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
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 prog...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.展开更多
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 f...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.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
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 p...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.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 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...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.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
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 Beijin...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.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
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 M...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?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
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
Earthquakes can cause significant damage and loss of life,necessitating immediate assessment of the resulting fatalities.Rapid assessment and timely revision of fatality estimates are crucial for effective emergency d...Earthquakes can cause significant damage and loss of life,necessitating immediate assessment of the resulting fatalities.Rapid assessment and timely revision of fatality estimates are crucial for effective emergency decisionmaking.This study using the February 6,2023,M_(S)8.0 and M_(S)7.9 Kahramanmaras,Türkiye earthquakes as an example to estimate the ultimate number of fatalities.An early Quick Rough Estimate(QRE)based on the number of deaths reported by the Disaster and Emergency Management Presidency of Türkiye(AFAD)is conducted,and it dynamically adjusts these estimates as new data becomes available.The range of estimates of the final number of deaths can be calculated as 31384–56475 based on the"the QRE of the second day multiplied by 2–3" rule,which incorporates the reported final deaths 50500.The Quasi-Linear and Adaptive Estimation(QLAE)method adaptively adjusts the final fatality estimate within two days and predicts subsequent reported deaths.The correct order of magnitude of the final death toll can be estimated as early as 13 hr after the M_(S)8.0 earthquake.In addition,additional earthquakes such as May 12,2008,M_(S)8.1 Wenchuan earthquake(China),September 8,2023,M_(S)7.2 Al Haouz earthquake(Morocco),November 3,2023,M_(S)5.8 Mid-Western Nepal earthquake,December 18,2023,M_(S)6.1 Jishishan earthquake(China),January 1,2024,M_(S)7.2 Noto Peninsula earthquake(Japan)and August 8,2023,Maui,Hawaii,fires are added again to verified the correctness of the model.The fatalities from the Maui fires are found to be approximately equivalent to those resulting from an M_(S)7.4 earthquake.These methods complement existing frameworks such as Quake Loss Assessment for Response and Mitigation(QLARM)and Prompt Assessment of Global.展开更多
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...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.展开更多
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 de...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.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
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.
文摘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.
文摘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.
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.
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.
基金supported by National Cheng Kung University Hospital(NCKUH-10505033)
文摘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.
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.
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the research deputy of Jahrom University of Medical Sciences for financial support and confirmation of the project(Project identification code IR.JUMS.REC.1398.120)
文摘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?
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.
基金supported by the National Natural Science Foundation of China(NSFC,grant number U2039207).
文摘Earthquakes can cause significant damage and loss of life,necessitating immediate assessment of the resulting fatalities.Rapid assessment and timely revision of fatality estimates are crucial for effective emergency decisionmaking.This study using the February 6,2023,M_(S)8.0 and M_(S)7.9 Kahramanmaras,Türkiye earthquakes as an example to estimate the ultimate number of fatalities.An early Quick Rough Estimate(QRE)based on the number of deaths reported by the Disaster and Emergency Management Presidency of Türkiye(AFAD)is conducted,and it dynamically adjusts these estimates as new data becomes available.The range of estimates of the final number of deaths can be calculated as 31384–56475 based on the"the QRE of the second day multiplied by 2–3" rule,which incorporates the reported final deaths 50500.The Quasi-Linear and Adaptive Estimation(QLAE)method adaptively adjusts the final fatality estimate within two days and predicts subsequent reported deaths.The correct order of magnitude of the final death toll can be estimated as early as 13 hr after the M_(S)8.0 earthquake.In addition,additional earthquakes such as May 12,2008,M_(S)8.1 Wenchuan earthquake(China),September 8,2023,M_(S)7.2 Al Haouz earthquake(Morocco),November 3,2023,M_(S)5.8 Mid-Western Nepal earthquake,December 18,2023,M_(S)6.1 Jishishan earthquake(China),January 1,2024,M_(S)7.2 Noto Peninsula earthquake(Japan)and August 8,2023,Maui,Hawaii,fires are added again to verified the correctness of the model.The fatalities from the Maui fires are found to be approximately equivalent to those resulting from an M_(S)7.4 earthquake.These methods complement existing frameworks such as Quake Loss Assessment for Response and Mitigation(QLARM)and Prompt Assessment of Global.
文摘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.
文摘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.
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.
