scholarly journals Estimating COVID-19 Prevalence in the United States: A Sample Selection Model Approach

2020 ◽  
Author(s):  
David Benatia ◽  
Raphael Godefroy ◽  
Joshua Lewis
Keyword(s):  
Public Health ◽  
United States ◽  
Sample Selection ◽  
The United States ◽  
Underlying Disease ◽  
Selection Model ◽  
Sample Period ◽  
Infection Rates ◽  
Public Health Response ◽  
Sample Selection Model

SummaryBackgroundPublic health efforts to determine population infection rates from coronavirus disease 2019 (COVID-19) have been hampered by limitations in testing capabilities and the large shares of mild and asymptomatic cases. We developed a methodology that corrects observed positive test rates for non-random sampling to estimate population infection rates across U.S. states from March 31 to April 7.MethodsWe adapted a sample selection model that corrects for non-random testing to estimate population infection rates. The methodology compares how the observed positive case rate vary with changes in the size of the tested population, and applies this gradient to infer total population infection rates. Model identification requires that variation in testing rates be uncorrelated with changes in underlying disease prevalence. To this end, we relied on data on day-to-day changes in completed tests across U.S. states for the period March 31 to April 7, which were primarily influenced by immediate supply-side constraints. We used this methodology to construct predicted infection rates across each state over the sample period. We also assessed the sensitivity of the results to controls for state-specific daily trends in infection rates.ResultsThe median population infection rate over the period March 31 to April 7 was 0.9% (IQR 0.64 1.77). The three states with the highest prevalence over the sample period were New York (8.5%), New Jersey (7.6%), and Louisiana (6.7%). Estimates from mod-els that control for state-specific daily trends in infection rates were virtually identical to the baseline findings. The estimates imply a nationwide average of 12 population infections per diagnosed case. We found a negative bivariate relationship (corr. = -0.51) between total per capita state testing and the ratio of population infections per diagnosed case.InterpretationThe effectiveness of the public health response to the coronavirus pandemic will depend on timely information on infection rates across different regions. With increasingly available high frequency data on COVID-19 testing, our methodology could be used to estimate population infection rates for a range of countries and subnational districts. In the United States, we found widespread undiagnosed COVID-19 infection. Expansion of rapid diagnostic and serological testing will be critical in preventing recurrent unobserved community transmission and identifying the large numbers individuals who may have some level of viral immunity.FundingSocial Sciences and Humanities Research Council.

scholarly journals Early introductions and community transmission of SARS-CoV-2 variant B.1.1.7 in the United States

2021 ◽  
Author(s):  
Tara Alpert ◽  
Erica Lasek-Nesselquist ◽  
Anderson F. Brito ◽  
Andrew L. Valesano ◽  
Jessica Rothman ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
New York ◽  
Diagnostic Testing ◽  
The United States ◽  
International Travel ◽  
Health Concern ◽  
Public Health Response ◽  
Community Transmission ◽  
The Uk

SummaryThe emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a national public health concern in the United States because of its increased transmissibility. Over 500 COVID-19 cases associated with this variant have been detected since December 2020, but its local establishment and pathways of spread are relatively unknown. Using travel, genomic, and diagnostic testing data, we highlight the primary ports of entry for B.1.1.7 in the US and locations of possible underreporting of B.1.1.7 cases. New York, which receives the most international travel from the UK, is likely one of the key hubs for introductions and domestic spread. Finally, we provide evidence for increased community transmission in several states. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.

scholarly journals A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States

2019 ◽  
Vol 116 (8) ◽  
pp. 3146-3154 ◽  
Author(s):  
Nicholas G. Reich ◽  
Logan C. Brooks ◽  
Spencer J. Fox ◽  
Sasikiran Kandula ◽  
Craig J. McGowan ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Real Time ◽  
Forecast Accuracy ◽  
The United States ◽  
Public Health Response ◽  
Public Health Decision ◽  
Health Decision Making ◽  
Health Response ◽  
Health Decision

Influenza infects an estimated 9–35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making.

scholarly journals Clinical Response, Outbreak Investigation, and Epidemiology of the Fungal Meningitis Epidemic in the United States: Systematic Review

2015 ◽  
Vol 10 (1) ◽  
pp. 145-151 ◽  
Author(s):  
Kaja M. Abbas ◽  
Nargesalsadat Dorratoltaj ◽  
Margaret L. O’Dell ◽  
Paige Bordwine ◽  
Thomas M. Kerkering ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Systematic Review ◽  
Clinical Response ◽  
Policy Development ◽  
The United States ◽  
Outbreak Investigation ◽  
Public Health Response ◽  
Fungal Meningitis ◽  
Health Response

AbstractWe conducted a systematic review of the 2012–2013 multistate fungal meningitis epidemic in the United States from the perspectives of clinical response, outbreak investigation, and epidemiology. Articles focused on clinical response, outbreak investigation, and epidemiology were included, whereas articles focused on compounding pharmacies, legislation and litigation, diagnostics, microbiology, and pathogenesis were excluded. We reviewed 19 articles by use of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. The source of the fungal meningitis outbreak was traced to the New England Compounding Center in Massachusetts, where injectable methylprednisolone acetate products were contaminated with the predominant pathogen, Exserohilum rostratum. As of October 23, 2013, the final case count stood at 751 patients and 64 deaths, and no additional cases are anticipated. The multisectoral public health response to the fungal meningitis epidemic from the hospitals, clinics, pharmacies, and the public health system at the local, state, and federal levels led to an efficient epidemiological investigation to trace the outbreak source and rapid implementation of multiple response plans. This systematic review reaffirms the effective execution of a multisectoral public health response and efficient delivery of the core functions of public health assessment, policy development, and service assurances to improve population health.(Disaster Med Public Health Preparedness. 2016;10:145–151)

scholarly journals A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S

2019 ◽  
Author(s):  
Nicholas G Reich ◽  
Craig J McGowan ◽  
Teresa K Yamana ◽  
Abhinav Tushar ◽  
Evan L Ray ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Seasonal Influenza ◽  
Influenza Season ◽  
Forecast Accuracy ◽  
The United States ◽  
Model Ensemble ◽  
Public Health Response ◽  
Health Response ◽  
Component Models

AbstractSeasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is based on that component’s forecast accuracy in past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

scholarly journals Quantifying Public Interest in Police Reforms by Mining Internet Search Data Following George Floyd’s Death (Preprint)

2020 ◽  
Author(s):  
John W Ayers ◽  
Benjamin M Althouse ◽  
Adam Poliak ◽  
Eric C Leas ◽  
Alicia L Nobles ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Police Training ◽  
The United States ◽  
Policy Reforms ◽  
Police Reform ◽  
Public Health Response ◽  
Health Response ◽  
2016 Election ◽  
Search Data

BACKGROUND The death of George Floyd while in police custody has resurfaced serious questions about police conduct that result in the deaths of unarmed persons. OBJECTIVE Data-driven strategies that identify and prioritize the public’s needs may engender a public health response to improve policing. We assessed how internet searches indicative of interest in police reform changed after Mr Floyd’s death. METHODS We monitored daily Google searches (per 10 million total searches) that included the terms “police” and “reform(s)” (eg, “reform the police,” “best police reforms,” etc) originating from the United States between January 1, 2010, through July 5, 2020. We also monitored searches containing the term “police” with “training,” “union(s),” “militarization,” or “immunity” as markers of interest in the corresponding reform topics. RESULTS The 41 days following Mr Floyd’s death corresponded with the greatest number of police “reform(s)” searches ever recorded, with 1,350,000 total searches nationally. Searches increased significantly in all 50 states and Washington DC. By reform topic, nationally there were 1,220,000 total searches for “police” and “union(s)”; 820,000 for “training”; 360,000 for “immunity”; and 72,000 for “militarization.” In terms of searches for all policy topics by state, 33 states searched the most for “training,” 16 for “union(s),” and 2 for “immunity.” States typically in the southeast had fewer queries related to any police reform topic than other states. States that had a greater percentage of votes for President Donald Trump during the 2016 election searched more often for police “union(s)” while states favoring Secretary Hillary Clinton searched more for police “training.” CONCLUSIONS The United States is at a historical juncture, with record interest in topics related to police reform with variability in search terms across states. Policy makers can respond to searches by considering the policies their constituencies are searching for online, notably police training and unions. Public health leaders can respond by engaging in the subject of policing and advocating for evidence-based policy reforms.

scholarly journals Quantifying Public Interest in Police Reforms by Mining Internet Search Data Following George Floyd’s Death

10.2196/22574 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e22574
Author(s):  
John W Ayers ◽  
Benjamin M Althouse ◽  
Adam Poliak ◽  
Eric C Leas ◽  
Alicia L Nobles ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Police Training ◽  
The United States ◽  
Policy Reforms ◽  
Police Reform ◽  
Public Health Response ◽  
Health Response ◽  
2016 Election ◽  
Search Data

Background The death of George Floyd while in police custody has resurfaced serious questions about police conduct that result in the deaths of unarmed persons. Objective Data-driven strategies that identify and prioritize the public’s needs may engender a public health response to improve policing. We assessed how internet searches indicative of interest in police reform changed after Mr Floyd’s death. Methods We monitored daily Google searches (per 10 million total searches) that included the terms “police” and “reform(s)” (eg, “reform the police,” “best police reforms,” etc) originating from the United States between January 1, 2010, through July 5, 2020. We also monitored searches containing the term “police” with “training,” “union(s),” “militarization,” or “immunity” as markers of interest in the corresponding reform topics. Results The 41 days following Mr Floyd’s death corresponded with the greatest number of police “reform(s)” searches ever recorded, with 1,350,000 total searches nationally. Searches increased significantly in all 50 states and Washington DC. By reform topic, nationally there were 1,220,000 total searches for “police” and “union(s)”; 820,000 for “training”; 360,000 for “immunity”; and 72,000 for “militarization.” In terms of searches for all policy topics by state, 33 states searched the most for “training,” 16 for “union(s),” and 2 for “immunity.” States typically in the southeast had fewer queries related to any police reform topic than other states. States that had a greater percentage of votes for President Donald Trump during the 2016 election searched more often for police “union(s)” while states favoring Secretary Hillary Clinton searched more for police “training.” Conclusions The United States is at a historical juncture, with record interest in topics related to police reform with variability in search terms across states. Policy makers can respond to searches by considering the policies their constituencies are searching for online, notably police training and unions. Public health leaders can respond by engaging in the subject of policing and advocating for evidence-based policy reforms.

Sign in / Sign up

Export Citation Format

Share Document