Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models

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.

Supervised Machine Learning-Based Prediction of COVID-19

COVID-19 turned out to be an infectious and life-threatening viral disease,and its swift and overwhelming spread has become one of the greatest challenges for the world.As yet,no satisfactory vaccine or medication has been developed that could guarantee its mitigation,though several efforts and trials are underway.Countries around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely treatment.In this regard,healthcare experts,researchers and scientists have delved into the investigation of existing as well as new technologies.The situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this disease.The state-of-the-art research in Artificial intelligence(AI),Machine learning(ML)and cloud computing have encouraged healthcare experts to find effective detection schemes.This study aims to provide a comprehensive review of the role of AI&ML in investigating prediction techniques for the COVID-19.A mathematical model has been formulated to analyze and detect its potential threat.The proposed model is a cloud-based smart detection algorithm using support vector machine(CSDC-SVM)with cross-fold validation testing.The experimental results have achieved an accuracy of 98.4%with 15-fold cross-validation strategy.The comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency.

Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

In this study,we have proposed an artificial neural network(ANN)model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17,2020.The proposed model is based on the existing data(training data)published in the Saudi Arabia Coronavirus disease(COVID-19)situation—Demographics.The Prey-Predator algorithm is employed for the training.Multilayer perceptron neural network(MLPNN)is used in this study.To improve the performance of MLPNN,we determined the parameters of MLPNN using the prey-predator algorithm(PPA).The proposed model is called the MLPNN–PPA.The performance of the proposed model has been analyzed by the root mean squared error(RMSE)function,and correlation coefficient(R).Furthermore,we tested the proposed model using other existing data recorded in Saudi Arabia(testing data).It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia.The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789.The number of recoveries will be 2000 to 4000 per day.

Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19

Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients.This study aimed to develop a COVID-19 diagnosis and prediction(AIMDP)model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography(CT)scans.The proposed system uses convolutional neural networks(CNNs)as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment.We employed the whale optimization algorithm(WOA)to select the most relevant patient signs.A set of experiments validated AIMDP performance.It demonstrated the superiority of AIMDP in terms of the area under the curve-receiver operating characteristic(AUC-ROC)curve,positive predictive value(PPV),negative predictive rate(NPR)and negative predictive value(NPV).AIMDP was applied to a dataset of hundreds of real data and CT images,and it was found to achieve 96%AUC for diagnosing COVID-19 and 98%for overall accuracy.The results showed the promising performance of AIMDP for diagnosing COVID-19 when compared to other recent diagnosing and predicting models.

Prediction of Covid-19 Based on Chest X-Ray Images Using Deep Learning with CNN

The COVID-19 pandemic has caused trouble in people’s daily lives andruined several economies around the world, killing millions of people thus far. Itis essential to screen the affected patients in a timely and cost-effective manner inorder to fight this disease. This paper presents the prediction of COVID-19 withChest X-Ray images, and the implementation of an image processing systemoperated using deep learning and neural networks. In this paper, a Deep Learning,Machine Learning, and Convolutional Neural Network-based approach for predicting Covid-19 positive and normal patients using Chest X-Ray pictures is proposed. In this study, machine learning tools such as TensorFlow were used forbuilding and training neural nets. Scikit-learn was used for machine learning fromend to end. Various deep learning features are used, such as Conv2D, Dense Net,Dropout, Maxpooling2D for creating the model. The proposed approach had aclassification accuracy of 96.43 percent and a validation accuracy of 98.33 percentafter training and testing the X-Ray pictures. Finally, a web application has beendeveloped for general users, which will detect chest x-ray images either as covidor normal. A GUI application for the Covid prediction framework was run. Achest X-ray image can be browsed and fed into the program by medical personnelor the general public.

Prediction of COVID-19 Pandemic Spread in Kingdom of Saudi Arabia

A significant increase in the number of coronavirus cases can easily be noticed in most of the countries around the world.Inspite of the consistent preventive initiatives being taken to contain the spread of this virus,the unabated increase in the cases is both alarming and intriguing.The role of mathematical models in predicting and estimating the spread of the virus,and identifying various preventive factors dependencies has been found important and effective in most of the previous pandemics like Severe Acute Respiratory Syndrome(SARS)2003.In this research work,authors have proposed the Susceptible-Infectected-Removed(SIR)model variation in order to forecast the pattern of coronavirus disease(COVID-19)spread for the upcoming eight weeks in perspective of Saudi Arabia.The study has been performed by using SIR model with a proposed simplification using average progression for further estimation ofβandγvalues for better curve fittings ratios.The predictive results of this study clearly show that under the current public health interventions,there will be an increase in the COVID-19 cases in Saudi Arabia in the next four weeks.Hence,a set of strong health primitives and precautionary measures are recommended in order to avoid and prevent the further spread of COVID-19 in Saudi Arabia.

Prediction of COVID-19 Transmission in the United States Using Google Search Trends

Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources.Due to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature.To accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access dataset.Our objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms.Specifically,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset.Considering the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,respectively.On the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,respectively.For 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,respectively.We found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 transmission.We also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.

Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method

The fast spread of coronavirus disease(COVID-19)caused by SARSCoV-2 has become a pandemic and a serious threat to the world.As of May 30,2020,this disease had infected more than 6 million people globally,with hundreds of thousands of deaths.Therefore,there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems.This study uses gradient boosting regression(GBR)to build a trained model to predict the daily total confirmed cases of COVID-19.The GBR method can minimize the loss function of the training process and create a single strong learner from weak learners.Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22,2020,to May 30,2020.The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR method.The results reveal that the GBR model achieves 0.00686 root mean square error,the lowest among several comparative models.

Influencing Factors and Mortality Prediction of Covid-19 Contact Infection Rate in China

Taking the COVID-19 data from 2020-1-23 to 3-21 days released by the China Health Protection Committee as the sample,the hospital remaining rate,mortality rate and cure rate are selected as covariates,and the contact infection rate is used as response variable to establish a high precision ADL model,results of return substitution show that the predicted value of contact infection rate almost coincides with the sample value,and shows three stages of change characteristics.After March 1,2020,the overall trend is downward,stable below 12%.Main influencing factors of contact infection rate are analyzed quantitatively.Based on this,the ARIMA(1,2,2)model is established to analyze and predict the mortality change trend.The results showed that the domestic COVID-19 mortality rate is stable near 4%after 2020-3-27.

Predictive Factors of Renal Failure in COVID-19 Patients at the Anti-COVID Center in Lome, Togo

Background: Angiotensin-converting enzyme 2 has been identified as the receptor that allows the entry of SarsCov2 into the human cell. Its expression in the kidney is 100 times higher than in the lung;thus, making the kidney an excellent target for SarsCov2 infection manifesting as renal failure (RF). The objective of this study was to determine the predictive factors of RF during COVID-19 in the Togolese context. Patients and Methods: This was a retrospective descriptive and analytical study conducted at the Lomé Anti-COVID Center including the records of patients hospitalized for COVID-19, of age ≥ 18 years and having performed a creatinemia. RF was defined by a GFR 2 calculated according to the MDRD formula. Patients were randomized into 2 groups according to GFRResults: 482 patients were selected for this study with a mean age of 58.02 years. Sixty-five percent of the patients were men, i.e., a sex ratio of 1.88. Fifty-two patients had RF, i.e., a frequency of 10.8%.There were 65% men (315 cases), for a sex ratio (M/F) of 1.88. Risk factors for renal failure in COVID-19 were age ≥ 65 years (ORa 2.42;CIa95% [1.17 - 4.95];p = 0.016), anemia (ORa 2.49;CIa95% [1.21 - 5.26];p = 0.015), moderate (ORa 13;CIa95% [2.30 - 2.44];p = 0.017), severe (ORa 26.2;CIa95% [4.85 - 4.93];p = 0.002) and critical (ORa 108;CIa95% [16.5 - 21.76];p Conclusion: Renal failure would therefore be related to the severity of COVID-19 and is the most formidable factor, conditioning the course of the disease and the patient’s vital prognosis.

COVID-19 Outbreak Prediction by Using Machine Learning Algorithms

COVID-19 is a contagious disease and its several variants put under stress in all walks of life and economy as well.Early diagnosis of the virus is a crucial task to prevent the spread of the virus as it is a threat to life in the whole world.However,with the advancement of technology,the Internet of Things(IoT)and social IoT(SIoT),the versatile data produced by smart devices helped a lot in overcoming this lethal disease.Data mining is a technique that could be used for extracting useful information from massive data.In this study,we used five supervised ML strategies for creating a model to analyze and forecast the existence of COVID-19 using the Kaggle dataset“COVID-19 Symptoms and Presence.”RapidMiner Studio ML software was used to apply the Decision Tree(DT),Random Forest(RF),K-Nearest Neighbors(K-NNs)and Naive Bayes(NB),Integrated Decision Tree(ID3)algorithms.To develop the model,the performance of each model was tested using 10-fold cross-validation and compared to major accuracy measures,Cohan’s kappa statistics,properly or mistakenly categorized cases and root means square error.The results demonstrate that DT outperforms other methods,with an accuracy of 98.42%and a root mean square error of 0.11.In the future,a devisedmodel will be highly recommendable and supportive for early prediction/diagnosis of disease by providing different data sets.

A Deep Learning Drug-Target Binding Affinity Prediction Based on Compound Microstructure and Its Application in COVID-19 Drug Screening

Drug target relationship(DTR)prediction is a rapidly evolving area of research in com-putational drug discovery.Despite recent advances in computational solutions that have overcome the challenges of in vitro and in vivo experiments,most computational methods still focus on binary classification.They ignore the importance of binding affinity,which correctly distinguishes between on-targets and off-targets.In this study,we propose a deep learning model based on the microstruc-ture of compounds and proteins to predict drug-target binding affinity(DTA),which utilizes topo-logical structure information of drug molecules and sequence semantic information of proteins.In this model,graph attention network(GAT)is used to capture the deep features of the compound molecular graph,and bidirectional long short-term memory(BiLSTM)network is used to extract the protein sequence features,and the pharmacological context of DTA is obtained by combining the two.The results show that the proposed model has achieved superior performance in both cor-rectly predicting the value of interaction strength and correctly discriminating the ranking of bind-ing strength compared to the state-of-the-art baselines.A case study experiment on COVID-19 con-firms that the proposed DTA model can be used as an effective pre-screening tool in drug discovery.

Prediction System for Diagnosis and Detection of Coronavirus Disease-2019(COVID-19):A Fuzzy-Soft Expert System

In early December 2019,a new virus named“2019 novel coronavirus(2019-nCoV)”appeared in Wuhan,China.The disease quickly spread worldwide,resulting in the COVID-19 pandemic.In the currentwork,we will propose a novel fuzzy softmodal(i.e.,fuzzy-soft expert system)for early detection of COVID-19.Themain construction of the fuzzy-soft expert systemconsists of five portions.The exploratory study includes sixty patients(i.e.,fortymales and twenty females)with symptoms similar to COVID-19 in(Nanjing Chest Hospital,Department of Respiratory,China).The proposed fuzzy-soft expert systemdepended on five symptoms of COVID-19(i.e.,shortness of breath,sore throat,cough,fever,and age).We will use the algorithm proposed by Kong et al.to detect these patients who may suffer from COVID-19.In this way,the present system is beneficial to help the physician decide if there is any patient who has COVID-19 or not.Finally,we present the comparison between the present system and the fuzzy expert system.

Prediction for risk of disease progression among hospitalized COVID-19 patients

Objective:The COVID-19 pandemic poses a significant threat to global health.Given the lack of studies on risk factors for COVID-19 progression at present,this study aimed to build a predictive model to predict the progression risk among hospitalized COVID-19 patients.Methods:We extracted data from 1074 mild and moderate COVID-19 patients from Electronic Health Records(EHRs)in a designated Wuhan hospital including demographic characteristics and clinical and laboratory information.Disease progression was defined as progressing to severe critical illness after admission.The LASSO regression was used to select the predicted variables and a logistic regression model was applied to build the predictive model.Nomogram was used to show the results.Results:Seven variables were included in the predictive model:age per 10 years(OR,1.15;95%CI,1.03-1.29),lactate dehydrogenase(OR,1.73;95%CI,1.14-2.62),neutrophil-to-lymphocyte ratio(OR,2.07;95%CI,1.42-3.02),eosinophil count(OR,2.10;95%CI,1.20-3.69),albumin(OR,2.37;95%CI,1.65-3.45),hemoglobin(OR,1.50;95%CI,1.10-2.05),D-dimer(OR,1.63;95%CI,1.19-2.23).The mean area under the receiver operating characteristic curve of the predictive model was 0.72(95%CI,0.69-0.76).Conclusions:This study built a predictive model that could effectively predict the progression risk among hospitalized COVID-19 patients.

基于CiteSpace及VOSviewer的COVID-19相关心律失常的文献计量学分析

目的:分析新型冠状病毒感染(COVID-19)相关心律失常的文献,探索该领域的研究现状、热点并预测未来的趋势,为后来的研究者提供借鉴。方法:选择Web of Science的核心合集数据库,每项研究都进行了文献计量和视觉分析,使用CiteSpace和VOSviewer软件生成知识图谱。结果:共鉴定出768篇文章,发文涉及美国、意大利和中国为首的319个国家/地区和4 366个机构,领先的研究机构是梅奥诊所和哈佛医学院。New England Journal of Medicine是该领域最常被引用的期刊。在6 687位作者中,Arbelo Elena撰写的研究最多,Guo T被共同引用的次数最多,心房纤颤是最常见的关键词。结论:随着COVID-19的暴发,对COVID-19所致新发/进行性心律失常事件的研究蓬勃发展,未来的研究者可能会对COVID-19感染后新发或遗留的快速性心律失常/缓慢性心律失常的发生机制进行进一步的探索。

麻杏石甘汤干预COVID-19肺炎的网络药理学研究及分子对接分析

目的:运用网络药理学与分子对接研究方法探讨麻杏石甘汤干预COVID-19肺炎的作用机制。方法:经TCMSP、HTDocking数据库检索麻杏石甘汤所有药物的成分并进行ADME筛选得到活性成分,在Uniport数据库检索其发挥治疗作用可能的基因靶点;借助GeneCards、PharmGKB等数据库检索COVID-19肺炎相关基因靶点,利用Cytoscape软件构建药物-活性成分-靶基因-疾病网络;借助String系统构建PPI网络,并进行拓扑学特征分析,筛选核心基因;利用R语言的clusterProfiler包对核心基因进行GO、KEGG富集分析;利用AutoDock Vina软件对疾病靶蛋白及药物活性成分进行分子对接验证。结果:共筛选出麻杏石甘汤与COVID-19肺炎交集基因靶点67个。PPI网络分析显示,其发挥治疗作用的核心基因靶点有MAPK1、MAPK3、STAT1、JUN等8个。GO富集分析显示,相关生物过程1863个,分子功能95个,细胞成分36个;KEGG富集分析显示,相关作用通路167条。分子对接结果显示,麻杏石甘汤活性成分槲皮素、山奈酚、熊果酸等与核心基因靶点JUN、MAK1、IL2等有较好的结合活性。结论:麻杏石甘汤可能以槲皮素、山奈酚等为物质基础,多成分、多靶点、多通路、相互协同发挥抑制病毒活性,降低炎症反应,保护肺部组织,从而对COVID-19肺炎起到积极的干预作用。

COVID-19诊疗信息、中医证型分布及组方用药规律的文献研究

目的利用数据挖掘技术深入探究新型冠状病毒感染(COVID-19,简称新冠感染)患者的诊断、中医证型分布以及药物使用规律,以期为中医药治疗新冠感染提供有效的参考依据。方法通过搜索,可以获取来自国家卫生健康委员会和全国各地的《新型冠状病毒肺炎诊疗方案》内涉及的中医证型和中医药防治方案,以及中国生物医学文献服务系统、知网、维普、万方数据库收录的治疗新冠感染相关文献共249份。对文献通过筛选、整理和去重并建立中药复方数据库、诊疗信息数据库、证型数据库,运用频数分析、频率分析进行探究。结果新冠感染患者常见的症状频数较高的为咳嗽、咽干咽痛、发热、纳差、乏力;大多数患者呈淡红舌、红舌,脉象为滑脉、滑数脉;中医证型频数较高的有湿毒郁肺证、湿热蕴肺证、肺脾气虚证、寒湿郁肺证、气阴两伤证、兼夹瘀血证。此外,共纳入491首治疗新冠感染中药复方,涉及中药227味,得到高频中药共64个,药物类别以清热药、补益药、解表药、化痰止咳平喘药、化湿药为主;药性以温、平、寒为主,药味以甘、辛、苦为主,归经中归肺、脾、胃经中药居多;聚类分析结果根据中药性能将治疗新冠感染的高频药物聚为8类较好。结论中医药治疗新型冠状病毒感染用药具有以下特点:补益药用药次数较多体现攻邪不忘扶正;解表、清热、攻下、化湿、利湿、渗湿药物俱全体现多种逐邪之法;药类以清热药、补益药、化湿药、化痰止咳平喘药、解表药为主,彰显新冠感染基本治法为清热化湿、止咳平喘、补养气阴。可为指导临床用药及研发新药提供一定的参考与借鉴。

低频脉冲磁场诱导TRPC1改善COVID-19患者康复期下肢的肌肉无力症状

背景:肌肉无力是新型冠状病毒(COVID-19)感染后的常见症状,影响康复期人体日常活动能力。在强度1.5 mT,频率3300 Hz的低频脉冲磁场刺激下可通过诱导和激活经典瞬时感受器电位通1(classical transient receptor potential channel 1,TRPC1),提升人体骨骼肌的最大自主收缩力与力量耐力,对肌肉组织产生一系列生理支持效应,该手段是否会改善新型冠状病毒肺炎患者康复期的肌无力症状尚无研究。目的:选用低频脉冲磁场对新型冠状病毒肺炎患者下肢肌群进行磁刺激,以观察该刺激对新型冠状病毒肺炎患者康复期下肢肌群肌无力改善的影响。方法:招募胶体金法抗原检测试剂(COVID-19)为阳性并伴有肌肉无力症状的新型冠状病毒(奥密克戎毒株)感染患者14例,将所有受试者随机分成2组,分别为接受磁场刺激的试验组和接受假治疗的对照组。试验总时长3周,试验组每隔48 h对腿部进行低频脉冲磁刺激,对照组与试验组干预流程一致但给予假刺激,两组患者均不被告知磁刺激仪器是否运行,两组患者共进行9次操作,随后观察两组患者下肢局部肌群最大自主收缩力、腿部爆发力与力量耐力的变化情况。结果与结论:①在采集的8个局部肌群中,试验组患者7个局部肌群在经过3周的低频脉冲磁场刺激,最大自主收缩力值均增长。对照组除3个肌群最大自主收缩力自行增长改善以外,其他肌群肌力无提升。②试验组的左腿前群与双腿后群提升率显著高于对照组。③两组的纵跳摸高高度与膝关节峰值角速度相比试验前测均提升,试验组摸高高度提升率高于对照组。④在疲劳状态下,试验组膝关节峰值角速度下降率显著下降,对照组膝关节峰值角速度下降率无显著性变化;试验组摸高高度下降率显著下降,而对照组摸高高度下降率无显著性变化。⑤上述数据证实,在强度1.5 mT,频率3300 Hz的低频脉冲磁场刺激方案下,新型冠状病毒肺炎患者在康复期经过3周的低频脉冲磁场刺激相比人体自愈过程可使更多的下肢局部肌群肌力获得提升,对基于腿部爆发力的全身协调发力能力及功能状态明显改善。因此,低频脉冲磁场刺激可作为一种改善新冠感染患者下肢肌肉无力症状的有效、非运动的康复手段。

全球COVID-19疫情主要预测模型比较分析

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

COVID-19疫情前后28个国家生育率变化及影响因素研究:线性模型和中断时间序列分析

目的分析COVID-19疫情暴发前后不同国家经季节和日历调整后的生育率(seasonally and calendar adjusted fertility rate,SAFR)趋势的变化及其影响因素。方法使用国际人类生育力数据库(Human Fertility Database,HFD)中28个国家自2012年1月至2022年12月的月度SAFR数据,以2020年12月(2020年3月疫情暴发起点加9个月妊娠过程)为节点划分为疫情前(2012.1-2020.11)和疫情后(2020.12-2022.12)进行比较,使用中断时间序列方法分析各国疫情前后的SAFR趋势(短期波动和长期趋势)是否发生变化,使用秩和检验分析疫情前SAFR、人均GDP、公共卫生和社会措施(public health and social measures,PHSM)和失业率是否与SAFR趋势变化有关。结果疫情后28个国家中19个国家的SAFR出现短期下降,随后反弹。对于长期趋势,2个国家由下降趋势转为上升趋势,8个国家由上升趋势转为下降趋势,6个国家的SAFR保持不变。SAFR变化率下降主要集中在部分中欧国家以及地中海西岸的国家,而SAFR变化率增加的国家主要分布在北欧以及西欧地区。SAFR无短期波动的国家疫情前的SAFR低于有短期波动的国家(P=0.041),SAFR变化率下降国家的疫情前SAFR(P=0.005)与人均GDP(P=0.027)均低于SAFR变化率上升国家。未发现SAFR短期波动或长期趋势与PHSM严重程度指数或失业率存在关联。结论COVID-19疫情对28个国家的SAFR造成了不同的短期和长期影响,特别是经济水平和疫情前SAFR相对较低的国家可能更易遭到进一步打击。COVID-19疫情对各国人口的更长期影响值得进一步关注。