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.

Case Fatality Rate of Severe Acute Respiratory Syndromes in Beijing

To describe the case fatality rate of SARS in Beijing. Methods Data of SARS cases notified from Beijing Center for Disease Control and Prevention （BCDC） and supplemented by other channels were collected. The data were analyzed by rate calculation. Results The case fatality rate of SARS in Beijing was 7.66%, and had an ascending trend while the age of cases was getting older, and a descending trend while the epidemic developmem. The case fatality rate in Beijing was lower than that in other main epidemic countries or regions. Conclusions The risk of death increases with the increment of age of SARS patients. Beijing is successful in controlling and treating SARS.

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.

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.

Effect of in-hospital medical complications on case fatality post-acute ischemic stroke： data from the China National Stroke Registry

Background In-hospital medical complications are associated with poorer clinical outcomes for stroke patients after disease onset. However, few studies from China have reported the effect of these complications on the mortality of patients with acute ischemic stroke. In this prospective work, the China National Stroke Registry Study, we investigated the effect of medical complications on the case fatality of patients with acute ischemic stroke. Methods From September 2007 to August 2008, we prospectively obtained the data of patients with acute stroke from 132 clinical centers in China. Medical complications, case fatality and other information recorded at baseline, during hospitalisation, and at 3, 6, and 12 months after stroke onset. Multivariable Logistic regression was performed to analyze the effect of medical complications on the case fatality of patients with acute ischemic stroke. Results There were 39741 patients screened, 14526 patients with acute ischemic stroke recruited, and 11 560 ischemic stroke patients without missing data identified during the 12-month follow-up. Of the 11 560 ischemic patients, 15.8% （1826） had in-hospital medical complications. The most common complication was pneumonia （1373; 11.9% of patients）, followed by urinary tract infection and gastrointestinal bleeding. In comparison with patients without complications, stroke patients with complications had a significantly higher risk of death during their hospitalization, and at 3, 6 and 12 months post-stroke. Having any one in-hospital medical complication was an independent risk factor for death in patients with acute ischemic stroke during hospital period （adjusted OR=6.946; 95% CI 5.181 to 9.314）, at 3 months （adjusted OR=3.843; 95% C/3.221 to 4.584）, 6 months （adjusted OR=3.492; 95% CI 2.970 to 4.106）, and 12 months （adjusted OR= 3.511; 95% CI 3.021 to 4.080）. Having multiple complications strongly increased the death risk of patients. Conclusion Short-term and long-term outcomes of acute stroke patients are affected by in-hospital medical complications.

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.

Exploring the percentage of COVID-19 cases reported in the community in Canada and associated case fatality ratios

While surveillance can identify changes in COVID-19 transmission patterns over time and space,sections of the population at risk,and the efficacy of public health measures,reported cases of COVID-19 are generally understood to only capture a subset of the actual number of cases.Our primary objective was to estimate the percentage of cases reported in the general community,considered as those that occurred outside of long-term care facilities(LTCFs),in specific provinces and Canada as a whole.We applied a methodology using the delay-adjusted case fatality ratio(CFR)to all cases and deaths,as well as those representing the general community.Our second objective was to assess whether the assumed CFR(mean=1.38%)was appropriate for calculating underestimation of cases in Canada.Estimates were developed for the period from March 11th,2020 to September 16th,2020.Estimates of the percentage of cases reported(PrCR)and CFR varied spatially and temporally across Canada.For the majority of provinces,and for Canada as a whole,the PrCR increased through the early stages of the pandemic.The estimated PrCR in general community settings for all of Canada increased from 18.1%to 69.0%throughout the entire study period.Estimates were greater when considering only those data from outside of LTCFs.The estimated upper bound CFR in general community settings for all of Canada decreased from 9.07%on March 11th,2020 to 2.00%on September 16th,2020.Therefore,the true CFR in the general community in Canada was likely less than 2%on September 16th.According to our analysis,some provinces,such as Alberta,Manitoba,Newfoundland and Labrador,Nova Scotia,and Saskatchewan reported a greater percentage of cases as of September 16th,compared to British Columbia,Ontario,and Quebec.This could be due to differences in testing rates and criteria,demographics,socioeconomic factors,race,and access to healthcare among the provinces.Further investigation into these factors could reveal differences among provinces that could partially explain the variation in estimates of PrCR and CFR identified in our study.The estimates provide context to the summative state of the pandemic in Canada,and can be improved as knowledge of COVID-19 reporting rates and disease characteristics are advanced.

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.

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.

The cholera epidemic of 2004 in Douala, Cameroon: A lesson learned

Objective: To describe the outbreak of 2004 with a view of retrospectively identifying factors that might explain the low case fatality rate. Methods: Outbreak data from 4 915 Cholera patients from registers of the Regional Health Delegation in Douala were analyzed using SPSS. Chi-square test, univariate and multivariate analysis were applied. Results: The outbreak started January 2004, peaking at 187 cases per week in February. After a decrease in April, case numbers rose to 688 cases per week in June. The outbreak was over in September 2004( <10 cases per week). The case fatality rate was higher in treatment centers with fewer than one nurse per two patients, than in those with more nursing staff. A temporary staff reduction after the first wave of the epidemic was associated with the increase of the case fatality rate during the second wave. This increase was reversed after re-instating full staff capacity.Conclusions: Providing sufficient nursing staff helps to lower the case fatality rate of cholera. Besides a lack of staff, age above 40 years is a risk factor for death in this disease.

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.

Interest of Procalcitonin Measurement in Children with Cerebral Malaria in Southern Benin

Introduction: Cerebral malaria is a major complication of the Plasmodium falciparum infection with a high case fatality rate. The objective of this study was to determine the relationship between cerebral malaria and high serum procalcitonin (PCT) level in children. Method: This was a prospective descriptive and analytical cohort study conducted over 12 months, on a series of PCT blood tests in children aged 6 months to 15 years old hospitalized for cerebral malaria in the pediatric wards of four hospitals in southern Benin. The cerebral malaria diagnosis was done based on WHO criteria. Blood samples for PCT measurement were collected on admission, 24 hours and 48 hours after the malaria therapy initiation. Student’s test, Pearson’s chi2 test, Fisher’s test and Kruskal-Wallis test were used where appropriate. For all comparisons the difference was significant when p was less than 5%. Results: Sixty-five children were included in the study with a sex ratio of 1.41. The age group of children under 5 years was the most represented, at 57%. PCT levels were high in 92.3% of children at admission, 90.8% at 24 hours and 84.6% at 48 hours. Forty-nine children had a positive clinical outcome while 16 died (24.6%). PCT levels were generally high over the three days of hospitalization, but higher at admission in case of death (p = 0.000). The association between PCT level and parasitemia at admission was significant (p = 0.04). Conclusion: In the view of the results, blood PCT level measured at admission could be predictive of the disease outcome in children with cerebral malaria.

Early Warning Factors of Death in COVID-19 Patients

The infectious coronavirus disease 2019(COVID-19)has spread all over the world and been persistently evolving so far.The number of deaths in the whole world has been rising rapidly.However,the early warning factors for mortality have not been well ascertained.In this retrospective,single-centre cohort study,we included some adult inpatients(≥18 years old)with laboratory-confirmed COVID-19 from Renmin Hospital of Wuhan University who had been discharged or had died by Apr.8,2020.Demographic,clinical and laboratory data at admission were extracted from electronic medical records and compared between survivors and non-survivors.We used univariable analysis,Cox proportional hazard model analysis and receiver operating characteristic(ROC)curve to explore the early warning factors associated with in-hospital death.A total of 159 patients were included in this study,of whom 86 were discharged and 73 died in hospital.Hypertension(52.1%vs.29.1%,P=0.003)and coronary heart disease(28.8%vs.12.8%,P=0.012)were more frequent among non-survived patients than among survived patients.The proportions of patients with dyspnoea(67.1%vs.25.6%,P<0.001),chest distress(58.9%vs.26.7%,P<0.001)and fatigue(64.4%vs.25.6%,P<0.001)were significantly higher in the non-survived group than in the survived group.Regression analysis with the Cox proportional hazards mode revealed that increasing odds of in-hospital death were associated with higher IL-6(odds ratio 10.87,95%CI 1.41–83.59;P=0.022),lactate(3.59,1.71–7.54;P=0.001),older age(1.86,1.03–3.38;P=0.041)and lower lymphopenia(5.44,2.71–10.93;P<0.001)at admission.The areas under the ROC curve(AUCs)of IL-6,lymphocyte,age and lactate were 0.933,0.928,0.786 and 0.753 respectively.The AUC of IL-6 was significantly higher than that of age(z=3.332,P=0.0009)and lactate(z=4.441,P<0.0001)for outcome prediction.There was no significant difference between the AUCs of IL-6 and lymphocyte for outcome prediction(z=0.372,P=0.7101).It was concluded that the potential risk factors of higher IL-6,lactate,older age and lower lymphopenia at admission could help clinicians to identify patients with poor prognosis at an early stage.

Spatiotemporal patterns and trends of community transmission of the pandemic COVID-19 in South Asia: Bangladesh as a case study

South Asian(SA)countries have been fighting with the pandemic novel coronavirus disease 2019(COVID-19)since January 2020.Earlier,the country-specific descriptive study has been done.Nevertheless,as transboundary infection,the border sharing,shared cultural and behavioral practice,effects on the temporal and spatial distribution of COVID-19 in SA is still unveiled.Therefore,this study has been revealed the spatial hotspot along with descriptive output on different parameters of COVID-19 infection.We extracted data from theWHO and the worldometer database from the onset of the outbreak up to 15 May,2020.Europe has the highest case fatality rate(CFR,9.22%),whereas Oceania has the highest(91.15%)recovery rate from COVID-19.Among SA countries,India has the highest number of cases(85,790),followed by Pakistan(38,799)and Bangladesh(20,065).However,the number of tests conducted was minimum in this region in comparison with other areas.The highest CFR was recorded in India(3.21%)among SA countries,whereas Nepal and Bhutan had no death record due to COVID-19 so far.The recovery rate varies from 4.75%in the Maldives to 51.02%in Sri Lanka.In Bangladesh,community transmission has been recorded,and the highest number of cases were detected in Dhaka,followed by Narayanganj and Chattogram.We detected Dhaka and its surrounding six districts,namely Gazipur,Narsingdi,Narayanganj,Munshiganj,Manikganj,and Shariatpur,as the 99%confidence-based hotspot where Faridpur and Madaripur district as the 95%confidence-based spatial hotspots of COVID-19 in Bangladesh.However,we did not find any cold spots in Bangladesh.We identified three hotspots and three cold spots at different confidence levels in India.Findings from this study suggested the“Test,Trace,and Isolation”approach for earlier detection of infection to prevent further community transmission of COVID-19.

Screening for volatile sulphur compounds in a fatal accident case

Acute fatal poisoning due to the inhalation of toxic gas frequently occurs in China.Volatile sulphur compounds(VSCs)are toxic to humans.In fatal poisoning investigations,such as those in industrial settings,a number of VSCs,including methanethiol(MT),dimethyl sulphide(DMS),dimethyl disulphide(DMDS)and dimethyl trisulphide(DMTS),can coexist.To date,there is limited data regarding these compounds in post-mortem cases.In the present study,we report toxicological findings in a fatal accident case with two victims.Headspace gas chromatography/flame ionization detector with two columns of different polarities was utilized to screen MT,DMS,DMDS and DMTS in blood.The limits of detection in both methods were 0.05 mg/mL.No sulphur compounds were detected in the blood samples of the two victims.DMS and DMDS were detected in the lungs at concentrations of 0.5 and 1.3 mg/g and 2.2 and 4.1 mg/g,respectively.DMDS liver concentrations were 2.5 and 6.5 mg/g.In addition to hydrogen sulphide,screening for additional VSCs could help establish the cause of death.

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.