The COVID-19 pandemic,caused by the SARS-CoV-2 virus,has triggered a global health crisis,necessitating accurate predictive models to forecast disease severity and aid in clinical decision-making.This study introduces...The COVID-19 pandemic,caused by the SARS-CoV-2 virus,has triggered a global health crisis,necessitating accurate predictive models to forecast disease severity and aid in clinical decision-making.This study introduces an innovative machine learning approach,the bDWPLO-FKNN model,designed to predict the severity of COVID-19 pneumonia in patients.The model incorporates the Differential Weibull Polar Lights Optimizer(DWPLO),an enhancement of the Polar Lights Optimizer(PLO)with the differential evolution operator and the Weibull flight operator,to perform effective feature selection.The DWPLO’s performance was rigorously tested against IEEE CEC 2017 benchmark functions,demonstrating its robust optimization capabilities.The binary version of DWPLO(bDWPLO)was then integrated with the Fuzzy K-Nearest Neighbors(FKNN)algorithm to form the predictive model.Using a dataset from the People’s Hospital Affiliated with Ningbo University,the model was trained to identify patients at risk of developing severe pneumonia due to COVID-19.The bDWPLO-FKNN model exhibited exceptional predictive accuracy,with an accuracy of 84.036% and a specificity of 88.564%.The analysis revealed key predictors,including albumin,albumin to globulin ratio,lactate dehydrogenase,urea nitrogen,gamma-glutamyl transferase,and inorganic phosphorus,which were significantly associated with disease severity.The integration of DWPLO with FKNN not only enhances feature selection but also bolsters the model’s predictive power,providing a valuable tool for clinicians to assess patient risk and allocate healthcare resources effectively during the COVID-19 pandemic.展开更多
BACKGROUND Previous studies have reported the high predictive accuracy of 4C Mortality Score derived at hospital admission in coronavirus disease 2019(COVID-19)patients.Very few studies have assessed it at intensive c...BACKGROUND Previous studies have reported the high predictive accuracy of 4C Mortality Score derived at hospital admission in coronavirus disease 2019(COVID-19)patients.Very few studies have assessed it at intensive care unit(ICU)admission and compared it with the Acute Physiology and Chronic Health Evaluation(APACHE)II score.There are no studies comparing its accuracy with APACHE III score.AIM To describe the characteristics and outcomes of patients admitted to ICU with COVID-19 infection and to compare the accuracy of 4C score and APACHE score in predicting mortality in these patients.METHODS We conducted this retrospective cohort study using an electronic database in a tertiary ICU in Sydney.We included all adult patients(age>16 years)admitted to ICU with COVID-19 infection over a 5-month period(July 1,2021 to November 30,2021).We collected the data on demographics,clinical characteristics,interventions and outcomes for all patients.We calculated the 4C Mortality Score for each patient using eight variables as described previously.We compared the predictive accuracy of 4C Mortality Score at hospital and ICU admission and APACHE II and III scores by area under the receiver operating characteristic curve(AUROC).We determined the optimal cut-off value for each of these scores using the‘nearest’method and its 95%confidence interval by bootstrapping.RESULTS A total of 140 patients(62%males,mean age 56±17 years,mean APACHE II score 13±57)were included in the study.Nineteen(13.6%)of 140 patients died in the hospital.Compared to survivors,the non-survivors were older,males,had more comorbidities,higher rate of mechanical ventilation and vasopressor use.The AUROC for the 4C Mortality Score at hospital and ICU admission and APACHE II and II score was 0.75,0.80.0.75 and 0.79 respectively.The optimal cut-off value for these four scores was 9,10,14 and 56 respectively.The cut-point for all the scores had higher sensitivity than specificity.CONCLUSION The 4C score at ICU admission had a higher accuracy in predicting mortality than the 4C score at hospital admission.The predictive accuracy was similar to that for APACHE III score.The 4C score at ICU admission needs to be validated in future studies.展开更多
In response to an outbreak of coronavirus disease 2019(COVID-19)within a cluster of Navy personnel in Sri Lanka commencing from 22nd April 2020,an aggressive outbreak management program was launched by the Epidemiolog...In response to an outbreak of coronavirus disease 2019(COVID-19)within a cluster of Navy personnel in Sri Lanka commencing from 22nd April 2020,an aggressive outbreak management program was launched by the Epidemiology Unit of the Ministry of Health.To predict the possible number of cases within the susceptible population under four social distancing scenarios,the COVID-19 Hospital Impact Model for Epidemics(CHIME)was used.With increasing social distancing,the epidemiological curve flattened,and its peak shifted to the right.The observed or actually reported number of cases was above the projected number of cases at the onset;however,subsequently,it fell below all predicted trends.Predictive modelling is a useful tool for the control of outbreaks such as COVID-19 in a closed community.展开更多
Corona Virus Disease-2019(COVID-19)was reported at first in Wuhan city,China by December 2019.World Health Organization(WHO)declared COVID-19 as a pandemic i.e.,global health crisis onMarch 11,2020.The outbreak of COV...Corona Virus Disease-2019(COVID-19)was reported at first in Wuhan city,China by December 2019.World Health Organization(WHO)declared COVID-19 as a pandemic i.e.,global health crisis onMarch 11,2020.The outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread,not only affected the economic status of a number of countries,but it also resulted in increased levels of Depression,Anxiety,and Stress(DAS)among people.Therefore,there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear;with tremendously-limitingmeasures of social distancing and lockdown in force;and with high rates of new cases and mortalities.With this motivation,the current study aims at investigating theDAS levels among college students during COVID-19 lockdown since they are identified as a highly-susceptible population.The current study proposes to develop Intelligent Feature Subset Selection withMachine Learning-based DAS predictive(IFSSML-DAS)model.The presented IFSSML-DAS model involves data preprocessing,Feature Subset Selection(FSS),classification,and parameter tuning.Besides,IFSSML-DAS model uses Group Gray Wolf Optimization based FSS(GGWO-FSS)technique to reduce the curse of dimensionality.In addition,Beetle Swarm Optimization based Least Square Support Vector Machine(BSO-LSSVM)model is also employed for classification in which the weight and bias parameters of the LSSVM model are optimally adjusted using BSO algorithm.The performance of the proposed IFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different measures.The outcome of the study suggests the development of specialized programs to handleDAS among population so as to overcome COVID-19 crisis.展开更多
The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination ...The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.展开更多
The COVID-19 pandemic posed a challenge for clinical management of a new lung disease that was characterized by inflammation,endothelial cell dysfunction,and thrombosis,which occur after the replication phase of infec...The COVID-19 pandemic posed a challenge for clinical management of a new lung disease that was characterized by inflammation,endothelial cell dysfunction,and thrombosis,which occur after the replication phase of infection of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).There are many laboratory models of active SARS-CoV-2 infection in mice,reflecting an acute lung injury in an otherwise healthy animal,but there is a lack of accurate animal models of the postviral inflammatory phase of the COVID-19 lung reflecting severe disease.The monocrotaline(MCT)-treated rat is a widely used laboratory model of pulmonary hypertension(PH).Not often discussed,however,are the observed changes in inflammation,edema,fibrosis,and microthrombosis in the lung prior to PH.At the cellular level,there is loss of pneumocytes and endotheliopathy,and at the molecular level the MCT rat lung is characterized by a proinflammatory cytokine profile,namely elevated interleukin 6,transforming growth factorβand tumor necrosis factor,M1 macrophage phenotype,and dysregulation of the angiotensin converting enzyme(ACE)/ACE2 balance.The systems-level pathophysiology of the MCT-treated rat includes progressive cardiopulmonary dysfunction.The MCT-treated rat clearly differs from the COVID-19 lung in terms of the triggers for pathology,but there are many parallels apparent in both the MCT-treated rat and the COVID-19 lung.The MCT-treated rat lung as a model of the COVID-19 lung may provide an in-depth understanding of the factors that drive the lung to more severe pathology,treatments that benefit lung recovery,or the factors that prove a useful research platform for future emerging respiratory threats of similar pathology.展开更多
This paper aims to provide a window opportunity to share a reflection and learning from different countries and from other disciplines with the focus on resilience.There is also an attempt to theorize the concept of l...This paper aims to provide a window opportunity to share a reflection and learning from different countries and from other disciplines with the focus on resilience.There is also an attempt to theorize the concept of learning from spurious success and failure in the context of COVID-19.The main emphasis is to provide understanding of the causal factors and the identification of improved measures and modelling approaches to prevent and mitigate against future pandemics.Proposed decision tools of resilience and bowtie modelling as enablers for decision makers to prevent hazards and protect against their consequences.展开更多
COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different...COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,respectively.Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty.The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and Random Forest Model.The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021.The model has been developed to obtain the forecast values till September 2021.This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country.In India,the cases are rapidly increasing day-by-day since mid of Feb 2021.The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave.To empower the prediction for future validation,the proposed model works effectively.展开更多
In 2020,the COVID-19 pandemic spreads rapidly around the world.To accurately predict the number of daily new cases in each country,Lanzhou University has established the Global Prediction System of the COVID-19 Pandem...In 2020,the COVID-19 pandemic spreads rapidly around the world.To accurately predict the number of daily new cases in each country,Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic(GPCP).In this article,the authors use the ensemble empirical mode decomposition(EEMD)model and autoregressive moving average(ARMA)model to improve the prediction results of GPCP.In addition,the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease,whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model.Judging from the results,the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP.For countries such as El Salvador with a small number of cases,the absolute values of the relative errors of prediction become smaller.Therefore,this article concludes that this method is more effective for improving prediction results and direct prediction.展开更多
COVID-19 comes from a large family of viruses identied in 1965;to date,seven groups have been recorded which have been found to affect humans.In the healthcare industry,there is much evidence that Al or machine learni...COVID-19 comes from a large family of viruses identied in 1965;to date,seven groups have been recorded which have been found to affect humans.In the healthcare industry,there is much evidence that Al or machine learning algorithms can provide effective models that solve problems in order to predict conrmed cases,recovered cases,and deaths.Many researchers and scientists in the eld of machine learning are also involved in solving this dilemma,seeking to understand the patterns and characteristics of virus attacks,so scientists may make the right decisions and take specic actions.Furthermore,many models have been considered to predict the Coronavirus outbreak,such as the retro prediction model,pandemic Kaplan’s model,and the neural forecasting model.Other research has used the time series-dependent face book prophet model for COVID-19 prediction in India’s various countries.Thus,we proposed a prediction and analysis model to predict COVID-19 in Saudi Arabia.The time series dependent face book prophet model is used to t the data and provide future predictions.This study aimed to determine the pandemic prediction of COVID-19 in Saudi Arabia,using the Time Series Analysis to observe and predict the coronavirus pandemic’s spread daily or weekly.We found that the proposed model has a low ability to forecast the recovered cases of the COVID-19 dataset.In contrast,the proposed model of death cases has a high ability to forecast the COVID-19 dataset.Finally,obtaining more data could empower the model for further validation.展开更多
Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span.Corona Virus Disease-2019(COVID-19),also known as novel coronavirus or SARS...Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span.Corona Virus Disease-2019(COVID-19),also known as novel coronavirus or SARSCoV-2(Severe Acute Respiratory Syndrome-Coronavirus-2),is a respiratory based touch contiguous disease.The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally.The whole world is making tremendous efforts to combat this life-threatening disease.For taking remedial action and planning preventive measures on time,there is an urgent need for efficient prediction models to confront the COVID-19 outbreak.A deep learning-based ARIMA-LSTM hybrid model is proposed in this article for predicting the COVID-19 outbreak by utilizing real-time information from the WHO’s daily bulletin report as well as provides information regarding clinical trials across the world.To evaluate the suitability and performance of our proposed model compared to other well-established prediction models,an experimental study has been performed.To estimate the prediction results,the three performance measures,i.e.,Root Mean Square Error(RMSE),Coefficient of determination(R2 Score),and Mean Absolute Percentage Error(MAPE)have been employed.The prediction results of fifty countries substantiated the fact that the proposed ARIMA-LSTM hybrid model performs very well as compared to other models.The proposed model archives the lowest RMSE,lowest MAPE,and highest R2 Score throughout the testing,under varied selection criteria(country-wise).This article aims to contribute a deep learning-based solution for the wellbeing of livings and to provide the current status of clinical trials across the globe.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
The COVID-19 pandemic is a curse and a threat to global health, development, the economy, and peaceful society because of its massive transmission and high rates of mutation. More than 220 countries have been affected...The COVID-19 pandemic is a curse and a threat to global health, development, the economy, and peaceful society because of its massive transmission and high rates of mutation. More than 220 countries have been affected by COVID-19. The world is now facing a drastic situation because of this ongoing virus. Bangladesh is also dealing with this issue, and due to its dense population, it is particularly vulnerable to the spread of COVID-19. Recently, many non-linear systems have been proposed to solve the SIR (Susceptible, Infected, and Recovered) model for predicting Coronavirus cases. In this paper, we have discussed the fractional order SIR epidemic model of a non-fatal disease in a population of a constant size. Using the Laplace Adomian Decomposition method, we get an approximate solution to the model. To predict the dynamic transmission of COVID-19 in Bangladesh, we provide a numerical argument based on real data. We also conducted a comparative analysis among susceptible, infected, and recovered people. Furthermore, the most sensitive parameters for the Basic Reproduction Number (<em>R</em><sub>0</sub>) are graphically presented, and the impact of the compartments on the transmission dynamics of the COVID-19 pandemic is thoroughly investigated.展开更多
The COVID-19 pandemic has caused hundreds of thousands of deaths,millions of infections worldwide,and the loss of trillions of dollars for many large economies.It poses a grave threat to the human population with an e...The COVID-19 pandemic has caused hundreds of thousands of deaths,millions of infections worldwide,and the loss of trillions of dollars for many large economies.It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope.Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses.In the same vein,a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of,and role of age groups and gender on,the probability of COVID-19 infection.This study aimed to review,analyze,and critically appraise published works that report on various factors to explain their relationship with COVID-19.Such studies span a wide range,including descriptive analyses,ratio analyses,cohort,prospective and retrospective studies.Various studies that describe indicators to determine the probability of infection among the general population,as well as the risk factors associated with severe illness and mortality,are critically analyzed and these ndings are discussed in detail.A comprehensive analysis was conducted on research studies that investigated the perceived differences in vulnerability of different age groups and genders to severe outcomes of COVID-19.Studies incorporating important demographic,health,and socioeconomic characteristics are highlighted to emphasize their importance.Predominantly,the lack of an appropriated dataset that contains demographic,personal health,and socioeconomic information implicates the efcacy and efciency of the discussed methods.Results are overstated on the part of both exclusion of quarantined and patients with mild symptoms and inclusion of the data from hospitals where the majority of the cases are potentially ill.展开更多
In this work,we present a model that uses the fractional order Caputo derivative for the novel Coronavirus disease 2019(COVID-19)with different hospitalization strategies for severe and mild cases and incorporate an a...In this work,we present a model that uses the fractional order Caputo derivative for the novel Coronavirus disease 2019(COVID-19)with different hospitalization strategies for severe and mild cases and incorporate an awareness program.We generalize the SEIR model of the spread of COVID-19 with a private focus on the transmissibility of people who are aware of the disease and follow preventative health measures and people who are ignorant of the disease and do not follow preventive health measures.Moreover,individuals with severe,mild symptoms and asymptomatically infected are also considered.The basic reproduction number(R0)and local stability of the disease-free equilibrium(DFE)in terms of R0 are investigated.Also,the uniqueness and existence of the solution are studied.Numerical simulations are performed by using some real values of parameters.Furthermore,the immunization of a sample of aware susceptible individuals in the proposed model to forecast the effect of the vaccination is also considered.Also,an investigation of the effect of public awareness on transmission dynamics is one of our aim in this work.Finally,a prediction about the evolution of COVID-19 in 1000 days is given.For the qualitative theory of the existence of a solution,we use some tools of nonlinear analysis,including Lipschitz criteria.Also,for the numerical interpretation,we use the Adams-Moulton-Bashforth procedure.All the numerical results are presented graphically.展开更多
<strong>Objective:</strong> To assess the diagnostic accuracy and predictive values of clinical symptoms in patients with suspected mild COVID-19 to identify target groups for self-isolation and outpatient...<strong>Objective:</strong> To assess the diagnostic accuracy and predictive values of clinical symptoms in patients with suspected mild COVID-19 to identify target groups for self-isolation and outpatient treatment without additional testing. <strong>Methods:</strong> We conducted an open-label prospective study in patients aged 18 to 72 years with suspected mild COVID-19. The clinical diagnosis was based on the acute onset of such symptoms as olfactory dysfunction, hyperthermia, myalgia, nasal congestion, nasal discharge, cough, rhinolalia, sore throat, without pneumonia in persons in contact with a confirmed case of COVID-19. The physician assessed clinical symptoms using a 4-point scale. The patient self-assessed clinical symptoms using a ten-point visual analogue scale (VAS). All enrolled patients underwent laboratory testing to confirm the diagnosis of COVID-19. <strong>Results:</strong> Of the 120 patients underwent testing, the diagnosis of mild COVID-19 was confirmed in 96 patients and ruled out in 24 patients. When assessing symptoms by a physician according to the correlation analysis, hyperthermia, myalgia, nasal congestion and rhinolalia have a positive predictive value with a significance level of more than 0.6. When self-assessing symptoms by a patient, fever, myalgia and nasal congestion have a diagnostic accuracy with a significance level of more than 0.5. Nasal discharge, cough and sore throat have negative predictive values. <strong>Conclusion: </strong>The presence of these symptoms in patients with an acute onset of the disease can help to make a clinical diagnosis of coronavirus disease and identify target groups for self-isolation and outpatient treatment without additional testing. Highly suspect asymptomatic patients are not considered as those who have possible mild COVID-19 infection.展开更多
In this study, the mathematical SIR model (Susceptible-Infected-Recovered (cured and deceased)) was applied to the case of Senegal during the first two waves of the COVID-19 pandemic. During this period, from March 1,...In this study, the mathematical SIR model (Susceptible-Infected-Recovered (cured and deceased)) was applied to the case of Senegal during the first two waves of the COVID-19 pandemic. During this period, from March 1, 2020, to March 30, 2021, the transmission and recovery rates as well as the number of reproduction were calculated and analyzed for the impact of the decisions taken by the Senegalese government. In both waves, the variation of the basic reproduction number as a function of time, with values below one towards the end of each study period, confirms the success of the Senegalese government in controlling the epidemic. The results show that the solution of mandatory mask-wearing is the best decision to counter the spread of the disease. Indeed, the mean number of reproduction is 2.11 in the first wave, and the second wave has a lower mean value of 1.23, while the decisions are less restrictive during this latter wave. Also, a short-term prediction model (about 4 months) was validated on the second wave. The validation criteria of this model reveal a good match between the results of the simulated model and the COVID-19 data reported via the Ministry of Health, Solidarity, and Social Action of Senegal.展开更多
Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urg...Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.展开更多
In this paper, a Negative Binomial (NB) Integer-valued Autoregressive model of order 1, INAR (1), is used to model and forecast the cumulative number of confirmed COVID-19 infected cases in Kenya independently for the...In this paper, a Negative Binomial (NB) Integer-valued Autoregressive model of order 1, INAR (1), is used to model and forecast the cumulative number of confirmed COVID-19 infected cases in Kenya independently for the three waves starting from 14<sup>th</sup> March 2020 to 1<sup>st</sup> February 2021. The first wave was experienced from 14<sup>th</sup> March 2020 to 15<sup>th</sup> September 2020, the second wave from around 15<sup>th</sup> September 2020 to 1<sup>st</sup> February 2021 and the third wave was experienced from 1<sup>st</sup> February 2021 to 3<sup>rd</sup> June 2021. 5, 10, and 15-day-ahead forecasts are obtained for these three waves and the performance of the NB-INAR (1) model analysed.展开更多
The higher concentration of PM_(10) and PM_(2.5) in the lower atmosphere is severely harmful for human health and it also makes visibility diminution along with weather and climate modifications.The main objective is ...The higher concentration of PM_(10) and PM_(2.5) in the lower atmosphere is severely harmful for human health and it also makes visibility diminution along with weather and climate modifications.The main objective is to find out the spatiotemporal variation and dispersal of PM_(10) and PM_(2.5) along with COVID-19 infection in the dusty city Kolkata.The consecutive two years PM_(10) and PM_(2.5) data of different stations have been obtained from State Pollution Control Board,Govt.of West Bengal.Forward trajectory analysis has been done through HYSPLIT(Hybrid Single Particle Lagrangian Integrated Trajectory)model to find the path and direction of air particles.The result showed that the various meteorological or environmental factors(such as temperature,relative humidity,wind,wind speed,pressure and gusty wind)and geographical location regulate the spatiotemporal variation of PM_(10) and PM_(2.5).These factors like high temperature with relative humidity and strong wind influence to disperse the particulate matters from north to south direction from city to outside during summer in Kolkata metropolitan city.During summer(both pre and lockdown years),the height of particles is extended up to 1000 m owing to active atmospheric ventilation whereas in winter it is confined within 100 m.The HYSPLIT model clearly specified that the particles dispersed from south,south-west to north and north east direction due to strong wind.The constant magnification of PM_(10) and PM_(2.5) in the lower atmosphere leads to greater frequency of COVID-19 infections and deaths.In Kolkata,the one of the crucial reasons of high infection and deaths(COVID-19)is co-morbidity of people.展开更多
基金supported by the Key Scientific Research Project of Wenzhou Polytechnic(No.WZY2025010).
文摘The COVID-19 pandemic,caused by the SARS-CoV-2 virus,has triggered a global health crisis,necessitating accurate predictive models to forecast disease severity and aid in clinical decision-making.This study introduces an innovative machine learning approach,the bDWPLO-FKNN model,designed to predict the severity of COVID-19 pneumonia in patients.The model incorporates the Differential Weibull Polar Lights Optimizer(DWPLO),an enhancement of the Polar Lights Optimizer(PLO)with the differential evolution operator and the Weibull flight operator,to perform effective feature selection.The DWPLO’s performance was rigorously tested against IEEE CEC 2017 benchmark functions,demonstrating its robust optimization capabilities.The binary version of DWPLO(bDWPLO)was then integrated with the Fuzzy K-Nearest Neighbors(FKNN)algorithm to form the predictive model.Using a dataset from the People’s Hospital Affiliated with Ningbo University,the model was trained to identify patients at risk of developing severe pneumonia due to COVID-19.The bDWPLO-FKNN model exhibited exceptional predictive accuracy,with an accuracy of 84.036% and a specificity of 88.564%.The analysis revealed key predictors,including albumin,albumin to globulin ratio,lactate dehydrogenase,urea nitrogen,gamma-glutamyl transferase,and inorganic phosphorus,which were significantly associated with disease severity.The integration of DWPLO with FKNN not only enhances feature selection but also bolsters the model’s predictive power,providing a valuable tool for clinicians to assess patient risk and allocate healthcare resources effectively during the COVID-19 pandemic.
文摘BACKGROUND Previous studies have reported the high predictive accuracy of 4C Mortality Score derived at hospital admission in coronavirus disease 2019(COVID-19)patients.Very few studies have assessed it at intensive care unit(ICU)admission and compared it with the Acute Physiology and Chronic Health Evaluation(APACHE)II score.There are no studies comparing its accuracy with APACHE III score.AIM To describe the characteristics and outcomes of patients admitted to ICU with COVID-19 infection and to compare the accuracy of 4C score and APACHE score in predicting mortality in these patients.METHODS We conducted this retrospective cohort study using an electronic database in a tertiary ICU in Sydney.We included all adult patients(age>16 years)admitted to ICU with COVID-19 infection over a 5-month period(July 1,2021 to November 30,2021).We collected the data on demographics,clinical characteristics,interventions and outcomes for all patients.We calculated the 4C Mortality Score for each patient using eight variables as described previously.We compared the predictive accuracy of 4C Mortality Score at hospital and ICU admission and APACHE II and III scores by area under the receiver operating characteristic curve(AUROC).We determined the optimal cut-off value for each of these scores using the‘nearest’method and its 95%confidence interval by bootstrapping.RESULTS A total of 140 patients(62%males,mean age 56±17 years,mean APACHE II score 13±57)were included in the study.Nineteen(13.6%)of 140 patients died in the hospital.Compared to survivors,the non-survivors were older,males,had more comorbidities,higher rate of mechanical ventilation and vasopressor use.The AUROC for the 4C Mortality Score at hospital and ICU admission and APACHE II and II score was 0.75,0.80.0.75 and 0.79 respectively.The optimal cut-off value for these four scores was 9,10,14 and 56 respectively.The cut-point for all the scores had higher sensitivity than specificity.CONCLUSION The 4C score at ICU admission had a higher accuracy in predicting mortality than the 4C score at hospital admission.The predictive accuracy was similar to that for APACHE III score.The 4C score at ICU admission needs to be validated in future studies.
文摘In response to an outbreak of coronavirus disease 2019(COVID-19)within a cluster of Navy personnel in Sri Lanka commencing from 22nd April 2020,an aggressive outbreak management program was launched by the Epidemiology Unit of the Ministry of Health.To predict the possible number of cases within the susceptible population under four social distancing scenarios,the COVID-19 Hospital Impact Model for Epidemics(CHIME)was used.With increasing social distancing,the epidemiological curve flattened,and its peak shifted to the right.The observed or actually reported number of cases was above the projected number of cases at the onset;however,subsequently,it fell below all predicted trends.Predictive modelling is a useful tool for the control of outbreaks such as COVID-19 in a closed community.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 2/25/42),www.kku.edu.sa.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.
文摘Corona Virus Disease-2019(COVID-19)was reported at first in Wuhan city,China by December 2019.World Health Organization(WHO)declared COVID-19 as a pandemic i.e.,global health crisis onMarch 11,2020.The outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread,not only affected the economic status of a number of countries,but it also resulted in increased levels of Depression,Anxiety,and Stress(DAS)among people.Therefore,there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear;with tremendously-limitingmeasures of social distancing and lockdown in force;and with high rates of new cases and mortalities.With this motivation,the current study aims at investigating theDAS levels among college students during COVID-19 lockdown since they are identified as a highly-susceptible population.The current study proposes to develop Intelligent Feature Subset Selection withMachine Learning-based DAS predictive(IFSSML-DAS)model.The presented IFSSML-DAS model involves data preprocessing,Feature Subset Selection(FSS),classification,and parameter tuning.Besides,IFSSML-DAS model uses Group Gray Wolf Optimization based FSS(GGWO-FSS)technique to reduce the curse of dimensionality.In addition,Beetle Swarm Optimization based Least Square Support Vector Machine(BSO-LSSVM)model is also employed for classification in which the weight and bias parameters of the LSSVM model are optimally adjusted using BSO algorithm.The performance of the proposed IFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different measures.The outcome of the study suggests the development of specialized programs to handleDAS among population so as to overcome COVID-19 crisis.
文摘The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.
基金College of Medicine and Public Health,MD,Advanced Studies ProgramAustralian National Health and Medical Research Council(NHMRC),Grant/Award Number:GNT2003683。
文摘The COVID-19 pandemic posed a challenge for clinical management of a new lung disease that was characterized by inflammation,endothelial cell dysfunction,and thrombosis,which occur after the replication phase of infection of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).There are many laboratory models of active SARS-CoV-2 infection in mice,reflecting an acute lung injury in an otherwise healthy animal,but there is a lack of accurate animal models of the postviral inflammatory phase of the COVID-19 lung reflecting severe disease.The monocrotaline(MCT)-treated rat is a widely used laboratory model of pulmonary hypertension(PH).Not often discussed,however,are the observed changes in inflammation,edema,fibrosis,and microthrombosis in the lung prior to PH.At the cellular level,there is loss of pneumocytes and endotheliopathy,and at the molecular level the MCT rat lung is characterized by a proinflammatory cytokine profile,namely elevated interleukin 6,transforming growth factorβand tumor necrosis factor,M1 macrophage phenotype,and dysregulation of the angiotensin converting enzyme(ACE)/ACE2 balance.The systems-level pathophysiology of the MCT-treated rat includes progressive cardiopulmonary dysfunction.The MCT-treated rat clearly differs from the COVID-19 lung in terms of the triggers for pathology,but there are many parallels apparent in both the MCT-treated rat and the COVID-19 lung.The MCT-treated rat lung as a model of the COVID-19 lung may provide an in-depth understanding of the factors that drive the lung to more severe pathology,treatments that benefit lung recovery,or the factors that prove a useful research platform for future emerging respiratory threats of similar pathology.
基金supported by the project“Societal and Economic Resilience within multi-hazards environment in Romania”funded by European Union-Next generation EU and Romanian Government,under National Recovery and Resilience Plan for Romania,contract no.760050/23.05.2023,cod PNRR-C9-I8-CF 267/29.11.2022through the Romanian Ministry of Research,Innovation and Digitalization,within Component 9,Investment I8.
文摘This paper aims to provide a window opportunity to share a reflection and learning from different countries and from other disciplines with the focus on resilience.There is also an attempt to theorize the concept of learning from spurious success and failure in the context of COVID-19.The main emphasis is to provide understanding of the causal factors and the identification of improved measures and modelling approaches to prevent and mitigate against future pandemics.Proposed decision tools of resilience and bowtie modelling as enablers for decision makers to prevent hazards and protect against their consequences.
基金This work was supported by the Taif University Researchers supporting Project Number(TURSP-2020/254).
文摘COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,respectively.Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty.The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and Random Forest Model.The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021.The model has been developed to obtain the forecast values till September 2021.This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country.In India,the cases are rapidly increasing day-by-day since mid of Feb 2021.The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave.To empower the prediction for future validation,the proposed model works effectively.
基金This work was jointly supported by the National Natural Science Foundation of China[grant numbers 41521004 and 41875083]the Gansu Provincial Special Fund Project for Guiding Scientific and Technological Innovation and Development[grant number 2019ZX-06].
文摘In 2020,the COVID-19 pandemic spreads rapidly around the world.To accurately predict the number of daily new cases in each country,Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic(GPCP).In this article,the authors use the ensemble empirical mode decomposition(EEMD)model and autoregressive moving average(ARMA)model to improve the prediction results of GPCP.In addition,the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease,whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model.Judging from the results,the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP.For countries such as El Salvador with a small number of cases,the absolute values of the relative errors of prediction become smaller.Therefore,this article concludes that this method is more effective for improving prediction results and direct prediction.
文摘COVID-19 comes from a large family of viruses identied in 1965;to date,seven groups have been recorded which have been found to affect humans.In the healthcare industry,there is much evidence that Al or machine learning algorithms can provide effective models that solve problems in order to predict conrmed cases,recovered cases,and deaths.Many researchers and scientists in the eld of machine learning are also involved in solving this dilemma,seeking to understand the patterns and characteristics of virus attacks,so scientists may make the right decisions and take specic actions.Furthermore,many models have been considered to predict the Coronavirus outbreak,such as the retro prediction model,pandemic Kaplan’s model,and the neural forecasting model.Other research has used the time series-dependent face book prophet model for COVID-19 prediction in India’s various countries.Thus,we proposed a prediction and analysis model to predict COVID-19 in Saudi Arabia.The time series dependent face book prophet model is used to t the data and provide future predictions.This study aimed to determine the pandemic prediction of COVID-19 in Saudi Arabia,using the Time Series Analysis to observe and predict the coronavirus pandemic’s spread daily or weekly.We found that the proposed model has a low ability to forecast the recovered cases of the COVID-19 dataset.In contrast,the proposed model of death cases has a high ability to forecast the COVID-19 dataset.Finally,obtaining more data could empower the model for further validation.
文摘Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span.Corona Virus Disease-2019(COVID-19),also known as novel coronavirus or SARSCoV-2(Severe Acute Respiratory Syndrome-Coronavirus-2),is a respiratory based touch contiguous disease.The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally.The whole world is making tremendous efforts to combat this life-threatening disease.For taking remedial action and planning preventive measures on time,there is an urgent need for efficient prediction models to confront the COVID-19 outbreak.A deep learning-based ARIMA-LSTM hybrid model is proposed in this article for predicting the COVID-19 outbreak by utilizing real-time information from the WHO’s daily bulletin report as well as provides information regarding clinical trials across the world.To evaluate the suitability and performance of our proposed model compared to other well-established prediction models,an experimental study has been performed.To estimate the prediction results,the three performance measures,i.e.,Root Mean Square Error(RMSE),Coefficient of determination(R2 Score),and Mean Absolute Percentage Error(MAPE)have been employed.The prediction results of fifty countries substantiated the fact that the proposed ARIMA-LSTM hybrid model performs very well as compared to other models.The proposed model archives the lowest RMSE,lowest MAPE,and highest R2 Score throughout the testing,under varied selection criteria(country-wise).This article aims to contribute a deep learning-based solution for the wellbeing of livings and to provide the current status of clinical trials across the globe.
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The COVID-19 pandemic is a curse and a threat to global health, development, the economy, and peaceful society because of its massive transmission and high rates of mutation. More than 220 countries have been affected by COVID-19. The world is now facing a drastic situation because of this ongoing virus. Bangladesh is also dealing with this issue, and due to its dense population, it is particularly vulnerable to the spread of COVID-19. Recently, many non-linear systems have been proposed to solve the SIR (Susceptible, Infected, and Recovered) model for predicting Coronavirus cases. In this paper, we have discussed the fractional order SIR epidemic model of a non-fatal disease in a population of a constant size. Using the Laplace Adomian Decomposition method, we get an approximate solution to the model. To predict the dynamic transmission of COVID-19 in Bangladesh, we provide a numerical argument based on real data. We also conducted a comparative analysis among susceptible, infected, and recovered people. Furthermore, the most sensitive parameters for the Basic Reproduction Number (<em>R</em><sub>0</sub>) are graphically presented, and the impact of the compartments on the transmission dynamics of the COVID-19 pandemic is thoroughly investigated.
基金supported by the Researchers Supporting Project Number(RSP-2020/250),King Saud University,Riyadh,Saudi Arabia.
文摘The COVID-19 pandemic has caused hundreds of thousands of deaths,millions of infections worldwide,and the loss of trillions of dollars for many large economies.It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope.Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses.In the same vein,a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of,and role of age groups and gender on,the probability of COVID-19 infection.This study aimed to review,analyze,and critically appraise published works that report on various factors to explain their relationship with COVID-19.Such studies span a wide range,including descriptive analyses,ratio analyses,cohort,prospective and retrospective studies.Various studies that describe indicators to determine the probability of infection among the general population,as well as the risk factors associated with severe illness and mortality,are critically analyzed and these ndings are discussed in detail.A comprehensive analysis was conducted on research studies that investigated the perceived differences in vulnerability of different age groups and genders to severe outcomes of COVID-19.Studies incorporating important demographic,health,and socioeconomic characteristics are highlighted to emphasize their importance.Predominantly,the lack of an appropriated dataset that contains demographic,personal health,and socioeconomic information implicates the efcacy and efciency of the discussed methods.Results are overstated on the part of both exclusion of quarantined and patients with mild symptoms and inclusion of the data from hospitals where the majority of the cases are potentially ill.
基金The authors Kamal Shah,and Thabet Abdeljawad would like to thank Prince Sultan University for paying the APC.
文摘In this work,we present a model that uses the fractional order Caputo derivative for the novel Coronavirus disease 2019(COVID-19)with different hospitalization strategies for severe and mild cases and incorporate an awareness program.We generalize the SEIR model of the spread of COVID-19 with a private focus on the transmissibility of people who are aware of the disease and follow preventative health measures and people who are ignorant of the disease and do not follow preventive health measures.Moreover,individuals with severe,mild symptoms and asymptomatically infected are also considered.The basic reproduction number(R0)and local stability of the disease-free equilibrium(DFE)in terms of R0 are investigated.Also,the uniqueness and existence of the solution are studied.Numerical simulations are performed by using some real values of parameters.Furthermore,the immunization of a sample of aware susceptible individuals in the proposed model to forecast the effect of the vaccination is also considered.Also,an investigation of the effect of public awareness on transmission dynamics is one of our aim in this work.Finally,a prediction about the evolution of COVID-19 in 1000 days is given.For the qualitative theory of the existence of a solution,we use some tools of nonlinear analysis,including Lipschitz criteria.Also,for the numerical interpretation,we use the Adams-Moulton-Bashforth procedure.All the numerical results are presented graphically.
文摘<strong>Objective:</strong> To assess the diagnostic accuracy and predictive values of clinical symptoms in patients with suspected mild COVID-19 to identify target groups for self-isolation and outpatient treatment without additional testing. <strong>Methods:</strong> We conducted an open-label prospective study in patients aged 18 to 72 years with suspected mild COVID-19. The clinical diagnosis was based on the acute onset of such symptoms as olfactory dysfunction, hyperthermia, myalgia, nasal congestion, nasal discharge, cough, rhinolalia, sore throat, without pneumonia in persons in contact with a confirmed case of COVID-19. The physician assessed clinical symptoms using a 4-point scale. The patient self-assessed clinical symptoms using a ten-point visual analogue scale (VAS). All enrolled patients underwent laboratory testing to confirm the diagnosis of COVID-19. <strong>Results:</strong> Of the 120 patients underwent testing, the diagnosis of mild COVID-19 was confirmed in 96 patients and ruled out in 24 patients. When assessing symptoms by a physician according to the correlation analysis, hyperthermia, myalgia, nasal congestion and rhinolalia have a positive predictive value with a significance level of more than 0.6. When self-assessing symptoms by a patient, fever, myalgia and nasal congestion have a diagnostic accuracy with a significance level of more than 0.5. Nasal discharge, cough and sore throat have negative predictive values. <strong>Conclusion: </strong>The presence of these symptoms in patients with an acute onset of the disease can help to make a clinical diagnosis of coronavirus disease and identify target groups for self-isolation and outpatient treatment without additional testing. Highly suspect asymptomatic patients are not considered as those who have possible mild COVID-19 infection.
文摘In this study, the mathematical SIR model (Susceptible-Infected-Recovered (cured and deceased)) was applied to the case of Senegal during the first two waves of the COVID-19 pandemic. During this period, from March 1, 2020, to March 30, 2021, the transmission and recovery rates as well as the number of reproduction were calculated and analyzed for the impact of the decisions taken by the Senegalese government. In both waves, the variation of the basic reproduction number as a function of time, with values below one towards the end of each study period, confirms the success of the Senegalese government in controlling the epidemic. The results show that the solution of mandatory mask-wearing is the best decision to counter the spread of the disease. Indeed, the mean number of reproduction is 2.11 in the first wave, and the second wave has a lower mean value of 1.23, while the decisions are less restrictive during this latter wave. Also, a short-term prediction model (about 4 months) was validated on the second wave. The validation criteria of this model reveal a good match between the results of the simulated model and the COVID-19 data reported via the Ministry of Health, Solidarity, and Social Action of Senegal.
文摘Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.
文摘In this paper, a Negative Binomial (NB) Integer-valued Autoregressive model of order 1, INAR (1), is used to model and forecast the cumulative number of confirmed COVID-19 infected cases in Kenya independently for the three waves starting from 14<sup>th</sup> March 2020 to 1<sup>st</sup> February 2021. The first wave was experienced from 14<sup>th</sup> March 2020 to 15<sup>th</sup> September 2020, the second wave from around 15<sup>th</sup> September 2020 to 1<sup>st</sup> February 2021 and the third wave was experienced from 1<sup>st</sup> February 2021 to 3<sup>rd</sup> June 2021. 5, 10, and 15-day-ahead forecasts are obtained for these three waves and the performance of the NB-INAR (1) model analysed.
文摘The higher concentration of PM_(10) and PM_(2.5) in the lower atmosphere is severely harmful for human health and it also makes visibility diminution along with weather and climate modifications.The main objective is to find out the spatiotemporal variation and dispersal of PM_(10) and PM_(2.5) along with COVID-19 infection in the dusty city Kolkata.The consecutive two years PM_(10) and PM_(2.5) data of different stations have been obtained from State Pollution Control Board,Govt.of West Bengal.Forward trajectory analysis has been done through HYSPLIT(Hybrid Single Particle Lagrangian Integrated Trajectory)model to find the path and direction of air particles.The result showed that the various meteorological or environmental factors(such as temperature,relative humidity,wind,wind speed,pressure and gusty wind)and geographical location regulate the spatiotemporal variation of PM_(10) and PM_(2.5).These factors like high temperature with relative humidity and strong wind influence to disperse the particulate matters from north to south direction from city to outside during summer in Kolkata metropolitan city.During summer(both pre and lockdown years),the height of particles is extended up to 1000 m owing to active atmospheric ventilation whereas in winter it is confined within 100 m.The HYSPLIT model clearly specified that the particles dispersed from south,south-west to north and north east direction due to strong wind.The constant magnification of PM_(10) and PM_(2.5) in the lower atmosphere leads to greater frequency of COVID-19 infections and deaths.In Kolkata,the one of the crucial reasons of high infection and deaths(COVID-19)is co-morbidity of people.
