A Probabilistic Description of the Impact of Vaccine-Induced Immunity in the Dynamics of COVID-19 Transmission

The recent outbreak of COVID-19 has caused millions of deaths worldwide and a huge societal and economic impact in virtually all countries. A large variety of mathematical models to describe the dynamics of COVID-19 transmission have been reported. Among them, Bayesian probabilistic models of COVID-19 transmission dynamics have been very efficient in the interpretation of early data from the beginning of the pandemic, helping to estimate the impact of non-pharmacological measures in each country, and forecasting the evolution of the pandemic in different potential scenarios. These models use probability distribution curves to describe key dynamic aspects of the transmission, like the probability for every infected person of infecting other individuals, dying or recovering, with parameters obtained from experimental epidemiological data. However, the impact of vaccine-induced immunity, which has been key for controlling the public health emergency caused by the pandemic, has been more challenging to describe in these models, due to the complexity of experimental data. Here we report different probability distribution curves to model the acquisition and decay of immunity after vaccination. We discuss the mathematical background and how these models can be integrated in existing Bayesian probabilistic models to provide a good estimation of the dynamics of COVID-19 transmission during the entire pandemic period.

Quantitative Analysis of the Effectiveness of Antigen-and Polymerase Chain Reaction-Based Combination Strategies for Containing COVID-19 Transmission in a Simulated Community

The number of coronavirus disease 2019(COVID-19)cases continues to surge,overwhelming healthcare systems and causing excess mortality in many countries.Testing of infectious populations remains a key strategy to contain the COVID-19 outbreak,delay the exponential spread of the disease,and flatten the epidemic curve.Using the Omicron variant outbreak as a background,this study aimed to evaluate the effectiveness of testing strategies with different test combinations and frequencies,analyze the factors associated with testing effectiveness,and optimize testing strategies based on these influencing factors.We developed a stochastic,agent-based,discrete-time susceptible–latent–infectious–recovered model simulating a community to estimate the association between three levels of testing strategies and COVID-19 transmission.Antigen testing and its combination strategies were more efficient than polymerase chain reaction(PCR)-related strategies.Antigen testing also showed better performance in reducing the demand for hospital beds and intensive care unit beds.The delay in the turnaround time of test results had a more significant impact on the efficiency of the testing strategy compared to the detection limit of viral load and detection-related contacts.The main advantage of antigen testing strategies is the short turnaround time,which is also a critical factor to be optimized to improve PCR strategies.After modifying the turnaround time,the strategies with less frequent testing were comparable to daily testing.The choice of testing strategy requires consideration of containment goals,test efficacy,community prevalence,and economic factors.This study provides evidence for the selection and optimization of testing strategies in the post-pandemic era and provides guidance for optimizing healthcare resources.

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.

Potential of Arbidol for Post-exposure Prophylaxis of COVID-19 Transmission:A Preliminary Report of a Retrospective Cohort Study

The efficient transmission of severe acute respiratory syndrome-2 coronavirus(SARS-CoV-2)from patients to health care workers or family members has been a worrisome and prominent feature of the ongoing outbreak.On the basis of clinical practice and in-vitro studies,we postulated that post-exposure prophylaxis(PEP)using Arbidol is associated with decreased infection among individuals exposed to confirmed cases of COVID-19 infection.We conducted a retrospective cohort study on family members and health care workers who were exposed to patients confirmed to have SARS-CoV-2 infection by real-time RT-PCR and chest computed tomography(CT)from January 1 to January 16,2020.The last follow-up date was Feb.26,2020.The emergence of fever and/or respiratory symptoms after exposure to the primary case was collected.The correlations between post-exposure prophylaxis and infection in household contacts and health care workers were respectively analyzed.A total of 66 members in 27 families and 124 health care workers had evidence of close exposure to patients with confirmed COVID-19.The Cox regression based on the data of the family members and health care workers with Arbidol or not showed that Arbidol PEP was a protective factor against the development of COVID-19(HR 0.025,95%CI 0.003-0.209,P=0.0006 for family members and HR 0.056,95%CI 0.005-0.662,P=0.0221 for health care workers).Our findings suggest Arbidol could reduce the infection risk of the novel coronavirus in hospital and family settings.This treatment should be promoted for PEP use and should be the subject of further investigation.

Temporal Dynamics in COVID-19 Transmission: Case of Some African Countries

Coronavirus disease 2019 (COVID-19) has become a global threat to public health and economy. The potential burden of this pandemic in developing world, particularly the African countries, is much concerning. With the aim of providing supporting evidence for decision making, this paper studies the dynamics of COVID-19 transmission through time in selected African countries. Time-dependent reproduction number (Rt) is one of the tools employed to quantify temporal dynamics of the disease. Pattern of the estimated reproduction numbers showed that transmissibility of the disease has been fluctuating through time in most of the countries included in this study. In few countries such as South Africa and Democratic Republic of Congo (DRC), these estimates dropped quickly and stayed stable, but greater than 1, for months. Regardless of their variability through time, the estimated reproduction numbers remain greater than or nearly equal to 1 in all countries. Another Statistical model used in this study, namely Autoregressive Conditional Poisson (ACP) model, showed that expected (mean) number of new cases is significantly dependent on short range change in new cases in all countries. In countries where there is no persistent trend in new cases, current mean number of new cases (on day t) depend on both previous observation and previous mean (day t - 1). In countries where there is continued trend in new cases, current mean is more affected by number of new cases on preceding day.

COVID-19 transmission and control in land public transport:A literature review

Land public transport is an important link within and between cities,and how to control the transmission of COVID-19 in land public transport is a critical issue in our daily lives.However,there are still many inconsistent opinions and views about the spread of SARS-CoV-2 in land public transport,which limits our ability to implement effective interventions.The purpose of this review is to overview the literature on transmission characteristics and routes of the epidemic in land public transport,as well as to investigate factors affecting its spread and provide feasible measures to mitigate the infection risk of passengers.We obtained 898 papers by searching the Web of Science,Pubmed,and WHO global COVID database by keywords,and finally selected 45 papers that can address the purpose of this review.Land public transport is a high outbreak area for COVID-19 due to characteristics like crowding,inadequate ventilation,long exposure time,and environmental closure.Different from surface touch transmission and drop spray transmission,aerosol inhalation transmission can occur not only in short distances but also in long distances.Insufficient ventilation is the most important factor influencing long-distance aerosol transmission.Other transmission factors(e.g.,interpersonal distance,relative orientation,and ambient conditions)should be noticed as well,which have been summarized in this paper.To address various influencing factors,it is essential to suggest practical and efficient preventive measures.Among these,increased ventilation,particularly the fresh air(i.e.,natural ventilation),has proven to effectively reduce indoor infection risk.Many preventive measures are also effective,such as enlarging social distance,avoiding face-to-face orientation,setting up physical partitions,disinfection,avoiding talking,and so on.As research on the epidemic has intensified,people have broken down many perceived barriers,but more comprehensive studies on monitoring systems and prevention measures in land public transport are still needed.

Epidemiological impact of revoking mask-wearing recommendation on COVID-19 transmission in Tokyo,Japan

Despite the global implementation of COVID-19 mitigation measures,the disease continues to maintain transmission.Although mask wearing became one of the key measures for preventing the transmission of COVID-19 early in the pandemic period,many countries have relaxed the mandatory or recommended wearing of masks.The objective of the present study was to estimate the epidemiological impact of removing the mask-wearing recommendation in Japan.We developed a model to assess the consequences of declining mask-wearing coverage after the government revoked its recommendation in February 2023.The declining mask-wearing coverage was estimated using serial cross-sectional data,and a mathematical model was devised to determine the age-specific incidence of COVID-19 using the observed case count in Tokyo from week of October 3,2022 to October 30,2023.We explored model-based counterfactual scenarios to measure hypothetical situations in which the mask-wearing coverage decreases or increases relative to the observed coverage.The results show that mask-wearing coverage declined from 97%to 69%by the week of October 30,2023,and that if the mask-wearing recommendation had continued,427 lives could have been saved in Tokyo.If the mask-wearing coverage had declined to 25%of the observed level,the model suggests there might have been 1587 additional deaths.Thus,revoking the mask-wearing recommendation had a substantial epidemiological impact.In future pandemics,our proposed approach could provide a realtime quantification of the effects of relaxing countermeasures.

Forecast of peak infection and estimate of excess deaths in COVID-19 transmission and prevalence in Taiyuan City,2022 to 2023

In this paper,with the method of epidemic dynamics,we assess the spread and prevalence of COVID-19 after the policy adjustment of prevention and control measure in December 2022 in Taiyuan City in China,and estimate the excess population deaths caused by COVID-19.Based on the transmission mechanism of COVID-19 among individuals,a dynamic model with heterogeneous contacts is established to describe the change of control measures and the population's social behavior in Taiyuan city.The model is verified and simulated by basing on reported case data from November 8th to December 5th,2022 in Taiyuan city and the statistical data of the questionnaire survey from December 1st to 23rd,2022 in Neijiang city.Combining with reported numbers of permanent residents and deaths from 2017 to 2021 in Taiyuan city,we apply the dynamic model to estimate theoretical population of 2022 under the assumption that there is no effect of COVID-19.In addition,we carry out sensitivity analysis to determine the propagation character of the Omicron strain and the effect of the control measures.As a result of the study,it is concluded that after adjusting the epidemic policy on December 6th,2022,three peaks of infection in Taiyuan are estimated to be from December 22nd to 31st,2022,from May 10th to June 1st,2023,and from September 5th to October 13th,2023,and the corresponding daily peaks of new cases can reach 400000,44000 and 22000,respectively.By the end of 2022,excess deaths can range from 887 to 4887,and excess mortality rate can range from 3.06%to 14.82%.The threshold of the infectivity of the COVID-19 variant is estimated 0.0353,that is if the strain infectivity is above it,the epidemic cannot be control with the previous normalization measures.

COVID-19 transmission driven by age-group mathematical model in Shijiazhuang City of China

Background:A COVID-19 outbreak in the rural areas of Shijiazhuang City was attributed to the complex interactions among vaccination,host,and non-pharmaceutical interventions(NPIs).Herein,we investigated the epidemiological characteristics of all reported symptomatic cases by picking Shijiazhuang City,Hebei Province in Northern China as research objective.In addition,we established an age-group mathematical model to perform the optimal fitting and to investigate the dynamical profiles under three scenarios.Methods:All reported symptomatic cases of Shijiazhuang epidemic(January 2-February 3,2021)were investigated in our study.The cases were classified by gender,age group and location,the distributions were analyzed by epidemiological characteristics.Furthermore,the reported data from Health Commission of Hebei Province was also analyzed by using an age-group mathematical model by two phases and three scenarios.Results:Shijiazhuang epidemic caused by SARS-CoV-2 wild strain was recorded with the peak 84 cases out of 868 reported symptomatic cases on January 11,2021,which was implemented with strong NPIs by local government and referred as baseline situation in this study.The research results showed that R0 under baseline situation ranged from 4.47 to 7.72,and Rt of Gaocheng Distinct took 3.72 with 95%confidence interval from 3.23 to 4.35 on January 9,the declining tendencies of Rt under baseline situation were kept till February 3,the value of Rt reached below 1 on January 19 and remained low value up to February 3 for Gaocheng District and Shijiazhuang City during Shijiazhuang epidemic.This indicated Shijiazhuang epidemic was under control on January 19.However,if the strong NPIs were kept,but remote isolation operated on January 11 was not implemented as of February 9,then the scale of Shijiazhuang epidemic reached 9,482 cases from age group who were 60 years old and over out of 31,017 symptomatic cases.The investigation also revealed that Shijiazhuang epidemic reached 132,648 symptomatic cases for age group who were 60 years old and over(short for G2)under risk-based strategies(Scenario A),58,048 symptomatic cases for G2 under late quarantine strategies(Scenario B)and 207,124 symptomatic cases for G2 under late quarantine double risk strategies(Scenario C),and that the corresponding transmission tendencies of Rt for three scenarios were consistently controlled on Jan 29,2021.Compared with baseline situation,the dates for controlling Rt below 1 under three scenarios were delayed 10 days.Conclusions:Shijiazhuang epidemic was the first COVID-19 outbreak in the rural areas in Hebei Province of Northern China.The targeted interventions adopted in early 2021 were effective to halt the transmission due to the implementation of a strict and village-wide closure.However we found that age group profile and NPIs played critical rules to successfully contain Shijiazhuang epidemic,which should be considered by public health policies in rural areas of China's Mainland during the dynamic zero-COVID policy.

COVID-19 transmission in U.S.transit buses:A scenario-based approach with agent-based simulation modeling(ABSM)

The transit bus environment is considered one of the primary sources of transmission of the COVID-19(SARSCoV-2)virus.Modeling disease transmission in public buses remains a challenge,especially with uncertainties in passenger boarding,alighting,and onboard movements.Although there are initial findings on the effectiveness of some of the mitigation policies(such as face-covering and ventilation),evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways,boarding and alighting patterns,and seating capacity control.This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic in USA,in which it brings crucial insights on combating current and future epidemics.We use an agent-based simulation model(ABSM)based on standard design characteristics for urban buses in USA and two different service frequency settings(10-min and 20-min headways).We find that wearing face-coverings(surgical masks)significantly reduces onboard transmission rates,from no mitigation rates of 85%in higher-frequency buses and 75%in lower-frequency buses to 12.5%.The most effective prevention outcome is the combination of KN-95 masks,open window policies,and half-capacity seating control during higher-frequency bus services,with an outcome of nearly 0%onboard infection rate.Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design,which is crucial to ensuring passenger safety.The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies.

TransCode:Uncovering COVID-19 transmission patterns via deep learning

Background The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale,especially in densely populated regions.In this study,we aim to discover such fine-scale transmission patterns via deep learning.Methods We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors.First,in Hong Kong,China,we construct the mobility trajectories of confirmed cases using their visiting records.Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution.Integrating the spatial and temporal information,we represent the TransCode via spatiotemporal transmission networks.Further,we propose a deep transfer learning model to adapt the TransCode of Hong Kong,China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises:New York City,San Francisco,Toronto,London,Berlin,and Tokyo,where fine-scale data are limited.All the data used in this study are publicly available.Results The TransCode of Hong Kong,China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns(e.g.,the imported and exported transmission intensities)at the district and constituency levels during different COVID-19 outbreaks waves.By adapting the TransCode of Hong Kong,China to other data-limited densely populated metropolises,the proposed method outperforms other representative methods by more than 10%in terms of the prediction accuracy of the disease dynamics(i.e.,the trend of case numbers),and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level.Conclusions The fine-scale transmission patterns due to the metapopulation level mobility(e.g.,travel across different districts)and contact behaviors(e.g.,gathering in social-economic centers)are one of the main contributors to the rapid spread of the virus.Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.

History,origin,transmission,genome structure,replication,epidemiology,pathogenesis,clinical features,diagnosis,and treatment of COVID-19:A review

In December,2019,pneumonia triggered by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)surfaced in Wuhan,China.An acute respiratory illness named coronavirus disease 2019(COVID-19)is caused by a new coronavirus designated as SARS-CoV-2.COVID-19 has surfaced as a major pandemic in the 21st century as yet.The entire world has been affected by this virus.World Health Organization proclaimed COVID-19 pandemic as a public health emergency of international concern on January 30,2020.SARS-CoV-2 shares the same genome as coronavirus seen in bats.Therefore,bats might be its natural host of this virus.It primarily disseminates by means of the respiratory passage.Evidence revealed human-to-human transmission.Fever,cough,tiredness,and gastrointestinal illness are the manifestations in COVID-19-infected persons.Senior citizens are more vulnerable to infections which can lead to dangerous consequences.Various treatment strategies including antiviral therapies are accessible for the handling of this disease.In this review,we organized the most recent findings on COVID-19 history,origin,transmission,genome structure,replication,epidemiology,pathogenesis,clinical features,diagnosis,and treatment strategies.

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。

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。

显微镜下多血管炎相关间质性肺病合并COVID-19患者疫苗保护作用初探

目的分析显微镜下多血管炎(MPA)相关间质性肺病(ILD)患者合并新型冠状病毒肺炎(COVID-19)的临床特征,初步探究新型冠状病毒(SARS-CoV-2)疫苗的保护作用。方法纳入确诊的MPA-ILD患者,同时符合《新型冠状病毒感染诊疗方案(第十版)》制定的COVID-19诊断标准,且均有明确SARS-CoV-2抗原或核酸检测阳性证据。收集并分析患者临床资料,ILD影像学分型,SARS-CoV-2疫苗接种情况、临床分型及预后。结果共纳入37例MPA-ILD患者,女14例,男23例。32例在并发COVID-19时MPA病情处于缓解期,31例同时维持免疫抑制剂或生物制剂治疗。ILD的影像学类型以寻常型间质性肺炎(15例)和非特异性间质性肺炎(14例)为主,剩余8例分型不确定。COVID-19分型中,27例为轻症,5例因COVID-19死亡。11例未接种疫苗,26例接种SARS-CoV-2灭活疫苗,其中11例完成3剂次加强免疫。接种疫苗患者COVID-19轻症比例高于未接种疫苗组(P=0.038)。多因素分析显示接种疫苗为MPA-ILD患者合并COVID-19住院(OR=0.045,95%CI:0.004~0.472,P=0.010)及非轻症感染(OR=0.049,95%CI:0.005~0.517,P=0.012)的独立保护因素。同时MPA处于缓解期为COVID-19非轻症感染的保护因素(OR=0.021,95%CI:0.001~0.459,P=0.014)。结论接种SARS-CoV-2疫苗及MPA病情处于缓解期有可能减轻MPA患者合并COVID-19的严重程度。

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。

Assessment of COVID-19 aerosol transmission in a university campus food environment using a numerical method

With the prevalence of COVID-19,the phenomenon of viruses spreading through aerosols has become a focus of attention.Diners in university dining halls have a high risk of exposure to respiratory droplets from others without the protection of face masks,which greatly increases the risk of COVID-19 transmission.Therefore,the transmission mechanism of respiratory droplets in extremely crowded dining environments should be investigated.In this study,a numerical simulation of coughing at dining tables under two conditions was performed,namely the presence and absence of protective partitions,and the evaporation and condensation of aerosol droplets in the air were examined.By using the numerical method,we analyzed and verified the isolation effect of dining table partitions in the propagation of aerosol droplets.The effect of changes in room temperature on the diffusion of coughed aerosols when partitions were present was analyzed.We demonstrated how respiratory droplets spread through coughing and how these droplets affect others.Finally,we proposed a design for a dining table partition that minimizes the transmission of COVID-19.