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 c...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.展开更多
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-compartmen...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.展开更多
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 fata...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.展开更多
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...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.展开更多
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 we...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.展开更多
<strong>Importance:</strong> 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 outbr...<strong>Importance:</strong> 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. <strong>Methods: </strong>This is a record based retrospective cohort study. <strong>Table 1</strong> was extracted from <a href="https://www.worldometers.info/coronavirus/" target="_blank">https://www.worldometers.info/coronavirus/</a> and from the Corona virus resource center (<strong>Table 2</strong>, <strong>Figures 1-3</strong>), Johns Hopkins University. Data for <strong>Table 1</strong> includes all countries which reported >1000 cases and <strong>Table 2</strong> 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. <strong>Results:</strong> From <strong>Table 1</strong>, we could infer that as on 4<sup>th</sup> 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%). <strong>Table 2</strong> depicts the scenario as on 8<sup>th</sup> 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. <strong>Conclusion:</strong> 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.展开更多
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 hi...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.展开更多
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...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.展开更多
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 measur...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.展开更多
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 c...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.展开更多
Marburg virus disease(MVD)is a highly fatal illness,with a case fatality rate of up to 88%,though this rate can be significantly reduced with prompt and effective patient care.The disease was first identified in 1967 ...Marburg virus disease(MVD)is a highly fatal illness,with a case fatality rate of up to 88%,though this rate can be significantly reduced with prompt and effective patient care.The disease was first identified in 1967 during concurrent outbreaks in Marburg and Frankfurt,Germany,and in Belgrade,Serbia,linked to laboratory use of African green monkeys imported from Uganda.Subsequent outbreaks and isolated cases have been reported in various African countries,including Angola,the Democratic Republic of the Congo,Equatorial Guinea,Ghana,Guinea,Kenya,Rwanda,South Africa(in an individual with recent travel to Zimbabwe),Tanzania,and Uganda.Initial human MVD infections typically occur due to prolonged exposure to mines or caves inhabited by Rousettus aegyptiacus fruit bats,the natural hosts of the virus.展开更多
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 statisti...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.展开更多
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 4915 Cholera patients from registers of the Region...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 4915 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.展开更多
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 ...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.展开更多
COVID-19 has become one of the critical health issues globally,which surfaced first in latter part of the year 2019.It is the topmost concern for many nations’governments as the contagious virus started mushrooming o...COVID-19 has become one of the critical health issues globally,which surfaced first in latter part of the year 2019.It is the topmost concern for many nations’governments as the contagious virus started mushrooming over adjacent regions of infected areas.In 1980,a vaccine called Bacillus Calmette-Guérin(BCG)was introduced for preventing tuberculosis and lung cancer.Countries that have made the BCG vaccine mandatory have witnessed a lesser COVID-19 fatality rate than the countries that have not made it compulsory.This paper’s initial research shows that the countries with a longtermcompulsory BCGvaccination system are less affected by COVID-19 than those without a BCG vaccination system.This paper discusses analytical data patterns for medical applications regarding COVID-19 impact on countries with mandatory BCG status on fatality rates.The paper has tackled numerous analytical challenges to realize the full potential of heterogeneous data.An analogy is drawn to demonstrate how other factors can affect fatality and infection rates other than BCG vaccination only,such as age groups affected,other diseases,and stringency index.The data of Spain,Portugal,and Germany have been taken for a case study of BCG impact analysis.展开更多
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 ...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 chi<sup>2</sup> 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.展开更多
Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and qua...Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and quality of data on disease burden are limited during an epidemic.Methods We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing.We demonstrate the robustness,accuracy,and precision of this framework,and apply it to the United States(U.S.)COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs.Results The estimators for the numbers of infections and IFRs showed high accuracy and precision;for instance,when applied to simulated validation data sets,across counties,Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928,respectively,and they showed strong robustness to model misspecification.Applying the county-level estimators to the real,unsimulated COVID-19 data spanning April 1,2020 to September 30,2020 from across the U.S.,we found that IFRs varied from 0 to 44.69,with a standard deviation of 3.55 and a median of 2.14.Conclusions The proposed estimation framework can be used to identify geographic variation in IFRs across settings.展开更多
Accurate prediction of fatal events in car accidents has significant health management implications.This research article explores the application of imbalanced data handling techniques in machine learning to enhance ...Accurate prediction of fatal events in car accidents has significant health management implications.This research article explores the application of imbalanced data handling techniques in machine learning to enhance prediction performance.By implementing these techniques on car accident data,health organizations can identify and forecast a fatal event,enabling more efficient and effective allocation of limited health resources.Concurrently,enhancing the performance of machine learning models through imbalanced data handling techniques can impact health management decisions.Our findings highlight the significance of imbalanced data handling techniques in predicting fatality in car accidents,ultimately contributing to improved road safety and better management of health resources.Moreover,the effective use of imbalanced data demonstrates a substantial improvement in the specificity of the prediction.Addressing the impact of machine learning techniques on imbalanced car accident data can significantly improve overall health outcomes.展开更多
Introduction:Mushroom poisoning represents a significant food safety concern in China.Over the past decade,China has established an effective mushroom poisoning control and prevention system involving government agenc...Introduction:Mushroom poisoning represents a significant food safety concern in China.Over the past decade,China has established an effective mushroom poisoning control and prevention system involving government agencies,clinicians,CDC experts,and mycologists.Methods:Under the system of mushroom poisoning control and prevention,information of mushroom poisoning incidents were systematically collected,with identifing species,detecting toxins,and analyzing the spatial and temporal distribution characteristics and species diversity.Results:In 2024,the China CDC investigated 599 mushroom poisoning incidents across 28 provinciallevel administrative divisions(PLADs),involving 1,486 patients and resulting in 13 deaths,with a case fatality rate of 0.87%.The number of cases per incident ranged from 1 to 14(median=2),with 11 incidents involving more than 10 patients.Among these cases,50 patients(including 2 fatalities)from 17 incidents consumed poisonous mushrooms purchased from markets,37 patients from 14 incidents were poisoned after consuming dried mushrooms,and 15 patients from 10 incidents consumed uncooked wild mushrooms.Temporal analysis revealed that mushroom poisoning incidents occurred throughout the year,with the highest frequency between June and October,peaking in June.Geographically,incidents were reported in 28 PLADs,with 13 PLADs reporting more than 10 incidents.Sichuan,Yunnan,Hunan,Guizhou,and Chongqing were the five most affected regions.Deaths were reported in Guizhou(5 deaths),Sichuan(2 deaths),Chongqing(2 deaths),Heilongjiang(2 deaths),Hunan(1 death),and Jiangsu(1 death).A total of 110 poisonous mushroom species causing 7 distinct clinical syndromes were identified,including 8 newly documented poisonous species,bringing the total number of mushroom species involved in poisoning incidents in China to approximately 246 by the end of 2024.The five most lethal mushrooms were Amanita exitialis,A.molliuscula,A.subpallidorosea,Lepiota brunneoincarnata,and Russula subnigricans,each causing 2 deaths.Chlorophyllum molybdites was responsible for the highest number of poisoning incidents(147 incidents affecting 269 patients).Paxillus obscurosporus,which causes hemolysis,was identified in a poisoning incident for the first time.Conclusions:The persistent severity of mushroom poisoning in China underscores the need for continued public education efforts,strengthened market supervision,and enhanced collaboration across departments and disciplines to reduce the incidence of mushroom poisoning.展开更多
文摘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.
基金The work has been supported by a grant received from the Ministry of Education,Government of India under the Scheme for the Promotion of Academic and Research Collaboration(SPARC)(ID:SPARC/2019/1396).
文摘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.
文摘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.
文摘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.
文摘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.
文摘<strong>Importance:</strong> 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. <strong>Methods: </strong>This is a record based retrospective cohort study. <strong>Table 1</strong> was extracted from <a href="https://www.worldometers.info/coronavirus/" target="_blank">https://www.worldometers.info/coronavirus/</a> and from the Corona virus resource center (<strong>Table 2</strong>, <strong>Figures 1-3</strong>), Johns Hopkins University. Data for <strong>Table 1</strong> includes all countries which reported >1000 cases and <strong>Table 2</strong> 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. <strong>Results:</strong> From <strong>Table 1</strong>, we could infer that as on 4<sup>th</sup> 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%). <strong>Table 2</strong> depicts the scenario as on 8<sup>th</sup> 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. <strong>Conclusion:</strong> 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.
基金partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region,China(HKU C7123-20G)。
文摘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.
文摘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.
文摘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.
文摘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.
文摘Marburg virus disease(MVD)is a highly fatal illness,with a case fatality rate of up to 88%,though this rate can be significantly reduced with prompt and effective patient care.The disease was first identified in 1967 during concurrent outbreaks in Marburg and Frankfurt,Germany,and in Belgrade,Serbia,linked to laboratory use of African green monkeys imported from Uganda.Subsequent outbreaks and isolated cases have been reported in various African countries,including Angola,the Democratic Republic of the Congo,Equatorial Guinea,Ghana,Guinea,Kenya,Rwanda,South Africa(in an individual with recent travel to Zimbabwe),Tanzania,and Uganda.Initial human MVD infections typically occur due to prolonged exposure to mines or caves inhabited by Rousettus aegyptiacus fruit bats,the natural hosts of the virus.
文摘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.
文摘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 4915 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.
文摘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.
文摘COVID-19 has become one of the critical health issues globally,which surfaced first in latter part of the year 2019.It is the topmost concern for many nations’governments as the contagious virus started mushrooming over adjacent regions of infected areas.In 1980,a vaccine called Bacillus Calmette-Guérin(BCG)was introduced for preventing tuberculosis and lung cancer.Countries that have made the BCG vaccine mandatory have witnessed a lesser COVID-19 fatality rate than the countries that have not made it compulsory.This paper’s initial research shows that the countries with a longtermcompulsory BCGvaccination system are less affected by COVID-19 than those without a BCG vaccination system.This paper discusses analytical data patterns for medical applications regarding COVID-19 impact on countries with mandatory BCG status on fatality rates.The paper has tackled numerous analytical challenges to realize the full potential of heterogeneous data.An analogy is drawn to demonstrate how other factors can affect fatality and infection rates other than BCG vaccination only,such as age groups affected,other diseases,and stringency index.The data of Spain,Portugal,and Germany have been taken for a case study of BCG impact analysis.
文摘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 chi<sup>2</sup> 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.
基金K.A.and J.L.were supported by a grant from the Benioff Center for Microbiome MedicineThis research used resources of the Oak Ridge Leadership Computing Facility,which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725+5 种基金This manuscript has been coauthored by UT-Battelle,LLC under contract no.DE-AC05-00OR22725 with the U.S.Department of EnergyThe United States Government retains and the publisher,by accepting the article for publication,acknowledges that the United States Government retains a nonexclusive,paid-up,irrevocable,world-wide license to publish or reproduce the published form of this manuscript,or allow others to do so,for United States Government purposesThe Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan(http://energy.gov/downloads/doe-public-access-plan,last accessed September 16,2020)Work at Oak Ridge and Lawrence Berkeley National Laboratories was supported by the DOE Office of Science through the National Virtual Biotechnology Laboratory,a consortium of DOE national laboratories focused on response to COVID-19,with funding provided by the Coronavirus CARES Actwas facilitated by previous breakthroughs obtained through the Laboratory Directed Research and Development Programs of the Lawrence Berkeley and Oak Ridge National Laboratories.M.P.J.was supported by a grant from the Laboratory Directed Research and Development(LDRD)Program of Lawrence Berkeley National Laboratory under U.S.Department of Energy Contract No.DE-AC02-05CH11231Oak Ridge National Laboratory would also like to acknowledge funding from the U.S.National Science Foundation(EF-2133763).
文摘Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and quality of data on disease burden are limited during an epidemic.Methods We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing.We demonstrate the robustness,accuracy,and precision of this framework,and apply it to the United States(U.S.)COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs.Results The estimators for the numbers of infections and IFRs showed high accuracy and precision;for instance,when applied to simulated validation data sets,across counties,Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928,respectively,and they showed strong robustness to model misspecification.Applying the county-level estimators to the real,unsimulated COVID-19 data spanning April 1,2020 to September 30,2020 from across the U.S.,we found that IFRs varied from 0 to 44.69,with a standard deviation of 3.55 and a median of 2.14.Conclusions The proposed estimation framework can be used to identify geographic variation in IFRs across settings.
文摘Accurate prediction of fatal events in car accidents has significant health management implications.This research article explores the application of imbalanced data handling techniques in machine learning to enhance prediction performance.By implementing these techniques on car accident data,health organizations can identify and forecast a fatal event,enabling more efficient and effective allocation of limited health resources.Concurrently,enhancing the performance of machine learning models through imbalanced data handling techniques can impact health management decisions.Our findings highlight the significance of imbalanced data handling techniques in predicting fatality in car accidents,ultimately contributing to improved road safety and better management of health resources.Moreover,the effective use of imbalanced data demonstrates a substantial improvement in the specificity of the prediction.Addressing the impact of machine learning techniques on imbalanced car accident data can significantly improve overall health outcomes.
基金Ministry of Science and Technology of the People’s Republic of China National Natural Science Foundation of China 32270021.
文摘Introduction:Mushroom poisoning represents a significant food safety concern in China.Over the past decade,China has established an effective mushroom poisoning control and prevention system involving government agencies,clinicians,CDC experts,and mycologists.Methods:Under the system of mushroom poisoning control and prevention,information of mushroom poisoning incidents were systematically collected,with identifing species,detecting toxins,and analyzing the spatial and temporal distribution characteristics and species diversity.Results:In 2024,the China CDC investigated 599 mushroom poisoning incidents across 28 provinciallevel administrative divisions(PLADs),involving 1,486 patients and resulting in 13 deaths,with a case fatality rate of 0.87%.The number of cases per incident ranged from 1 to 14(median=2),with 11 incidents involving more than 10 patients.Among these cases,50 patients(including 2 fatalities)from 17 incidents consumed poisonous mushrooms purchased from markets,37 patients from 14 incidents were poisoned after consuming dried mushrooms,and 15 patients from 10 incidents consumed uncooked wild mushrooms.Temporal analysis revealed that mushroom poisoning incidents occurred throughout the year,with the highest frequency between June and October,peaking in June.Geographically,incidents were reported in 28 PLADs,with 13 PLADs reporting more than 10 incidents.Sichuan,Yunnan,Hunan,Guizhou,and Chongqing were the five most affected regions.Deaths were reported in Guizhou(5 deaths),Sichuan(2 deaths),Chongqing(2 deaths),Heilongjiang(2 deaths),Hunan(1 death),and Jiangsu(1 death).A total of 110 poisonous mushroom species causing 7 distinct clinical syndromes were identified,including 8 newly documented poisonous species,bringing the total number of mushroom species involved in poisoning incidents in China to approximately 246 by the end of 2024.The five most lethal mushrooms were Amanita exitialis,A.molliuscula,A.subpallidorosea,Lepiota brunneoincarnata,and Russula subnigricans,each causing 2 deaths.Chlorophyllum molybdites was responsible for the highest number of poisoning incidents(147 incidents affecting 269 patients).Paxillus obscurosporus,which causes hemolysis,was identified in a poisoning incident for the first time.Conclusions:The persistent severity of mushroom poisoning in China underscores the need for continued public education efforts,strengthened market supervision,and enhanced collaboration across departments and disciplines to reduce the incidence of mushroom poisoning.
