An Adaptive Anomaly Detection Algorithm Based on CFSFDP

CFSFDP(Clustering by fast search and find of density peak)is a simple and crisp density clustering algorithm.It does not only have the advantages of density clustering algorithm,but also can find the peak of cluster automatically.However,the lack of adaptability makes it difficult to apply in intrusion detection.The new input cannot be updated in time to the existing profiles,and rebuilding profiles would waste a lot of time and computation.Therefore,an adaptive anomaly detection algorithm based on CFSFDP is proposed in this paper.By analyzing the influence of new input on center,edge and discrete points,the adaptive problem mainly focuses on processing with the generation of new cluster by new input.The improved algorithm can integrate new input into the existing clustering without changing the original profiles.Meanwhile,the improved algorithm takes the advantage of multi-core parallel computing to deal with redundant computing.A large number of experiments on intrusion detection on Android platform and KDDCUP 1999 show that the improved algorithm can update the profiles adaptively without affecting the original detection performance.Compared with the other classical algorithms,the improved algorithm based on CFSFDP has the good basic performance and more room of improvement.

Characterizing Human Collective Behaviors During COVID-19 — Hong Kong SAR, China, 2020

Summary What is already known about this topic?People are likely to engage in collective behaviors online during extreme events,such as the coronavirus disease 2019(COVID-19)crisis,to express awareness,take action,and work through concerns.

基于多阶邻居传播度量和拓扑特征的高影响力节点识别

如何定量评估复杂网络中节点的影响力是一个重要的研究课题,因为它有助于深入理解网络的结构和功能.现有的多数方法主要基于网络固有拓扑的分析建立,缺少对多阶邻居节点的传播性质和拓扑信息的综合利用,然而它们对影响力节点识别有重要影响.为此,本文提出了一种综合多阶邻居传播度量和拓扑特征(multi-order neighbor propagation metrics and topological features,MNPMTF)的算法来有效识别复杂网络中的影响力节点.首先,该算法结合传播模型和最短路径来刻画邻居节点的传播概率,从而量化节点之间信息传播的可能性.其次,考虑多阶邻居中的邻居重叠比形成邻居重叠度,进而量化信息在邻居网络中的传播路径.再次,利用节点的k壳、h指数和聚类系数构成新指标KHC系数,以此来描述节点的拓扑特征.最后,算法综合3阶邻居范围内的传播概率、邻居重叠度和拓扑特征以评估节点的影响力.在9个真实网络上的大量实验表明,所提算法在排序准确性、有效性和区分能力等多方面均优于7种具有代表性的方法,为复杂网络中节点影响力评估提供了一种新的思路.

Heterogeneous influence of individuals’ behavior on mask efficacy in gathering environments

Wearing masks is an easy way to operate and popular measure for preventing epidemics.Although masks can slow down the spread of viruses,their efficacy in gathering environments involving heterogeneous person-to-person contacts remains unknown.Therefore,we aim to investigate the epidemic prevention effect of masks in different real-life gathering environments.This study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering environments.The transmission of COVID-19 is simulated using the Monte Carlo simulation method.The heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general society.Furthermore,the heterogeneity of gathering environments causes the epidemic prevention effect of masks to differ.Wearing masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools,high schools,and hospitals.However,the use of masks alone in primary schools and hospitals cannot control outbreaks.In high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse,masks can meet the need for prevention.Given the heterogeneity of individual behavior,if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing,the epidemic prevention effect of masks can be improved.Finally,asymptomatic infection has varying effects on the prevention effect of masks in different environments.The effect can be weakened or eliminated by increasing the usage rate of masks in high schools and workplaces.However,the effect on primary schools and hospitals cannot be weakened.This study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention.

Nowcasting and Forecasting Seasonal Influenza Epidemics—China,2022-2023

Background:Seasonal influenza resurged in China in February 2023,causing a large number of hospitalizations.While influenza epidemics occurred across China during the coronavirus disease 2019(COVID-19)pandemic,the relaxation of COVID-19 containment measures in December 2022 may have contributed to the spread of acute respiratory infections in winter 2022/2023.Methods:Using a mathematical model incorporating influenza activity as measured by influenza-like illness(ILI)data for northern and southern regions of China,we reconstructed the seasonal influenza incidence from October 2015 to September 2019 before the COVID-19 pandemic.Using this trained model,we predicted influenza activities in northern and southern China from March to September 2023.Results:We estimated the effective reproduction number Re as 1.08[95%confidence interval(CI):0.51,1.65]in northern China and 1.10(95%CI:0.55,1.67)in southern China at the start of the 2022-2023 influenza season.We estimated the infection attack rate of this influenza wave as 18.51%(95%CI:0.00%,37.78%)in northern China and 28.30%(95%CI:14.77%,41.82%)in southern China.Conclusions:The 2023 spring wave of seasonal influenza in China spread until July 2023 and infected a substantial number of people.

Travel-related Importation and Exportation Risks of SARS-CoV-2 Omicron Variant in 367 Prefectures(Cities)-China,2022

Introduction:Minimizing the importation and exportation risks of coronavirus disease 2019(COVID-19)is a primary concern for sustaining the“Dynamic COVID-zero”strategy in China.Risk estimation is essential for cities to conduct before relaxing border control measures.Methods:Informed by the daily number of passengers traveling between 367 prefectures(cities)in China,this study used a stochastic metapopulation model parameterized with COVID-19 epidemic characteristics to estimate the importation and exportation risks.Results:Under the transmission scenario(R0=5.49),this study estimated the cumulative case incidence of Changchun City,Jilin Province as 3,233(95%confidence interval:1,480,4,986)before a lockdown on March 14,2022,which is close to the 3,168 cases reported in real life by March 16,2022.In a total of 367 prefectures(cities),127(35%)had high exportation risks according to the simulation and could transmit the disease to 50%of all other regions within a period from 17 to 94 days.The average time until a new infection arrives in a location in 1 of the 367 prefectures(cities)ranged from 26 to 101 days.Conclusions:Estimating COVID-19 importation and exportation risks is necessary for preparedness,prevention,and control measures of COVID-19—especially when new variants emerge.

Epidemic Surveillance of Influenza Infections:A Network-Free Strategy—Hong Kong Special Administrative Region,China,2008–2011

Introduction:The ease of coronavirus disease 2019(COVID-19)non-pharmacological interventions and the increased susceptibility during the past COVID-19 pandemic could be a precursor for the resurgence of influenza,potentially leading to a severe outbreak in the winter of 2022 and future seasons.The recent increased availability of data on Electronic Health Records(EHR)in public health systems,offers new opportunities to monitor individuals to mitigate outbreaks.Methods:We introduced a new methodology to rank individuals for surveillance in temporal networks,which was more practical than the static networks.By targeting previously infected nodes,this method used readily available EHR data instead of the contactnetwork structure.Results:We validated this method qualitatively in a real-world cohort study and evaluated our approach quantitatively by comparing it to other surveillance methods on three temporal and empirical networks.We found that,despite not explicitly exploiting the contacts’network structure,it remained the best or close to the best strategy.We related the performance of the method to the public health goals,the reproduction number of the disease,and the underlying temporal-network structure(e.g.,burstiness).Discussion:The proposed strategy of using historical records for sentinel surveillance selection can be taken as a practical and robust alternative without the knowledge of individual contact behaviors for public health policymakers.