Abstract Background In the Inner Mongolia Autonomous Region, men who have sex with men (MSM) have become a high-risk group for HIV transmission due to their complex, secretive sexual relationships, posing challenges to HIV-1 prevention. Objective We analyzed factors associated with molecular transmission clustering and pretreatment drug resistance (PDR) among newly reported HIV-1–infected MSM in Baotou, China, and to provide locally relevant evidence for targeted public health interventions. Methods We conducted a subgroup cross-sectional molecular epidemiological analysis of all newly reported HIV-1 cases attributed to homosexual transmission in Baotou between January 2022 and December 2024. The sample comprised 213 treatment-naïve MSM, from whom plasma samples were obtained. A 1.3-kb fragment of the HIV-1 pol gene was amplified and sequenced using next-generation sequencing. Inclusion criteria were a newly reported HIV-1 infection, no antiretroviral therapy at the time of this writing, and self-reported same-sex sexual behavior. Primary outcomes were clustering in a genetic distance–based molecular transmission network and the presence of PDR according to the Stanford HIV Drug Resistance Database. Results In total, 176 pol gene sequences were successfully obtained, with circulating recombinant form (CRF)–07_BC being the most prevalent subtype, accounting for 56.82% of the total sequences. The molecular transmission network was constructed using a pairwise genetic distance of 1.30%, with 52 of 176 (29.55%) sequences forming 19 clusters within the network. The network exhibited significant assortative mixing by age (ra=0.41; P =.001), marital status (ra=0.408; P =.002), and educational attainment (ra=0.385; P =.001), rather than random mixing. Multivariate logistic regression analysis revealed that CRF07_BC subtype–infected individuals had a significantly higher risk of clustering (AOR 4.98; 95% CI 1.92‐12.94; P <.01). Compared to never-married individuals, divorced or widowed individuals were less likely to have clustered cases (AOR 0.27; 95% CI 0.08‐0.92; P <.05). The prevalence of PDR was 6.57% (n=14). Nonnucleoside reverse transcriptase inhibitor resistance was the most common (n=8, 3.76%), followed by protease inhibitor resistance (n=5, 2.35%), and nucleoside reverse transcriptase inhibitor resistance (n=3, 1.14%). Drug-resistant strains were more frequently detected among individuals infected with the CRF07_BC subtype ( χ 2 8 =90.44; P< .001). Conclusions Unlike previous studies primarily focused on high-burden southeastern coastal regions of China, we investigated the molecular transmission characteristics of HIV-1 among MSM in the Inner Mongolia Autonomous Region. Our findings demonstrate that transmission clustering and drug resistance patterns previously observed are also present in northern border areas. This study integrated HIV-1 molecular transmission network analysis with next-generation sequencing technologies to identify key populations involved in local HIV transmission, thereby supporting targeted public health interventions. In addition, surveillance of PDR among high-risk populations may facilitate the development of evidence-based antiretroviral treatment strategies and improve the efficient allocation of limited health care resources.
Abstract Background Testing arriving international travelers for COVID-19 paired with whole-genome sequencing (WGS) may identify new introductions of SARS-CoV-2 variants of concern in California, enabling a timely public health response. Traveler genomic surveillance may be a way to improve gaps in worldwide disease genomic surveillance. Objective This paper aimed to describe a pilot surveillance program to identify SARS-CoV-2 variants in arriving international travelers at San Francisco International Airport (SFO). Methods The California Department of Public Health implemented a pilot program at SFO to voluntarily test arriving international travelers for COVID-19 from June 2021 to November 2021 to identify SARS-CoV-2 variants. Arriving international travelers were offered self-collect COVID-19 antigen tests upon arrival at SFO. Travelers with positive COVID-19 antigen tests or with COVID-19 symptoms were recommended to self-collect and mail an additional specimen for diagnostic molecular testing. WGS of SARS-CoV-2 was performed on RNA extracts from positive tests to identify variants. Results In total, 2.1% (19,008/892,558) of all arriving international travelers at SFO from 133 different countries of departure enrolled in the program. The COVID-19 test positivity among this group was 0.7% (33/5038), and the weekly test positivity range, 0% to 1.5%, was lower than the weekly test positivity across the state of California during the same time frame. Arriving international travelers departing from Turkey (3/145, 2.1%), Mexico (7/602, 1.2%), and the United Kingdom (6/626, 1.0%) had the highest test positivity. Of the 14 positive molecular tests, 10 (71.4%) were sequenced, and all were found to belong to the SARS-CoV-2 Delta B.1.617.2 lineage, the most prevalent SARS-CoV-2 variant in California and worldwide at the time. Conclusions Voluntary testing of arriving international travelers paired with WGS was operationally and logistically feasible. Although the number of sequenced positive COVID-19 specimens in this program was limited, the lessons learned from this pilot informed improvements to subsequent iterations of traveler viral genomic surveillance in California and highlighted the potential value of traveler viral genomic surveillance to support early identification and characterization of emerging infectious diseases entering a region.
The COVID-19 pandemic has triggered major global challenges, highlighting the need for timely surveillance and accurate forecasting to curb disease transmission and reduce the health burden. Traditional modeling approaches usually rely on aggregated data on confirmed cases. However, testing data can be unreliable due to low availability and uptake, as well as registration delays. In Sweden, the 1177 health care helpline provides valuable syndromic surveillance data that can capture early signals of infection prior to confirmation through testing. This study aims to evaluate the effect of integrating syndromic surveillance data on short-term COVID-19 forecast accuracy. We developed a novel epidemiological model named SIsIcHRD (susceptible-infectedsuspected-infectedconfirmed -hospitalized-recovered-deceased), incorporating data from the 1177 health care helpline cross-linked with national registers on COVID-19 testing, hospitalizations, and deaths. In the novel model, the population with infection is divided into suspected infections (Is; those reporting symptoms to 1177) and confirmed infections (Ic; those who tested positive). Accuracy metrics of this model are compared to a model without data from the 1177 health care helpline. To evaluate model performance across different pandemic phases between March 2 and December 31, 2020, we used a daily rolling-window approach with a 60-day fitting period to predict confirmed infections, hospitalizations, and deaths over the subsequent 30 days. Prediction performance was evaluated using mean absolute error and mean absolute percentage error (MAPE). The proposed SIsIcHRD model successfully captured the temporal dynamics of confirmed, hospitalized, and death cases. The model achieved a median mean MAPE of 61.6% (IQR 37.6%-79.6%) for confirmed cases, 37.5% (IQR 18.9%-57.5%) for hospitalizations and for deaths, the model achieved a median MAPE of 0.65% (IQR 0.3%-1.9%), indicating substantially higher predictive accuracy for severe outcomes compared to confirmed infections and hospital admissions. Compared to the model without syndromic surveillance data, short-term forecasting performance of the SIsIcHRD model reduced MAPE by an average of 15.7 percentage points for confirmed cases, 8.17 percentage points for hospitalizations, and 1.99 percentage points for deaths. Overall, the results demonstrate predictive stability for severe outcomes during the early phase of the pandemic. The proposed model demonstrated predictive performance by integrating individual-level data with health care helpline syndromic data on testing and severe disease outcomes, capturing early signals from symptomatic individuals and distinguishing suspected cases from subsequently confirmed infections, leading to better forecasts of severe outcomes such as hospitalizations and deaths. This framework can support public health decision-making in future outbreaks.
BackgroundHemorrhagic fever with renal syndrome (HFRS) is a severe natural focal infectious disease that remains a major public health concern in Shandong Province, China. Understanding the epidemiological characteristics of HFRS and forecasting its incidence are crucial to provide guidance for policymaking and implement timely interventions against this disease. ObjectiveThis study examined the long-term epidemiological characteristics of HFRS based on 15 years of surveillance data; the analysis used 4 time series models to predict its incidence trends. The aim was to provide a scientific basis for local disease control departments, thereby supporting more accurate prediction and effective prevention and control of HFRS in this region. MethodsHFRS case surveillance data in Shandong Province from 2010 to 2024 were collected through the China Information System for Disease Control and Prevention. Descriptive epidemiological methods were used to analyze the basic characteristics of HFRS. Four forecasting models, namely, seasonal autoregressive integrated moving average, Prophet, the Holt-Winters exponential smoothing method, and Extreme Gradient Boosting, were implemented in R to fit the time series and generate predictions. Their predictive performance was evaluated and compared using the root mean squared error, mean absolute error, and mean absolute percentage error. ResultsFrom 2010 to 2024, a total of 15,184 HFRS cases were reported in Shandong Province, with 188 deaths. The annual incidence rate ranged from 0.45 to 1.84 per 100,000 population, showing an overall fluctuating downward trend. The peak incidence occurred during the autumn and winter months from October to December each year. Spatially, the eastern and southeastern regions of Shandong Province were the primary high-incidence areas. Demographically, the high-risk populations were mainly middle-aged and older adults, male individuals, and farmers. In the 2024 test set, the Holt-Winters model yielded the lowest prediction error metrics among the 4 models, with root mean squared error, mean absolute error, and mean absolute percentage error values of 7.56, 6.43, and 34.56%, respectively. ConclusionsThe Holt-Winters model showed the lowest prediction error metrics among the 4 models, suggesting its potential utility for forecasting HFRS incidence. It is recommended to apply this model to enhance precise monitoring and implement targeted prevention and control measures focusing on key areas, peak periods, and high-risk populations.
BackgroundLung cancer is the leading cause of cancer-related death globally, with a significant burden in China, which accounts for more than one-third of the world’s new cases and 40% of related mortalities. Despite evidence that low-dose computed tomography screening can reduce mortality, its limited uptake in China means that most patients are diagnosed at an advanced stage, contributing to a low 5-year survival rate compared to high-income countries. Although overall incidence trends have been well documented, there is a scarcity of population-based data on the temporal trends of different histological subtypes of lung cancer in China. ObjectiveThis study aimed to analyze population-based cancer registry data to assess lung cancer incidence trends by histological subtype and sex from 2001 to 2020 and to forecast future incidence rates through 2030 in Xihu District, Hangzhou, Zhejiang Province, eastern China. MethodsThe study used population-based surveillance data from the Xihu Cancer Registry from 2001 to 2020. Statistical analyses were conducted to assess the burden of each tumor subtype, including the average annual percentage change (AAPC) to evaluate temporal trends and the Bayesian age-period-cohort (BAPC) model to forecast incidence rates through 2030. ResultsLung cancer incidence rates were generally low among individuals aged <45 years but escalated markedly with age, reaching a peak of 567.6 per 100,000 person-years in among those aged 80 to 84 years. Notably, a significant rise in incidence was observed among those aged 30 to 44 years in recent years. Sex-based differences in histological distribution were pronounced. Adenocarcinoma accounted for 66.65% (2322/3484) of female cases compared to 37.1% (1795/4838) of male cases, while squamous-cell carcinoma was substantially less common in female cases (86/3484, 2.47% compared to 829/4838, 17.13% in male cases). Among male cases, the age-standardized incidence rates (ASIRs) of adenocarcinoma increased from 1.39 to 42.18 per 100,000 between 2001 and 2020; among female cases, the corresponding increase was even more pronounced, rising from 2.81 to 67.37 per 100,000. Joinpoint regression analysis demonstrated a consistent long-term increase in adenocarcinoma (AAPC=35.1, 95% CI 14.2-50.2), with peak acceleration observed from 2005 to 2012 (annual percentage change [APC]=76.7, 95% CI 45.2-124.2), and a steady increase in large-cell and other specified carcinomas (AAPC=32.7, 95% CI 12.5-45.7), while unspecified carcinoma showed a long-term decline (AAPC=−3.1, 95% CI −14.2 to −1.1). BAPC projections indicated that adenocarcinoma ASIR will continue to surge between 2020 and 2030, emerging as the dominant histological subtype, while unspecified carcinoma, squamous-cell carcinoma, and small-cell lung cancer are anticipated to decline modestly. ConclusionsThe increase in lung cancer incidence, particularly among female participants, underscores the need for effective prevention and control strategies, including targeted interventions to reduce smoking.
