Accurate short-term influenza forecasting is important for early warning and public health preparedness, but routine sentinel surveillance may be delayed and may not fully capture behavioural and environmental signals related to influenza activity. This study aimed to develop and evaluate multi-source machine-learning models for short-term influenza forecasting in Hunan Province, China. Weekly influenza-like illness plus (ILI+) data from 54 sentinel hospitals across 14 prefecture-level cities in Hunan Province were used to characterise influenza activity from 2015 to 2024. For forecasting during 2020–2024, ILI+ data were integrated with Baidu Search Index indicators, meteorological variables and air quality index data. ILI + was calculated as the product of the weekly ILI proportion and weekly influenza virus test positivity rate. Five models, including LSTM, GRU, stacked autoencoder, random forest and LightGBM, were compared for 1–3-week-ahead forecasting under four feature settings. Performance was evaluated using RMSE, MAE, MAPE, SMAPE and R², with SeasonalNaive52 as the baseline comparator. Influenza activity showed pronounced winter–spring seasonality with substantial interannual and city-level heterogeneity. At the provincial level, the all-features RF model achieved the lowest absolute errors among the machine-learning models (RMSE = 0.00882, MAE = 0.00389) and an out-of-sample R² of 0.723. Compared with the SeasonalNaive52 baseline, the machine-learning models showed lower RMSE, MAE and SMAPE and higher out-of-sample R². In city-level analyses, RF ranked highest in 11 of 14 cities based on the descriptive summary score, whereas GRU ranked highest in Yueyang, Zhangjiajie and Hengyang, indicating that model suitability may vary across local epidemic settings. We developed a city-level multi-source framework for short-term influenza forecasting in Hunan Province by integrating surveillance, Baidu Search Index, meteorological and air-quality data. Multi-source predictors improved forecasting beyond a seasonal naive baseline, with RF providing a robust option for reducing absolute weekly errors. City-level performance differences highlight the need for local validation before operational deployment. With prospective validation, this framework could support 1–2-week-ahead early warning and targeted influenza preparedness.

Understanding the acquisition and dissemination of microbiomes and antimicrobial resistance genes (ARGs) that circulate across human-animal-environment interfaces remains a central One Health challenge, largely because of complex ecological interactions and multiple confounding factors. Although occupational exposure is known to influence the microbiomes and resistomes of farmers, how environmental compartments involve in this system is unclear. Here, we conducted a one-year longitudinal study combining strain-resolved metagenomics (500 metagenomes) with isolate-based whole-genome sequencing (28 isolates) in an ecologically managed, antibiotic-free farming ecosystem spanning animals, farmers, environmental compartments and non-exposed individuals. Assembling 6,075 species-level genomes, we show that animal-associated occupancy reshapes the microbiome and resistome of occupationally exposed farmers and their surrounding environments. Animals and their associated habitats formed the dominant interface for both strain sharing and ARG dissemination across connected ecological compartments, whereas village residents and surrounding river samples - used as ecological controls - showed limited integration into this sharing network. Tracking a frequently shared lineage further revealed within-lineage genetic turnover together with selection-consistent changes following cross-species spread, suggestive of ecological selection across hosts and habitats. Finally, we identify Klebsiella pneumoniae as the most widespread ESKAPE pathogen in this ecosystem, with repeated occurrence across animal, human and environmental compartments, consistent with a neglected but clinically critical broad profile of ecological generalist. Together, these findings identify animals as central interfaces for microbiome and resistome sharing and show how agricultural ecosystems can sustain circulation of opportunistic pathogens and resistance determinants across human-animal-environment interfaces even in the absence of routine antibiotic use.

INTRODUCTION:This study explored transmission patterns of brucellosis in Hubei Province, a Class II region in China, through analysis of spatiotemporal distribution and environmental factors to provide evidence for precise prevention. METHODS:We analysed epidemiological data from Hubei Province (2010-2023), including human cases, demographics, livestock numbers and environmental factors. The MaxEnt model was used for spatial risk prediction, while the seasonal autoregressive integrated moving average (SARIMA) model analysed temporal patterns and warning effectiveness. RESULTS:Human brucellosis showed a 'rapid rise-effective control-local rebound' pattern, with incidence rates rising from 0.0087/100,000 (2010) to 0.5932/100,000 (2015), declining to 0.1535/100,000 (2019) and rebounding to 0.4508/100,000 (2023). The epidemic displayed seasonal peaks (April-September) and affected primarily males (71.13%) and farmers (76.54%). Spatial distribution showed a 'high-north low-south' pattern, spreading from Suizhou to neighbouring areas. The MaxEnt model identified high-risk areas (probability ≥ 0.713) in livestock-intensive regions, with sheep density (55.1%) and annual mean temperature (9.9%) as key factors. The SARIMA model indicated earliest warnings in high-risk areas (mid-late April), followed by medium-risk (early-mid April) and low-risk areas (late May). CONCLUSIONS:Brucellosis transmission in Hubei is influenced by the livestock industry, control measures and climate. The integrated MaxEnt-SARIMA model provides risk classification and differentiated warning schemes, offering guidance for targeted prevention across different risk areas.

Pertussis remains a global threat for infants, and recent increases in notifications have renewed interest in optimising vaccination strategies and improving vaccines. At the biological level, the rationale for maternal pertussis immunization extends beyond passive antibody transfer alone and may also involve broader maternal-infant immune interactions; however, at the population level these mechanisms are operationalized through policy adoption, timing recommendations, and coverage. We compared incidence constructs from WHO routine notifications and Global Burden of Disease (GBD) modelled estimates and assessed how associations with vaccination policy indicators change across outcome definitions. Trends were characterised with Joinpoint regression; policy associations were estimated using Bayesian hierarchical models fitted separately to each dataset with Universal Health Coverage stratified random intercepts. Incidence levels and trends differed markedly between WHO and GBD. Unadjusted analyses showed heterogeneous, sometimes opposing, associations for maternal immunization and schedule timing. After adjustment, most schedule parameters were small and imprecise, whereas DTP3 coverage remained strongly inversely associated with incidence in GBD but not in WHO. Surveillance and modelled estimates should not be interpreted interchangeably; harmonized constructs and routine implementation and ascertainment metadata are needed for robust cross-country inference.

Urban wastewater systems connect hospitals, residential communities, transport hubs, and wastewater treatment plants, creating opportunities for the dissemination of microorganisms and antibiotic resistance genes (ARGs). Here, we conduct a three-month, citywide metagenomic survey of wastewater in Xiamen, China, comprising 252 samples from seven hospital sites (n = 16), 27 residential sites (n = 55), 16 wastewater treatment plant sites (n = 159), and individual international flights (n = 22). Genome-resolved analyses reveal source-specific microbial community structures, resistome profiles, lineage-sharing patterns, and associations between ARGs and mobile genetic elements across wastewater sources. Hospital wastewater harbors the most diverse resistome, while international flight wastewater introduces microbial taxa and ARGs absent from local wastewater networks. Wastewater treatment plants accumulate ARGs from multiple upstream sources, exhibiting frequent lineage sharing and signals of potential horizontal gene transfer. Compared with within-environment sharing, cross-environment lineage sharing is associated with lower nucleotide diversity, consistent with possible genetic bottlenecks. Among potential correlates, pH shows strong non-linear associations with microbial diversity and resistome composition. These findings indicate that urban wastewater systems function as interconnected networks for microbial and ARG dissemination and identify potential hotspots for targeted antimicrobial resistance surveillance.

Abstract In 2026, a Bundibugyo ebolavirus (BDBV) outbreak emerged in the Democratic Republic of the Congo (DRC), with 4,566 confirmed cases and 2,128 deaths reported as of 11 August, potentially becoming the largest Ebola outbreak on record globally. We developed a susceptible-exposed-infectious-deceased-recovered (SEIDR) model incorporating incorporating three categories of interventions—public self-protection, safe burial, and treatment and convalescence—to assess early transmission dynamics, the current epidemic trajectory, and cross-border spillover risk, and to inform the formulation of control strategies. Based on cumulative confirmed case data up to 31 July, sensitivity analyses across multiple candidate start dates identified 28 March as the optimal start date of sustained transmission, with 31 March to 3 April as the most likely onset window. As of 31 July, the basic reproduction number (ℛ 0 ) was 1.83 (95% CI: 1.81–1.84). When 58.12% of the susceptible population adopted protective behaviours, the transmission chain could be effectively interrupted. By integrating the non-dominated sorting genetic algorithm II (NSGA-II) with Pontryagin’s minimum principle (PMP), we derived a time-varying optimal control strategy, with adjustments every two weeks, that could shorten the epidemic duration by approximately 7 months. Using International Migrant Stock data and Facebook IP-based mobility data with the Prophet forecasting model, we assessed spillover risk. Four countries were identified as very high risk at the end of July. Compared with the status quo scenario, the optimised control strategy could substantially reduce global importation risk. Enhanced entry screening and preparedness are warranted in neighbouring countries of the DRC in Africa, France in Europe, and Canada in North America.

As natural hosts of avian influenza viruses, wild birds pose an increasing threat to public health. Here, using surveillance data from wild bird infections across the United States (2022-2025), we show that HPAI-H5 transmission exhibits strong interspecific variation, seasonality, and spatial heterogeneity linked to migratory flyways. Phylogeographic analysis reveals that viral genotypes evolve from early, limited transmission along single migratory routes to a nationwide dispersal pattern spanning multiple migratory flyways. Anseriformes exhibits the highest number of infections but the lowest transmission risk, whereas Strigiformes demonstrates the greatest transmission risk. The HPAI-H5 transmission in wild birds along migratory flyways exhibits significant spatial heterogeneity and is associated with bird migration. Meteorological conditions are correlated with outbreak timing and may inform early warning efforts; however, these relationships are nonlinear. These findings provide a foundation for risk assessment, early warning systems, and integrated management of avian influenza in wild bird populations.

Introduction Global human papillomavirus (HPV) prevalence among women rose significantly from 14% (2019) to 24% (2024), underscoring the need to understand transmission dynamics and public health impact. Although multi-genotype infections are increasingly documented, evidence remains limited on their combined effect on cervical lesion severity and transmission, especially in regional populations. Using clinical datasets from Xiamen, China, this study evaluates co-infection patterns through integrated statistical and dynamical models to quantify associations with histological severity and transmissibility. Methods Data were sourced from positive HPV nucleic acid tests at two hospitals in Xiamen. Genotyping employed multiplex PCR-based flow fluorescence hybridisation. Cumulative link models (CLMs) were used to assess associations between multi-genotype infections and cervical lesion severity. Concurrently, an ordinary differential equation transmission model was developed to estimate reproduction numbers, comparing transmission potential across infection types. Results Of 1 33 438 samples, 15 939 (11.9%) were HPV-positive, covering 27 genotypes. HPV 16, 52 and 58 were the most prevalent high-risk types. Co-infections involving these genotypes showed strong inter-hospital correlation in pairing patterns (Pearson’s r =0.851). Co-infection severity association was context-specific: the number of genotypes predicted severity in the screening population (eg, quadruple infections OR=1.47, 95% CI 1.23 to 1.71, p<0.01) but not in the referral population. Conversely, co-infections exhibited consistently higher relative transmissibility indices in both settings (eg, high-low-risk co-infection median model-derived R 0 : 2.57–6.82). Conclusions HPV co-infection impacts are modulated by patient population: relative transmission potential is broadly elevated, whereas histological severity effects are marked in screening cohorts but minimal in referral groups. Context-aware public health strategies—adapting co-infection screening and interventions to clinical setting—are urged for more effective and efficient disease control.

Between 2022 and 2025, nearly 130,000 confirmed monkeypox (mpox) cases, including over 280 deaths, were reported to the World Health Organization (WHO) from 130 countries and territories, prompting the WHO to declare it a public health emergency of international concern on two occasions. Hence, to enable a scientific approach to prevention and control, transmission dynamics through mathematical modeling must be elucidated. Through a comprehensive literature review on the modeling of mpox transmission dynamics, this study indicates that existing research primarily extends the susceptible-infectious-recovered (SIR) modeling framework and largely focuses on the 2022 global outbreak. The analysis revealed significant variations in mpox virulence, particularly dependent on the subtypes, and variations according to the descending order of clades: Ib>Ia>IIa>IIb. Interpersonal transmission capacity was notably higher for clade II than for clade I, highlighting geographical disparities, with the highest transmission capacity in the Americas, moderate in Europe and Oceania, and the lowest in Asia. In contrast, Africa maintained consistently low but non-declining transmission levels. Furthermore, the study confirmed significant co-infection patterns between mpox and sexually transmitted diseases, such as Human Immunodeficiency Virus (HIV) and syphilis, along with evidence of synergistic transmission interactions among multiple pathogens. This study provides crucial modeling evidence for mpox regulation. By quantifying high-risk populations, evaluating intervention effectiveness, and identifying the risks associated with subtypes and geographic areas, this study offers a comprehensive reference for understanding the interface between model complexity and practical application. These advancements have enhanced the strategic focus and precision of public health responses during outbreaks.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging public health concern with increasing incidence and geographic spread. Quantitative assessment of its cross-species transmission among humans, ticks, and animal hosts remains limited. Here, we develop and apply a multi-population, multi-route dynamic model using data from 3,883 reported cases in Anhui Province, China, from 2019 to 2023 to quantify transmissibility and characterize spatiotemporal patterns. We estimate an average annual incidence of 1.3 per 100,000 and a case fatality rate of 3.1%. Cases are concentrated from April to September, and the transmission season extends into early spring and late autumn. Farmers and older adults are the main high-risk groups, and transmission risk is highest in central and southern Anhui. Model-based estimates indicate increasing transmissibility, with the basic reproduction number exceeding 1 from 2022 and reaching 1.5 in 2023; the highest prefecture-level estimate is 3.3 in Chuzhou. These findings show sustained expansion of SFTS transmission in a high-endemic region and support earlier, geographically targeted interventions for cross-species transmission control.

Abstract Background Dengue fever is a major mosquito-borne disease that is spreading rapidly from endemic regions to previously low-incidence regions. The regions at the interface between Southeast Asia and China represent critical hotspots for the cross-border and subsequent local transmission of dengue fever. In this study, a cross-population transmission model was constructed to characterize the transmission dynamics in China's border regions adjacent to Myanmar. Methods Dengue case data from the dengue fever case reporting system were collected for Dehong Dai and Jingpo Autonomous Prefecture (DH) and Lincang City (LC), which are situated along the China-Myanmar border in southwestern China (2014–2023). We analyzed spatiotemporal patterns of dengue transmission and developed a transmission dynamics model that accounts for vertical transmission in mosquitoes to quantify transmission dynamics at three levels: the overall disease system, mosquito-to-human, and human-to-mosquito. Model parameters were estimated using least-squares fitting to observed case data, and model performance was evaluated using the coefficient of determination (R 2). Results From 2014 to 2023, a total of 10,180 dengue cases were documented across two China's border prefectures adjacent to Myanmar, 83.1% of which were locally acquired infections. There were 7,893 cases reported in DH (906 international imports, 6,961 local) and 2,287 cases reported in LC (717 international imports, 1,497 local). Transmission exhibited pronounced seasonality, peaking between July and November, and a strong temporal correlation between imported and local cases was observed. By using a dynamic transmission model incorporating mosquito vertical transmission, we achieved statistically significant model fits (P < 0.001) and quantified temporal changes in transmissibility. During the rising phases of the outbreak, the overall transmissibility consistently exceeded 1. Analysis of directional transmission revealed a marked temporal shift: human-to-mosquito transmissibility was predominant in earlier outbreak years (2017: 1–5 in DH, 10–15 in LC), whereas mosquito-to-human transmissibility has increased substantially in recent years (2023: 5–15 in DH, 10–20 in LC). Sensitivity analyses demonstrated that while overall transmissibility estimates remained robust across different initial mosquito population assumptions, the directional transmission components were sensitive to the ratio of exposed to infected mosquitoes, reflecting the inherent identifiability challenges of the model in the absence of entomological surveillance data. Conclusions Our findings reveal the dengue transmission dynamics in China's border regions adjacent to Myanmar over the 2014–2023 study period. Our results underscore the necessity of integrating entomological monitoring with case-based surveillance and support enhanced cross-border coordination for effective outbreak prevention in high-risk frontier zones. Graphical Abstract

Abstract Against the backdrop of global climate change and accelerating population mobility in 2025, chikungunya fever (CHIKF) exhibited a trend of worldwide spread, significantly increasing the difficulty of controlling tropical mosquito-borne diseases. To enhance the precision of intervention strategies, this study developed an age- and sex-structured human–mosquito interaction dynamic model based on data from the largest CHIKF outbreak ever recorded in China, and conducted a targeted analysis of prevention and control strategies. By decomposing the basic reproduction number and examining population heterogeneity, asymptomatic males aged 15–59 years were identified as the core transmission group. Optimal control analysis revealed that the synergistic implementation of three measures— reducing the effective human-to-mosquito transmission rate, reducing the effective mosquito-to-human transmission rate, and suppressing mosquito population density—could reduce the overall infection rate by 95.7586%. Among these, mosquito population suppression should be prioritized as a universal core strategy; however, its protective effect on females aged 60 years and above was relatively weak, warranting particular attention. The study further demonstrated that asymmetric intensity combinations targeting these three intervention pathways—such as intensity profiles of “10%, 90%, 90%” or “60%, 80%, 90%”—could achieve effective outbreak control. This research elucidates population-specific transmission patterns and key pathways for intervention intensity, providing a theoretical and strategic foundation for the precise control of mosquito-borne diseases. It also provides actionable operational insights to support rapid response and strategy optimization for future emerging outbreaks. Author summary CHIKF is a mosquito-borne viral disease that is gradually spreading from tropical regions to other areas. To achieve more precise control of this disease, we developed an age- and sex-structured analytical model based on the largest CHIKF outbreak in China, aiming to provide a scientific basis for responding to potential future outbreaks with inherent uncertainties. The study found that asymptomatic males aged 15–59 years were the primary drivers of transmission and should be prioritized as a key population for reducing viral spread in prevention efforts. When evaluating the effectiveness of different intervention strategies, females aged 60 years and above were the least affected by the implemented measures, indicating that this group should strengthen personal protection to lower their infection risk. Among all control measures, mosquito suppression was the most effective, suggesting that vector control strategies should be prioritized in future outbreak responses.

Background: China‘s influenza vaccination coverage remains at a low rate, with significant regional socioeconomic disparities, lacking targeted distribution strategies and achievable coverage targets. This study aims to provide scientific evidence for formulating differentiated and feasible vaccination strategies across Chinese provinces based on regional economic gradients. Methods: We employed the Susceptible-Vaccinated-Exposed-Asymptomatic-Infectious-Critical-Fatal-Recovered/Removed (SVEAICFR) model to simulate various vaccination strategies, analyzing the reduction in disease burden and vaccine dose requirements across underdeveloped, developing, and developed regions. The optimal strategy and achievable coverage targets were subsequently determined. Results: The 31 provinces were clustered into three categories based on economic levels, showing significant spatiotemporal differences in epidemics (Kruskal–Wallis test, all p < 0.001). Developed regions showed the earliest onset and highest peaks (influenza-like illness positive (ILI+) index ≈ 12–13, Baidu Influenza Search Index (BISI) ≈ 310,000). Developing regions exhibited moderate lagging by 1–2 weeks, while underdeveloped regions had the lowest peaks (ILI+ 3–4) and longer epidemic cycles. During the 2023–2024 influenza season, the national predicted vaccination rate was only 2.89% with marked regional disparities. Baseline incidence, severity, and mortality rates were 13,374.93, 49.52, and 8.37 cases per 100,000 population, respectively. Modeling indicates that increasing influenza vaccination coverage rates for populations aged <18 and ≥65 to a theoretical threshold (39.73% of the total population) before the season could reduce incidence, severity, and mortality rate by 99.26%,99.42%, and 99.46%, respectively. Conclusions: Influenza prevalence in China exhibits significant regional heterogeneity, necessitating differentiated measures based on regional economic gradients. Regional support mechanisms should be implemented to promote equitable vaccine distribution. Priority vaccination for high-risk populations (aged <18 and ≥65), to reach a 40% theoretical national coverage target, is recommended via realistic implementation pathways to minimize the disease burden of influenza.

Background The largest documented chikungunya fever (CF) outbreak in China occurred in Foshan, yet evidence on outbreak-wide epidemiological characteristics and intervention-associated effects has remained limited. Methods We conducted a longitudinal analysis from 16 June to 10 August 2025, integrating case and vector epidemiology, and generalised estimating equation (GEE) models. Results A total of 8,964 cases were reported, with no deaths. Females had a higher incidence than males (1,027.12 vs 919.71 per 100,000), and males had lower infection odds (OR 0.89, 95% CI = 0.86–0.93). Adults older than 60 years showed the highest incidence (2371.62 vs. <1402.00 per 100,000) and infection risk (OR < 1 in other age groups). Cases were concentrated in Shunde District (82.42%). The onset-to-diagnosis interval shortened after interventions. 2.60% were asymptomatic; fever, rash and arthralgia were the predominant symptoms. The effective reproduction number (\(\:{R}_{t}\)) rose rapidly (3.12, 95% CI = 0.08–11.51) and declined below 1 by 1 August (0.87, 95% CI = 0.80–0.94). Breteau index (BI) and adult mosquito density Index (ADI) were decreased and high in the border areas between districts. For non-lagged GEE, one-unit changes in ADI and BI were associated with 9.28% (95% CI= -13.40–37.92; P = 0.45) and 0.60% (95% CI =-1.48–2.73; p = 0.57) changes in case numbers. One-unit changes in ADI were associated with a 10.73% change in \(\:{R}_{t}\) (95% CI = 3.23–18.78; p = 0.004), and 7.50% in BI (95% CI =-5.15–21.84; p = 0.26). For lagged GEE, lagged ADI remained associated with \(\:{R}_{t}\) (8.74%, 95% CI = 1.14–16.91; p = 0.023), while lagged tOD showed no significant association with case numbers (2.68%, 95% =CI -8.21 to 14.86; p = 0.64) and \(\:{R}_{t}\) (2.17%, 95% CI= -1.70 to 6.20; p = 0.28). Conclusions This outbreak showed high transmissibility, concentration in Shunde, and elevated risk in females and older adults. The key area for vector control was the border of different districts. Prioritising routine and targeted adult-mosquito surveillance and control, while maintaining larval source management and accelerating case detection, not only for this outbreak but also for other non-endemic urban settings facing comparable arboviral threats.

To investigate the transmission dynamics of respiratory multi-pathogen co-infection, this study developed a dual-pathogen co-infection transmission dynamics model comprising 10 compartments, incorporating an exponentially decaying time-varying effective transmission rate. The model was systematically calibrated and validated using epidemiological surveillance data from a major Chinese metropolis—Wuhan—covering approximately 2.72 million cases between January 1, 2023 and January 1, 2025. The analysis focused on the 13 most common pathogen combinations, which accounted for the top 1% of infection cases. Results revealed three distinct winter-centered epidemic peaks in Wuhan over the two-year period. Co-infection cases were most frequently observed as virus–bacteria and virus–other pathogen combinations. Analysis of the major co-infection combinations showed that the mean basic reproduction number of the system ranged between 1.19 and 1.88, while the median ranged from 1.08 to 1.35. Transmission peaks in each combination were primarily driven by the most transmissible pathogen within the pair. Transmission route analysis further indicated that when individuals were infected with multiple pathogens, sequential secondary infection was the dominant pattern, whereas direct simultaneous co-infection occurred at a relatively low proportion. This study suggests that respiratory pathogens continue to exhibit sustained transmission potential. The findings provide an important evidence based reference for designing risk oriented, precision prevention and control strategies in the future.

Objectives: This study aimed to elucidate the global epidemic trends and evolutionary characteristics of nine major non-polio enterovirus serotypes (CVA2, CVA4, CVA6, CVA10, CVA16, CVB3, CVB5, EV-A71, and EV-D68) through genomic data mining, focusing on their spatiotemporal distribution and evolutionary dynamics. Design: We employed a data mining framework integrating programming, phylogenetic analysis, Bayesian evolutionary modeling, and selection pressure assessment. Over 40,000 genomic sequences from GenBank were analyzed to reconstruct temporal phylogenies, estimate evolutionary rates, and characterize amino acid variability in the capsid protein VP1. Seasonal decomposition and spatial-temporal trend modeling were applied to evaluate epidemic patterns across the six WHO regions. Results: Key findings include [1]: Distinct biennial or triennial epidemic cycles for EV-D68 and clear seasonal peaks for HFMD-associated serotypes [2]; A preliminary observation termed the “60% Transcendence” phenomenon, where once cumulative VP1 nucleotide mutations reach approximately 60%, the cumulative non-synonymous amino acid mutations begin to exceed this threshold [3]; Evidence of episodic positive selection at critical VP1 codons, suggesting immune-driven evolution [4]; Divergent trends in relative genetic diversity, with EV-A71, CVA16, and CVA6 showing sustained expansion, while the diversity of CVB5 and EV-D68 declined sharply during the COVID-19 pandemic. Conclusions: This study provides valuable insights into the changing landscape of global enterovirus infections and underscores the critical role of genomic epidemiology in tracking their spread. Sustained research in this field is essential for developing effective strategies to prevent and control enterovirus-related diseases worldwide.

What is already known about this topic?:Given the proportion of undiagnosed human immunodeficiency virus (HIV) cases and regional disparities in Fujian Province, achieving the 2030 targets to end acquired immunodeficiency syndrome (AIDS) remains uncertain. What is added by this report?:HIV transmission in Fujian has stabilized through treatment and viral suppression, achieving the "95-95-95" targets; however, a diagnostic gap persists (86.30%). Transmissibility recently increased (R eff =1.03), with males and adults aged 20-70 years as the primary high-risk populations, threatening elimination goals. What are the implications for public health practice?:Improving diagnostic coverage is essential for epidemic elimination. Targeted screening and self-testing must be intensified among key populations, particularly middle-aged and older adults, while maintaining treatment achievements to prevent resurgence.

Background:Recently, surges in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfections in China have raised public concern. We investigated the epidemiological features and risk factors for SARS-CoV-2 reinfection in China. Methods:A retrospective cohort study was conducted in Xiamen, China (2021-2023) with two subcohorts: Delta-Omicron (cohort 1) and Omicron-Omicron (cohort 2). Descriptive analysis and ensemble modeling were employed to evaluate reinfections. Results:A total of 327 cases without fatalities were included. Reinfections accounted for 14.68% of cases, with 22.51% in cohort 1 and 3.68% in cohort 2. Compared with primary infections (PIs) (99.69% symptomatic, 56.54% hospitalized), reinfections were less severe, with fewer symptomatic instances (47.92%) and hospitalizations (4.65%). The majority of reinfections (83.33%) occurred following the relaxation of strict public health and social measures. The median time interval between PI and reinfection was longer for cohort 1 (462 days) than for cohort 2 (280 days). Reinfection risks were noted among lesser developed regions, those without persistent PI, those with primary Delta variant infection, government and hospital workers, and unvaccinated individuals. Conclusions:SARS-CoV-2 reinfections are generally less severe and are influenced by the relaxation of control measures, viral evolution, and changing patterns of population immunity and contact; this underscores the need for ongoing surveillance and targeted public health strategies to manage future infection waves.