scholarly journals Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model

2021 ◽  
Vol 18 (3) ◽  
pp. 1090
Author(s):  
Zhenghong Peng ◽  
Siya Ao ◽  
Lingbo Liu ◽  
Shuming Bao ◽  
Tao Hu ◽  
...  
Keyword(s):  
United States ◽  
Transmission Rate ◽  
Fixed Time ◽  
The United States ◽  
Ordinary Least Squares ◽  
Time Varying ◽  
County Level ◽  
Infection Rates ◽  
Level Data ◽  
The Us

Background: Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. Methods: Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. Results: Based on the US county-level COVID-19 data from 22 January (T1) to 20 August (T212) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007–0.157 (mean = 0.048), 7.31–185.6 (mean = 38.89), and 0.04–2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7–14.0) and IFR was 0.70% (95%CI 0.52–0.95%) at T212. Interpretation: Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.

scholarly journals Estimating Unreported COVID-19 Cases in the United States Based on the tvSIRu Model

2020 ◽  
Author(s):  
Lingbo Liu ◽  
Tao Hu ◽  
Shuming Bao ◽  
Hao Wu ◽  
Zhenghong Peng ◽  
...  
Keyword(s):  
United States ◽  
Transmission Rate ◽  
Fixed Time ◽  
The United States ◽  
Rate Equations ◽  
Time Varying ◽  
County Level ◽  
Infection Rates ◽  
Country Level ◽  
Level Data

Abstract BackgroundThe potential unreported infection may impair and mislead policymaking for COVID-19，and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that may be underestimated based on county-level data to take better countermeasures against COVID-19. We suggested taking time-varying SIR models with unreported infection rates (UIR)to estimate the factual COVID-19 cases in the United States.MethodsSIR integrated with unreported infection rates (SIRu) of fixed time effect and SIR with time-varying parameters (tvSIRu)were applied to estimate and compare the value of transmission rate(TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. ResultsBased on US county-level COVID-19 data from January 22 (T1) to August 20 (T212) in 2020, SIRu was first tested and verified by a general OLS regression. The further regression of SIRu at the country-level showed that the average values of TR, UIR, and IFR were 0.034,19.5, 0.51% respectively. The range of TR, UIR, IFR of all states ranged were 0.007-0.157 (mean=0.048) ,7.31-185.6 (mean=38.89), and 0.04%-2.22% (mean=0.22%). Among time-varying transmission rate equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the UIR has an estimate of 9.1(95%CI = 5.7-14.0), and IFR was 0.70% (0.52%-0.95%) at T212.InterpretationDespite the decline in TR and IFR, the UIR of the United States is still on the rise, which had been supposed to decrease with sufficient tests or improved countersues. The US medical system may be largely affected by severe cases in the rapid spread of COVDI-19.

scholarly journals It’s complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Sean Jewell ◽  
Joseph Futoma ◽  
Lauren Hannah ◽  
Andrew C. Miller ◽  
Nicholas J. Foti ◽  
...  
Keyword(s):  
Cell Phone ◽  
The United States ◽  
Spatiotemporal Variation ◽  
Policy Implications ◽  
Time Varying ◽  
Infection Rates ◽  
Local Effects ◽  
Level Data ◽  
The Us ◽  
Model Flexibility

AbstractRestricting in-person interactions is an important technique for limiting the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Although early research found strong associations between cell phone mobility and infection spread during the initial outbreaks in the United States, it is unclear whether this relationship persists across locations and time. We propose an interpretable statistical model to identify spatiotemporal variation in the association between mobility and infection rates. Using 1 year of US county-level data, we found that sharp drops in mobility often coincided with declining infection rates in the most populous counties in spring 2020. However, the association varied considerably in other locations and across time. Our findings are sensitive to model flexibility, as more restrictive models average over local effects and mask much of the spatiotemporal variation. We conclude that mobility does not appear to be a reliable leading indicator of infection rates, which may have important policy implications.

Abstract P636: Social Determinants of Stroke Hospitalization and Mortality in the United States

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Randhir Sagar Yadav ◽  
Durgesh Chaudhary ◽  
Shima Shahjouei ◽  
Jiang Li ◽  
Vida Abedi ◽  
...  
Keyword(s):  
United States ◽  
Income Inequality ◽  
Social Determinants ◽  
Hemorrhagic Stroke ◽  
The United States ◽  
Care Physician ◽  
County Level ◽  
Medicare Beneficiaries ◽  
Level Data ◽  
Control And Prevention

Introduction: Stroke hospitalization and mortality are influenced by various social determinants. This ecological study aimed to determine the associations between social determinants and stroke hospitalization and outcome at county-level in the United States. Methods: County-level data were recorded from the Centers for Disease Control and Prevention as of January 7, 2020. We considered four outcomes: all-age (1) Ischemic and (2) Hemorrhagic stroke Death rates per 100,000 individuals (ID and HD respectively), and (3) Ischemic and (4) Hemorrhagic stroke Hospitalization rate per 1,000 Medicare beneficiaries (IH and HH respectively). Results: Data of 3,225 counties showed IH (12.5 ± 3.4) and ID (22.2 ± 5.1) were more frequent than HH (2.0 ± 0.4) and HD (9.8 ± 2.1). Income inequality as expressed by Gini Index was found to be 44.6% ± 3.6% and unemployment rate was 4.3% ± 1.5%. Only 29.8% of the counties had at least one hospital with neurological services. The uninsured rate was 11.0% ± 4.7% and people living within half a mile of a park was only 18.7% ± 17.6%. Age-adjusted obesity rate was 32.0% ± 4.5%. In regression models, age-adjusted obesity (OR for IH: 1.11; HH: 1.04) and number of hospitals with neurological services (IH: 1.40; HH: 1.50) showed an association with IH and HH. Age-adjusted obesity (ID: 1.16; HD: 1.11), unemployment (ID: 1.21; HD: 1.18) and income inequality (ID: 1.09; HD: 1.11) showed an association with ID and HD. Park access showed inverse associations with all four outcomes. Additionally, population per primary-care physician was associated with HH while number of pharmacy and uninsured rate were associated with ID. All associations and OR had p ≤0.04. Conclusion: Unemployment and income inequality are significantly associated with increased stroke mortality rates.

scholarly journals The evolution of infant mortality inequality in the United States, 1960–2016

2020 ◽  
Vol 6 (29) ◽  
pp. eaba5908
Author(s):  
Nick Turner ◽  
Kaveh Danesh ◽  
Kelsey Moran
Keyword(s):  
United States ◽  
Infant Mortality ◽  
The United States ◽  
Mortality Rates ◽  
County Level ◽  
Level Data ◽  
Antipoverty Programs ◽  
The Relationship ◽  
Over Time ◽  
Mortality Inequality

What is the relationship between infant mortality and poverty in the United States and how has it changed over time? We address this question by analyzing county-level data between 1960 and 2016. Our estimates suggest that level differences in mortality rates between the poorest and least poor counties decreased meaningfully between 1960 and 2000. Nearly three-quarters of the decrease occurred between 1960 and 1980, coincident with the introduction of antipoverty programs and improvements in medical care for infants. We estimate that declining inequality accounts for 18% of the national reduction in infant mortality between 1960 and 2000. However, we also find that level differences between the poorest and least poor counties remained constant between 2000 and 2016, suggesting an important role for policies that improve the health of infants in poor areas.

scholarly journals Explaining COVID-19 Outbreaks with Reactive SEIRD Models

2021 ◽  
Author(s):  
Kunal Menda ◽  
Lucas Laird ◽  
Mykel J. Kochenderfer ◽  
Rajmonda S. Caceres
Keyword(s):  
Neural Network ◽  
United States ◽  
Infection Rate ◽  
Expectation Maximization ◽  
The United States ◽  
County Level ◽  
Training Set ◽  
Single Model ◽  
Partial Observability ◽  
Level Data

AbstractCOVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. In this work, we seek to explain the diversity in epidemic progressions by considering an extension to the compartmental SEIRD model. The model we propose uses a neural network to predict the infection rate as a function of time and of the prevalence of the disease. We provide a methodology for fitting this model to available county-level data describing aggregate cases and deaths. Our method uses Expectation-Maximization in order to overcome the challenge of partial observability—that the system’s state is only partially reflected in available data. We fit a single model to data from multiple counties in the United States exhibiting different behavior. By simulating the model, we show that it is capable of exhibiting both single peak and multi-peak behavior, reproducing behavior observed in counties both in and out of the training set. We also numerically compare the error of simulations from our model with a standard SEIRD model, showing that the proposed extensions are necessary to be able to explain the spread of COVID-19.

scholarly journals Concealed Handgun Licensing and Crime in Four States

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Charles D. Phillips ◽  
Obioma Nwaiwu ◽  
Szu-hsuan Lin ◽  
Rachel Edwards ◽  
Sara Imanpour ◽  
...  
Keyword(s):  
United States ◽  
Policy Issue ◽  
The United States ◽  
Crime Rates ◽  
State Legislators ◽  
County Level ◽  
Contextual Variables ◽  
Research Tradition ◽  
Level Data ◽  
Firearm Policy

Firearm policy in the United States has long been a serious policy issue. Much of the previous research on crime and firearms focused on the effects of states’ passage of concealed handgun licensing (CHL) legislation. Today, given the proliferation of CHL legislation and growing strength of the “pro-gun” movement, the primary policy focus has changed. State legislators now face issues concerning whether and how to increase access to CHLs. Because of this transformation, this research moves away from the research tradition focused on the effect of a legislative change allowing CHLs. Instead, we consider two issues more policy relevant in the current era: What are the dynamics behind CHL licensing? Do increases in concealed handgun licensing affect crime rates? Using county-level data, we found that the density of gun dealers and other contextual variables, rather than changing crime rates, had a significant effect on increases of the rates at which CHLs were issued. We also found no significant effect of CHL increases on changes in crime rates. This research suggests that the rate at which CHLs are issued and crime rates are independent of one another—crime does not drive CHLs; CHLs do not drive crime.

scholarly journals Characterizing the spatiotemporal heterogeneity of the COVID-19 vaccination landscape

2021 ◽  
Author(s):  
Andrew Tiu ◽  
Zachary Susswein ◽  
Alexes Merritt ◽  
Shweta Bansal
Keyword(s):  
United States ◽  
Vaccination Coverage ◽  
Vaccination Campaign ◽  
The United States ◽  
County Level ◽  
Spatial Clusters ◽  
Southern Us ◽  
The Us ◽  
Target Locations ◽  
County Scale

AbstractIt is critical that we maximize vaccination coverage across the United States so that SARS-CoV-2 transmission can be suppressed, and we can sustain the recent reopening of the nation. Maximizing vaccination requires that we track vaccination patterns to measure the progress of the vaccination campaign and target locations that may be undervaccinated. To improve efforts to track and characterize COVID-19 vaccination progress in the United States, we integrate CDC and state-provided vaccination data, identifying and rectifying discrepancies between these data sources. We find that COVID-19 vaccination coverage in the US exhibits significant spatial heterogeneity at the county level and statistically identify spatial clusters of undervaccination, all with foci in the southern US. Vaccination progress at the county level is also variable; many counties stalled in vaccination into June 2021 and few recovered by July, with transmission of the Delta variant rapidly rising. Using a comparison with a mechanistic growth model fitted to our integrated data, we classify vaccination dynamics across time at the county scale. Our findings underline the importance of curating accurate, fine-scale vaccination data and the continued need for widespread vaccination in the US, especially in the wake of the highly transmissible Delta variant.

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