HIV incidence among men who have sex with men using geosocial networking smartphone application in Beijing, China: an open cohort study

Background:Sexual transmission among men who have sex with men(MSM)is the dominant route of HIV transmission in China.Extensive use of geosocial networking(GSN)smartphone application(app)has dramatically changed the pattern of sexual behaviors and HIV risk among MSM;but data on HIV incidence and the changing risk behaviors of GSN app-using MSM are limited.We aims to assess the HIV incidence and its correlates among gay GSN app-using MSM in China.Methods:We constructed an open cohort which was initiated and maintained using a GSN app to assess the HIV incidence among app-using MSM,recruited from June 2017 to December 2018.MSM completed an online questionnaire on their sociodemographic characteristics,sexual behaviors,recreational drug use and sexually transmitted infections status.Then each man had an HIV test,and those tested negatives were enrolled into the cohort.Participants completed follow-ups with additional HIV tests though the app during the study period,and were censored at HIV seroconversion or study end date.HIV incidence was calculated by dividing the sum of observed HIV seroconversions by the observed person-time.Univariate(Chi-square test and Fisher's exact test)and multivariate(proportional hazards regression)analyses were used to examine correlates of HIV incidence.Results:A total of 6957 HIV negative MSM were enrolled in the open cohort,37 seroconversions occurred among 1937 men contributing 1065 observed person-years:HIV incidence was 3.47 per 100 person-years[95%confidence interval(Cl):2.37-4.57].More than five sexual partners[hazard ratio(HR)=2.65,95%Cl:1.04-6.67],and sex with HIV positive partners(HR=3.82,95%Cl:1.16-12.64)in the preceding six months were positively associated with HIV seroconversion.Consistent condom use for anal sex(HR=0.27,95%Cl:0.07-0.96),and reporting insertive anal sex only(HR=0.23,95%Cl:0.08-0.62)in the preceding six months were protective factors for HIV seroconversion.Conclusions:Tailored interventions targeting app-using MSM are urgently needed given their high risk of HIV.As a new tool for accessing MSM at higher HIV risk,GSN smartphone app could play an important role in HIV research among MSM.

Exploring spatiotemporal patterns of geosocial media data for urban functional zone identification

Urban Functional Zones(UFZs)can be identified by measuring the spatiotemporal patterns of activities that occur within them.Geosocial media data possesses abundant spatial and temporal information for activity mining.Identifying UFZs from geosocial media data aids urban planning,infrastructure,resource allocation,and transportation modernization in the complex urban system.In this work,we proposed an integrated approach by combining the spatiotemporal clustering method with a machine learning classifier.The spatiotemporal clustering method was used to mine the spatiotemporal patterns of activities,of which the distinctive features were extracted as inputs into a machine learning classifier for UFZ identification.The results show that more than 80%of the UFZs can be correctly identified by our proposed method.It reveals that this work serves as a functional groundwork for future studies,facilitating the understanding of urban systems as well as promoting sustainable urban development.

A review of the use of geosocial media data in agent-based models for studying urban systems

Since the rapid growth of urban populations,the study of urban systems has gained considerable attention from researchers,decision makers,governments,and organizations.Urban systems are complex and dynamic such that they produce emergent patterns such as selforganization and nonlinearity.Agent-based modelling presents an approach to simulating and abstracting urban systems to reveal and study emergent patterns from urban-related entities.However,agent-based models are difficult to effectively optimize and validate without high quality real-world data.Geosocial media data provides agent-based models with location-enabled data at high volumes and frequencies.Integrating agent-based models with geosocial media data presents opportunities in advancing and developing studies in urban systems.This paper provides a general overview of concepts,review of recent applications,and discussion of challenges and opportunities in the context of using geosocial media data in agentbased models for urban systems.We argue that ABMs focused on studying urban systems can benefit greatly from geosocial media data,given that research moves towards standard guidelines that enable the comparison and effective use of ABMs,and geosocial media data under appropriate circumstances and applications.