Xing Li

Background: The 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. Methods: Using 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 R ² . Results: The multisource data-driven GAM exhibited high predictive accuracy across short-term forecasting horizons. For 1-week ahead forecasts, the model achieved an R ² of 0.85 (95% CI: 0.74–0.92). Notably, performance remained robust for 2- and 3-week forecasts, with R ² 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. Conclusion: The 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: Several studies have investigated the associations between ambient temperature and years of life lost (YLLs), but few focused on the difference of life loss attributable to temperature among different socioeconomic development levels. Objectives: We investigated the disparity in temperature-YLL rate relationships and life loss per death attributable to nonoptimal temperature in regions with various development levels. Methods: Three hundred sixty-four Chinese counties or districts were classified into 92 high-development regions (HDRs) and 272 low-development regions (LDRs) according to socioeconomic factors of each location using K-means clustering approach. We used distributed lag non-linear models (DLNM) and multivariate meta-analysis to estimate the temperature-YLL rate relationships. We calculated attributable fraction (AF) of YLL and temperature-related average life loss per death to compare mortality burden of temperature between HDRs and LDRs. Stratified analyses were conducted by region, age, sex and cause of death. Results: We found that non-optimal temperatures increased YLL rates in both HDRs and LDRs, but all subgroups in LDRs were more vulnerable. The disparity of cold effects between HDRs and LDRs was significant, while the difference in heat effect was insignificant. The overall AF of non-optimal temperature in LDRs [AF = 12.2, 95% empirical confidence interval (eCI):11.0–13.5%] was higher than that in HDRs (AF = 8.9, 95% eCI: 8.3–9.5%). Subgroups analyses found that most groups in LDRs had greater AFs than that in HDRs. The average life loss per death due to non-optimal temperature in LDRs (1.91 years, 95% eCI: 1.72–2.10) was also higher than that in HDRs (1.32 years, 95% eCI: 1.23–1.41). Most of AFs and life loss per death were caused by moderate cold in both HDRs and LDRs. Conclusions: Mortality burden caused by temperature was more significant in LDRs than that in HDRs, which means that more attention should be paid to vulnerable populations in LDRs in planning adaptive strategies.

(1) Background: Although the health effects of future climate change have been examined in previous studies, few have considered additive impacts of population expansion, ageing, and adaptation. We aimed to quantify the future heat-related years of life lost (YLLs) under different Representative Concentration Pathways (RCP) scenarios and global-scale General Circulation Models (GCMs), and further to examine relative contributions of population expansion, ageing, and adaptation on these projections. (2) Methods: We used downscaled and bias-corrected projections of daily temperature from 27 GCMs under RCP2.6, 4.5, and 8.5 scenarios to quantify the potential annual heat-related YLLs in Guangzhou, China in the 2030s, 2060s, and 2090s, compared to those in the 1980s as a baseline. We also explored the modification effects of a range of population expansion, ageing, and adaptation scenarios on the heat-related YLLs. (3) Results: Global warming, particularly under the RCP8.5 scenario, would lead to a substantial increase in the heat-related YLLs in the 2030s, 2060s, and 2090s for the majority of the GCMs. For the total population, the annual heat-related YLLs under the RCP8.5 in the 2030s, 2060s, and 2090s were 2.2, 7.0, and 11.4 thousand, respectively. The heat effects would be significantly exacerbated by rapid population expansion and ageing. However, substantial heat-related YLLs could be counteracted by the increased adaptation (75% for the total population and 20% for the elderly). (4) Conclusions: The rapid population expansion and ageing coinciding with climate change may present an important health challenge in China, which, however, could be partially counteracted by the increased adaptation of individuals.

The magnitude of excess mortality risk due to exposures to heavy air pollution during the red alert periods in Beijing remains unknown. A health impact assessment tool combined with the PM_2.5-mortality relationship was applied to estimate the number of excess deaths due to high air pollution exposure during two red alert periods in Beijing, China in December 2015. Daily PM_2.5 concentration increased from 80.2 µg/m³ to 159.8 µg/m³ during the first red alert period and from 61.9 µg/m³ to 226 µg/m³ during the second period in 2015 when compared to daily PM_2.5 concentrations during the same calendar date of 2013 and 2014. It was estimated that 26 to 42 excessive deaths (including 14 to 34 cardiovascular deaths, and four to 16 respiratory deaths) occurred during the first period, and 40 to 65 excessive deaths (22 to 53 cardiovascular deaths, and six to 13 respiratory deaths) occurred during the second period. The results show that heavy smog may have substantially increased the mortality risk in Beijing, suggesting more stringent air pollution controlling measures should be implemented to protect the public health.

Dengue fever is a severe public heath challenge in south China. A dengue outbreak was reported in Chaozhou city, China in 2015. Intensified interventions were implemented by the government to control the epidemic. However, it is still unknown the degree to which intensified control measures reduced the size of the epidemics, and when should such measures be initiated to reduce the risk of large dengue outbreaks developing? We selected Xiangqiao district as study setting because the majority of the indigenous cases (90.6%) in Chaozhou city were from this district. The numbers of daily indigenous dengue cases in 2015 were collected through the national infectious diseases and vectors surveillance system, and daily Breteau Index (BI) data were reported by local public health department. We used a compartmental dynamic SEIR (Susceptible, Exposed, Infected and Removed) model to assess the effectiveness of control interventions, and evaluate the control effect of intervention timing on dengue epidemic. A total of 1250 indigenous dengue cases was reported from Xiangqiao district. The results of SEIR modeling using BI as an indicator of actual control interventions showed a total of 1255 dengue cases, which is close to the reported number (n = 1250). The size and duration of the outbreak were highly sensitive to the intensity and timing of interventions. The more rigorous and earlier the control interventions implemented, the more effective it yielded. Even if the interventions were initiated several weeks after the onset of the dengue outbreak, the interventions were shown to greatly impact the prevalence and duration of dengue outbreak. This study suggests that early implementation of rigorous dengue interventions can effectively reduce the epidemic size and shorten the epidemic duration.

Background: Large spatial heterogeneity was observed in the dengue fever outbreak in Guangzhou in 2014, however, the underlying reasons remain unknown. We examined whether socio-ecological factors affected the spatial distribution and their interactive effects. Methods: Moran’s I was applied to first examine the spatial cluster of dengue fever in Guangzhou. Nine socio-ecological factors were chosen to represent the urbanization level, economy, accessibility, environment, and the weather of the 167 townships/streets in Guangzhou, and then the geographical detector was applied to analyze the individual and interactive effects of these factors on the dengue outbreak. Results: Four clusters of dengue fever were identified in Guangzhou in 2014, including one hot spot in the central area of Guangzhou and three cold spots in the suburban districts. For individual effects, the temperature (q = 0.33) was the dominant factor of dengue fever, followed by precipitation (q = 0.24), road density (q = 0.24), and water body area (q = 0.23). For the interactive effects, the combination of high precipitation, high temperature, and high road density might result in increased dengue fever incidence. Moreover, urban villages might be the dengue fever hot spots. Conclusions: Our study suggests that some socio-ecological factors might either separately or jointly influence the spatial distribution of dengue fever in Guangzhou.

Domestic incense burning is a common activity in China. Although it generates serious air pollution and has been linked to various health outcomes, it remains unknown whether it is associated with blood pressure and hypertension. A community-based survey including 1153 hypertensive subjects and 4432 normotensive participants in Guangdong (China) was used to examine this question. Two-level logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (CI). The analyses showed that, compared with non-users, OR of hypertension was 1.24 (95% CI: 1.03–1.50) for users, and 1.37 (95% CI: 1.04–1.80) for daily users with a clear dose-response relationship. The estimated increases in systolic and diastolic blood pressures were 1.02 mmHg (95% CI: 0.06–1.99) and 1.26 mmHg (95% CI: 0.69–1.83) for users, 0.67 mmHg (95% CI: −0.35–1.68) and 1.25 mmHg (95% CI: 0.66–1.85) for occasional users, and 2.09 mmHg (95% CI: 0.79–3.39) and 1.28 mmHg (95% CI: 0.52–2.05) for daily users, respectively. The results remained after adjusting for potential confounders and more pronounced associations were found among females. This study suggests that domestic incense burning may increase the risk of hypertension and blood pressure in the study population, and women are more vulnerable to these effects than men.