This study proposes a method for automated report generation in disease control agencies using R Markdown and demonstrates the entire workflow from data collection, analysis, and visualization to report generation through a case study on mosquito surveillance data. By integrating text, code, and output results, R Markdown significantly reduces manual operations and improves report accuracy and reproducibility. This study represents the first application of R Markdown in the automated report generation of mosquito surveillance, offering an efficient and reusable solution for public health. The findings highlight the method ability to enhance data processing efficiency, report flexibility, and real-time decision-making support.

Although increasing evidence suggests an association between particulate matter (PM) and tuberculosis (TB), epidemiological studies in southern China remain limited. Daily tuberculosis (TB) case numbers, air pollution, and meteorological data for four cities (Guangzhou, Shenzhen, Foshan and Jiangmen) in southern China during 2015–2019 were collected. A two-stage analytical approach was used to evaluate the association between PM and TB incidence, with stratification by sex, age, disease severity, and city. A total of 101,567 TB cases were reported in four cities. The relationships between PM2.5 and PM10 and TB incidence were approximately linear, with the highest risk observed at a cumulative lag of 0–2 days (lag02). Each 10ug/m3 increase in PM2.5 was associated with a 0.84

BackgroundInfectious diarrhea remains a significant public health challenge, with climatic factors potentially playing a crucial role in its epidemiological spread. However, the precise mechanisms through which the El Niño-Southern Oscillation (ENSO) influences diarrheal morbidity are still not fully understood.MethodsWe collected monthly other infectious diarrhea (OID) incidence and climatic data across the 31 provincial administrative divisions of mainland China (2005–2019). Wavelet analysis was employed to examine the periodicity of OID and the phase relationships between ENSO, climate factors and OID. Generalized additive models (GAM) and Peter and Clark Momentary Conditional Independence (PCMCI) algorithm were used to quantify exposure-response relationship and establish causal pathways in China.ResultsFrom 2005 to 2019, a total of 13,620,167 OID cases were reported in 31 provincial regions of China, with the highest incidence of OID concentrated in the southern and eastern regions of China. Wavelet analysis identified a significant periodicity between ENSO cycles and diarrhea incidence patterns, demonstrating that La Niña events (characterized by low ENSO index) were associated with subsequent increases in incidence with a 6-month lag. The exposure-response relationship showed an inverted J-shaped curve in North and East China, while a nearly linear relationship was observed in Northeast, Central, Southwest and Northwest China. PCMCI analysis elucidated that precipitation is an indirect link between ENSO and OID.ConclusionOur study suggests that a low ENSO index (La Niña) may drive the incidence of OID in China. The findings provide a scientific basis for predicting and warning of OID based on ENSO.

The relationship between urban vegetation and dengue risk remains unclear, partly because most studies rely on static residential exposure and coarse greenness measures. This study examined how street-view vegetation and human mobility jointly influence neighborhood-level dengue risk in Guangzhou, China. We analyzed 1,054 communities in Guangzhou using 3,972 locally acquired dengue cases from 2015 to 2019. Street-view images were used to derive grass, plant, and tree cover, while Landsat imagery provided NDVI. Row-standardized origin-destination mobility networks were used to construct mobility-weighted vegetation metrics. Negative binomial regression models with a population offset were fitted to estimate associations of local and mobility-weighted vegetation with dengue risk. Sub-group analyses were conducted by sex and age, and sensitivity analyses tested the robustness of the findings. In the local-only model, plant cover was significantly associated with lower dengue risk (IRR = 0.883, 95

BackgroundThe COVID-19 pandemic has reshaped the global epidemiology of respiratory infectious diseases, posing new challenges for influenza forecasting. Existing studies are often limited by reliance on single data sources, poor interpretability, or failure to account for nonlinear relationships among variables, which restricts their ability to balance prediction accuracy and practical utility for public health decision-making. This study aimed to develop and validate a multisource data-integrated generalized additive model (GAM) to forecast influenza activity in Shenzhen, China.MethodsUsing surveillance and auxiliary data from 2023 to 2025, we developed GAM models incorporating local and Hong Kong influenza surveillance, cross-boundary mobility metric, meteorological factors, and Baidu Search Index data. The predictive performance of the GAM was compared with Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model. Model accuracy was evaluated using root mean square error (RMSE), mean absolute percentage error (MAPE), and R2.ResultsThe multisource data-driven GAM exhibited high predictive accuracy across short-term forecasting horizons. For 1-week ahead forecasts, the model achieved an R2 of 0.85 (95% CI: 0.74–0.92). Notably, performance remained robust for 2- and 3-week forecasts, with R2 values of 0.80 (95% CI: 0.69–0.87) and 0.74 (95% CI: 0.62–0.83), respectively. The GAM demonstrated superior overall performance compared with SARIMAX.ConclusionThe multisource data-integrated GAM provides robust and stable influenza forecasts for Shenzhen up to 3 weeks in advance. This approach provides a valuable tool to support cross-boundary public health collaboration between Hong Kong and Shenzhen, and might serve as a reference for the development of broader regional public health strategies in future research.

BACKGROUND:China implemented diverse varicella vaccination strategies from 2012 to 2022, with unclear protective effects. The study aimed to evaluate the effects of two varicella vaccination (VarV) (the two-dose self-paid VarV and the two-dose free VarV) strategies implemented in Guangdong Province, China. RESEARCH DESIGN AND METHODS:We collected data on varicella cases and doses administered to children aged 0-14 in Guangzhou, Shenzhen, and Foshan from 2012 to 2022. Using Bayesian Structured Time Series (BSTS) model, we estimated the effects of the two VarV strategies in Guangzhou and Shenzhen starting from 2018, by referencing Foshan. RESULTS:Post-implementation of the two-dose self-paid VarV strategy 36,749 (95% CI: 29070, 44428) and 24,179 (95% CI: 16400, 31958) varicella cases were averted in Guangzhou and Shenzhen, with a protection rate of 41.8% (95% CI: 36.3%, 46.5%) and 38.9% (95% CI: 30.2%, 45.7%), respectively. After the adoption of the two-dose free VarV strategy, a substantial relative protection rate of 64.2% (95% CI: 58.0%, 68.7%) in varicella cases was observed in Shenzhen, with 38,828 (95% CI: 29979, 47677) cases averted by 2022. CONCLUSIONS:The two-dose VarV strategies have proven highly effective in reducing varicella incidence. The experience in Shenzhen underscores the benefits of a two-dose free VarV strategy.

BACKGROUND:During the coronavirus disease 2019 (COVID-19) pandemic, the implementation of public health intervention measures have reshaped the transmission patterns of other infectious diseases. We aimed to analyze the epidemiological characteristics of dengue in Guangdong Province, China, and to investigate the temporal shifts in dengue epidemic in Guangdong Province during the COVID-19 pandemic. METHODS:Based on the data of dengue reported cases, meteorological factors, and mosquito vector density in Guangdong Province from 2012 to 2022, wavelet analysis was applied to investigate the relationship between the dengue incidence in Southeast Asian (SEA) countries and the local dengue incidence in Guangdong Province. We constructed the dengue importation risk index to assess the monthly risk of dengue importation. Based on the counterfactual framework, we constructed the Bayesian structural time series (BSTS) model to capture the epidemic trends of dengue. RESULTS:Wavelet analysis showed that the local dengue incidence in Guangdong Province was in phase correlation with the dengue incidence of the prior month in relative SEA countries. The dengue importation risk index showed an increasing trend from 2012 to 2019, then decreased to a low level during the COVID-19 pandemic. From 2020 to 2022, the average annual number of reported imported cases and local cases of dengue in Guangdong Province were 26 and 2, respectively, with a decrease of 95.62% and 99.94% compared to the average during 2017-2019 (594 imported cases and 3,118 local cases). According to BSTS model estimates, 6557 local dengue cases may have been reduced in Guangdong Province from 2020 to 2022, with a relative reduction of 99.91% (95%CI: 98.85-99.99%). CONCLUSION:The incidence of dengue in Guangdong notably declined from 2020 to 2022, which may be related to the co-benefits of COVID-19 intervention measures and the intensified interventions against dengue during that period. Furthermore, our findings further supported that dengue is not currently endemic in Guangdong.

BackgroundAs of July 22, 2025, the chikungunya virus transmission has been documented across 119 countries and territories of the world. In 2025, an outbreak of chikungunya fever (CF) occurred in Foshan, Guangdong Province, China. We aimed to analyze the epidemiological characteristics and transmission dynamics during the early stage of this outbreak.MethodsWe collected the data of CF cases in Foshan from July 8 to July 26, 2025. Case data were extracted from the National Notifiable Infectious Disease Reporting System. Demographics and tempo-spatial distributions of cases, incidence rates and the onset-to-report interval times were analyzed. Global spatial autocorrelation (Moran's I) to assess township-level clustering; Kruskal-Wallis tests with Dunn's post-hoc comparisons (Bonferroni-corrected) to analyze onset-to-report intervals across four epidemic phases. The basic reproduction number (R0) was calculated using a maximum likelihood method, which was also compared with the R0 from the CF outbreak in Dongguan City of Guangdong Province in 2010.ResultsA total of 4,754 local cases were reported during the study period. Persons aged 65 years or above had the highest incidence (116.57 per 100,000 population). Most cases were business/service workers, homemakers, and retirees. The median onset-to-report interval decreased from 4 days to 1 day after outbreak control measures were implemented. The outbreak, initially detected in Shunde District, spread rapidly to other districts of Foshan, forming a significant spatial cluster (Moran's I = 0.152, P = 0.029). The estimated R0 was 16.3 (95% confidence interval: 15.0 to 17.5), substantially higher than the estimated R0 of 5.5 for the Dongguan outbreak in 2010.ConclusionsThis outbreak was characterized by high transmissibility, with older persons being a primary high-risk group. The rapid reduction in case reporting delay highlights the effectiveness of response interventions. Sustained, integrated and prompt response has been essential to control the outbreak.

Generating fine-scale risk maps for mosquito-borne diseases vectors is an essential tool for guiding spatially targeted vector control interventions in urban settings, given the limited public health resources. This study aimed to generate fine-scale risk maps for dengue vectors using routine vector surveillance data collected at the township scale. We integrated monthly township-specific Breteau Index (BI) data from Guangzhou city (2019 to 2020) with covariates extracted from remote sensing imagery and other geospatial datasets to develop an original random forest (RF) model for predicting hotspot areas (BI ≥ 5). We implemented three data resampling techniques (undersampling, oversampling, and hybrid sampling) to improve the model’s performance and evaluate it using the ROC-AUC, Recall, Specificity, and G-means metrics. Finally, we generated a downscaled risk maps for BI hotspot areas at a 1000 m grid scale by applying the optimal model to fine-scale input data. Our findings indicate the following: (1) data resampling techniques significantly improved the prediction accuracy of the original RF model, demonstrating robust spatial downscaling capabilities for fine-scale grids; (2) the spatial distribution of BI hotspot areas within townships exhibits significant heterogeneity. The fine-scale risk mapping approach overcomes the limitations of previous coarse-scale risk maps and provides critical evidence for policymakers to better understand the distribution of BI hotspot areas, facilitating pixel-level spatially targeted vector control interventions in intra-urban areas.

Background:The growing burden of ovarian cancer is attracting widespread attention; the impact factors and the evolution trend of ovarian cancer burden need to be further studied. Methods:Ovarian cancer disease burden data for Chinese women were obtained from the Global Burden of Disease study 2021. We performed Age-Period-Cohort (APC) analysis to evaluate evolution trends across age, period, and cohort dimensions and identify contributing factors. Using the Bayesian Age-Period-Cohort (BAPC) model, we projected incidence and mortality trends through 2040. Results:In 2021, China recorded approximately 41,240 new ovarian cancer cases and 25,140 related deaths. From 1990 to 2021, age-standardized rates (ASRs) for incidence, mortality, and disability-adjusted life years fluctuated but increased steadily after 2015, with annual percentage changes of 1.6% (95%CI: 1.4%, 1.8%), 1.6% (95%CI: 1.4%, 1.9%), and 1.5% (95%CI: 1.3%, 1.6%), respectively. The APC model revealed a significant age effect with peak incidence occurring at 65-69 years; a period effect showing incidence and mortality rates resurged after 2015; and the cohort effects demonstrating bimodal incidence peaks in the birth cohorts of 1910-1914 and 1935-1939. Specifically, a 1% increase in the obesity rate was associated with a 3.06 (95%CI: 0.84, 5.28; p = 0.007) per 100,000 rise in ovarian cancer incidence. BAPC projections suggest that the ASRs of incidence and mortality of ovarian cancer in China will continue rising through 2040, possibly exceeding global trends. Conclusions:The burden of ovarian cancer in China remains significant; the increasing obesity rate in women may be a driver. The ovarian cancer burden has resurged in China since 2015, and it is projected to continue increasing by 2040.

Hand, foot and mouth disease (HFMD) remains a major public health challenge in China, exhibiting distinct seasonal patterns. This study integrates meteorological, behavioural and social determinants to elucidate the transmission dynamics of HFMD in Guangzhou. Utilizing surveillance data from 2012 to 2022, we employed regression analysis and developed a mechanistic transmission model incorporating absolute humidity (AH), the Baidu search index (BDI) as a proxy for health-seeking behaviour and holiday effects. The model, calibrated via Markov chain Monte Carlo methods, explained 91.4% of the case variance and estimated a mean time-varying reproduction number of 2.29. Our findings demonstrate that AH and BDI act as significant nonlinear drivers of transmission, while holidays reduced incidence by an average of 21.3%. The implementation of non-pharmaceutical interventions during the COVID-19 pandemic was associated with a substantial reduction in HFMD incidence, with cases declining by 88.1% in 2020, 36.6% in 2021 and 72.2% in 2022. This integrative modelling framework effectively captures the multifactorial drivers of HFMD seasonality and provides a robust tool for forecasting outbreaks and informing targeted public health interventions.

Human papillomavirus (HPV) infection remains a critical public health challenge in China, particularly in Guangdong Province, where HPV-52, 16, and 58 genotypes predominate, and male infection rates exceed 40%. Despite the successful implementation of a government-funded school-based program that has achieved 88% HPV vaccine coverage among adolescent girls, several persistent barriers, including genotype mismatch (the free HPV vaccine covers < 50% of high-risk local strains), regional disparities (80% vs. 60% for first-dose coverage), and exclusion of males, thwart progress toward herd immunity. Financial sustainability risks pose an even more significant threat to the expansion of HPV vaccination programs, especially in Guangdong province where annual expenditures exceed CNY 200 million. This review delves into Guangdong’s pioneering efforts and proposes practical solutions: accelerating domestic multivalent HPV vaccine development, adopting gender-neutral vaccination policies, and leveraging mobile clinics for remote populations. These strategies not only provide a roadmap for China but also serve as valuable insight for other LMICs striving to overcome HPV-related inequalities.

Fine particulate matter (PM2.5) pollution threatens urban sustainability. Few cohort studies have assessed hypertension risks linked to lagged and cumulative exposure to PM2.5 components. Using data from a cohort study of 36,271 individuals in South China (2015-2020), we examined the individual associations between timevarying PM2.5 and six components (NO3- , SO42- , BC, CL-, NH4+, and OM) with hypertension hospitalization through Cox proportional hazards regression. Mixed associations of simultaneous exposure to these components were analyzed at lag 0, lag 1, lag 2, lag 0-1, and lag 0-2 years using quantile-based g-computation models. Individual-effect analysis revealed strong associations, with each quantile increase in CL-, NH4+, SO42- , and NO3 linked to 17 %-32 % higher hypertension risks across different time windows. Co-exposure to PM2.5 components at different lag times increased hospital admissions for overall hypertension, with hazard ratios (95 % confidence intervals) of 1.151 (1.136-1.166), 1.221 (1.205-1.238), 1.257 (1.241-1.273), 1.087 (1.073-1.101), and 1.197 (1.182-1.212). Secondary water-soluble ions (NO3- , SO42- , NH4+, CL-) were major contributors. Increased susceptibility was observed among those under 45, men, individuals with lower education, unhealthy weight, or limited green space exposure. These findings highlight the lagged and cumulative impacts of simultaneous exposure to PM2.5 component on hypertension.

Objective:To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis (TB) in the Guangdong Province between 2010 and 2019. Method:Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering. Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive (ST-CAR) model. Results:Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000 in 2019. Spatial hotspots were found in northeastern Guangdong, particularly in Heyuan, Shanwei, and Shantou, while Shenzhen, Dongguan, and Foshan had the lowest rates in the Pearl River Delta. The ST-CAR model showed that the TB risk was lower with higher per capita Gross Domestic Product (GDP) [Relative Risk ( RR), 0.91; 95% Confidence Interval ( CI): 0.86-0.98], more the ratio of licensed physicians and physician ( RR, 0.94; 95% CI: 0.90-0.98), and higher per capita public expenditure ( RR, 0.94; 95% CI: 0.90-0.97), with a marginal effect of population density ( RR, 0.86; 95% CI: 0.86-1.00). Conclusion:The incidence of TB in Guangdong varies spatially and temporally. Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection. Strategies focusing on equitable health resource distribution and economic development are the key to TB control.

The opportunity of long-term exposure to non-optimal temperature and relative humidity (RH) may increase which might contribute to elevations in blood pressure (BP). This study aimed to examine the joint effect of long-term exposure to temperature and RH on BP which help better adaption to climate change. Data were collected from 9272 participants in global AGEing and adult health cohort study across eight provinces in China from 2007 to 2019. Annual meteorological data were treated as indicators which derived from European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5). Generalized linear mixed models (GLMM) and quantile-based g-computation models were employed to estimate the individual and joint associations between long-term exposure to temperature and RH with BP. In joint effect analysis, we found a U-shaped pattern. The minimum blood pressure percentile (MBPP) for both systolic blood pressure (SBP) and diastolic blood pressure (DBP) was observed at a temperature of approximately 15.4 °C and RH of 72.8

There is limited evidence on the relationship of diabetes burden with fine particulate matter (PM2.5) and its components, which is not conducive to sustainable development in the context of rapid urbanization. To obtain relevant clues in the United States (US), we collected annual county-level diabetes incidence and mortality, concentrations of PM2.5 and five major components (including elemental carbon, organic carbon, sulfate, nitrate, and ammonium), temperature, and socioeconomic factors during 2008-2017. Through an integrating method of difference-in-differences approach and quantile G-computation, we observed that (i) long-term PM2.5 components mixture exposure was associated with diabetes mortality, but not incidence, with percent risk increase (IR%) of 3.58% (95%CI: 1.84%, 5.36%); (ii) among the five components of PM2.5, sulfate was estimated to have the largest weight (0.519); (iii) the effect of PM2.5 and its components mixture was higher when the summer mean temperature was 2 or 3 degrees below the 10-year average temperature; (iv) in counties with higher health insurance coverage, nitrate was the most important component (with the greatest weight of 0.829). Our findings suggest that long-term PM2.5 exposure is associated with increased diabetes mortality, and reducing sulfate and nitrate emission could effectively alleviate the burden of PM2.5-related diabetes mortality in the US.

Fine particulate matter (PM2.5) is one of the major threats to human health, and may partly responsible for intentional self-harm deaths, while the limited results seemed contradictory. Further analysis on PM2.5 constituents may provide more reliable evidence. Heavy metals are crucial toxic components of PM2.5 that may induce suicide behavior. What role do PM2.5-bound heavy metals play in a threat to intentional self-harm death is still unclear. Two-year data of daily PM2.5-bound heavy metals (including metalloids) and daily intentional self-harm deaths were collected in Guangzhou. Bayesian kernel machine regression, weighted quantile sum, and quantile-based g-computation models were employed to depict the relationships between heavy metals and intentional self-harm deaths. The number of intentional self-harm deaths was 217 and 283 for 2015 and 2016, respectively. A positive correlation was found between the combined effect of the 13 heavy metals and intentional self-harm deaths. Nickel, cadmium, and iron were the primary contributors to this positive correlation. Heavy metal components play significant roles in PM2.5-related intentional self-harm deaths, and targeted source control measures are warranted to protect residents from suicide.

Objectives:The prevention and control of dengue fever (DF) has been a major public health issue in Guangdong (GD) province, China. This study aims to analyze the return period (RP) and the return level (RL) of DF epidemic in GD, to help the formulation of prevention and control plan. Methods:Three models, namely Lognormal distribution (Lognor D.), normal distribution (Norm D.), and generalized logistic distribution (GLD) were selected to fit the annual number of indigenous DF cases in GD from 1978 to 2021. The coefficient of determination (R2), the root mean squared error (RMSE), and the Akaike information criterion (AIC) were used to evaluate the goodness of fit. We predicted the RP of 45130 historical maximum cases that occurred in 2014 and the RP of 4884 peak cases that occurred in 2019 over the 5 years up to 2021. Results:Fitting through the three models, the R2 was 0.98, 0.98, and 0.96, respectively. The predicted RLs of the annual DF case number were between 297 and 43234, 297 and 43233, 362 and 41868 for the RPs of 2-45 years. The predicted RPs of DF outbreaks exceeding the historical maximum were 43, 43, and 44 years, and the RPs of DF epidemic exceeding the peak in 2019 were 7, 7, and 8 years, respectively. Therefore, we predicted that GD would experience a DF outbreak beyond the historical maximum in the next 35 or 36 years from 2022. And in the next 4 or 5 years from 2022, there would be a DF epidemic exceeding the peak in 2019. Conclusions:The study discloses a temporal periodicity inherent to the DF epidemic in GD. The three models are applicable for forecasting and evaluating the RP and RL of DF epidemic in GD, separately.

Coronavirus disease 2019(COVID-19),caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is an acute respiratory infection.The majority of infected individuals present with either asymptomatic or mild symptoms,such as dry throat,sore throat,cough,and fever.However,a small percentage of patients may progress to severe illness and even mortality.This study conducted a comprehensive review of relevant reports and literature on COVID-19-related deaths published nationally and internationally between January 1,2020 and December 31,2022.The factors influencing death in COVID-19 patients are summarized from five perspectives:clinical manifestations,individual characteristics,socio-cultural influences,governmental interventions,and viral strains.The objective is to provide scientific evidence for the prevention of adverse outcomes in COVID-19 patients and theoretical support for the enhancement of treatment protocols.

Background: The evaluation of HPV vaccine effectiveness is essential for informing public health strategies, yet there remains a gap in understanding humoral immune responses generated by different HPV vaccine formulations in regional populations. This study addresses this gap by evaluating the immunogenicity of the newly developed HPV vaccine Cecolin (Wantai), alongside various imported vaccines, including bivalent, quadrivalent, and nonavalent options available in China. Methods: From March 2023 to June 2024, a total of 352 participants were enrolled, including 87 females aged 9–14 years who received two doses of the bivalent HPV vaccine (Cecolin), 215 females aged 15–45 years who were fully vaccinated with various HPV vaccines, and 50 non-recipients. Follow-up assessments were conducted at six timepoints during the administration of Cecolin. Serum was collected at enrollment and at each follow-up visit for antibody assessments using a pseudovirion-based neutralization assay (PBNA). Findings: The longitudinal follow-up of females aged 9–14 years revealed a 100% conversion rate for neutralizing antibodies against HPV types 16 and 18 after the second dose, compared to 94.3% and 97.1% conversion rates six months after the first dose. Compared to participants who received full doses of quadrivalent and nonavalent vaccines, females who received two or three doses of Cecolin exhibited higher neutralizing antibody geometric mean titers (GMTs) and non-vaccine-type (HPV31 and HPV33) antibody seroconversion rates. Interpretation: The domestically produced HPV vaccine Cecolin in China demonstrates strong immunogenicity and holds promise for the large-scale vaccination of females in developing countries to prevent cervical cancer.