Country-based modelling of COVID-19 case fatality rate:A multiple regression analysis

BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.

SEIHCRD Model for COVID-19 Spread Scenarios,Disease Predictions and Estimates the Basic Reproduction Number,Case Fatality Rate,Hospital,and ICU Beds Requirement

We have proposed a new mathematical method,the SEIHCRD model,which has an excellent potential to predict the incidence of COVID-19 diseases.Our proposed SEIHCRD model is an extension of the SEIR model.Three-compartments have added death,hospitalized,and critical,which improves the basic understanding of disease spread and results.We have studiedCOVID-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.After estimating model parameters based on available clinical data,the modelwill propagate and forecast dynamic evolution.Themodel calculates the Basic reproduction number over time using logistic regression and the Case fatality rate based on the selected countries’age-category scenario.Themodel calculates two types of Case fatality rate one is CFR daily,and the other is total CFR.The proposed model estimates the approximate time when the disease is at its peak and the approximate time when death cases rarely occur and calculate how much hospital beds and ICU beds will be needed in the peak days of infection.The SEIHCRD model outperforms the classic ARXmodel and the ARIMA model.RMSE,MAPE,andRsquaredmatrices are used to evaluate results and are graphically represented using Taylor and Target diagrams.The result shows RMSE has improved by 56%–74%,and MAPE has a 53%–89%improvement in prediction accuracy.

A Study on the Global Scenario of COVID-19 Related Case Fatality Rate, Recovery Rate and Prevalence Rate and Its Implications for India—A Record Based Retrospective Cohort Study

Importance: Corona virus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pandemic claiming millions of lives since the first outbreak was reported in Wuhan, China during December 2019. It is thus important to make cross-country comparison of the relevant rates and understand the socio-demographic risk factors. Methods: This is a record based retrospective cohort study. Table 1 was extracted from https://www.worldometers.info/coronavirus/ and from the Corona virus resource center (Table 2, Figures 1-3), Johns Hopkins University. Data for Table 1 includes all countries which reported >1000 cases and Table 2 includes 20 countries reporting the largest number of deaths. The estimation of CFR, RR and PR of the infection, and disease pattern across geographical clusters in the world is presented. Results: From Table 1, we could infer that as on 4th May 2020, COVID-19 has rapidly spread world-wide with total infections of 3,566,423 and mortality of 248,291. The maximum morbidity is in USA with 1,188,122 cases and 68,598 deaths (CFR 5.77%, RR 15% and PR 16.51%), while Spain is at the second position with 247,122 cases and 25,264 deaths (CFR 13.71%, RR 38.75%, PR 9.78%). Table 2 depicts the scenario as on 8th October 2020, where-in the highest number of confirmed cases occurred in US followed by India and Brazil (cases per million population: 23,080, 5007 & 23,872 respectively). For deaths per million population: US recorded 647, while India and Brazil recorded 77 and 708 respectively. Conclusion: Studying the distribution of relevant rates across different geographical clusters plays a major role for measuring the disease burden, which in-turn enables implementation of appropriate public healthcare measures.

Subgroup comparison of COVID-19 case and mortality with associated factors in Mississippi: findings from analysis of the first four months of public data

We compared subgroup differences in COVID-19 case and mortality and investigated factors associated with case and mortality rate(MR)measured at the county level in Mississippi.Findings were based on data published by the Mississippi State Department of Health between March 11 and July 16,2020.The COVID-19 case rate and case fatality rate(CFR)differed by gender and race,while MR only differed by race.Residents aged 80 years or older and those who live in a non-metro area had a higher case rate,CFR,and MR.After controlling for selected factors,researchers found that the percent of residents who are obese,low income,or with certain chronic conditions were associated with the county COVID-19 case rate,CFR,and/or MR,though some were negatively related.The findings may help the state to identify counties with higher COVID-19 case rate,CFR,and MR based on county demographics and the degree of its chronic conditions.

A simple approach to estimate the instantaneous case fatality ratio:Using the publicly available COVID-19 surveillance data in Canada as an example

The case fatality ratio(CFR)is one of the key measurements to evaluate the clinical severity of infectious diseases.The CFR may vary due to change in factors that affect the mortality risk.In this study,we developed a simple likelihood-based framework to estimate the instantaneous CFR of infectious diseases.We used the publicly available COVID-19 surveillance data in Canada for demonstration.We estimated the mean fatality ratio of reported COVID-19 cases(rCFR)in Canada was estimated at 6.9%(95%CI:4.5e10.6).We emphasize the extensive implementation of the constructed instantaneous CFR that is to identify the key determinants affecting the mortality risk.

Estimation of the case fatality rate of COVID-19 epidemiological data in Nigeria using statistical regression analysis

Following the emergence of COVID-19 outbreak,numbers of studies have been conducted to curtail the global spread of the virus by identifying epidemiological changes of the disease through developing statistical models,estimation of the basic reproduction number,displaying the daily reports of confirmed and deaths cases,which are closely related to the present study.Reliable and comprehensive estimation method of the epidemiological data is required to understand the actual situation of fatalities caused by the epidemic.Case fatality rate(CFR)is one of the cardinal epidemiological parameters that adequately explains epidemiology of the outbreak of a disease.In the present study,we employed two statistical regression models such as the linear and polynomial models in order to estimate the CFR,based on the early phase of COVID-19 outbreak in Nigeria(44 days since first reported COVID-19 death).The estimate of the CFR was determined based on cumulative number of confirmed cases and deaths reported from 23 March to 30 April,2020.The results from the linear model estimated that the CFR was 3.11%(95%CI:2.59%-3.80%)with R2 value of 90%and p-value of<0.0001.The findings from the polynomial model suggest that the CFR associated with the Nigerian outbreak is 3.0%and may range from 2.23%to 3.42%with R2 value of 93%and p-value of<0.0001.Therefore,the polynomial regression model with the higher R2 value fits the dataset well and provides better estimate of CFR for the reported COVID-19 cases in Nigeria.

Mathematical Model of the Spread of the Coronavirus Disease 2019 (COVID-19) in Burkina Faso

In this paper, we develop a mathematical model of the COVID-19 pandemic in Burkina Faso. We use real data from Burkina Faso National Health Commission against COVID-19 to predict the dynamic of the disease and also the cumulative number of reported cases. We use public policies in model in order to reduce the contact rate, this allows to show how the reduction of the daily report of infectious cases goes, so we would like to draw the attention of decision makers for a rapid treatment of reported cases.

A new method for accurate calculation of case fatality rates during a pandemic:Mathematical deduction based on population-level big data

Background:During the course of an epidemic of a potentially fatal disease,it is difficult to accurately estimate the case fatality rate(CFR)because many calculation methods do not account for the delay between case confirmation and disease outcome.Taking the coronavirus disease-2019(COVID-19)as an example,this study aimed to develop a new method for CFR calculation while the pandemic was ongoing.Methods:We developed a new method for CFR calculation based on the following formula:number of deaths divided by the number of cases T days before,where T is the average delay between case confirmation and disease outcome.An objective law was found using simulated data that states if the hypothesized T is equal to the true T,the calculated real-time CFR remains constant;whereas if the hypothesized T is greater(or smaller)than the true T,the real-time CFR will gradually decrease(or increase)as the days progress until it approaches the true CFR.Results:Based on the discovered law,it was estimated that the true CFR of COVID-19 at the initial stage of the pandemic in China,excluding Hubei Province,was 0.8%;and in Hubei Province,it was 6.6%.The calculated CFRs predicted the death count with almost complete accuracy.Conclusions:The method could be used for the accurate calculation of the true CFR during a pandemic,instead of waiting until the end of the pandemic,whether the pandemic is under control or not.It could provide those involved in outbreak control a clear view of the timeliness of case confirmations.

COVID-19 case fatality risk by age and gender in a high testing setting in Latin America: Chile, March-August 2020

Background:Early severity estimates of coronavirus disease 2019(COVID-19)are critically needed to assess the potential impact of the on going pandemic in differe nt demographic groups.Here we estimate the real-time delayadjusted case fatality rate across nine age groups by gender in Chile,the country with the highest testing rate for COVID-19 in Latin America.Methods:We used a publicly available real-time daily series of age-stratified COVID-19 cases and deaths reported by the Ministry of Health in Chile from the beginning of the epidemic in March through August 31,2020.We used a robust likelihood function and a delay distribution to estimate real-time delay-adjusted case-fatality risk and estimate model parameters using a Monte Carlo Markov Chain in a Bayesian framework.

Finding the real COVID-19 case-fatality rates for SAARC countries

The crude case fatality rate(CFR),because of the calculation method,is the most accurate when the pandemic is over since there is a possibility of the delay between disease onset and outcomes.Adjusted crude CFR measures can better explain the pandemic situation by improving the CFR estimation.However,no study has thoroughly investigated the COVID-19 adjusted CFR of the South Asian Association For Regional Cooperation(SAARC)countries.This study estimated both survival interval and underreporting adjusted CFR of COVID-19 for these countries.Moreover,we assessed the crude CFR between genders and across age groups and observed the CFR changes due to the imposition of fees on COVID-19 tests in Bangladesh.Using the daily records up to October 9,we implemented a statistical method to remove the delay between disease onset and outcome bias,and due to asymptomatic or mild symptomatic cases,reporting rates lower than 50%(95%CI:10%–50%)bias in crude CFR.We found that Afghanistan had the highest CFR,followed by Pakistan,India,Bangladesh,Nepal,Maldives,and Sri Lanka.Our estimated crude CFR varied from 3.708%to 0.290%,survival interval adjusted CFR varied from 3.767%to 0.296%and further underreporting adjusted CFR varied from 1.096%to 0.083%.Furthermore,the crude CFRs for men were significantly higher than that of women in Afghanistan(4.034%vs.2.992%)and Bangladesh(1.739%vs.1.337%)whereas the opposite was observed in Maldives(0.284%vs.0.390%),Nepal(0.006%vs.0.007%),and Pakistan(2.057%vs.2.080%).Besides,older age groups had higher risks of death.Moreover,crude CFR increased from 1.261%to 1.572%after imposing the COVID-19 test fees in Bangladesh.Therefore,the authorities of countries with higher CFR should be looking for strategic counsel from the countries with lower CFR to equip themselves with the necessary knowledge to combat the pandemic.Moreover,caution is needed to report the CFR.

Quasi-Binomial Regression Model for the Analysis of Data with Extra-Binomial Variation

Objectives: Developing inference procedures on the quasi-binomial distribution and the regression model. Methods: Score testing and the method of maximum likelihood for regression parameters estimation. Data: Several examples are included, based on published data. Results: A quasi-binomial model is used to model binary response data which exhibit extra-binomial variation. A partial score test on the binomial hypothesis versus the quasi-binomial alternative is developed and illustrated on three data sets. The extended logit transformation on the binomial parameter is introduced and the large sample dispersion matrix of the estimated parameters is derived. The Nonlinear Mixed Procedure (NLMIXED) in SAS is shown to be very appropriate for the estimation of nonlinear regression.

Power-law growth of the COVID-19 fatality incidents in Europe

We report on the dynamic scaling of the diffusion growth phase of the COVID-19 epidemic in Europe.During this initial diffusion stage,the European countries implemented unprecedented mitigation polices to delay and suppress the disease contagion,although not in a uniform way or timing.Despite this diversity,we find that the reported fatality cases grow following a power law in all European countries we studied.The difference among countries is the value of the power-law exponent 3.5<α<8.0.This common attribute can prove a practical diagnostic tool,allowing reasonable predictions for the growth rate from very early data at a country level.We propose a model for the disease-causing interactions,based on a mechanism of human decisions and risk taking in interpersonal associations.The model describes the observed statistical distribution and contributes to the discussion on basic assumptions for homogeneous mixing or for a network perspective in epidemiological studies of COVID-19.

Study on the COVID-19 epidemic in China's Mainland between November 2022 and January 2023,with prediction of its tendency

The prediction system EpiSIX was used to study the COVID-19 epidemic in China's Mainland between November 2022 and January 2023,based on reported data from December 9,2022,to January 30,2023,released by The Chinese Center for Disease Control and Prevention on February 1,2023.Three kinds of reported data were used for model fitting:the daily numbers of positive nucleic acid tests and deaths,and the daily number of hospital beds taken by COVID-19 patients.It was estimated that the overall infection rate was 87.54%and the overall case fatality rate was 0.078%–0.116%(median 0.100%).Assuming that a new COVID-19 epidemic outbreak would start in March or April of 2023,induced by a slightly more infectious mutant strain,we predicted a possible large rebound between September and October 2023,with a peak demand of between 800,000 and 900,000 inpatient beds.If no such new outbreak was induced by other variants,then the current COVID-19 epidemic course in China's Mainland would remain under control until the end of 2023.However,it is suggested that the necessary medical resources be prepared to manage possible COVID-19 epidemic emergencies in the near future,especially for the period between September and October 2023.

An evaluation of mathematical models for the outbreak of COVID-19

Mathematical modelling performs a vital part in estimating and controlling the recent outbreak of coronavirus disease 2019(COVID-19).In this epidemic,most countries impose severe intervention measures to contain the spread of COVID-19.The policymakers are forced to make difficult decisions to leverage between health and economic development.How and when tomake clinical and public health decisions in an epidemic situation is a challenging question.The most appropriate solution is based on scientific evidence,which is mainly dependent on data and models.So one of the most critical problems during this crisis is whether we can develop reliable epidemiological models to forecast the evolution of the virus and estimate the effectiveness of various intervention measures and their impacts on the economy.There are numerous types of mathematical model for epidemiological diseases.In this paper,we present some critical reviews on mathematical models for the outbreak of COVID-19.Some elementary models are presented as an initial formulation for an epidemic.We give some basic concepts,notations,and foundation for epidemiological modelling.More related works are also introduced and evaluated by considering epidemiological features such as disease tendency,latent effects,susceptibility,basic reproduction numbers,asymptomatic infections,herd immunity,and impact of the interventions.

Global trends in COVID-19

The pandemic COVID-19 is certainly one of the most severe infectious diseases in human history.In the last 2 years,the COVID-19 pandemic has caused over 418.6 million confirmed cases and 5.8 million deaths world-wide.Young people make up the majority of all infected COVID-19 cases,but the mortality rate is relatively lower compared to older age groups.Currently,about 55.04%individuals have been fully vaccinated rapidly approaching to herd immunity globally.The challenge is that new SARS-CoV-2 variants with potential to evade immunity from natural infection or vaccine continue to emerge.Breakthrough infections have occurred in both SARS-CoV-2 naturally infected and vaccinated individuals,but breakthrough infections tended to exhibit mild or asymptomatic symptoms and lower mortality rates.Therefore,immunity from natural infection or vaccination can reduce SARS-CoV-2 pathogenicity,but neither can completely prevent SARS-CoV-2 infection/reinfection.Fortunately,the morbidity and mortality of COVID-19 continue to decline.The 7-day average cumulative case fatality of COVID-19 has decreased from 12.3%on the February 25,2020,to 0.27%on January 09,2022,which could be related to a decreased SARS-CoV-2 variant virulence,vaccine immunization,and/or better treatment of patients.In conclusion,elimination of SARS-CoV-2 in the world could be impossible or at least an arduous task with a long way to go.The best strategy to prevent COVID-19 pandemic is to expand inoculation rate of effective vaccines.As the population reaches herd immunity,the mortality rate of COVID-19 may continue to decrease,and COVID-19 could eventually become another common cold.

Impact of Lockdown Measures and Meteorological Parameters on the COVID-19 Incidence and Mortality Rate in Bangladesh

The coronavirus disease 2019(COVID-19)pandemic has become a public health crisis and a global catastrophe for human societies.In the absence of a vaccine,non-pharmaceutical interventions have been implemented across the world to reduce COVID-19 transmission.Recently,several studies have articulated the influence of meteorological parameters on COVID-19 infections in several countries.The purpose of this study was to investigate the effect of lockdown measures and meteorological parameters on COVID-19 daily confirmed cases and deaths in Bangladesh.Different parameters,such as case fatality rate,recovery rate,number of polymerase chain reaction tests,and percentages of confirmed cases were calculated for data covering March to September 2020.The meteorological data include daily average temperature,humidity,and wind speed,and their effects on COVID-19 data were analyzed after 0,3,7,and 14 days.A linear regression analysis revealed that all the studied meteorological parameters were positively correlated with the daily new cases and deaths in Bangladesh,while the highest correlations were observed for the 14 days incubation period.These results provide useful implications for the healthcare authorities to contain the pandemic in Bangladesh and beyond.