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Background: Following the global mpox outbreak in 2022, multiple regions in Asia have been reporting ongoing mpox cases within high-risk groups, including gay, bisexual, and other men who have sex with men (GBMSM). An optimal level of vaccination rate is essential to prevent further mpox outbreaks. However, no existing studies have examined mpox vaccine uptake among GBMSM in East Asia., Methods: A cross-sectional survey was conducted among a sample of 531 GBMSM in Hong Kong, China, between March and October 2023. The study used multivariable logistic regression models to investigate the associations between mpox-related disease perceptions, exposures to sources and contents of mpox-related information, and mpox vaccine uptake., Results: The prevalence of mpox vaccine uptake among GBMSM in Hong Kong was 21.7%, with 7.7% completing one dose and 13.9% completing two doses. GBMSM who were younger or earning less monthly income were less likely to have been vaccinated. After adjusting for confounding variables, participants who perceived more negative impacts on their lives if they were to contract mpox, more severe symptoms, and a more coherent understanding of mpox were positively associated with mpox vaccine uptake. In addition, more frequent exposure to information through the following sources: TV, newspaper, radio and posters, government websites, news websites or apps, other people's social media, and communication over the phone or face-to-face was positively associated with mpox vaccine uptake. Finally, more frequent exposure to the following information contents: mpox statistics from other countries, the Hong Kong government's responses to mpox cases, negative information about patients with mpox, and information on prevention and treatment of mpox were positively associated with mpox vaccine uptake., Conclusions: This study provides timely and evidence-based implications to address health communication and messaging needs in promoting mpox vaccination among GBMSM in Hong Kong, relevant to regions with similar sociocultural contexts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: HIV self-testing (HIVST) has been rapidly scaled up and additional strategies further expand testing uptake. Secondary distribution involves people (defined as "indexes") applying for multiple kits and subsequently sharing them with people (defined as "alters") in their social networks. However, identifying key influencers is difficult., Objective: This study aimed to develop an innovative ensemble machine learning approach to identify key influencers among Chinese men who have sex with men (MSM) for secondary distribution of HIVST kits., Methods: We defined three types of key influencers: (1) key distributors who can distribute more kits, (2) key promoters who can contribute to finding first-time testing alters, and (3) key detectors who can help to find positive alters. Four machine learning models (logistic regression, support vector machine, decision tree, and random forest) were trained to identify key influencers. An ensemble learning algorithm was adopted to combine these 4 models. For comparison with our machine learning models, self-evaluated leadership scales were used as the human identification approach. Four metrics for performance evaluation, including accuracy, precision, recall, and F 1 -score, were used to evaluate the machine learning models and the human identification approach. Simulation experiments were carried out to validate our approach., Results: We included 309 indexes (our sample size) who were eligible and applied for multiple test kits; they distributed these kits to 269 alters. We compared the performance of the machine learning classification and ensemble learning models with that of the human identification approach based on leadership self-evaluated scales in terms of the 2 nearest cutoffs. Our approach outperformed human identification (based on the cutoff of the self-reported scales), exceeding by an average accuracy of 11.0%, could distribute 18.2% (95% CI 9.9%-26.5%) more kits, and find 13.6% (95% CI 1.9%-25.3%) more first-time testing alters and 12.0% (95% CI -14.7% to 38.7%) more positive-testing alters. Our approach could also increase the simulated intervention's efficiency by 17.7% (95% CI -3.5% to 38.8%) compared to that of human identification., Conclusions: We built machine learning models to identify key influencers among Chinese MSM who were more likely to engage in secondary distribution of HIVST kits., Trial Registration: Chinese Clinical Trial Registry (ChiCTR) ChiCTR1900025433; https://www.chictr.org.cn/showproj.html?proj=42001., (©Fengshi Jing, Yang Ye, Yi Zhou, Yuxin Ni, Xumeng Yan, Ying Lu, Jason Ong, Joseph D Tucker, Dan Wu, Yuan Xiong, Chen Xu, Xi He, Shanzi Huang, Xiaofeng Li, Hongbo Jiang, Cheng Wang, Wencan Dai, Liqun Huang, Wenhua Mei, Weibin Cheng, Qingpeng Zhang, Weiming Tang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.11.2023.)

Traditional surveys only provide local observations about the topological structure of isolated individuals. This study aims to develop a novel data-driven approach to reconstructing the social network of men who have sex with men (MSM) communities from locally observed information by surveys. A large social network consisting of 1075 users and their public relationships was obtained manually from BlueD.com. We followed the same survey-taking procedure to sample locally observed information and adapted an Exponential Random Graph Model (ERGM) to model the full structure of the BlueD social network (number of local nodes N  = 1075, observed average degree k  = 6.46). The parameters were learned and then used to reconstruct the MSM social networks by two real-world survey datasets in Hong Kong ( N  = 600, k  = 5.61) and Guangzhou ( N  = 757, k  = 5). Our method performed well on reconstructing the BlueD social network, with a high accuracy (90.3%). In conclusion, this study demonstrates the feasibility of using parameters learning methods to reconstruct the social networks of HIV key populations. The method has the potential to inform data-driven intervention programs that need global social network structures.

Background: Estimating the population sizes of key populations(people who inject drugs, men who have sex with men, transgender persons, and commercial sex workers) is critical for understanding the overall Human Immunodeficiency Virus burden. This scoping review aims to synthesize existing methods for population size estimation among key populations, and provide recommendations for future application of the existing methods., Methods: Relevant studies published from 1st January 2000 to 4th August 2020 and related to key population size estimation were retrieved and 120 of 688 studies were assessed. After reading the full texts, 81 studies were further excluded. Therefore, 39 studies were included in this scoping review. Estimation methods included five digital methods, one in-person method, and four hybrid methods., Finding: We summarized and organized the methods for population size estimateion into the following five categories: methods based on independent samples (including capture-recapture method and multiplier method), methods based on population counting (including Delphi method and mapping method), methods based on the official report (including workbook method), methods based on social network (including respondent-driven sampling method and network scale-up method) and methods based on data-driven technologies (Bayesian estimation method, Stochastic simulation method, and Laska, Meisner, and Siegel estimation method). Thirty-six (92%) articles were published after 2010 and 23 (59%) used multiple methods. Among the articles published after 2010, 11 in high-income countries and 28 in low-income countries. A total of 10 estimated the size of commercial sex workers, 14 focused on men who have sex with men, and 10 focused on people who inject drugs., Conclusions: There was no gold standard for population size estimation. Among 120 studies that were related to population size estimation of key populations, the most commonly used population estimation method is the multiplier method (26/120 studies). Every method has its strengths and biases. In recent years, novel methods based on data-driven technologies such as Bayesian estimation have been developed and applied in many surveys., (© 2022. The Author(s).)

Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several innovative implementations of HIVST have been developed and piloted among some HIV high-risk populations like men who have sex with men (MSM) to meet the global testing target. One innovative strategy is the secondary distribution of HIVST, in which individuals (defined as indexes) were given multiple testing kits for both self-use (i.e.self-testing) and distribution to other people in their MSM social network (defined as alters). Studies about secondary HIVST distribution have mainly concentrated on developing new intervention approaches to further increase the effectiveness of this relatively new strategy from the perspective of traditional public health discipline. There are many points of HIVST secondary distribution in which mathematical modelling can play an important role. In this study, we considered secondary HIVST kits distribution in a resource-constrained situation and proposed two data-driven integer linear programming models to maximize the overall economic benefits of secondary HIVST kits distribution based on our present implementation data from Chinese MSM. The objective function took expansion of normal alters and detection of positive and newly-tested 'alters' into account. Based on solutions from solvers, we developed greedy algorithms to find final solutions for our linear programming models. Results showed that our proposed data-driven approach could improve the total health economic benefit of HIVST secondary distribution. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

Objectives: Internet search engine data have been widely used to monitor and predict infectious diseases. Existing studies have found correlations between search data and HIV/AIDS epidemics. We aimed to extend the literature through exploring the feasibility of using search data to monitor and predict the number of newly diagnosed cases of HIV/AIDS, syphilis and gonorrhoea in China., Methods: This paper used vector autoregressive model to combine the number of newly diagnosed cases with Baidu search index to predict monthly newly diagnosed cases of HIV/AIDS, syphilis and gonorrhoea in China. The procedures included: (1) keywords selection and filtering; (2) construction of composite search index; (3) modelling with training data from January 2011 to October 2016 and calculating the prediction performance with validation data from November 2016 to October 2017., Results: The analysis showed that there was a close correlation between the monthly number of newly diagnosed cases and the composite search index (the Spearman's rank correlation coefficients were 0.777 for HIV/AIDS, 0.590 for syphilis and 0.633 for gonorrhoea, p<0.05 for all). The R 2 were all more than 85% and the mean absolute percentage errors were less than 11%, showing the good fitting effect and prediction performance of vector autoregressive model in this field., Conclusions: Our study indicated the potential feasibility of using Baidu search data to monitor and predict the number of newly diagnosed cases of HIV/AIDS, syphilis and gonorrhoea in China., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

There are multiple modes for human immunodeficiency virus (HIV) transmissions, each of which is usually associated with a certain key population (e.g., needle sharing among people who inject drugs). Recent field studies revealed the merging trend of multiple key populations, making HIV intervention difficult because of the existence of multiple transmission modes in such complex multiplex social networks. In this paper, we aim to address this challenge by developing a multiplex social network framework to capture the multimode transmission across two key populations. Based on the multiplex social network framework, we propose a new random search method, named partition-based random search with network and memory prioritization (PRS-NMP), to identify the optimal subset of high-value individuals in the social network for interventions. Numerical experiments demonstrated that the proposed PRS-NMP-based interventions could effectively reduce the scale of HIV transmissions. The performance of PRS-NMP-based interventions is consistently better than the benchmark nested partitions method and network-based metrics.

Objectives: Internet data are important sources of abundant information regarding HIV epidemics and risk factors. A number of case studies found an association between internet searches and outbreaks of infectious diseases, including HIV. In this research, we examined the feasibility of using search query data to predict the number of new HIV diagnoses in China., Design: We identified a set of search queries that are associated with new HIV diagnoses in China. We developed statistical models (negative binomial generalised linear model and its Bayesian variants) to estimate the number of new HIV diagnoses by using data of search queries (Baidu) and official statistics (for the entire country and for Guangdong province) for 7 years (2010 to 2016)., Results: Search query data were positively associated with the number of new HIV diagnoses in China and in Guangdong province. Experiments demonstrated that incorporating search query data could improve the prediction performance in nowcasting and forecasting tasks., Conclusions: Baidu data can be used to predict the number of new HIV diagnoses in China up to the province level. This study demonstrates the feasibility of using search query data to predict new HIV diagnoses. Results could potentially facilitate timely evidence-based decision making and complement conventional programmes for HIV prevention., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: A large and growing body of "big data" is generated by internet search engines, such as Google. Because people often search for information about public health and medical issues, researchers may be able to use search engine data to monitor and predict public health problems, such as HIV. We sought to assess the feasibility of using Google search data to analyze and predict new HIV diagnoses cases in the United States., Methods and Findings: From 2007 to 2014, we collected search volume data on HIV-related Google search keywords across the United States. State-level new HIV diagnoses data were collected from the Centers for Disease Control and Prevention (CDC) and AIDSVu.org. We developed a negative binomial model to predict HIV cases using a subset of significant predictor keywords identified by LASSO. The Google search data were combined with state-level HIV case reports provided by the CDC. We use historical data to train the model and predict new HIV diagnoses from 2011 to 2014, with an average R2 value of 0.99 between predicted versus actual cases, and average root-mean-square error (RMSE) of 108.75., Conclusions: Results indicate that Google Trends is a feasible tool to predict new cases of HIV at the state level. We discuss the implications of integrating visualization maps and tools based on these models into public health and HIV monitoring and surveillance., Competing Interests: The authors have declared that no competing interests exist.

The HIV transmissions between multiple key populations make interventions difficult, particularly with multiple transmission behaviors. It remains unclear how significant the role of bridge individuals (who connect multiple communities) is in HIV transmission, and how to develop more effective intervention strategies targeting different transmission modes across key populations. In this research, we proposed a 2-layer social network framework to simulate the HIV transmissions across female sex workers (FSWs) and persons who inject drugs (PWID) through two behaviors: unprotected sex and needle-sharing. We proposed a set of intervention strategies based on the topological properties of individuals in the social network and estimated the efficacy of these strategies. Simulation studies demonstrated that bridge individuals played a significant role in HIV transmissions across the two networks. Prevention on such bridge individuals could help reduce both the scale and speed of HIV transmissions.

• Edoxaban use is associated with lower risks of new onset hip fractures, medically attended falls and mortality risks compared to warfarin. • This relationship was observed after propensity score matching adjusting for significant confounders. The three direct oral anticoagulants (DOAC), rivaroxaban, apixaban and dabigatran have been associated with lower risks of fractures compared to warfarin. However, no large scale studies have explored the associations with the newest DOAC, edoxaban, with fracture risk. The present study aims to elucidate the effects of edoxaban on the risk of hip fracture amongst elderly patients by comparing the incidence of new onset hip fracture between edoxaban and warfarin users in a Chinese population. This was a retrospective population-based cohort study of patients with edoxaban or warfarin use between January 1st, 2016 and December 31st, 2019 in Hong Kong, China. Patients with less than one-month exposure, medication switching between warfarin and edoxaban, those who died within 30 days after drug exposure, prior human immunodeficiency virus infection, age <50 years old, and those with prior hip fractures were excluded. Propensity score matching (1:2) between edoxaban and warfarin users using the nearest neighbour method was performed based on demographics, prior comorbidities, and use of different medications. The study outcomes were new onset hip fractures, medically attended falls and all-cause mortality. A total of 5014 patients including 579 edoxaban users and 4435 warfarin users (median age: 70 years old [interquartile range (IQR): 62–79], 56.66% males) with a median follow-up of 637.5 (IQR: 320–1073) days were included. In the matched cohort, edoxaban users had significantly lower rates of new onset hip fractures, medically attended falls and all-cause mortality. The protective value of edoxaban use against new onset hip fracture (hazard ratio [HR]: 0.13, 95% confidence interval [CI]: [0.03–0.54], p = 0.0051), medically attended falls (HR: 0.47, [0.29–0.75], p = 0.0018) and all-cause mortality (HR: 0.61, [0.42–0.87], p = 0.0059) in comparison to warfarin use persisted after matching. The significant relationship between edoxaban use and lower fracture risk was preserved in all sensitivity analyses using different approaches using the propensity score. Edoxaban use is associated with lower risks of new onset hip fractures, medically attended falls and mortality risks compared to warfarin after propensity score matching. [ABSTRACT FROM AUTHOR]