UNASSIGNED: To conduct a comprehensive evaluation of the Dengue Fever Surveillance and Early Warning System deployed in Ningbo City during 2023, focusing on its capacity for timely identification and reporting of dengue fever cases, particularly imported cases from endemic regions.
UNASSIGNED: A detailed data of patient clinical features and blood profile trends was collected from clinical records and surveillance reports, focusing on the rapid diagnostic processes and surveillance rigor. This study assessed the effectiveness of the system in identifying and reporting dengue cases and identified the limitations of the existing framework through a basic statistical approach.
UNASSIGNED: The system demonstrated timely identification and reporting of dengue fever cases, with a particular emphasis on imported cases. However, several limitations were identified, including the need for more precise monitoring criteria and improved coordination with medical entities.
UNASSIGNED: The study underscores the critical role of public health bodies in managing disease outbreaks and advocates for enhanced methodologies to refine epidemic control efforts. The findings contribute to the advancement of early warning mechanisms and the improvement of proactive infectious disease monitoring in metropolitan environments, providing valuable insights for fortifying the Dengue Fever Surveillance and Early Warning System in Ningbo City.

In August 2023, we identified a case of dengue fever in Yantai City, which was imported from Xishuangbanna, China. To investigate its evolutionary history and population dynamics, we utilized the metatranscriptomic method to obtain the virus\' whole genome sequence. Together with 367 selected dengue virus whole genome sequences from the NCBI database, we constructed a time-scaled Maximum Clade Credibility (MCC) tree. We found that our sequence exhibited a high homology with a sequence of DENV1 (OR418422.1) uploaded by the Guangzhou Center for Disease Control and Prevention in 2023, with an estimated divergence time around 2019 (95% HPD: 2017-2023), coinciding with the emergence of SARS-CoV-2. The DENV strain obtained in this study belongs to genotype I of DENV1. Its ancestors experienced a global epidemic around 2005 (95% HPD: 2002-2010), and its progeny strains have spread extensively in Southeast Asia and China since around 2007 (95% HPD: 2006-2011). The Bayesian skyline plot indicates that the current population of DENV1 has not been affected by SARS-CoV-2 and is expected to maintain stable transmission. Hence, it is imperative to track and monitor its epidemiological trends and genomic variations to prevent potential large-scale outbreaks in the post-SARS-CoV-2 era.

BACKGROUND: Dengue fever (DF) has emerged as a significant public health concern in China. The spatiotemporal patterns and underlying influencing its spread, however, remain elusive. This study aims to identify the factors driving these variations and to assess the city-level risk of DF epidemics in China.
METHODS: We analyzed the frequency, intensity, and distribution of DF cases in China from 2003 to 2022 and evaluated 11 natural and socioeconomic factors as potential drivers. Using the random forest (RF) model, we assessed the contributions of these factors to local DF epidemics and predicted the corresponding city-level risk.
RESULTS: Between 2003 and 2022, there was a notable correlation between local and imported DF epidemics in case numbers (r = 0.41, P < 0.01) and affected cities (r = 0.79, P < 0.01). With the increase in the frequency and intensity of imported epidemics, local epidemics have become more severe. Their occurrence has increased from five to eight months per year, with case numbers spanning from 14 to 6641 per month. The spatial distribution of city-level DF epidemics aligns with the geographical divisions defined by the Huhuanyong Line (Hu Line) and Qin Mountain-Huai River Line (Q-H Line) and matched well with the city-level time windows for either mosquito vector activity (83.59%) or DF transmission (95.74%). The RF models achieved a high performance (AUC = 0.92) when considering the time windows. Importantly, they identified imported cases as the primary influencing factor, contributing significantly (24.82%) to local DF epidemics at the city level in the eastern region of the Hu Line (E-H region). Moreover, imported cases were found to have a linear promoting impact on local epidemics, while five climatic and six socioeconomic factors exhibited nonlinear effects (promoting or inhibiting) with varying inflection values. Additionally, this model demonstrated outstanding accuracy (hitting ratio = 95.56%) in predicting the city-level risks of local epidemics in China.
CONCLUSIONS: China is experiencing an increasing occurrence of sporadic local DF epidemics driven by an unavoidably higher frequency and intensity of imported DF epidemics. This research offers valuable insights for health authorities to strengthen their intervention capabilities against this disease.

Dengue fever is a mosquito-borne viral disease caused by the dengue virus (DENV). It poses a public health threat globally and, while most people with dengue have mild symptoms or are asymptomatic, approximately 5% of affected individuals develop severe disease and need hospital care. However, knowledge of the molecular mechanisms underlying dengue infection and the interaction between the virus and its host remains limited. In the present study, we performed a quantitative proteomic and N-glycoproteomic analysis of serum from 19 patients with dengue and 11 healthy people. The results revealed distinct proteomic and N-glycoproteomic landscapes between the two groups. Notably, we report for the first time the changes in the serum N glycosylation pattern following dengue infection and provide abundant information on glycoproteins, glycosylation sites, and intact N-glycopeptides using recently developed site-specific glycoproteomic approaches. Furthermore, a series of key functional pathways in proteomic and N-glycoproteomic were identified. Collectively, our findings significantly improve understanding of host and DENV interactions and the general pathogenesis and pathology of DENV, laying a foundation for functional studies of glycosylation and glycan structures in dengue infection.

Dengue fever is a viral illness, mainly transmitted by Aedes aegypti and Aedes albopictus. With climate change and urbanisation, more urbanised areas are becoming suitable for the survival and reproduction of dengue vector, consequently are becoming suitable for dengue transmission in China. Chongqing, a metropolis in southwestern China, has recently been hit by imported and local dengue fever, experiencing its first local outbreak in 2019. However, the genetic evolution dynamics of dengue viruses and the spatiotemporal patterns of imported and local dengue cases have not yet been elucidated. Hence, this study implemented phylogenetic analyses using genomic data of dengue viruses in 2019 and 2023 and a spatiotemporal analysis of dengue cases collected from 2013 to 2022. We sequenced a total of 15 nucleotide sequences of E genes. The dengue viruses formed separate clusters and were genetically related to those from Guangdong Province, China, and countries in Southeast Asia, including Laos, Thailand, Myanmar and Cambodia. Chongqing experienced a dengue outbreak in 2019 when 168 imported and 1,243 local cases were reported, mainly in September and October. Few cases were reported in 2013-2018, and only six were imported from 2020 to 2022 due to the COVID-19 lockdowns. Our findings suggest that dengue prevention in Chongqing should focus on domestic and overseas population mobility, especially in the Yubei and Wanzhou districts, where airports and railway stations are located, and the period between August and October when dengue outbreaks occur in endemic regions. Moreover, continuous vector monitoring should be implemented, especially during August-October, which would be useful for controlling the Aedes mosquitoes. This study is significant for defining Chongqing\'s appropriate dengue prevention and control strategies.

UNASSIGNED: The goal of this study is to analyze the epidemiological patterns of dengue fever across different districts and counties in Yunnan Province from 2010 to 2021.
UNASSIGNED: In this study, we employed joinpoint regression analysis, spatial autocorrelation analysis, and space-time scan analysis to illustrate the spatio-temporal propagation and demographic influence of dengue fever, using both graphical and tabular presentations to clearly demonstrate the findings.
UNASSIGNED: Yunnan Province reported 14,098 cases of dengue fever during the period from 2010 to 2021. Of these, 11,513 cases were caused by local transmission, 2,566 were imported internationally, and 19 were inter-provincial imports. Seasonal trends emerged, revealing a surge in incidences during the summer and autumn months. The sex ratio of male to female cases was 1:0.88, with a significant majority of 82.00% of cases involving individuals belonging to the age group of 15-60. Commercial service workers constituted the most impacted occupational group, forming 20.96% of total cases. A spatio-temporal scan identified significant clustering of dengue fever cases across space and time, with the most pronounced cluster observed in southern Yunnan, primarily between 2015 and 2019.
UNASSIGNED: Dengue fever in Yunnan Province manifests as biennial outbreaks, underscoring the necessity for increased surveillance, particularly in counties bordering other regions.

UNASSIGNED: This study employs the Geographically and Temporally Weighted Regression (GTWR) model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks, emphasizing the spatial-temporal variability of these factors in border regions.
UNASSIGNED: We conducted a descriptive analysis of dengue fever\'s temporal-spatial distribution in Yunnan border areas. Utilizing annual data from 2013 to 2019, with each county in the Yunnan border serving as a spatial unit, we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region.
UNASSIGNED: The GTWR model, proving more effective than Ordinary Least Squares (OLS) analysis, identified significant spatial and temporal heterogeneity in factors influencing dengue fever\'s spread along the Yunnan border. Notably, the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence, meteorological variables, and imported cases across different counties.
UNASSIGNED: In the Yunnan border areas, local dengue incidence is affected by temperature, humidity, precipitation, wind speed, and imported cases, with these factors\' influence exhibiting notable spatial and temporal variation.

BACKGROUND: Tanreqing injection (TRQ) has been employed in clinical practice as a treatment for dengue fever (DF). Nevertheless, the precise pharmacological mechanism underlying its efficacy remains elusive.
METHODS: Network pharmacology, molecular docking, transcriptome sequencing, and experimental evaluation were employed to analyze and study the inhibitory potential of TRQ against dengue virus (DENV).
RESULTS: We found that TRQ inhibited the replication of DENV in human umbilical vein endothelial cells, Huh-7 cells, and Hep3B cells. In addition, TRQ prolonged the survival duration of AG129 mice infected with DF, decreased the viral load in serum and organs, and alleviated organ damage. Subsequently, ultra-high-performance liquid chromatography-tandem mass spectrometry analysis of TRQ was performed to identify 314 targets associated with 36 active compounds present in TRQ. Integration of multiple databases yielded 47 DF-related genes. Then, 15 hub targets of TRQ in DF were determined by calculating the network topology parameters (Degree). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that these pathways were primarily enriched in the processes of cytokine activation and leukocyte cross-endothelial migration, with significant enrichment of cell adhesion molecules. Molecular docking revealed favorable binding affinity between TRQ\'s key active compounds and the predicted hub targets. Transcriptome sequencing results showed TRQ\'s ability to restore the expression of vascular cell adhesion molecule-1 (VCAM-1) post-DENV infection. Finally, TRQ was found to modulate the immune status by regulating the nuclear factor kappa-B (NF-κB)- intercellular cell adhesion molecule-1 (ICAM-1)/VCAM-1 axis, as well as reduce immune cell alterations, inflammatory factor secretion, vascular permeability, and bleeding tendencies induced by DENV infection.
CONCLUSIONS: Our research suggests that TRQ exerts therapeutic effects on DF by regulating the NF-κB-ICAM-1/VCAM-1 axis.

Dengue fever is a viral disease caused by one of four dengue stereotypes (Flavivirus: Flaviviridae) that are primarily transmitted by Aedes albopictus (Skuse) and Aedes aegypti (L.). To safeguard public health, it is crucial to conduct surveys that examine the factors favouring the presence of these species. Our study surveyed 42 councils across four towns within the Bhakkar district of Punjab Province, by inspecting man-made or natural habitats containing standing water. First, door-to-door surveillance teams from the district health department were assigned to each council to surveillance Aedes species and dengue cases. Second, data collection through surveillance efforts, and validation procedures were implemented, and the verified data was uploaded onto the Dengue Tracking System by Third Party Validation teams. Third, data were analysed to identify factors influencing dengue fever cases. The findings demonstrated the following: (1) Predominantly, instances were discerned among individuals who had a documented history of having travelled beyond the confines of the province. (2) Containers associated with evaporative air coolers and tyre shops were responsible for approximately 30% of the Aedes developmental sites. (4) Variability in temperature was responsible for approximately 45% of the observed differences in the quantity of recorded Aedes mosquito developmental sites. (5) Implementation of dengue prevention initiatives precipitated a 50% reduction in Aedes-positive containers, alongside a notable 70% decline in reported cases of dengue fever during the period spanning 2019 to 2020, while the majority of reported cases were of external origin. Aedes control measures substantially curtailed mosquito populations and lowered vector-virus interactions. Notably, local dengue transmission was eliminated through advanced and effective Aedes control efforts, emphasising the need for persistent surveillance and eradication of larval habitats in affected regions.

OBJECTIVE: This study aimed to improve dengue fever predictions in Singapore using a machine learning model that incorporates meteorological data, addressing the current methodological limitations by examining the intricate relationships between weather changes and dengue transmission.
METHODS: Using weekly dengue case and meteorological data from 2012 to 2022, the data was preprocessed and analyzed using various machine learning algorithms, including General Linear Model (GLM), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) were employed.
RESULTS: From 2012 to 2022, there was a total of 164,333 cases of dengue fever. Singapore witnessed a fluctuating number of dengue cases, peaking notably in 2020 and revealing a strong seasonality between March and July. An analysis of meteorological data points highlighted connections between certain climate variables and dengue fever outbreaks. The correlation analyses suggested significant associations between dengue cases and specific weather factors such as solar radiation, solar energy, and UV index. For disease predictions, the XGBoost model showed the best performance with an MAE = 89.12, RMSE = 156.07, and R2 = 0.83, identifying time as the primary factor, while 19 key predictors showed non-linear associations with dengue transmission. This underscores the significant role of environmental conditions, including cloud cover and rainfall, in dengue propagation.
CONCLUSIONS: In the last decade, meteorological factors have significantly influenced dengue transmission in Singapore. This research, using the XGBoost model, highlights the key predictors like time and cloud cover in understanding dengue\'s complex dynamics. By employing advanced algorithms, our study offers insights into dengue predictive models and the importance of careful model selection. These results can inform public health strategies, aiming to improve dengue control in Singapore and comparable regions.