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.

Fatality in Kikuchi-Fujimoto disease: A rare phenomenon

Kikuchi-Fujimoto disease(KFD), also known as histiocytic necrotizing lymphadenitis, is an uncommon condition, typically characterized by lymphadenopathy and fevers. It usually has a benign course; however, it may progress to fatality in extremely rare occasions. The diagnosis is made via lymph node biopsy and histopathology. Our patient was a young female who presented with shortness of breath, fever, and malaise. Physical examination revealed significant cervical and axillary lymphadenopathy. Chest X-ray displayed multilobar pneumonia. She required intubation and mechanical ventilation for progressive respiratory distress. Histopathology of lymph nodes demonstrated variable involvement of patchy areas of necrosis within the paracortex composed of karyorrhectic debris with abundant histiocytes consistent with KFD. After initial stabilization, the patient's condition quickly deteriorated with acute anemia, thrombocytopenia and elevated prothrombin time, partial prothrombin time, and D-dimer levels. Disseminated intravascular coagulopathy(DIC) ensued resulting in the patient's fatality. DIC in KFD is not well understood, but it is an important cause of mortality in patients with aggressive disease.

Fatality from Road Traffic Accident in Guinea: A Retrospective Descriptive Analysis

Objectives: Causes and risk factors that result in fatal road traffic accident have not been described at the national level in Guinea yet. The goal of this study is to explore the causes and risk factors related to fatal road traffic accident, identified most vulnerable road users, and inform the road traffic prevention policy in Guinea. Methods: We made a retrospective descriptive analysis based on national fatal road traffic accident data from the Department of Health Information at the Guinean Ministry of Health for year 2011. Results: In 2011, road traffic accident was responsible for an aggregate number of 1655 deaths with an overall death rate of 15.3 per 100,000 population. Male experienced more than twice the risk of death from road traffic accidents (21.9 deaths per 100,000 population) compared with female (9.0 deaths per 100,000 population). While taking the population as a whole, the highest death rate was found among the middle aged in 35 - 49 age group accounting for (29.7 deaths per 100,000 population), followed successively by young adults age group 25 - 34 years (24.6 deaths per 100,000 population), and the middle aged in 50 - 64 age group (22.9 deaths per 100,000 population). Principally, occupants, motorcyclists and pedestrians sustained considerable burden of deaths respectively (9.2;2.9;2.2 per 100,000 population). In re-gional setting, the highest death rate was found in Upper Guinea (19.5 per 100,000 population), followed by Forest Guinea (18.7 per 100,000 population) and Middle Guinea (16.8 per 100,000 population). A large proportion of male was killed as motorcyclist than female while high per-centage of female died as occupant than male for all age group. The regional distribution showed that when a remarkable number of occupant death were observed in Upper and Forest Guinea, more people died as pedestrian and pedal cyclist in Conakry. Conclusions: This study demonstrated that most of the deaths were among occupants, motorcyclists and pedestrians, and the productive workforce aged 25 - 49 years. It was found that majority of the deaths happened in Upper Guinea followed by Forest Guinea. Improvement of roads design, strict enforcement of road safety laws and raising the awareness of general public about the causes and risks factors of road traffic accident through various channels are highly required which will promote economic growth in the local communities and then help people escape the poverty trap.

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.

Use of Finite Element Analysis for the Prediction of Driver Fatality Ratio Based on Vehicle Intrusion Ratio in Head-On Collisions

To estimate the aggressivity of vehicles in frontal crashes, national highway traffic safety administration (NHTSA) has introduced the driver fatality ratio, DFR, for different vehicle-to-vehicle categories. The DFR proposed by NHTSA is based on the actual crash statistical data, which makes it difficult to evaluate for other vehicle categories newly introduced to the market, as they do not have sufficient crash statistics. A finite element (FE) methodology is proposed in this study based on computational reconstruction of crashes and some objective measures to predict the relative risk of DFR associated with any vehicle-to-vehicle crash. The suggested objective measures include the ratios of maximum intrusion in the passenger compartments of the vehicles in crash, and the transmitted peak deceleration of the vehicles’ center of gravity, which are identified as the main influencing parameters on occupant injury. The suitability of the proposed method is established for a range of bullet light truck and van (LTV) categories against a small target passenger car with published data by NHTSA. A mathematical relation between the objective measures and DFR is then developed. The methodology is then extended to predict the relative risk of DFR for a crossover category vehicle, a light pick-up truck, and a mid-size car in crash against a small size passenger car. It is observed that the ratio of intrusions produces a reasonable estimate for the DFR, and that it can be utilized in predicting the relative risk of fatality ratios in head-on collisions. The FE methodology proposed in this study can be utilized in design process of a vehicle to reduce the aggressivity of the vehicle and to increase the on-road fleet compatibility in order to reduce the occupant injury out- come.

No Effects of Meteorological Factors on the SARS-CoV-2 Infection Fatality Rate

Objective Previous studies have shown that meteorological factors may increase COVID-19 mortality,likely due to the increased transmission of the virus.However,this could also be related to an increased infection fatality rate(IFR).We investigated the association between meteorological factors(temperature,humidity,solar irradiance,pressure,wind,precipitation,cloud coverage)and IFR across Spanish provinces(n=52)during the first wave of the pandemic(weeks 10–16 of 2020).Methods We estimated IFR as excess deaths(the gap between observed and expected deaths,considering COVID-19-unrelated deaths prevented by lockdown measures)divided by the number of infections(SARS-CoV-2 seropositive individuals plus excess deaths)and conducted Spearman correlations between meteorological factors and IFR across the provinces.Results We estimated 2,418,250 infections and 43,237 deaths.The IFR was 0.03%in<50-year-old,0.22%in 50–59-year-old,0.9%in 60–69-year-old,3.3%in 70–79-year-old,12.6%in 80–89-year-old,and26.5%in≥90-year-old.We did not find statistically significant relationships between meteorological factors and adjusted IFR.However,we found strong relationships between low temperature and unadjusted IFR,likely due to Spain’s colder provinces’aging population.Conclusion The association between meteorological factors and adjusted COVID-19 IFR is unclear.Neglecting age differences or ignoring COVID-19-unrelated deaths may severely bias COVID-19 epidemiological analyses.

A cohort study of hospitalized adult dengue patients with fatality in Taiwan: The elderly and febrile characteristics matter for prognosis

Objective:To identify the febrile characteristics and clinical presentations associated with fatality in hospitalized adult patients with dengue virus(DENV)infections.Methods:A total of 289 adult hospitalized patients with laboratoryconfirmed DENV infections were examined,of which 22 were fatal and 267 were non-fatal.A comparison of the clinical and laboratory characteristics was retrospectively conducted of the deceased and surviving individuals.Multivariate logistic regression and receiver operating characteristic curve analysis were performed to identify predictors of fatality.Results:Fatal patients exhibited significantly more comorbidities,particularly renal and cardiac comorbidities,and they were,in general,older than control individuals(P<0.0001).The results of logistic regression analysis showed that febrile duration of less than four days before arriving in the Emergency Department(OR=5.34;95%CI:1.39–20.6),episode of hypotension in the Emergency Department(OR=6.95;95%CI:2.40–20.1),and comorbidity with congestive heart failure(OR=11.26;95%CI:2.31–54.79)were all significantly associated with inpatient fatality due to DENV infection.The ROC curve analysis indicated that the final prognostic model yielded an area under the curve of 0.87(95%CI:0.79–0.97)for fatality.Conclusions:The aforementioned clinical findings may help clinicians predict fatality among adult inpatients with DENV infection.

Predicting COVID-19 fatality rate based on age group using LSTM

Objective: To predict the daily incidence and fatality rates based on long short-term memory(LSTM) in 4 age groups of COVID-19 patients in Mazandaran Province, Iran.Methods: To predict the daily incidence and fatality rates by age groups, this epidemiological study was conducted based on the LSTM model. All data of COVID-19 disease were collected daily for training the LSTM model from February 22, 2020 to April 10, 2021 in the Mazandaran University of Medical Sciences. We defined 4 age groups, i.e., patients under 29, between 30 and 49, between 50 and 59, and over 60 years old. Then, LSTM models were applied to predict the trend of daily incidence and fatality rates from 14 to 40 days in different age groups. The results of different methods were compared with each other.Results: This study evaluated 5 0826 patients and 5 109 deaths with COVID-19 daily in 20 cities of Mazandaran Province. Among the patients, 25 240 were females(49.7%), and 25 586 were males(50.3%). The predicted daily incidence rates on April 11, 2021 were 91.76, 155.84, 150.03, and 325.99 per 100 000 people, respectively;for the fourteenth day April 24, 2021, the predicted daily incidence rates were 35.91, 92.90, 83.74, and 225.68 in each group per 100 000 people. Furthermore, the predicted average daily incidence rates in 40 days for the 4 age groups were 34.25, 95.68, 76.43, and 210.80 per 100 000 people, and the daily fatality rates were 8.38, 4.18, 3.40, 22.53 per 100 000 people according to the established LSTM model. The findings demonstrated the daily incidence and fatality rates of 417.16 and 38.49 per 100 000 people for all age groups over the next 40 days. Conclusions: The results highlighted the proper performance of the LSTM model for predicting the daily incidence and fatality rates. It can clarify the path of spread or decline of the COVID-19 outbreak and the priority of vaccination in age groups.

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.

Analyzing Pedestrian Fatality Risk in Accidents at Mid-Blocks

Objective: Pedestrian safety is considered as one of the greatest concerns, especially for developing countries. In the year of 2015, about 48% pedestrian accidents with 56% fatalities occurred at mid-blocks in Beijing. Since the high frequency and fatality risk, this study focused on pedestrian accidents taking place at mid-blocks and aimed at identifying significant factors. Methods: Based on total 10,948 crash records, a binary logit model was established to explore the impact of various factors on the probability of pedestrian’s death. Furthermore, first-degree interaction effects were introduced into the basic model. The Hosmer-Lemeshow goodness-of-fit test was used to assess the model performance. Odds ratio was calculated for categorical variables to compare significant accident conditions with the conference level. Variables within consideration in this study included weather, area type, road type, speed limit, pedestrian location, lighting condition, vehicle type, pedestrian gender and pedestrian age. Results: The calibration results of the model show that the increased fatality chances of an accident at mid-blocks are associated with normal weather, rural area, two-way divided road, crossing elsewhere in carriageway, darkness (especially for no street lighting), light vehicle, large vehicle and male pedestrian. With road speed limit increasing by 10 km/h, the probability of death accordingly increases by 46%. Older victims have higher chances of being killed in a crash. Moreover, three interaction effects are found significant: rural area and two-way divided, rural area and crossing elsewhere as well as speed limit and pedestrian age. Conclusions: This study has analyzed police accident data and identified factors significant to the death probability of pedestrians in accidents occurred at mid-blocks. Recommendations and improving measures were proposed correspondingly. Behaviors of different road users at mid-blocks should be taken into account in the future research.

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.

Case fatality rate of coronavirus disease 2019(COVID-19) in Iran-a term of caution

Coronavirus disease 2019 (COVID-19) has spread to 72 countries by the time of writing this report on 4th March 2020[1].On 20th February 2020,the first two confirmed deaths from COVID-19were reported in Iran.Till 4th March 2020,2 922 confirmed and92 death cases have also been reported till 4th March 2020 in Iran(Figure 1)[1].A key question that remains unanswered or controversial among the public,media,and researchers is the exact COVID-19 case fatality rate (CFR) in Iran.Why does the CFR in Iran appear to be higher compared to the rest of the world until now?Or why the fatality rate is high at the beginning of the epidemic in Iran?

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.

Comparison of Block Design Nonparametric Subset Selection Rules Based on Alternative Scoring Rules

This article compares the size of selected subsets using nonparametric subset selection rules with two different scoring rules for the observations. The scoring rules are based on the expected values of order statistics of the uniform distribution (yielding rank values) and of the normal distribution (yielding normal score values). The comparison is made using state motor vehicle traffic fatality rates, published in a 2016 article, with fifty-one states (including DC as a state) and over a nineteen-year period (1994 through 2012). The earlier study considered four block design selection rules—two for choosing a subset to contain the “best” population (i.e., state with lowest mean fatality rate) and two for the “worst” population (i.e., highest mean rate) with a probability of correct selection chosen to be 0.90. Two selection rules based on normal scores resulted in selected subset sizes substantially smaller than corresponding rules based on ranks (7 vs. 16 and 3 vs. 12). For two other selection rules, the subsets chosen were very close in size (within one). A comparison is also made using state homicide rates, published in a 2022 article, with fifty states and covering eight years. The results are qualitatively the same as those obtained with the motor vehicle traffic fatality rates.

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.

Infection fatality rate and infection attack rate of COVID-19 in South American countries

Background:The ongoing COVID-19 pandemic hit South America badly with multiple waves.Different COVID-19 variants have been storming across the region,leading to more severe infections and deaths even in places with high vaccination coverage.This study aims to assess the spatiotemporal variability of the COVID-19 pandemic and estimate the infection fatality rate(IFR),infection attack rate(IAR)and reproduction number(R0)for twelve most affected South American countries.Methods:We fit a susceptible-exposed-infectious-recovered(SEIR)-based model with a time-varying transmission rate to the reported COVID-19 deaths for the twelve South American countries with the highest mortalities.Most of the epidemiological datasets analysed in this work are retrieved from the disease surveillance systems by the World Health Organization,Johns Hopkins Coronavirus Resource Center and Our World in Data.We investigate the COVID-19 mortalities in these countries,which could represent the situation for the overall South American region.We employ COVID-19 dynamic model with-and-without vaccination considering time-varying flexible transmission rate to estimate IFR,IAR and R0 of COVID-19 for the South American countries.Results:We simulate the model in each scenario under suitable parameter settings and yield biologically reasonable estimates for IFR(varies between 0.303% and 0.723%),IAR(varies between 0.03 and 0.784)and R0(varies between 0.7 and 2.5)for the 12 South American countries.We observe that the severity,dynamical patterns of deaths and time-varying transmission rates among the countries are highly heterogeneous.Further analysis of the model with the effect of vaccination highlights that increasing the vaccination rate could help suppress the pandemic in South America.Conclusions:This study reveals possible reasons for the two waves of COVID-19 outbreaks in South America.We observed reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions measures and human protective behavioral reaction to recent deaths.Thus,strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America.

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.