A model simulation on the SARS-CoV-2 Omicron variant containment in Beijing,China

Objective:The Omicron variant of SARS-COV-2 is replacing previously circulating variants around the world in 2022.Sporadic outbreaks of the Omicron variant into China have posed a concern how to properly response to battle against evolving coronavirus disease 2019(COVID-19).Methods:Based on the epidemic data from website announced by Beijing Center for Disease Control and Prevention for the recent outbreak in Beijing from April 22nd to June 8th in 2022,we developed a modified SEPIR model to mathematically simulate the customized dynamic COVID-zero strategy and project transmissions of the Omicron epidemic.To demonstrate the effectiveness of dynamic-changing policies deployment during this outbreak control,we modified the transmission rate into four parts according to policy-changing dates as April 22nd to May 2nd,May 3rd to 11st,May 12th to 21st,May 22nd to June 8th,and we adopted Markov chain Monte Carlo(MCMC)to estimate different transmission rate.Then we altered the timing and scaling of these measures used to understand the effectiveness of these policies on the Omicron variant.Results:The estimated effective reproduction number of four parts were 1.75(95%CI 1.66-1.85),0.89(95%CI 0.79-0.99),1.15(95%CI 1.05-1.26)and 0.53(95%CI 0.48-0.60),respectively.In the experiment,we found that till June 8th the cumulative cases would rise to 132,609(95%CI 59,667-250,639),73.39 times of observed cumulative cases number 1,807 if no policy were implemented on May 3rd,and would be 3,235(95%CI 1,909-4,954),increased by 79.03%if no policy were implemented on May 22nd.A 3-day delay of the implementation of policies would led to increase of cumulative cases by 58.28%and a 7-day delay would led to increase of cumulative cases by 187.00%.On the other hand,taking control measures 3 or 7 days in advance would result in merely 38.63%or 68.62%reduction of real cumulative cases.And if lockdown implemented 3 days before May 3rd,the cumulative cases would be 289(95%CI 211-378),reduced by 84%,and the cumulative cases would be 853(95%CI 578-1,183),reduced by 52.79%if lockdown implemented 3 days after May 3rd.Conclusion:The dynamic COVID-zero strategy might be able to effectively minimize the scale of the transmission,shorten the epidemic period and reduce the total number of infections.

Knowledge Graph:Applications in Tracing the Source of Large-Scale Outbreak—Beijing Municipality,China,2020–2021

Introduction:Tracing transmission paths and identifying infection sources have been effective in curbing the spread of coronavirus disease 2019(COVID-19).However,when facing a large-scale outbreak,this is extremely time-consuming and laborintensive,and resources for infection source tracing become limited.In this study,we aimed to use knowledge graph(KG)technology to automatically infer transmission paths and infection sources.Methods:We constructed a KG model to automatically extract epidemiological information and contact relationships from case reports.We then used an inference engine to identify transmission paths and infection sources.To test the model’s performance,we used data from two COVID-19 outbreaks in Beijing.Results:The KG model performed well for both outbreaks.In the first outbreak,20 infection relationships were identified manually,while 42 relationships were determined using the KG model.In the second outbreak,32 relationships were identified manually and 31 relationships were determined using the KG model.All discrepancies and omissions were reasonable.Discussion:The KG model is a promising tool for predicting and controlling future COVID-19 epidemic waves and other infectious disease pandemics.By automatically inferring the source of infection,limited resources can be used efficiently to detect potential risks,allowing for rapid outbreak control.

Multidimensional dynamic prediction model for hospitalized patients with the omicron variant in China

Purpose:To establish dynamic prediction models by machine learning using daily multidimensional data for coronavirus disease 2019(COVID-19)patients.Methods:Hospitalized COVID-19 patients at Peking Union Medical College Hospital from Nov 2nd,2022,to Jan 13th,2023,were enrolled in this study.The outcome was defined as deterioration or recovery of the patient's condition.Demographics,comorbidities,laboratory test results,vital signs,and treatments were used to train the model.To predict the following days,a separate XGBoost model was trained and validated.The Shapley additive explanations method was used to analyze feature importance.Results:A total of 995 patients were enrolled,generating 7228 and 3170 observations for each prediction model.In the deterioration prediction model,the minimum area under the receiver operating characteristic curve(AUROC)for the following 7 days was 0.786(95%CI 0.721-0.851),while the AUROC on the next day was 0.872(0.831-0.913).In the recovery prediction model,the minimum AUROC for the following 3 days was 0.675(0.583-0.767),while the AUROC on the next day was 0.823(0.770-0.876).The top 5 features for deterioration prediction on the 7th day were disease course,length of hospital stay,hypertension,and diastolic blood pressure.Those for recovery prediction on the 3rd day were age,D-dimer levels,disease course,creatinine levels and corticosteroid therapy.Conclusion:The models could accurately predict the dynamics of Omicron patients’conditions using daily multidimensional variables,revealing important features including comorbidities(e.g.,hyperlipidemia),age,disease course,vital signs,D-dimer levels,corticosteroid therapy and oxygen therapy.

Changing profile of lung cancer clinical characteristics in China:Over 8-year population-based study

Background:Although examinations and therapies for bronchial lung cancer,also called lung cancer(LC),have become more effective and precise,the morbidity and mortality of LC remain high worldwide.Describing the changing profile of LC characteristics over time is indispensable.This study aimed to understand the changes in real-world settings of LC and its characteristics in China.Methods:In this study,119,785 patients were enrolled from 2012 to 2020 in the Shanghai Pulmonary Hospital.The patients’medical records were extracted from the hospital’s database.Demographic characteristics,general clinicopathological information,and blood coagulation indices at the initial diagnoses were analyzed using the Kruskal-Wallis,Nemenyi,chi-squared,and Bonferroni tests.Changes in demographic characteristics during the 8-year study period,namely dynamic changes among different stages and different pathological types,were evaluated.Results:The percentages of female(from 38.50%[323/839]in 2012 to 48.29%[5112/10,585]in 2020)and non-smoking LC(from 69.34%[475/685]to 80.48%[8055/10,009])patients increased significantly during the study period,with a trend toward a younger age at diagnosis(from 3.58%[30/839]to 8.99%[952/10,585]).Over the study period,the proportion and absolute number of lung adenocarcinoma cases increased(from 67.97%[433/637]to 76.31%[6606/8657])while the proportion of lung squamous cell carcinoma decreased(from 21.19%[135/637]to 12.08%[1046/8657]).Comprehensive driver gene mutation examination became more common,and epidermal growth factor receptor(EGFR)mutation occurred more frequently in female vs.male(62.03%[12793/20625]vs.29.90%[8207/27,447])and non-smoking vs.smoking(53.54%[17,203/32,134]vs.23.73%[3322/13,997])patients(both P<0.001).The distribution of the common driver genes differed among different stages of LC.EGFR mutation was detected most frequently at each stage,and other driver gene alterations were more common in advanced stages(P<0.001).The combination of chemotherapy,targeted ther-apy,and immunotherapy,as a comprehensive management regimen,gradually became predominant over the study period(P<0.001).A hypercoagulable state was shown in advanced-stage LC patients and patients with the anaplastic lymphoma kinase fusion,indicated by significantly elevated levels of d-dimer,fibrinogen,and fibrinogen degradation products.Conclusions:This study comprehensively depicted the changing characteristics of Chinese LC patients over an 8-year period to provide preliminary insights into LC treatment.Trial registration:ClinicalTrials.gov,NCT05423236.