Background: Dengue has become one of the major vector-borne diseases, which has been an important public health concern. We aimed to estimate the disease burden of dengue in major endemic regions from 1990 to 2019, and explore the impact pattern of the socioeconomic factors on the burden of dengue based on the global burden of diseases, injuries, and risk factors study 2019 (GBD 2019). Methods: Using the analytical strategies and data from the GBD 2019, we described the incidence and disability-adjusted life years (DALYs) of dengue in major endemic regions from 1990 to 2019. Furthermore, we estimated the correlation between dengue burden and socioeconomic factors, and then established an autoregressive integrated moving average (ARIMA) model to predict the epidemic trends of dengue in endemic regions. All estimates were proposed as numbers and age-standardized rates (ASR) per 100,000 population, with uncertainty intervals (UIs). The ASRs of dengue incidence were compared geographically and five regions were stratified by a sociodemographic index (SDI). Results: A significant rise was observed on a global scale between 1990 and 2019, with the overall age-standardized rate (ASR) increasing from 557.15 (95% UI 243.32–1212.53) per 100,000 in 1990 to 740.4 (95% UI 478.2–1323.1) per 100,000 in 2019. In 2019, the Oceania region had the highest age-standardized incidence rates per 100,000 population (3173.48 (95% UI 762.33–6161.18)), followed by the South Asia region (1740.79 (95% UI 660.93–4287.12)), and then the Southeast Asia region (1153.57 (95% UI 1049.49–1281.59)). In Oceania, South Asia, and Southeast Asia, increase trends were found in the burden of dengue fever measured by ASRs of DALY which were consistent with ASRs of dengue incidence at the national level. Most of the countries with the heaviest burden of dengue fever occurred in areas with low and medium SDI regions. However, the burden in high-middle and high-SDI countries is relatively low, especially the Solomon Islands and Tonga in Oceania, the Maldives in South Asia and Indonesia in Southeast Asia. The age distribution results of the incidence rate and disease burden of dengue fever of major endemic regions showed that the higher risk and disease burden are mainly concentrated in people under 14 or over 70 years old. The prediction by ARIMA showed that the risk of dengue fever in South and Southeast Asia is on the rise, and further prevention and control is warranted. Conclusions: In view of the rapid population growth and urbanization in many dengue-endemic countries, our research results are of great significance for presenting the future trend in dengue fever. It is recommended to policy makers that specific attention needs to be paid to the negative impact of urbanization on dengue incidence and allocate more resources to the low-SDI areas and people under 14 or over 70 years old to reduce the burden of dengue fever.

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. Method: 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 ($R^2$) 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 $R^2$ = 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. Conclusion: 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.

Background: Zoonotic Babesia infections are an emerging public health threat globally. The geographical distribution, animal reservoirs and tick vectors vary greatly across Babesia species, and estimations of prevalence reported in works within the literature are also quite different. Better prevalence estimates and identification of moderators are needed to understand the global transmission risk of different zoonotic Babesia species, and to provide crucial background information for the diagnosis, treatment and control of zoonotic babesiosis. Methods: We conducted a systematic review and meta-analysis to determine the global nucleic acid prevalence of different zoonotic Babesia species in humans, animals and ticks. Relevant publications were obtained from several electronic databases and grey literature up to December 2021. Articles were included if they were published in English or Chinese and reported the nucleic acid prevalence of zoonotic Babesia species in humans, animals or ticks. The pooled estimates of prevalence were determined using a random effect model. Heterogeneity was investigated using subgroup analyses and random effect meta-regression models. Results: Of 3205 unique studies, 28 were included by the systematic review of zoonotic Babesia for humans, 79 for animals and 104 for ticks. The results showed overall pooled estimates of nucleic acid prevalence for the following: B. microti—1.93% (0.32–4.69%) in humans; B. microti—7.80% (5.25–10.77%), B. divergens—2.12% (0.73–4.08%) and B. venatorum—1.42% (0.30–3.16%) in animals; and B. microti—2.30% (1.59–3.13%), B. divergens—0.16% (0.05–0.32%), and B. venatorum—0.39% (0.26–0.54%) in questing ticks. The type of population, animal reservoir or tick vector, detecting method and continent were moderators possibly associated with heterogeneity, yet the remaining heterogeneity that was not explained was still substantial (all QE p values < 0.05). Conclusions:B. microti is the most prevalent and widely distributed zoonotic Babesia species globally. The wide range of suitable animal reservoirs and potential transmission vectors and high prevalence in animals and ticks may contribute to the worldwide distribution of B. microti. Other zoonotic Babesia species were relatively less prevalent and were reported in quite limited areas.

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