scholarly journals Refining reproduction number estimates to account for unobserved generations of infection in emerging epidemics

2021 ◽  
Author(s):  
Andrea Brizzi ◽  
Megan O'Driscoll ◽  
Ilaria Dorigatti
Keyword(s):  
Situational Awareness ◽  
Reproduction Number ◽  
Simulated Data ◽  
The United States ◽  
Original Method ◽  
Minimal Amount ◽  
Systematic Bias ◽  
Real World Data ◽  
Response Planning ◽  
The Impact

Background Estimating the transmissibility of infectious diseases is key to inform situational awareness and for response planning. Several methods tend to overestimate the basic (R_0) and effective (R_t) reproduction numbers during the initial phases of an epidemic. The reasons driving the observed bias are unknown. In this work we explore the impact of incomplete observations and underreporting of the first generations of infections during the initial epidemic phase. Methods We propose a debiasing procedure which utilises a linear exponential growth model to infer unobserved initial generations of infections and apply it to EpiEstim. We assess the performance of our adjustment using simulated data, considering different levels of transmissibility and reporting rates. We also apply the proposed correction to reported SARS-CoV-2 incidence data reported in Italy, Sweden, the United Kingdom and the United States of America. Results In all simulation scenarios, our adjustment outperforms the original EpiEstim method. The proposed correction reduces the systematic bias and the quantification of uncertainty is more precise, as better coverage of the true R_0 values is achieved with tighter credible intervals. When applied to real world data, the proposed adjustment produces basic reproduction number estimates which closely match the estimates obtained in other studies while making use of a minimal amount of data. Conclusions The proposed adjustment refines the reproduction number estimates obtained with the current EpiEstim implementation by producing improved, more precise estimates earlier than with the original method. This has relevant public health implications.

scholarly journals SEIHCRD Model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement

2020 ◽  
Author(s):  
Avaneesh Singh ◽  
Manish Kumar Bajpai
Keyword(s):  
Basic Reproduction Number ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
The United States ◽  
Fatality Rate ◽  
Hospital Beds ◽  
The Impact ◽  
Over Time ◽  
The Basic Reproduction Number

We have proposed a new mathematical method, SEIHCRD-Model that is an extension of the SEIR-Model adding hospitalized and critical twocompartments. SEIHCRD model has seven compartments: susceptible (S), exposed (E), infected (I), hospitalized (H), critical (C), recovered (R), and deceased or death (D), collectively termed SEIHCRD. We have studied COVID- 19 cases of six countries, where the impact of this disease in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. SEIHCRD model is estimating COVID-19 spread and forecasting under uncertainties, constrained by various observed data in the present manuscript. We have first collected the data for a specific period, then fit the model for death cases, got the values of some parameters from it, and then estimate the basic reproduction number over time, which is nearly equal to real data, infection rate, and recovery rate of COVID-19. We also compute the case fatality rate over time of COVID-19 most affected countries. SEIHCRD model computes two types of Case fatality rate one is CFR daily and the second one is total CFR. We analyze the spread and endpoint of COVID-19 based on these estimates. SEIHCRD model is time-dependent hence we estimate the date and magnitude of peaks of corresponding to the number of exposed cases, infected cases, hospitalized cases, critical cases, and the number of deceased cases of COVID-19 over time. SEIHCRD model has incorporated the social distancing parameter, different age groups analysis, number of ICU beds, number of hospital beds, and estimation of how much hospital beds and ICU beds are required in near future.

scholarly journals 412. Estimating the Impact of School Classroom Sizes on the Probability of Severe Acute Respiratory Syndrome Coronavirus-2 Infectivity or Exposure

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S307-S307
Author(s):  
Sanya J Thomas ◽  
Rebecca R Young ◽  
Ibukunoluwa Akinboyo ◽  
Michael J Smith ◽  
Tara Buckley ◽  
...  
Keyword(s):  
North Carolina ◽  
Severe Acute Respiratory Syndrome ◽  
Reproduction Number ◽  
The United States ◽  
Mitigation Strategies ◽  
Age Group ◽  
Cohort Size ◽  
Research Support ◽  
The Impact

Abstract Background Despite schools reopening across the United States in communities with low and high Coronavirus disease 2019 (COVID-19) prevalence, data remain scarce about the effect of classroom size on the transmission of severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) within schools. This study estimates the effect of classroom size on the risk of COVID-19 infection in a closed classroom cohort for varying age groups locally in Durham, North Carolina. Total number of Coronavirus Disease 2019 (COVID-19) infections over a 28-day follow-up period for varying classroom reproduction number (R0) and varying classroom cohort sizes of 15 students, 30 students and 100 students in Durham County, North Carolina. Methods Using publicly available population and COVID-19 case count data from Durham County, we calculated a weekly average number of new confirmed COVID-19 cases per week between May 3, 2020 and August 22, 2020 according to age categories: < 5 years, 5-9 years, 10-14 years, and 15-19 years. We collated average classroom cohort sizes and enrollment data for each age group by grade level of education for the first month of the 2019-2020 academic school year. Then, using a SEIR compartmental model, we calculated the number of susceptible (S), exposed (E), infectious (I) and recovered (R) students in a cohort size of 15, 30 and 100 students, modelling for classroom reproduction number (R0) of 0.5, 1.5 and 2.5 within a closed classroom cohort over a 14-day and 28-day follow-up period using age group-specific COVID-19 prevalence rates. Results The SEIR model estimated that the increase in cohort size resulted in up to 5 new COVID-19 infections per 10,000 students whereas the classroom R0 had a stronger effect, with up to 88 new infections per 10,000 students in a closed classroom cohort over time. When comparing different follow-up periods in a closed cohort with R0 of 0.5, we estimated 12 more infected students per 10,000 students over 28 days as compared to 14 days irrespective of cohort size. With a R0 of 2.5, there were 49 more infected students per 10,000 students over 28 days as compared to 14 days. Conclusion Classroom R0 had a stronger impact in reducing school-based COVID-19 transmission events as compared to cohort size. Additionally, earlier isolation of newly infected students in a closed cohort resulted in fewer new COVID-19 infections within that group. Mitigation strategies should target promoting safe practices within the school setting including early quarantine of newly identified contacts and minimizing COVID-19 community prevalence. Disclosures Michael J. Smith, MD, M.S.C.E, Merck (Grant/Research Support)Pfizer (Grant/Research Support)

scholarly journals Are we there yet? An adaptive SIR model for continuous estimation of COVID-19 infection rate and reproduction number in the United States (Preprint)

2020 ◽  
Author(s):  
Mark Shapiro ◽  
Fazle Karim ◽  
Guido Muscioni ◽  
Abel Saju Augustine
Keyword(s):  
Infection Rate ◽  
Reproduction Number ◽  
Infected Individual ◽  
Local Population ◽  
The United States ◽  
Mitigation Measures ◽  
Simple Method ◽  
Wide Range ◽  
Continuous Estimation ◽  
The Impact

BACKGROUND The dynamics of the COVID-19 epidemic vary due to local population density and policy measures. When making decisions, policy makers consider an estimate of the effective reproduction number R_t which is the expected number of secondary infections by a single infected individual. OBJECTIVE We propose a simple method for estimating the time-varying infection rate and reproduction number R_t . METHODS We use a sliding window approach applied to a Susceptible-Infectious-Removed model. The infection rate is estimated using the reported cases for a seven-day window to obtain continuous estimation of R_t. The proposed adaptive SIR (aSIR) model was applied to data at the state and county levels. RESULTS The aSIR model showed an excellent fit for the number of reported COVID-19 positive cases, a one-day forecast MAPE was less than 2.6% across all states. However, a seven-day forecast MAPE reached 16.2% and strongly overestimated the number of cases when the reproduction number was high and changing fast. The maximal R_t showed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5). We demonstrate that the aSIR model can quickly adapt to an increase in the number of tests and associated increase in the reported cases of infections. Our results also suggest that intensive testing may be one of the effective methods of reducing R_t. CONCLUSIONS The aSIR model provides a simple and accurate computational tool to obtain continuous estimation of the reproduction number and evaluate the impact of mitigation measures.

Real-world treatment and outcomes of frontline chemotherapy in patients with metastatic pancreatic cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16249-e16249
Author(s):  
Salwan Al Mutar ◽  
Muhammad Shaalan Beg ◽  
Eric Hansen ◽  
Andrew J. Belli ◽  
Maegan Vaz ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Clinical Trial ◽  
Real World ◽  
Univariate Analysis ◽  
The United States ◽  
Metastatic Pancreatic Cancer ◽  
Future Research ◽  
Real World Data ◽  
The Real ◽  
The Impact

e16249 Background: The difference between the FOLFIRINOX and gemcitabine/nab-paclitaxel (GnP) regimens’ clinical trial designs limit the ability to generate cross-study comparisons. Therefore, there is a significant need to understand the impact of various demographic and clinical characteristics on the effectiveness of these systemic therapies in the real-world treatment setting. This study seeks to compare the real-world outcomes of patients with metastatic pancreatic cancer treated with frontline FOLFIRINOX or GnP. Methods: Patients with primary metastatic pancreatic cancer who received first-line (1L) FOLFIRINOX or GnP were identified in the COTA real-world database. The COTA database is a de-identified database of real-world data (RWD) derived from the electronic health records of healthcare providers in the United States. Real-world overall response rate (rwORR) was calculated as the proportion of patients achieving complete response (CR) or partial response (PR). Overall survival (OS) was calculated using the Kaplan-Meier method and multivariate analyses utilized Cox proportional hazards. Results: The overall qualified cohort (n=236) was stratified by 1L FOLFIRINOX (n=109) or GnP (n=127). Select patient characteristics are shown in table. Patients treated with 1L FOLFIRINOX showed greater rwORR as compared to those treated with GnP (68.8% vs. 55.9%, p=0.04). Additionally, patients treated with 1L FOLFIRINOX had longer median OS (14.4 vs 11.4 mos, respectively). In univariate analysis, patients treated with GnP had a greater chance of mortality (HR: 1.3, 95% CI: 1.0, 1.8, p=0.05). This relationship strengthened in multivariate analysis (GnP treated HR: 1.6, 95% CI: 1.1, 2.1, p=0.01). Conclusions: Due to lack of enrollment of representative patients in clinical trials and in the absence of a comparative clinical trial, real-world experience with chemotherapy regimens provide critical insights on the outcome of treatments. In our cohort, patients treated with frontline GnP had a significantly greater chance of mortality as compared to patients treated with frontline FOLFIRINOX. The FOLFIRINOX cohort also showed greater rwORR. Future research will continue to expand on treatment patterns in subsequent lines of therapy, as well as emerging therapy types, in order to better understand the optimal treatment sequence in metastatic pancreatic cancer.[Table: see text]

EFFECTS OF OPA 90 RESPONSE PLANNING ON REPORTED SPILLS

1997 ◽  
Vol 1997 (1) ◽  
pp. 761-764 ◽  
Author(s):  
Dana Stalcup ◽  
Gary Yoshioka ◽  
Brad Kaiman ◽  
Adam Hall
Keyword(s):  
United States ◽  
Emergency Response ◽  
Oil Spills ◽  
Oil Pollution ◽  
The United States ◽  
Government Agencies ◽  
Notification System ◽  
Response Planning ◽  
The Impact ◽  
Spilled Oil

ABSTRACT In the years following the passage of the Oil Pollution Act of 1990 (OPA 90), government agencies and regulated parties in the United States have begun to implement spill prevention and preparedness programs. For this analysis, 7 years of oil spill data collected in the Emergency Response Notification System were used to measure the impact that OPA 90 has had on preventing large spills. Furthermore, relationships among the types, sources, and location of spilled oil are characterized. A comparison of the number of reported 10,000-gallon oil spills for the years 1992-1995 to that number for the years 1989-1991 indicates a decline, not only for vessels but also for pipelines and fixed facilities. The decline in large oil spills to water from various sources appears to indicate that the efforts of government and industry have had a measurable impact on environmental protection.

scholarly journals Atrial Fibrillation in Patients With Cardiomyopathy: Prevalence and Clinical Outcomes From Real‐World Data

2021 ◽  
Author(s):  
Benjamin J. R. Buckley ◽  
Stephanie L. Harrison ◽  
Dhiraj Gupta ◽  
Elnara Fazio‐Eynullayeva ◽  
Paula Underhill ◽  
...  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Clinical Outcomes ◽  
Restrictive Cardiomyopathy ◽  
The United States ◽  
Research Network ◽  
Real World Data ◽  
All Cause Mortality ◽  
Incident Heart Failure ◽  
The Impact

Background Cardiomyopathy is a common cause of atrial fibrillation (AF) and may also present as a complication of AF. However, there is a scarcity of evidence of clinical outcomes for people with cardiomyopathy and concomittant AF. The aim of the present study was therefore to characterize the prevalence of AF in major subtypes of cardiomyopathy and investigate the impact on important clinical outcomes. Methods and Results A retrospective cohort study was conducted using electronic medical records from a global federated health research network, with data primarily from the United States. The TriNetX network was searched on January 17, 2021, including records from 2002 to 2020, which included at least 1 year of follow‐up data. Patients were included based on a diagnosis of hypertrophic, dilated, or restrictive cardiomyopathy and concomitant AF. Patients with cardiomyopathy and AF were propensity‐score matched for age, sex, race, and comorbidities with patients who had a cardiomyopathy only. The outcomes were 1‐year mortality, hospitalization, incident heart failure, and incident stroke. Of 634 885 patients with cardiomyopathy, there were 14 675 (2.3%) patients with hypertrophic, 90 117 (7.0%) with restrictive, and 37 685 (5.9%) with dilated cardiomyopathy with concomitant AF. AF was associated with significantly higher odds of all‐cause mortality (odds ratio [95% CI]) for patients with hypertrophic (1.26 [1.13–1.40]) and dilated (1.36 [1.27–1.46]), but not restrictive (0.98 [0.94–1.02]), cardiomyopathy. Odds of hospitalization, incident heart failure, and incident stroke were significantly higher in all cardiomyopathy subtypes with concomitant AF. Among patients with AF, catheter ablation was associated with significantly lower odds of all‐cause mortality at 12 months across all cardiomyopathy subtypes. Conclusions Findings of the present study suggest AF may be highly prevalent in patients with cardiomyopathy and associated with worsened prognosis. Subsequent research is needed to determine the usefulness of screening and multisdisciplinary treatment of AF in this population.

scholarly journals An upper-bound estimate for the accuracy of glacier volume–area scaling

2013 ◽  
Vol 7 (6) ◽  
pp. 1707-1720 ◽  
Author(s):  
D. Farinotti ◽  
M. Huss
Keyword(s):  
Volume Change ◽  
Real World ◽  
Upper Bound ◽  
Systematic Bias ◽  
Volume Changes ◽  
Real World Data ◽  
Ice Volume ◽  
Lower Accuracy ◽  
Upper Bound Estimate ◽  
The Impact

Abstract. Volume–area scaling is the most popular method for estimating the ice volume of large glacier samples. Here, a series of resampling experiments based on different sets of synthetic data is presented in order to derive an upper-bound estimate (i.e. a level achieved only within ideal conditions) for its accuracy. For real-world applications, a lower accuracy has to be expected. We also quantify the maximum accuracy expected when scaling is used for determining the glacier volume change, and area change of a given glacier population. A comprehensive set of measured glacier areas, volumes, area and volume changes is evaluated to investigate the impact of real-world data quality on the so-assessed accuracies. For populations larger than a few thousand glaciers, the total ice volume can be recovered within 30% if all data currently available worldwide are used for estimating the scaling parameters. Assuming no systematic bias in ice volume measurements, their uncertainty is of secondary importance. Knowing the individual areas of a glacier sample for two points in time allows recovering the corresponding ice volume change within 40% for populations larger than a few hundred glaciers, both for steady-state and transient geometries. If ice volume changes can be estimated without bias, glacier area changes derived from volume–area scaling show similar uncertainties to those of the volume changes. This paper does not aim at making a final judgement on the suitability of volume–area scaling as such, but provides the means for assessing the accuracy expected from its application.

ILI-Related School Dismissal Monitoring System: An Overview and Assessment

2012 ◽  
Vol 6 (2) ◽  
pp. 104-112 ◽  
Author(s):  
Laura Kann ◽  
Steve Kinchen ◽  
Bill Modzelski ◽  
Madeline Sullivan ◽  
Dana Carr ◽  
...  
Keyword(s):  
United States ◽  
Monitoring System ◽  
Situational Awareness ◽  
The United States ◽  
State Monitoring ◽  
Direct Reports ◽  
School Year ◽  
The Us ◽  
Us Government ◽  
The Impact

ABSTRACTObjective: This report provides an overview and assessment of the School Dismissal Monitoring System (SDMS) that was developed by the Centers for Disease Control and Prevention (CDC) and the US Department of Education (ED) to monitor influenza-like illness (ILI)-related school dismissals during the 2009-2010 school year in the United States.Methods: SDMS was developed with considerable consultation with CDC's and ED's partners. Further, each state appointed a single school dismissal monitoring contact, even if that state also had its own school-dismissal monitoring system in place. The SDMS received data from three sources: (1) direct reports submitted through CDC's Web site, (2) state monitoring systems, and (3) media scans and online searches. All cases identified through any of the three data sources were verified.Results: Between August 3, 2009, and December 18, 2009, a total of 812 dismissal events (ie, a single school dismissal or dismissal of all schools in a district) were reported in the United States. These dismissal events had an impact on 1947 schools, approximately 623 616 students, and 40 521 teachers.Conclusions: The SDMS yielded real-time, national summary data that were used widely throughout the US government for situational awareness to assess the impact of CDC guidance and community mitigation efforts and to inform the development of guidance, resources, and tools for schools.(Disaster Med Public Health Preparedness. 2012;6:104-112)

scholarly journals Looking in the medicine cabinet: methods for using real-world data to assess the impact of measles, mumps and rubella (MMR) and recombinant adjuvanted varicella-zoster vaccines on coronavirus disease 2019 (COVID-19) prevention and case fatality

2021 ◽  
Vol 5 ◽  
pp. 115
Author(s):  
Douglas McNair ◽  
Hao Hu ◽  
Casey Selwyn
Keyword(s):  
Real World ◽  
Case Fatality ◽  
Data Interpretation ◽  
The United States ◽  
Zoster Vaccine ◽  
Real World Data ◽  
World Data ◽  
Varicella Zoster ◽  
Viral Vaccines ◽  
The Impact

Background: Analysis of real-world data can be used to identify promising leads and dead ends among products being repurposed for clinical practice for coronavirus disease 2019 (COVID-19).  This paper uses real-world data from Cerner Labs collected from 90 source institutions in the United States to assess the potential impact of live viral vaccines on COVID-19 case fatality rates. Methods: We identified 373,032 polymerase chase reaction (PCR)-positive COVID-19 cases in the Cerner Labs database between 01-MAR-2020 and 31-DEC-2020 and identified patients that had received measles, mumps and rubella (MMR) or a recombinant adjuvanted varicella-zoster vaccine within the previous 5 years. We calculated heterogeneity scores to support interpretation of results across institutions, and used stepwise forward variable selection to construct covariable-based propensity scores. These scores were used to match cases and control for biasing and confounding issues inherent in observational data. Results: Neither the recombinant adjuvanted varicella-zoster vaccine nor MMR showed significant efficacy in prevention of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We could not derive clinically significant results on the impact of MMR for case fatality rates due to persistently high rates of heterogeneity between institutions. However, we were able to achieve acceptable levels of heterogeneity for the analysis of the recombinant adjuvanted varicella-zoster vaccine, and found a clinically meaningful benefit of reduced case fatality rate, with an odds ratio of 0.43 (95% confidence interval [CI]: 0.38 – 0.48). Conclusions: Using propensity score matching and heterogeneity statistics can help guide our interpretation of real-world data, and rigorous statistical methods are needed to reduce bias or disparities in data interpretation. Applying these methods to the impact of live viral vaccines on COVID-19 case fatalities yields actionable findings for further analysis.

scholarly journals The impact of race and ethnicity on outcomes in 19,584 adults hospitalized with COVID-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254809
Author(s):  
Ann M. Navar ◽  
Stacey N. Purinton ◽  
Qingjiang Hou ◽  
Robert J. Taylor ◽  
Eric D. Peterson
Keyword(s):  
United States ◽  
Race And Ethnicity ◽  
The United States ◽  
Lower Mortality ◽  
Real World Data ◽  
Black Adults ◽  
Increased Risk ◽  
Hispanic Patients ◽  
The Impact ◽  
White Patients

Introduction At the population level, Black and Hispanic adults in the United States have increased risk of dying from COVID-19, yet whether race and ethnicity impact on risk of mortality among those hospitalized for COVID-19 is unclear. Methods Retrospective cohort study using data on adults hospitalized with COVID-19 from the electronic health record from 52 health systems across the United States contributing data to Cerner Real World DataTM. In-hospital mortality was evaluated by race first in unadjusted analysis then sequentially adjusting for demographics and clinical characteristics using logistic regression. Results Through August 2020, 19,584 patients with median age 52 years were hospitalized with COVID-19, including n = 4,215 (21.5%) Black and n = 5,761 (29.4%) Hispanic patients. Relative to white patients, crude mortality was slightly higher in Black adults [22.7% vs 20.8%, unadjusted OR 1.12 (95% CI 1.02–1.22)]. Mortality remained higher among Black adults after adjusting for demographic factors including age, sex, date, region, and insurance status (OR 1.13, 95% CI 1.01–1.27), but not after including comorbidities and body mass index (OR 1.07, 95% CI 0.93–1.23). Compared with non-Hispanic patients, Hispanic patients had lower mortality both in unadjusted and adjusted models [mortality 12.7 vs 25.0%, unadjusted OR 0.44(95% CI 0.40–0.48), fully adjusted OR 0.71 (95% CI 0.59–0.86)]. Discussion In this large, multicenter, EHR-based analysis, Black adults hospitalized with COVID-19 had higher observed mortality than white patients due to a higher burden of comorbidities in Black adults. In contrast, Hispanic ethnicity was associated with lower mortality, even in fully adjusted models.

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