths during the period of vaccine administration, and that increasing the vaccination rate from 5 to 11 million people per week could increase the averted burden by more than one third. These findings underscore the importance of maintaining non-pharmaceutical interventions and increasing vaccine administration rates.

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.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naive baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance StatementThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

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.

The importance of continued non-pharmaceutical interventions during the upcoming SARS-COV-2 vaccination campaign

In this communication we assess the potential benefit of SARS-COV-2 pandemic vaccination in the US and show how continued use of non-pharmaceutical interventions (NPIs) will be crucial during implementation.

Role of air temperature and humidity in the transmission of SARS-CoV-2 in the United States

Improved understanding of the effects of meteorological conditions on the transmission of SARS-CoV-2, the causative agent for COVID-19 disease, is urgently needed to inform mitigation efforts. Here, we estimated the relationship between air temperature or specific humidity (SH) and SARS-CoV-2 transmission in 913 U.S. counties with abundant reported infections from March 15 to August 31, 2020. Specifically, we quantified the associations of daily mean temperature and SH with daily estimates of the SARS-CoV-2 reproduction number (Rt) and calculated the fraction of Rt attributable to these meteorological conditions. Both lower temperature and lower SH were significantly associated with increased Rt. The fraction of Rt attributable to temperature was 5.10% (95% eCI: 5.00 - 5.18%), and the fraction of Rt attributable to SH was 14.47% (95% eCI: 14.37 - 14.54%). These fractions generally were higher in northern counties than in southern counties. Our findings indicate that cold and dry weather are moderately associated with increased SARS-CoV-2 transmissibility, with humidity playing a larger role than temperature.

COVID-19 Transmission Dynamics and Effectiveness of Public Health Interventions in New York City during the 2020 Spring Pandemic Wave

New York City experienced a large COVID-19 pandemic wave during March - May 2020. We model the transmission dynamics of COVID-19 in the city during the pandemic and estimate the effectiveness of public health interventions (overall and for each major intervention separately) for the entire population and by age group. We estimate that the overall effective reproductive number was 2.99 at the beginning of the pandemic wave and reduced to 0.93 one week after the stay-at-home mandate. Most age groups experienced similar reductions in transmission. Interventions reducing contact rates were associated with a 70.7% (95% CI: 65.0 - 76.4%) reduction of transmission overall and > 50% for all age groups during the pandemic. Face covering was associated with a 6.6% (95% CI: 0.8 - 12.4%) reduction of transmission overall and up to 20% for 65+ year-olds during the first month of implementation. Accounting for the amount of time masks are in use (i.e. mainly outside homes), these findings indicate universal masking could reduce transmission by up to 28-32% when lockdown-like measures are lifted, if the high effectiveness estimated for older adults were achieved for all ages. These estimates are verified by out-of-fit projections and support the need for implementing multiple interventions simultaneously in order to effectively mitigate the spread of COVID-19.

Mask Wearing and Control of SARS-CoV-2 Transmission in the United States

IntroductionCloth 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, research is 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. MethodsSerial cross-sectional surveys were administered June 3 through July 27, 2020 via 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. ResultsWe find an upward 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. Segmented regression analysis of mask wearing found no statistical change following mandates, however the positive trend of increased mask wearing over time was preserved. ConclusionWidespread 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.

Compound risks of hurricane evacuation amid the COVID-19 pandemic in the United States

Current projections and unprecedented storm activity to date suggest the 2020 Atlantic hurricane season will be extremely active and that a major hurricane could make landfall during the global COVID-19 pandemic. Such an event would necessitate a large-scale evacuation, with implications for the trajectory of the pandemic. Here we model how a hypothetical hurricane evacuation from four counties in southeast Florida would affect COVID-19 case levels. We find that hurricane evacuation increases the total number of COVID-19 cases in both origin and destination locations; however, if transmission rates in destination counties can be kept from rising during evacuation, excess evacuation-induced case numbers can be minimized by directing evacuees to counties experiencing lower COVID-19 transmission rates. Ultimately, the number of excess COVID-19 cases produced by the evacuation depends on the ability of destination counties to meet evacuee needs while minimizing virus exposure through public health directives.

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.