COVID-19大流行期间流感活动呈“断崖式”下降——佩戴口罩、人员流动变化及SARS-CoV-2干扰的作用

一般情况下,每年冬季是季节性流感高发季节,但在当前2019冠状病毒病(COVID-19)大流行期间,全球季节性流感活动呈“断崖式”下降。为应对即将到来的流感季节,亟需弄清这种前所未有的流感低水平流行的原因。本文中,我们探索了一种国家特异性推理模型,利用该模型估计中国、英国和美国这三个国家中佩戴口罩、人员流动变化(国际和国内)以及严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)干扰的影响。结果发现,在这些地区增加一周佩戴口罩的干预措施,流感活动可减少11.3%~35.2%。实施一周的限制人员流动措施对国际(1.7%~6.5%)和国内社区(1.6%~2.8%)的影响较小。2020年至2021年,仅佩戴口罩这一项干预措施就可使阳性率下降13.3%至19.8%。仅人员流动变化可使阳性率降低5.2%至14.0%,其中79.8%~98.2%归因于国际旅行限制。SARS-CoV-2干扰仅在2019年至2020年具有统计学显著效应。中国北方地区及英国的阳性百分比分别降低7.6%(2.4~14.4)和10.2%(7.2~13.6)。探索所得出的结果对了解非药物干预措施和其他呼吸系统疾病背景下的流感演变有一定意义,有助于制定卫生政策,并可为公共卫生措施的个性化设计提供信息。

应急状态下新冠流行曲线预测的方法学研究——基于北京市百度搜索和传统流感样病例监测

Surveillance is an essential work on infectious diseases prevention and control.When the pandemic occurred,the inadequacy of traditional surveillance was exposed,but it also provided a valuable opportunity to explore new surveillance methods.This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2(SARS-Co V-2)Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness(ILI)surveillance.A novel hybrid model(multiattention bidirectional gated recurrent unit(MABG)-susceptible-exposed-infected-removed(SEIR))was developed,which leveraged a deep learning algorithm(MABG)to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever,pyrexia,cough,sore throat,anti-fever medicine,and runny nose.By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019(COVID-19)cases,a transmission dynamics model(SEIR)was formulated to estimate the transmission dynamics and epidemic curve of SARS-Co V-2.During the COVID-19 pandemic,when conventional surveillance measures have been suspended temporarily,cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19.In the specific case of Beijing,it has been ascertained that cumulative infection attack rate surpass 80.25%(95%confidence interval(95%CI):77.51%-82.99%)since December 17,2022,with the apex of the outbreak projected to transpire on December 12.The culmination of existing patients is expected to occur three days subsequent to this peak.Effective reproduction number(Rt)represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic,remained below 1 since December 17,2022.The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support.Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks.Syndrome surveillance on COVID-19 should be established to following on the epidemic,clinical severity,and medical resource demand.

Assessing the Effect of Global Travel and Contact Restrictions on Mitigating the COVID-19 Pandemic

Travel restrictions and physical distancing have been implemented across the world to mitigate the coronavirus disease 2019(COVID-19)pandemic,but studies are needed to understand their effectiveness across regions and time.Based on the population mobility metrics derived from mobile phone geolocation data across 135 countries or territories during the first wave of the pandemic in 2020,we built a metapopulation epidemiological model to measure the effect of travel and contact restrictions on containing COVID-19 outbreaks across regions.We found that if these interventions had not been deployed,the cumulative number of cases could have shown a 97-fold(interquartile range 79–116)increase,as of May 31,2020.However,their effectiveness depended upon the timing,duration,and intensity of the interventions,with variations in case severity seen across populations,regions,and seasons.Additionally,before effective vaccines are widely available and herd immunity is achieved,our results emphasize that a certain degree of physical distancing at the relaxation of the intervention stage will likely be needed to avoid rapid resurgences and subsequent lockdowns.

Assessing spread risk of COVID-19 in early 2020

A novel coronavirus emerged in late 2019,named as the coronavirus disease 2019(COVID-19)by the World Health Organization(WHO).This study was originally conducted in January 2020 to estimate the potential risk and geographic range of COVID-19 spread at the early stage of the transmission.A series of connectivity and risk analyses based on domestic and international travel networks were conducted using historical aggregated mobile phone data and air passenger itinerary data.We found that the cordon sanitaire of the primary city was likely to have occurred during the latter stages of peak population numbers leaving the city,with travellers departing into neighbouring cities and other megacities in China.We estimated that there were 59,912 international air passengers,of which 834(95%uncertainty interval:478–1,349)had COVID-19 infection,with a strong correlation seen between the predicted risks of importation and the number of imported cases found.Given the limited understanding of emerging infectious diseases in the very early stages of outbreaks,our approaches and findings in assessing travel patterns and risk of transmission can help guide public health preparedness and intervention design for new COVID-19 waves caused by variants of concern and future pandemics to effectively limit transmission beyond its initial extent.

Epidemic intelligence trinity:Detection,risk assessment,and early warning

Emerging infectious diseases have been frequently observed.The occurrence of infectious diseases is highly uncertain because of the unpredictability of pathogen,the complexity of occurrence time and site,and the characteristics of transmission.Early detection and immediate implementation of effective interventions within a reasonable time are the keys to preventing pandemics.[1]However,due to the insufficiency of the traditional surveillance and early warning,which relied heavily on passive reporting by healthcare institutions,[2]the early warning time lags behind the risk factor identification,syndrome differentiation(pneumonia of unknown cause),and suspected case confirmation of the infectious disease at the gateway of clustered cases.This paper aimed to face the challenges mentioned above and propose a universal epidemic intelligence model to establish a novel infectious disease surveillance and early warning system based on the trinity of surveillance and detection,risk assessment,and early warning.

Human Brucellosis:An Ongoing Global Health Challenge

Brucellosis is one of the most common zoonotic diseases,caused by species of the genus Brucella,that affects domestic and farm livestock and a wide range of wild mammals(1-2).Endemic areas are primarily located in the low-and middle-income countries across the Mediterranean region,the Arabian Peninsula,Africa,Asia,and Central and South America,with major regional differences(3-5).The highest prevalence in animals was observed in countries of the Middle East and sub-Saharan Africa,China。

The Incoming Influenza Season—China,the United Kingdom,and the United States,2021–2022

Introduction:Seasonal influenza activity has declined globally since the widespread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)transmission.There has been scarce information to understand the future dynamics of influenza—and under different hypothesis on relaxation of nonpharmaceutical interventions(NPIs)in particular—after the disruptions to seasonal patterns.Methods:We collected data from public sources in China,the United Kingdom,and the United States,and forecasted the influenza dynamics in the incoming 2021–2022 season under different NPIs.We considered Northern China and Southern China separately,due to the sharp difference in the patterns of seasonal influenza.For the United Kingdom,data were collected for England only.Results:Compared to the epidemics in 2017–2019,longer and blunter influenza outbreaks could occur should NPIs be fully lifted,with percent positivity varying from 10.5 to 18.6 in the studying regions.The rebounds would be smaller if the maskwearing intervention continued or the international mobility stayed low,but sharper if the mask-wearing intervention was lifted in the middle of influenza season.Further,influenza activity could stay low under a much less stringent mask-wearing intervention coordinated with influenza vaccination.Conclusions:The results added to our understandings of future influenza dynamics after the global decline during the coronavirus disease 2019(COVID-19)pandemic.In light of the uncertainty on the incoming circulation strains and the relatively low negative impacts of mask wearing on society,our findings suggested that wearing mask could be considered as an accompanying mitigation measure in influenza prevention and control,especially for seasons after long periods of low-exposure to influenza viruses.Seasonal influenza activity declines globally during the coronavirus disease 2019(COVID-19)pandemic(1–4).For instance,in China,influenza activity,as measured by percentage of submitted specimens testing positive,dropped from 11.8%to 2.0%in 2020–2021 influenza season,compared to the past 5 years(5).The long-period of low-exposure to influenza viruses adds great uncertainty on preparedness for the incoming 2021–2022 influenza season.Influenza vaccination is one of the most effective measures in seasonal influenza prevention and control,but with only a few influenza viruses circulating,it could be difficult to determine the targeted strains for vaccination.In this context,it is of primary importance to identify alternative mitigation measures for the incoming 2021–2022 influenza season,the first season after long periods of virtually no influenza outbreaks worldwide.Using data from China,the United Kingdom,and the United States,we forecasted the influenza activity in the incoming 2021–2022 influenza season under hypothetical scenarios without non-pharmaceutical interventions(NPIs)and with different assumptions on mask-wearing and mobility levels.

Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019:A modelling and comparative study

Background Influenza is an acute respiratory infectious disease with a significant global disease burden.Additionally,the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions(NPIs)have introduced uncertainty to the spread of influenza.However,comparative studies on the performance of innovative models and approaches used for influenza prediction are limited.Therefore,this study aimed to predict the trend of influenza-like illness(ILI)in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance.Methods The generalized additive model(GAM),deep learning hybrid model based on Gate Recurrent Unit(GRU),and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA—GARCH)model were established to predict the trends of ILI 1-,2-,3-,and 4-week-ahead in Beijing,Tianjin,Shanxi,Hubei,Chongqing,Guangdong,Hainan,and the Hong Kong Special Administrative Region in China,based on sentinel surveillance data from 2011 to 2019.Three relevant metrics,namely,Mean Absolute Percentage Error(MAPE),Root Mean Squared Error(RMSE),and R squared,were calculated to evaluate and compare the goodness of fit and robustness of the three models.Results Considering the MAPE,RMSE,and R squared values,the ARMA—GARCH model performed best,while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China.Additionally,the models’predictive performance declined as the weeks ahead increased.Furthermore,blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting.Conclusions Our study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model.Therefore,in the future,the ARMA—GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones,thereby contributing to influenza control and prevention efforts.