scholarly journals Novel Uses of Three-Parameter Logistic Models and First-Derivative Models for the Coronavirus Disease (COVID-19) Epidemic in the United States, in Three Distinct Scenarios

2020 ◽  
Author(s):  
Bishoy T. Samuel
Keyword(s):  
Logistic Model ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Logistic Models ◽  
The United States ◽  
Fatality Rate ◽  
First Derivative ◽  
The Third ◽  
Variable Versus ◽  
Growth Deceleration

Abstract Background:Forecasting the current coronavirus disease (COVID-19) epidemic in the United States necessitates novel mathematical models for accurate predictions. This paper examines novel uses of three-parameter logistic models and first-derivative models through three distinct scenarios that have not been examined in the literature as of July 14, 2020.Methods:Using publicly available data, statistical software was used to conduct a non-linear least-squares estimate to generate a three-parameter logistic model, with a subsequently generated first-derivative model. In the first scenario a logistic model was used to examine the natural log of COVID-19 cases as the dependent variable (versus day number), on July 11 and May 1. Independent t-test analyses were used to test comparative coefficient differences across models. In the second scenario, a first-derivative model was derived from a base three-parameter logistic model for April 27, examining time to peak mortality and decrease in case fatality rate. In the third scenario, a first-derivative model of mortality through July 11 as the dependent variable, versus confirmed cases, was generated to look at case fatality rate relative to increasing cases.Results:All models generated were statistically significant with R2 > 99%. The logistic models in the first scenario best predicted time to growth deceleration in the natural log of cases in the U.S. (slowing of exponential growth), estimated at March 11, 2020. For the May 1 data, independent t-test analyses of comparative coefficients across models were useful to track improvements from implemented public health measures. The first-derivative model in the second scenario on April 27, when the epidemic was more controlled, showed peak mortality around April 12-13, with a case fatality rate of < 1,000 deaths and trending down. The first-derivative model in the third scenario estimated a near-zero case fatality rate to occur at 4 million confirmed cases. It has not been affected by fluctuations in mortality from June 29 through July 11.Conclusion:Three-parameter logistic models and first-derivative models have utility in predicting time to growth deceleration, and case fatality rates relative to cases. They can objectively assess improvements of implemented epidemiologic measures and have applicable public health safety implications.

scholarly journals Novel uses of three-parameter logistic models and first-derivative models for the Coronavirus Disease (COVID-19) epidemic in the United States, in three distinct scenarios

2020 ◽  
Author(s):  
Bishoy T. Samuel
Keyword(s):  
Logistic Model ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Logistic Models ◽  
The United States ◽  
Fatality Rate ◽  
First Derivative ◽  
The Third ◽  
Variable Versus ◽  
Growth Deceleration

Abstract Background Forecasting the current coronavirus disease (COVID-19) epidemic in the United States necessitates novel mathematical models for accurate predictions. This paper examines novel uses of three-parameter logistic models and first-derivative models through three distinct scenarios that have not been examined in the literature as of July 14, 2020. Methods Using publicly available data, statistical software was used to conduct a non-linear least-squares estimate to generate a three-parameter logistic model, with a subsequently generated first-derivative model. In the first scenario a logistic model was used to examine the natural log of COVID-19 cases as the dependent variable (versus day number), on July 11 and May 1. Independent t-test analyses were used to test comparative coefficient differences across models. In the second scenario, a first-derivative model was derived from a base three-parameter logistic model for April 27, examining time to peak mortality and decrease in case fatality rate. In the third scenario, a first-derivative model of mortality through July 11 as the dependent variable, versus confirmed cases, was generated to look at case fatality rate relative to increasing cases. Results All models generated were statistically significant with R2 > 99%. The logistic models in the first scenario best predicted time to growth deceleration in the natural log of cases in the U.S. (slowing of exponential growth), estimated at March 11, 2020. For the May 1 data, independent t-test analyses of comparative coefficients across models were useful to track improvements from implemented public health measures. The first-derivative model in the second scenario on April 27, when the epidemic was more controlled, showed peak mortality around April 12-13, with a case fatality rate of < 1,000 deaths and trending down. The first-derivative model in the third scenario estimated a near-zero case fatality rate to occur at 4 million confirmed cases. It has not been affected by fluctuations in mortality from June 29 through July 11. Conclusion Three-parameter logistic models and first-derivative models have utility in predicting time to growth deceleration, and case fatality rates relative to cases. They can objectively assess improvements of implemented epidemiologic measures and have applicable public health safety implications.

scholarly journals Novel uses of three-parameter logistic models and first-derivative models for the Coronavirus Disease (COVID-19) epidemic in the United States, in three distinct scenarios

2020 ◽  
Author(s):  
Bishoy T. Samuel
Keyword(s):  
Logistic Model ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Logistic Models ◽  
The United States ◽  
Fatality Rate ◽  
First Derivative ◽  
The Third ◽  
Variable Versus ◽  
Growth Deceleration

Abstract Background: Forecasting the current coronavirus disease (COVID-19) epidemic in the United States necessitates novel mathematical models for accurate predictions. This paper examines novel uses of three-parameter logistic models and first-derivative models through three distinct scenarios that have not been examined in the literature as of July 14, 2020.Methods: Using publicly available data, statistical software was used to conduct a non-linear least-squares estimate to generate a three-parameter logistic model, with a subsequently generated first-derivative model. In the first scenario a logistic model was used to examine the natural log of COVID-19 cases as the dependent variable (versus day number), on July 11 and May 1. Independent t-test analyses were used to test comparative coefficient differences across models. In the second scenario, a first-derivative model was derived from a base three-parameter logistic model for April 27, examining time to peak mortality and decrease in case fatality rate. In the third scenario, a first-derivative model of mortality through July 11 as the dependent variable, versus confirmed cases, was generated to look at case fatality rate relative to increasing cases.Results: All models generated were statistically significant with R2 > 99%. The logistic models in the first scenario best predicted time to growth deceleration in the natural log of cases in the U.S. (slowing of exponential growth), estimated at March 11, 2020. For the May 1 data, independent t-test analyses of comparative coefficients across models were useful to track improvements from implemented public health measures. The first-derivative model in the second scenario on April 27, when the epidemic was more controlled, showed peak mortality around April 12-13, with a case fatality rate of < 1,000 deaths and trending down. The first-derivative model in the third scenario estimated a near-zero case fatality rate to occur at 4 million confirmed cases. It has not been affected by fluctuations in mortality from June 29 through July 11.Conclusion: Three-parameter logistic models and first-derivative models have utility in predicting time to growth deceleration, and case fatality rates relative to cases. They can objectively assess improvements of implemented epidemiologic measures and have applicable public health safety implications.

scholarly journals Covid-19 Fatality Rate in Third World Countries: A Review of Environmental Challenges and Impacts on Public Health and Human Security

2021 ◽  
Vol 9 (1) ◽  
pp. 39-44
Author(s):  
Chukwuemeka E. Etodike ◽  
◽  
Elsie C. Ekeghalu ◽  
Kelechi Johnmary Ani ◽  
Emmanuel Mutambara
Keyword(s):  
Public Health ◽  
Case Fatality Rate ◽  
Third World ◽  
Case Fatality ◽  
Personal Hygiene ◽  
Fatality Rate ◽  
Environmental Challenges ◽  
The Third ◽  
The Third World ◽  
Sanitary Conditions

The novel coronavirus is far from being over; with the case-fatality rate (CFR) hitting more than 16,500 globally as of July, there is a worry that despite the fact that the global CFR curve is showing signs of flattening, the environmental peculiarities of the third world countries may be abetting global efforts towards containing the virus. Therefore, this review x-rayed these peculiarities in the light of their current concern in public health as per their contribution to the persistent surge in CFR in most developing nations. Given that the virus is transmitted via droplets, the review focused on how the state of public and environmental challenges such as air as well as water pollution and personal hygiene could be abetting the surge in coronavirus infections and morbidity. The review revealed, among other things, that challenges associated with poor sanitary conditions, lack of potable water, unventilated environments, air pollution, and poor inter-personal hygiene are devastating challenges in the fight against the pandemic. The implication is that since these conditions are systematic in nature, it may take more than average effort and public sacrifice to checkmate the case-fatality rate of the virus in the third world. Therefore, call for studies is necessary to establish empiricism for CFR patterns and ratio across areas in deplorable environmental and sanitary conditions.

CASE FATALITY IN INFANTS AND CHILDREN WITH PERTUSSIS, 1942-1946

PEDIATRICS ◽  
1950 ◽  
Vol 5 (5) ◽  
pp. 840-852
Author(s):  
JEROME L. KOHN ◽  
ALFRED E. FISCHER ◽  
HERBERT H. MARKS
Keyword(s):  
United States ◽  
Case Fatality Rate ◽  
Communicable Disease ◽  
Case Fatality ◽  
The United States ◽  
Fatality Rate ◽  
Older Children ◽  
Severity Of The Disease ◽  
One Year ◽  
In Infants And Children

Analysis of data on patients with pertussis during 1942-1946 obtained by means of a questionnaire from communicable disease hospitals and from health officers in a number of cities in the United States and Canada showed these results: Case fatality rates of patients admitted to hospitals for treatment have declined substantially in the period under review. This decline is general, both among infants under one year of age and among older children. In 1946, the case fatality rate of the infants hospitalized for the disease was 5.0% in those cities for which data for at least four years were available. This may be compared with the rate of 7.8% in 1942 and 11.1% in 1943. At ages one year and over, the rate was only 1.3% in 1946, as compared with 1.7% in 1942 and 3.7% in 1943. The rates in the hospitals with larger experiences were generally more favorable than in hospitals with smaller experiences. Despite the incomplete reporting of pertussis, which results in exaggerating the case fatality rate for the general population, the level of these rates in the community as a whole was lower than for hospitalized cases. This reflects the higher proportion of the severer cases in the hospitalized group. Indications are that in many places hospitalization is limited more and more to severe cases. Progress in the management of pertussis, especially of the severer cases admitted to hospitals, is believed to be the chief factor in the decline in case fatality of pertussis. A request contained in the questionnaire for an opinion on the severity of pertussis during the period studied elicited few replies, and these replies showed a division of opinion on the matter. It appears unlikely that there has been much of any change in the severity of the disease.

scholarly journals Clinical Characteristics and Outcomes of COVID-19–Infected Cancer Patients: A Systematic Review and Meta-Analysis

2020 ◽  
Author(s):  
Hua Zhang ◽  
Han Han ◽  
Tianhui He ◽  
Kristen E Labbe ◽  
Adrian V Hernandez ◽  
...  
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Case Fatality Rate ◽  
Meta Analysis ◽  
Univariate Analysis ◽  
Case Fatality ◽  
The United States ◽  
Fatality Rate ◽  
Patients With Cancer ◽  
Increased Risk

Abstract Background Previous studies have indicated coronavirus disease 2019 (COVID-19) patients with cancer have a high fatality rate. Methods We conducted a systematic review of studies that reported fatalities in COVID-19 patients with cancer. A comprehensive meta-analysis that assessed the overall case fatality rate and associated risk factors was performed. Using individual patient data, univariate and multivariable logistic regression analyses were used to estimate odds ratios (OR) for each variable with outcomes. Results We included 15 studies with 3019 patients, of which 1628 were men; 41.0% were from the United Kingdom and Europe, followed by the United States and Canada (35.7%), and Asia (China, 23.3%). The overall case fatality rate of COVID-19 patients with cancer measured 22.4% (95% confidence interval [CI] = 17.3% to 28.0%). Univariate analysis revealed age (OR = 3.57, 95% CI = 1.80 to 7.06), male sex (OR = 2.10, 95% CI = 1.07 to 4.13), and comorbidity (OR = 2.00, 95% CI = 1.04 to 3.85) were associated with increased risk of severe events (defined as the individuals being admitted to the intensive care unit, or requiring invasive ventilation, or death). In multivariable analysis, only age greater than 65 years (OR = 3.16, 95% CI = 1.45 to 6.88) and being male (OR = 2.29, 95% CI = 1.07 to 4.87) were associated with increased risk of severe events. Conclusions Our analysis demonstrated that COVID-19 patients with cancer have a higher fatality rate compared with that of COVID-19 patients without cancer. Age and sex appear to be risk factors associated with a poorer prognosis.

Incidence of and Risk Factors for Nosocomial Bloodstream Infections in Adults in the United States, 2003

2009 ◽  
Vol 30 (11) ◽  
pp. 1036-1044 ◽  
Author(s):  
Omar M. AL-Rawajfah ◽  
Frank Stetzer ◽  
Jeanne Beauchamp Hewitt
Keyword(s):  
United States ◽  
Risk Factors ◽  
Case Fatality Rate ◽  
Nationwide Inpatient Sample ◽  
Hospital Admissions ◽  
Case Fatality ◽  
The United States ◽  
Fatality Rate ◽  
The Us ◽  
National Estimates

Background.Although many studies have examined nosocomial bloodstream infection (BSI), US national estimates of incidence and case-fatality rates have seldom been reported.Objective.The purposes of this study were to generate US national estimates of the incidence and severity of nosocomial BSI and to identify risk factors for nosocomial BSI among adults hospitalized in the United States on the basis of a national probability sample.Methods.This cross-sectional study used the US Nationwide Inpatient Sample for the year 2003 to estimate the incidence and case-fatality rate associated with nosocomial BSI in the total US population. Cases of nosocomial BSI were defined by using 1 or more International Classification of Diseases, 9th Revision, Clinical Modification codes in the secondary field(s) that corresponded to BSIs that occurred at least 48 hours after admission. The comparison group consisted of all patients without BSI codes in their NIS records. Weighted data were used to generate US national estimates of nosocomial BSIs. Logistic regression was used to identify independent risk factors for nosocomial BSI.Results.The US national estimated incidence of nosocomial BSI was 21.6 cases per 1,000 admissions, while the estimated case-fatality rate was 20.6%. Seven of the 10 leading causes of hospital admissions associated with nosocomial BSI were infection related. We estimate that 541,081 patients would have acquired a nosocomial BSI in 2003, and of these, 111,427 would have died. The final multivariate model consisted of the following risk factors: central venous catheter use (odds ratio [OR], 4.76), other infections (OR, 4.61), receipt of mechanical ventilation (OR, 4.97), trauma (OR, 1.98), hemodialysis (OR, 4.83), and malnutrition (OR, 2.50). The total maximum rescaled R2 was 0.22.Conclusions.The Nationwide Inpatient Sample was useful for estimating national incidence and case-fatality rates, as well as examining independent predictors of nosocomial BSI.

scholarly journals The influence of SARS-CoV-2 variants of concern on national case fatality rates

2021 ◽  
Author(s):  
William A. Barletta
Keyword(s):  
Case Fatality Rate ◽  
Temporal Dynamics ◽  
Case Fatality ◽  
Quantitative Measure ◽  
The United States ◽  
World Health ◽  
Fatality Rate ◽  
Wide Spread ◽  
The Face ◽  
Health Organization

AbstractBackgroundDuring 2021 several new variants of the SARS-CoV-2 virus appeared with both increased levels of transmissibility and virulence with respect to the original wild variant. The Delta (B.1.617.2) variation, first seen in India, dominates COVID-19 infections in several large countries including the United States and India. Most recently, the Lambda variant of interest with increased resistance to vaccines has spread through much of South America.ObjectiveThis research explores the degree to which new variants of concern 1) generate spikes and waves of fluctuations in the daily case fatality rates (CFR) across countries in several regions in the face of increasing levels of vaccination of national populations and 2) may increase the vulnerability of persons with certain comorbidities.MethodsThis study uses new, openly available, epidemiological statistics reported to the relevant national and international authorities for countries across the Americas, Europe, Africa, Asia and the Middle East. Daily CFRs and correlations of fatal COVID-19 infections with potential cofactors are computed for the first half of 2021 that has been dominated by the wide spread of several “variants of concern” as denoted by the World Health Organization.ResultsThe analysis yields a new quantitative measure of the temporal dynamics of mortality due to SARS-CoV-2 infections in the form of variations of a proxy case fatality rate compared on a country to-country basis in the same region. It also finds minimal variation of correlation between the cofactors based on WHO data and on the average apparent case fatality rate.

scholarly journals 104. Plague Meningitis - A Systematic Review of Published Cases, Antimicrobial Treatment, and Outcomes

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S65-S65
Author(s):  
Katharine Cooley ◽  
Shannon Fleck-Derderian ◽  
Christina Nelson
Keyword(s):  
Systematic Review ◽  
Case Fatality Rate ◽  
Case Reports ◽  
Treatment Strategies ◽  
Case Fatality ◽  
The United States ◽  
Antimicrobial Treatment ◽  
Recent Analysis ◽  
Fatality Rate ◽  
Bubonic Plague

Abstract Background Plague meningitis is a rare but serious manifestation of infection with the bacterium Yersinia pestis. The risk factors, clinical evolution, and optimal treatment strategies of plague meningitis are not well understood, and data is limited to sporadic case reports. To advance knowledge of this condition and support clinical practice recommendations, we conducted a systematic review of published cases of plague meningitis. Methods We reviewed PubMed Central, Medline, Embase, and other databases for publications on plague meningitis in any language. Articles that contained reports of patients with plague meningitis plus information on patient outcome were included. Results Among 1,090 articles identified in our search, we found 54 articles describing 83 cases eligible for inclusion. Cases occurred between 1898 and 2015; mean age of patients was 20.5 years (range 6 wks - 64 yrs) and 65% were male. Most patients lived in the United States (23%), Argentina (18%), Vietnam (12%), or China (12%). Four patients (5%) had primary plague meningitis. More than half (59%) of patients developed meningitis secondary to primary bubonic plague; the remainder developed meningitis secondary to other or unknown forms of plague. Of patients with a bubo, 51% had an axillary bubo. The most common symptoms were fever (66%), nuchal rigidity (43%), and headache (35%); 23 patients had focal neurologic deficits such as cranial nerve abnormality. Case fatality rate was 96% (n=23/24) for patients who did not receive antimicrobial treatment and 42% (n=25/59) for patients treated with antimicrobials. Case fatality rate by antimicrobial received, including patients who received multiple antimicrobial classes, was 50% for sulfonamides (n= 38), 50% for fluoroquinolones (n=2), 19% for aminoglycosides (n=21), 11% for chloramphenicol (n=19), and 0% for tetracyclines (n=14). Conclusion Plague meningitis has a high fatality rate, but antimicrobial treatment can improve patient outcomes. Having an axillary bubo may be a risk factor for developing plague meningitis – in contrast to our findings, a recent analysis found that only 24% of patients with bubonic plague had buboes in the axillary region. Additional research would be helpful to investigate this association further. Disclosures All Authors: No reported disclosures

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 Omicron Impact in India: An Early Analysis of the Ongoing COVID-19 Third Wave

2022 ◽  
Author(s):  
Rajesh Ranjan
Keyword(s):  
Rapid Growth ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
Sir Model ◽  
Third Wave ◽  
Fatality Rate ◽  
Sequencing Data ◽  
The Third ◽  
Second Wave

India is currently experiencing the third wave of COVID-19, which began on around 28 Dec. 2021. Although genome sequencing data of a sufficiently large sample is not yet available, the rapid growth in the daily number of cases, comparable to South Africa, United Kingdom, suggests that the current wave is primarily driven by the Omicron variant. The logarithmic regression suggests the growth rate of the infections during the early days in this wave is nearly four times than that in the second wave. Another notable difference in this wave is the relatively concurrent arrival of outbreaks in all the states; the effective reproduction number (Rt) although has significant variations among them. The test positivity rate (TPR) also displays a rapid growth in the last 10 days in several states. Preliminary estimates with the SIR model suggest that the peak to occur in late January 2022 with peak caseload exceeding that in the second wave. Although the Omicron trends in several countries suggest a decline in case fatality rate and hospitalizations compared to Delta, a sudden surge in active caseload can temporarily choke the already stressed healthcare India is currently experiencing the third wave of COVID-19, which began on around 28 Dec. 2021. Although genome sequencing data of a sufficiently large sample is not yet available, the rapid growth in the daily number of cases, comparable to South Africa, United Kingdom, suggests that the current wave is primarily driven by the Omicron variant. The logarithmic regression suggests the growth rate of the infections during the early days in this wave is nearly four times than that in the second wave. Another notable difference in this wave is the relatively concurrent arrival of outbreaks in all the states; the effective reproduction number (Rt) although has significant variations among them. The test positivity rate (TPR) also displays a rapid growth in the last 10 days in several states. Preliminary estimates with the SIR model suggest that the peak to occur in late January 2022 with peak caseload exceeding that in the second wave. Although the Omicron trends in several countries suggest a decline in case fatality rate and hospitalizations compared to Delta, a sudden surge in active caseload can temporarily choke the already stressed healthcare infrastructure. Therefore, it is advisable to strictly adhere to COVID-19 appropriate behavior for the next few weeks to mitigate an explosion in the number of infections.

Sign in / Sign up

Export Citation Format

Share Document