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.

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.

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 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.

儿童急性坏死性脑病26例临床特征及预后分析

目的总结儿童急性坏死性脑病(ANEC)的临床特征、治疗过程和预后。方法回顾性分析2016年6月至2022年12月确诊ANEC患儿的临床资料。对存活患儿进行电话随访,应用儿童整体表现分类量表(POPC)评估患儿生存质量。结果共纳入26例ANEC患儿,男女比例为1.6∶1,中位年龄36.0(23.0~69.0)个月。发病季节以夏季常见(42.3%),其次为冬季(34.6%);前驱感染以流感病毒感染多见(34.6%),其次为人类疱疹病毒6B型及新型冠状病毒。所有患儿均有发热伴不同程度意识障碍,24例出现惊厥。神经影像学以对称性丘脑受累(100%)为典型改变,多伴有基底节区(50.0%)、脑干(61.5%)、小脑(26.9%)等部位受累。23例患儿接受甲基泼尼松龙激素冲击治疗,住院病死率为23.1%(6/26),出院6个月后累积病死率为30.7%(8/26)。6个月后随访到14例存活患儿,11例接受康复治疗,其中POPC评估2分1例,3分4例,4分6例,5分1例,6分2例。3例新型冠状病毒感染后ANEC患儿3个月随访POPC评估结果为2分2例,6分1例。结论华南地区ANEC于夏季和冬季高发,新型冠状病毒感染可诱发ANEC;临床表现为发热、抽搐、意识障碍三联征,典型影像学改变为双侧丘脑受累。早期免疫治疗及综合康复训练有助于减少神经系统后遗症的严重程度。

泸定地震人员死亡、失联数量的变化特征及地质灾害对其的影响

搜集了泸定6.8级地震人员死亡和失联资料,通过与近10年典型破坏性地震震例进行案例对比,定量分析了泸定地震人员死亡、失联数量随时间变化的特征。结合应急救援工作实际,梳理、归纳主要原因,同时系统分析了地质灾害对人员死亡率及失联率的影响。探讨山区地震灾害及救灾特点,提出山区复杂环境下地震灾害信息核查报送及人员搜救能力提升建议。

全球COVID-19疫情主要预测模型比较分析

目的新冠感染病死率预测对于深入理解新冠病毒严重性、合理配置医疗资源及开展针对性防疫策略有重大意义。方法本研究依据新冠病毒变异优势株,将疫情发展划分四个时期,选取美国、印度、巴西、墨西哥、秘鲁、中国六个国家以及全球平均水平的病死率为研究对象。运用灰色模型、指数平滑模型、ARIMA模型、支持向量机、Prophet和LSTM模型六个模型进行拟合预测,探讨各模型的优缺点和适用性,选取效果最优的模型对全球和重点国家的病死率进行预测。结果模型比较显示多种模型各有优缺点,经预测,多数国家的累计确诊人数和累计死亡人数增长速度减缓,发展趋势逐渐平稳。结论传统时间序列模型适于发展趋势平稳、有限样本的预测;而机器学习模型更适用于波动型变化数据,可进行大样本预测,进一步外推,运用到其他卫生领域的研究。

2018—2022年某综合性三甲医院住院死亡病例分析

目的 分析2018—2022年某综合性三甲医院住院患者死亡病例特征,为科学合理配置医疗资源,加强医疗质量管理提供参考。方法 从该院病案信息系统中提取2018年1月1日至2022年12月31日的所有出院患者信息,对出院患者和死亡病例总数、年龄、性别分布以及疾病诊断等,采用Excel 2007和SPSS 18.0软件进行数据整理和统计分析。结果 675例死亡病例中,男性病例443例(65.63%),女性232例(34.37%),住院患者病死率0.64%;死亡原因居前3位的疾病为循环系统疾病、呼吸系统疾病、肿瘤,分别占总死亡病例数的28.44%、28.30%和27.56%;死亡病例男女性别比1.91∶1,病死率男性高于女性,差异有统计学意义(χ2=46.068,P<0.001)。年龄越大,病死率越高,50岁以上病死率迅速上升。结论 医院住院患者病死率总体较低,老年人是该院的死亡高危人群,循环系统疾病、恶性肿瘤和呼吸系统疾病是最主要的死亡原因。在医疗工作中,应根据患者年龄、性别、死亡高发病种等特点,科学合理配置医疗资源,降低住院患者病死率。

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.

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.

红细胞分布宽度、胆碱酯酶、降钙素原对老年重症社区获得性肺炎病人28 d死亡风险的预测价值

目的探讨红细胞分布宽度(RDW)、胆碱酯酶(CHE)、降钙素原(PCT)对老年重症社区获得性肺炎(SCAP)病人28 d死亡风险的预测价值。方法回顾性分析2016年1月至2021年12月入住徐州医科大学附属医院的86例老年SCAP病人的临床资料。根据28 d转归情况将病人分为生存组(n=50)与死亡组(n=36)。比较两组病人的一般资料以及入院24 h内的化验结果。结果死亡组病人的急性生理学和慢性健康状况评价Ⅱ(APACHEⅡ)、脓毒症相关性器官功能衰竭评价(SOFA)、白细胞计数、中性粒细胞(NEU)计数、RDW[(15.61±1.82)%比(13.89±1.35)%]、PCT[14.71(2.37,23.64)μg/L比0.46(0.19,3.29)μg/L]、C-反应蛋白(CRP)、白细胞介素-6(IL-6)、乳酸(LAC)大于生存组,血小板(PLT)计数、白蛋白(ALB)、CHE[2920.00(1992.00,3638.00)IU/L比4443.00(3174.00,5275.00)IU/L]、pH、氧合指数(PaO2/FiO2)小于生存组(P<0.05)。多因素logistic回归分析显示RDW、CHE、PCT是老年SCAP病人死亡的独立危险因素(P<0.05)。Spearman相关分析显示,RDW与PCT呈正相关(r=0.30,P=0.005),RDW与CHE呈负相关(r=−0.30,P=0.005)。ROC曲线分析显示RDW、CHE、PCT预测老年SCAP病人死亡的AUC分别为0.75、0.71、0.75。联合指标行ROC曲线分析显示RDW+CHE、RDW+PCT、CHE+PCT、RDW+CHE+PCT预测老年SCAP病人死亡的AUC分别为0.81、0.87、0.84、0.92,联合指标对老年SCAP病人死亡的预测能力大于单一指标,RDW+CHE+PCT三项联合指标的预测能力最强。结论RDW和/或PCT升高、CHE降低是老年SCAP病人死亡的高危因素。而且,RDW、CHE、PCT对老年SCAP病人的28 d死亡风险存在预测价值,各指标联合预测价值更高。

改良的早期预警评分联合动脉血氧饱和度监测对门诊新型冠状病毒感染患者病情及预后的评估作用

目的评估改良的早期预警评分(MEWS)联合动脉血氧饱和度(SpO 2)监测判断门诊新型冠状病毒感染(COVID-19)重症患者的可行性,探讨适用于门诊COVID-19患者病情评估的可行性预警模型。方法回顾性收集2022年12月15日至2023年1月15日在郑州大学附属郑州中心医院新冠康复门诊就诊的COVID-19患者的一般资料,按照就诊前是否进行MEWS评分及SpO 2监测分为对照组及校正MEWS评分组。应用受试者工作特征(ROC)曲线分析校正MEWS评分判断门诊COVID-19患者重症转归的灵敏度、特异度及最佳临界值;比较两组患者门诊就诊后15 d内重症转归率、病死率、住院前门诊停留时间、住院时间等指标。结果校正MEWS评分预测门诊COVID-19患者就诊后15 d内重症转归的灵敏度为83.05%,特异度为87.32%,最佳临界值为4.5分;构成校正MEWS评分的各项因素中,SpO 2对重症转归组分类的特征重要性最高达42.20%;相对于对照组,校正MEWS评分组重症转归率较低,门诊停留时间和住院时间较短(P<0.05),但两组病死率差异无统计学意义(P>0.05)。结论以SpO 2作为校正因子的MEWS评分模型可有效识别潜在的门诊重症COVID-19患者并进行预警,这种预警作用可能有利于医疗人员采取更加积极的临床治疗决策,进而影响门诊COVID-19患者就诊后的临床转归和相关医疗经济指标。

清醒俯卧位通气联合高流量氧疗在轻中度ARDS患者中的临床效果

目的:探究清醒俯卧位通气联合高流量氧疗在轻中度急性呼吸窘迫综合征(ARDS)患者中的临床效果。方法:选取2023年7月—2024年5月上饶市人民医院收治的80例轻中度ARDS患者作为研究对象,按照治疗方法分为对照组及观察组,各40例。对照组给予高流量氧疗治疗。观察组给予清醒俯卧位联合高流量氧疗治疗。比较两组患者吸入气氧浓度(FiO_(2))、氧合指数(PaO_(2)/FiO_(2))、治疗依从性及气管插管率、重症监护室(ICU)住院时间和28 d病死率。结果:治疗第3天和治疗第7天观察组FiO_(2)均低于对照组,而PaO_(2)/FiO_(2)均高于对照组,差异均有统计学意义(P<0.05);治疗后观察组总依从性高于对照组,差异有统计学意义(P<0.05);治疗后两组28 d病死率比较差异无统计学意义(P>0.05);而观察组的气管插管率低于对照组,ICU住院时间短于对照组,差异均有统计学意义(P<0.05)。结论:清醒俯卧位通气联合高流量氧疗在轻中度ARDS患者中临床效果较好,可改善机体PaO_(2)/FiO_(2)和FiO_(2),同时还可以降低气管插管率和缩短ICU住院时间。

影响热射病患者90 d预后的危险因素分析

目的探讨热射病患者90 d预后的危险因素。方法回顾性分析2018年6月至2022年8月该院重症医学科收治的热射病患者89例,根据90 d死亡情况分为存活组(62例)和死亡组(27例),比较2组患者年龄、入院时体温及心率、降温时间、炎症及器官功能障碍的实验室数据、急性生理和慢性健康状况Ⅱ评分、序贯器官衰竭估计(SOFA)评分、格拉斯哥昏迷评分法评分等。结果89例患者中90 d内死亡27例(30.3%)。降温时间、入院时心率、SOFA评分是影响患者生存的独立危险因素(风险比=6.230、1.738、1.042,95%可信区间:2.561~14.521、1.026~2.114、1.011~1.427,P=0.004、0.002、0.023)。降温时间、入院时心率、SOFA评分联合预测热射病患者90 d病死率的受试者工作特征曲线下面积为0.993(95%可信区间:0.976~1.000,P<0.001),灵敏度为1.000,特异度为0.933。结论降温时间、入院时心率、SOFA评分是影响热射病患者90 d生存的因素,3个指标可联合使用预测患者90 d病死率和不良预后。

降低急性胰腺炎的病死率:基于日本2021版诊疗指南的探讨

如何降低急性胰腺炎的病死率是一项意义重大的课题。日本2021版诊疗指南提出了对急性胰腺炎患者的“集束化管理措施”,在此指导下,日本急性胰腺炎总体病死率下降至2.6%,重症急性胰腺炎病死率下降至6.1%,远低于全球各国报道的病死率。本文对该指南之诊疗方案进行分析,并与我国现行指南进行对比,以期对AP患者的临床诊治提供指导意义,并期待日后有更多更深入的该领域相关研究。

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.

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.