Impact of extreme weather on dengue fever infection in four Asian countries: A modelling analysis

Wolbachia: A bacterial weapon against dengue fever- a narrative review of risk factors for dengue fever outbreaks

Arboviruses constitute the largest known group of viruses and are responsible for various infections that impose significant socioeconomic burdens worldwide, particularly due to their link with insect-borne diseases. The increasing incidence of dengue fever in non-endemic regions underscores the urgent need for innovative strategies to combat this public health threat. Wolbachia, a bacterium, presents a promising biological control method against mosquito vectors, offering a novel approach to managing dengue fever. We systematically investigated biomedical databases (PubMed, Web of Science, Google Scholar, Science Direct, and Embase) using "AND" as a Boolean operator with keywords such as "dengue fever," "dengue virus," "risk factors," "Wolbachia," and "outbreak." We prioritized articles that offered significant insights into the risk factors contributing to the outbreak of dengue fever and provided an overview of Wolbachia's characteristics and functions in disease management, considering studies published until December 25, 2024. Field experiments have shown that introducing Wolbachia-infected mosquitoes can effectively reduce mosquito populations and lower dengue transmission rates, signifying its potential as a practical approach for controlling this disease.

Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing

Dengue fever has been spreading rapidly worldwide, with a notably high prevalence in South American countries such as Brazil. Its transmission dynamics are governed by the vector population dynamics and the interactions among humans, vectors, and pathogens, which are further shaped by environmental factors. Calculating these environmental indicators is challenging due to the limited spatial coverage of weather station observations and the time-consuming processes involved in downloading and processing local data, such as satellite imagery. This issue is exacerbated in large-scale studies, making it difficult to develop comprehensive and publicly accessible datasets of disease-influencing factors. Addressing this challenge necessitates the efficient data integration methods and the assembly of multi-factorial datasets to aid public health authorities in understanding dengue transmission mechanisms and improving risk prediction models. In response, we developed a population-weighted dataset of 12 dengue risk factors, covering 558 microregions in Brazil over 1252 epidemiological weeks from 2001 to 2024. This dataset and the associated methodology streamline data processing for researchers and can be adapted for other vector-borne disease studies.

Unraveling varying spatiotemporal patterns of Dengue Fever and associated exposure-response relationships with environmental variables in three Southeast Asian countries before and during COVID-19

The enforcement of COVID-19 interventions by diverse governmental bodies, coupled with the indirect impact of COVID-19 on short-term environmental changes (e.g., plant shutdowns lead to lower greenhouse gas emissions), influences the Dengue Fever (DF) vector. This provides a unique opportunity to investigate the indirect impact of COVID-19 on DF transmission and generate insights for targeted prevention measures. We aim to compare DF transmission patterns and the exposure-response relationship of environmental variables and DF incidence in the pre- and during-COVID-19 to identify variations and assess the indirect impact of COVID-19 on DF transmission. We initially visualized the overall trend of DF transmission from 2017-2022, then conducted two quantitative analyses to compare DF transmission pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). These analyses included time series analysis to assess DF seasonality, and a Distributed Lag Non-linear Model (DLNM) to quantify the exposure-response relationship between environmental variables and DF incidence. We observed a notable surge in Singapore during-COVID-19, particularly from May to August in 2020 and 2022, with cases multiplying several times compared to pre-COVID-19. All subregions in Thailand exhibited remarkable synchrony with a similar annual trend except 2021. Cyclic patterns remained generally consistent, but seasonal variability in Singapore has become increasingly pronounced. Monthly DF incidence in three countries varied significantly. Exposure-response relationships of DF and environmental variables show varying degrees of change, notably in Northern Thailand, where the peak relative risk for the maximum temperature-DF relationship rose from about 3-17, and the max RR of overall cumulative association 0-3 months of relative humidity increased from around 4-40. Our study is the first to compare DF transmission patterns and their relationship with environmental variables before and during COVID-19, demonstrating that the pandemic has affected DF transmission and altered the exposure-response relationship at both national and regional levels.

Disentangling climate's dual role in dengue dynamics: A multiregion causal analysis study

Dengue fever poses major public health challenges, with climate change complicating control efforts. Yet, the full extent of climate change's impact on dengue remains elusive. To investigate this, we used an advanced causal inference method to 16 diverse climatic regions in the Philippines. This method is capable of detecting nonlinear and joint effects of temperature and rainfall to dengue incidence. We found that temperature consistently increased dengue incidence throughout all the regions, while rainfall effects differed depending on the variation in dry season length, a factor previously overlooked. Specifically, our results showed that regions with low variation in dry season length experience a negative impact of rainfall on dengue incidence likely due to strong flushing effect on mosquito habitats, while regions with high variation in dry season length experience a positive impact, likely due to increased mosquito breeding sites. Our findings emphasize the need for tailored prevention strategies based on local climate conditions, rather than a one-size-fits-all approach.

Analysis of effects of meteorological variables on dengue incidence in Bangladesh using VAR and Granger causality approach

Background

Dengue fever is a serious public health issue in Bangladesh, where its incidence rises with the monsoon. Meteorological variables are believed to be responsible factors among others. Therefore, this study examines the effects of meteorological variables (temperature, rainfall, and humidity) on dengue incidence in Bangladesh. While previous studies have examined the relationship between dengue and meteorological variables using single model approaches, this study employs advanced econometric techniques to capture dynamic interactions. Furthermore, in the case of Bangladesh, this type of analysis is necessary due to the fact that dengue outbreak become one of the major issues. However, the analysis related to this issue is not available.

Methods

For estimation purposes, the Augmented Dickey-Fuller (ADF) test, Vector Autoregressive (VAR) model, Granger causality tests, Impulse Response Function (IRF), Variance Decomposition (VDC), and Vector Error Correction Model (VECM) are employed.

Results

Rainfall has a significant impact on dengue incidence compared to temperature and humidity. The Granger causality test demonstrates that rainfall and dengue incidence are causally related unidirectionally. Rainfall can potentially have a short-term and long-term effect on the incidence of dengue, as per the estimates of the VECM model.

Conclusions

These findings will assist policymakers in Bangladesh in developing a dengue fever early warning system depending on climate change. In order to efficiently avoid the spread of dengue in Bangladesh's dengue-endemic urban areas, this study suggests societal monitoring.

Demographic-environmental effect on dengue outbreaks in 11 countries

BACKGROUND

Dengue outbreaks are common in tropical or temperate countries, and climate change can exacerbate the problem by creating conditions conducive to the spread of mosquitoes and prolonging the transmission season. Warmer temperatures can allow mosquitoes to mature faster and increase their ability to spread disease. Additionally, changes in rainfall patterns can create more standing water, providing a breeding ground for mosquitoes.

OBJECTIVE

The objective of this study is to investigate the correlation between environmental and demographic factors and the dissemination of dengue fever. The study will use yearly data from 2000 to 2021 from 11 countries highly affected by dengue, considering multiple factors such as dengue cases, temperatures, precipitation, and population to better understand the impact of these variables on dengue transmission.

METHODS

In this research, Poisson regression (PR) and negative binomial regression (NBR) models are used to model count data and estimate the effect of different predictor variables on the outcome. Also, histogram plots and pairwise correlation plots are used to provide an initial overview of the distribution and relationship between the variables. Moreover, Goodness-of-fit tests, t-test analysis, diagnostic plots, influence plots, and residual vs. leverage plots are used to check the assumptions and validity of the models and identify any outliers or influential observations that may be affecting the results.

RESULTS

The findings indicate that mean temperature and log(Urban) had a positive impact on dengue infection rates, while maximum temperature, log(Precipitation), and population density had a negative impact. However, minimum temperature, log(Rural), and log(Total population) did not demonstrate any significant effects on the incidence of dengue.

CONCLUSION

The impact of demographic-environmental factors on dengue outbreaks in 11 Asian countries is illuminated by this study. The results highlight the significance of mean temperature (Tmean), maximum temperature (Tmax), log(Urban), log(Precipitation), and population density in influencing dengue incidence rates. However, further research is needed to gain a better understanding of the role of additional variables, such as immunity levels, awareness, and vector control measures, in the spread of dengue.

Temperature and humidity as drivers for the transmission of zoonotic diseases

Zoonotic diseases remain a persistent threat to global public health. Many major zoonotic pathogens exhibit seasonal patterns associated with climatic variations. Quantifying the impacts of environmental variables such as temperature and humidity on disease transmission dynamics is critical for improving prediction and control measures. This review synthesizes current evidence on the relationships between temperature and humidity and major zoonotic diseases, including malaria, dengue, rabies, anisakiasis, and influenza. Overall, this review highlighted some overarching themes across the different zoonotic diseases examined. Higher temperatures within suitable ranges were generally associated with increased transmission risks, while excessively high or low temperatures had adverse effects. Humidity exhibited complex nonlinear relationships, facilitating transmission in certain temperature zones but inhibiting it in others. Heavy rainfall and high humidity were linked to vector‐borne disease outbreaks such as malaria by enabling vector breeding. However, reduced incidence of some diseases like dengue fever was observed with high rainfall. To address existing knowledge gaps, future research efforts should prioritize several key areas: enhancing data quality through robust surveillance and the integration of high‐resolution microclimate data; standardizing analytical frameworks and leveraging advanced methodologies such as machine learning; conducting mechanistic studies to elucidate pathogen, vector, and host responses to climatic stimuli; adopting interdisciplinary approaches to account for interacting drivers; and projecting disease impacts under various climate change scenarios to inform adaptation strategies. Investing in these research priorities can propel the development of evidence‐based climate‐aware disease prediction and control measures, ultimately safeguarding public health more effectively.

Shifting spatial, temporal and demographic patterns of dengue incidence and associated meteorological factors in Jazan Region of Saudi Arabia from 2015-2020

BACKGROUND OBJECTIVES

Dengue poses a considerable public health threat in Saudi Arabia, with escalating outbreaks in Jazan, where seasonal rains create ideal mosquito breeding conditions. Elucidating local epidemiological dynamics is imperative to strengthen evidence-based prevention policies. This study analyzed the spatiotemporal, demographic, and meteorological patterns of dengue in Jazan from 2015-2020.

METHODS

This retrospective cross-sectional study utilized surveillance records for 3427 confirmed dengue cases. Descriptive analyses characterized geographic, seasonal, age, gender, and nationality distributions. Forecasting models project expected epidemics through 2025. Regression analysis identified climate factors associated with monthly case counts.

RESULTS

Dengue exhibited shifting seasonal peaks, transitioning into year-round transmission by 2019, indicating endemic establishment. Cases clustered in different high-burden sectors annually, requiring localized vector control. The majority of affected individuals were young male adults, with gender gaps narrowing over time. Saudi nationals had an escalating incidence, but non-citizens showed a higher risk, signaling importation threats. Seasonal outbreaks were associated with temperature, wind speed, and direction.

INTERPRETATION CONCLUSION

Enhanced surveillance, outbreak forecasting, targeted control activities, and integrated prevention policies grounded in continuous evidence assessment can effectively address endemic dengue transmission in Jazan. This study provides key insights to optimize data-driven decision-making for dengue control in Saudi Arabia.

Climate change and its impact on infectious diseases in Asia

ABSTRACT

Climate change, particularly increasing temperature, changes in rainfall, extreme weather events and changes in vector ecology, impacts the transmission of many climate-sensitive infectious diseases. Asia is the world's most populous, rapidly evolving and diverse continent, and it is already experiencing the effects of climate change. Climate change intersects with population, sociodemographic and geographical factors, amplifying the public health impact of infectious diseases and potentially widening existing disparities. In this narrative review, we outline the evidence of the impact of climate change on infectious diseases of importance in Asia, including vector-borne diseases, food- and water-borne diseases, antimicrobial resistance and other infectious diseases. We also highlight the imperative need for strategic intersectoral collaboration at the national and global levels and for the health sector to implement adaptation and mitigation measures, including responsibility for its own greenhouse gas emissions.

Projection of dengue fever transmissibility under climate change in South and Southeast Asian countries

Vector-borne infectious disease such as dengue fever (DF) has spread rapidly due to more suitable living environments. Considering the limited studies investigating the disease spread under climate change in South and Southeast Asia, this study aimed to project the DF transmission potential in 30 locations across four South and Southeast Asian countries. In this study, weekly DF incidence data, daily mean temperature, and rainfall data in 30 locations in Singapore, Sri Lanka, Malaysia, and Thailand from 2012 to 2020 were collected. The effects of temperature and rainfall on the time-varying reproduction number (Rt) of DF transmission were examined using generalized additive models. Projections of location-specific Rt from 2030s to 2090s were determined using projected temperature and rainfall under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585), and the peak DF transmissibility and epidemic duration in the future were estimated. According to the results, the projected changes in the peak Rt and epidemic duration varied across locations, and the most significant change was observed under middle-to-high greenhouse gas emission scenarios. Under SSP585, the country-specific peak Rt was projected to decrease from 1.63 (95% confidence interval: 1.39-1.91), 2.60 (1.89-3.57), and 1.41 (1.22-1.64) in 2030s to 1.22 (0.98-1.51), 2.09 (1.26-3.47), and 1.37 (0.83-2.27) in 2090s in Singapore, Thailand, and Malaysia, respectively. Yet, the peak Rt in Sri Lanka changed slightly from 2030s to 2090s under SSP585. The epidemic duration in Singapore and Malaysia was projected to decline under SSP585. In conclusion, the change of peak DF transmission potential and disease outbreak duration would vary across locations, particularly under middle-to-high greenhouse gas emission scenarios. Interventions should be considered to slow down global warming as well as the potential increase in DF transmissibility in some locations of South and Southeast Asia.

Unraveling varying spatiotemporal patterns of dengue and associated exposure-response relationships with environmental variables in Southeast Asian countries before and during COVID-19

The enforcement of COVID-19 interventions by diverse governmental bodies, coupled with the indirect impact of COVID-19 on short-term environmental changes (e.g. plant shutdowns lead to lower greenhouse gas emissions), influences the dengue vector. This provides a unique opportunity to investigate the impact of COVID-19 on dengue transmission and generate insights to guide more targeted prevention measures. We aim to compare dengue transmission patterns and the exposure-response relationship of environmental variables and dengue incidence in the pre- and during-COVID-19 to identify variations and assess the impact of COVID-19 on dengue transmission. We initially visualized the overall trend of dengue transmission from 2012-2022, then conducted two quantitative analyses to compare dengue transmission pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). These analyses included time series analysis to assess dengue seasonality, and a Distributed Lag Non-linear Model (DLNM) to quantify the exposure-response relationship between environmental variables and dengue incidence. We observed that all subregions in Thailand exhibited remarkable synchrony with a similar annual trend except 2021. Cyclic and seasonal patterns of dengue remained consistent pre- and during-COVID-19. Monthly dengue incidence in three countries varied significantly. Singapore witnessed a notable surge during-COVID-19, particularly from May to August, with cases multiplying several times compared to pre-COVID-19, while seasonality of Malaysia weakened. Exposure-response relationships of dengue and environmental variables show varying degrees of change, notably in Northern Thailand, where the peak relative risk for the maximum temperature-dengue relationship rose from about 3 to 17, and the max RR of overall cumulative association 0-3 months of relative humidity increased from around 5 to 55. Our study is the first to compare dengue transmission patterns and their relationship with environmental variables before and during COVID-19, showing that COVID-19 has affected dengue transmission at both the national and regional level, and has altered the exposure-response relationship between dengue and the environment.

Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China

Introduction

Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier.

Methods

In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China.

Results

Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24-55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02-1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7-121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43-0.79), occurring at a lag of 45 days.

Discussion

These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.

Association between environmental factors and dengue incidence in Lao People's Democratic Republic: a nationwide time-series study

BACKGROUND

Dengue fever is a vector-borne disease of global public health concern, with an increasing number of cases and a widening area of endemicity in recent years. Meteorological factors influence dengue transmission. This study aimed to estimate the association between meteorological factors (i.e., temperature and rainfall) and dengue incidence and the effect of altitude on this association in the Lao People's Democratic Republic (Lao PDR).

METHODS

We used weekly dengue incidence and meteorological data, including temperature and rainfall, from 18 jurisdictions in Lao PDR from 2015 to 2019. A two-stage distributed lag nonlinear model with a quasi-Poisson distribution was used to account for the nonlinear and delayed associations between dengue incidence and meteorological variables, adjusting for long-term time trends and autocorrelation.

RESULTS

A total of 55,561 cases were reported in Lao PDR from 2015 to 2019. The cumulative relative risk for the 90th percentile of weekly mean temperature (29 °C) over 22 weeks was estimated at 4.21 (95% confidence interval: 2.00-8.84), relative to the 25th percentile (24 °C). The cumulative relative risk for the weekly total rainfall over 12 weeks peaked at 82 mm (relative risk = 1.76, 95% confidence interval: 0.91-3.40) relative to no rain. However, the risk decreased significantly when heavy rain exceeded 200 mm. We found no evidence that altitude modified these associations.

CONCLUSIONS

We found a lagged nonlinear relationship between meteorological factors and dengue incidence in Lao PDR. These findings can be used to develop climate-based early warning systems and provide insights for improving vector control in the country.

Socio-economic factors and its influence on the association between temperature and dengue incidence in 61 Provinces of the Philippines, 2010-2019

BACKGROUND

Temperature has a significant impact on dengue incidence, however, changes on the temperature-dengue relationship across axes of socio-economic vulnerability is not well described. This study sought to determine the association between dengue and temperature in multiple locations in the Philippines and explore the effect modification by socio-economic factors.

METHOD

Nationwide dengue cases per province from 2010 to 2019 and data on temperature were obtained from the Philippines' Department of Health-Epidemiological Bureau and ERA5-land, respectively. A generalized additive mixed model (GAMM) with a distributed lag non-linear model was utilized to examine the association between temperature and dengue incidence. We further implemented an interaction analysis in determining how socio-economic factors modify the association. All analyses were implemented using R programming.

RESULTS

Nationwide temperature-dengue risk function was noted to depict an inverted U-shaped pattern. Dengue risk increased linearly alongside increasing mean temperature from 15.8 degrees Celsius and peaking at 27.5 degrees Celsius before declining. However, province-specific analyses revealed significant heterogeneity. Socio-economic factors had varying impact on the temperature-dengue association. Provinces with high population density, less people in urban areas with larger household size, high poverty incidence, higher health spending per capita, and in lower latitudes were noted to exhibit statistically higher dengue risk compared to their counterparts at the upper temperature range.

CONCLUSIONS

This observational study found that temperature was associated with dengue incidence, and that this association is more apparent in locations with high population density, less people in urban areas with larger household size, high poverty incidence, higher health spending per capita, and in lower latitudes. Differences with socio-economic conditions is linked with dengue risk. This highlights the need to develop interventions tailor-fit to local conditions.

Impact of climate change on dengue fever epidemics in South and Southeast Asian settings: A modelling study

The potential for dengue fever epidemic due to climate change remains uncertain in tropical areas. This study aims to assess the impact of climate change on dengue fever transmission in four South and Southeast Asian settings. We collected weekly data of dengue fever incidence, daily mean temperature and rainfall from 2012 to 2020 in Singapore, Colombo, Selangor, and Chiang Mai. Projections for temperature and rainfall were drawn for three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585) scenarios. Using a disease transmission model, we projected the dengue fever epidemics until 2090s and determined the changes in annual peak incidence, peak time, epidemic size, and outbreak duration. A total of 684,639 dengue fever cases were reported in the four locations between 2012 and 2020. The projected change in dengue fever transmission would be most significant under the SSP585 scenario. In comparison to the 2030s, the peak incidence would rise by 1.29 times in Singapore, 2.25 times in Colombo, 1.36 times in Selangor, and >10 times in Chiang Mai in the 2090s under SSP585. Additionally, the peak time was projected to be earlier in Singapore, Colombo, and Selangor, but be later in Chiang Mai under the SSP585 scenario. Even in a milder emission scenario of SSP126, the epidemic size was projected to increase by 5.94%, 10.81%, 12.95%, and 69.60% from the 2030s-2090s in Singapore, Colombo, Selangor, and Chiang Mai, respectively. The outbreak durations in the four settings were projected to be prolonged over this century under SSP126 and SSP245, while a slight decrease is expected in 2090s under SSP585. The results indicate that climate change is expected to increase the risk of dengue fever transmission in tropical areas of South and Southeast Asia. Limiting greenhouse gas emissions could be crucial in reducing the transmission of dengue fever in the future.

Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China

Background

Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions.

Methods

In this study, we use a distributed lag non-linear model to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China, stratified by prior water availability.

Results

Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24-55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 (95% credible interval (CI): 1.02-1.83) occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7-121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43-0.79), occurring at a lag of 45 days.

Conclusions

These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.

Effects of high temperatures and heatwaves on dengue fever: a systematic review and meta-analysis

BACKGROUND

Studies have shown that dengue virus transmission increases in association with ambient temperature. We performed a systematic review and meta-analysis to assess the effect of both high temperatures and heatwave events on dengue transmission in different climate zones globally.

METHODS

A systematic literature search was conducted in PubMed, Scopus, Embase, and Web of Science from January 1990 to September 20, 2022. We included peer reviewed original observational studies using ecological time series, case crossover, or case series study designs reporting the association of high temperatures and heatwave with dengue and comparing risks over different exposures or time periods. Studies classified as case reports, clinical trials, non-human studies, conference abstracts, editorials, reviews, books, posters, commentaries; and studies that examined only seasonal effects were excluded. Effect estimates were extracted from published literature. A random effects meta-analysis was performed to pool the relative risks (RRs) of dengue infection per 1 °C increase in temperature, and further subgroup analyses were also conducted. The quality and strength of evidence were evaluated following the Navigation Guide systematic review methodology framework. The review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO).

FINDINGS

The study selection process yielded 6367 studies. A total of 106 studies covering more than four million dengue cases fulfilled the inclusion criteria; of these, 54 studies were eligible for meta-analysis. The overall pooled estimate showed a 13% increase in risk of dengue infection (RR = 1.13; 95% confidence interval (CI): 1.11-1.16, I2 = 98.0%) for each 1 °C increase in high temperatures. Subgroup analyses by climate zones suggested greater effects of temperature in tropical monsoon climate zone (RR = 1.29, 95% CI: 1.11-1.51) and humid subtropical climate zone (RR = 1.20, 95% CI: 1.15-1.25). Heatwave events showed association with an increased risk of dengue infection (RR = 1.08; 95% CI: 0.95-1.23, I2 = 88.9%), despite a wide confidence interval. The overall strength of evidence was found to be "sufficient" for high temperatures but "limited" for heatwaves. Our results showed that high temperatures increased the risk of dengue infection, albeit with varying risks across climate zones and different levels of national income.

INTERPRETATION

High temperatures increased the relative risk of dengue infection. Future studies on the association between temperature and dengue infection should consider local and regional climate, socio-demographic and environmental characteristics to explore vulnerability at local and regional levels for tailored prevention.

FUNDING

Australian Research Council Discovery Program.

An Integrative Explainable Artificial Intelligence Approach to Analyze Fine-Scale Land-Cover and Land-Use Factors Associated with Spatial Distributions of Place of Residence of Reported Dengue Cases

Dengue fever is a prevalent mosquito-borne disease that burdens communities in subtropical and tropical regions. Dengue transmission is ecologically complex; several environmental conditions are critical for the spatial and temporal distribution of dengue. Interannual variability and spatial distribution of dengue transmission are well-studied; however, the effects of land cover and use are yet to be investigated. Therefore, we applied an explainable artificial intelligence (AI) approach to integrate the EXtreme Gradient Boosting and Shapley Additive Explanation (SHAP) methods to evaluate spatial patterns of the residences of reported dengue cases based on various fine-scale land-cover land-use types, Shannon's diversity index, and household density in Kaohsiung City, Taiwan, between 2014 and 2015. We found that the proportions of general roads and residential areas play essential roles in dengue case residences with nonlinear patterns. Agriculture-related features were negatively associated with dengue incidence. Additionally, Shannon's diversity index showed a U-shaped relationship with dengue infection, and SHAP dependence plots showed different relationships between various land-use types and dengue incidence. Finally, landscape-based prediction maps were generated from the best-fit model and highlighted high-risk zones within the metropolitan region. The explainable AI approach delineated precise associations between spatial patterns of the residences of dengue cases and diverse land-use characteristics. This information is beneficial for resource allocation and control strategy modification.

Correlation of Dengue and Meteorological Factors in Bangladesh: A Public Health Concern

Dengue virus (DENV) is an enveloped, single-stranded RNA virus, a member of the Flaviviridae family (which causes Dengue fever), and an arthropod-transmitted human viral infection. Bangladesh is well known for having some of Asia's most vulnerable Dengue outbreaks, with climate change, its location, and it's dense population serving as the main contributors. For speculation about DENV outbreak characteristics, it is crucial to determine how meteorological factors correlate with the number of cases. This study used five time series models to observe the trend and forecast Dengue cases. Current data-based research has also applied four statistical models to test the relationship between Dengue-positive cases and meteorological parameters. Datasets were used from NASA for meteorological parameters, and daily DENV cases were obtained from the Directorate General of Health Service (DGHS) open-access websites. During the study period, the mean of DENV cases was 882.26 ± 3993.18, ranging between a minimum of 0 to a maximum of 52,636 daily confirmed cases. The Spearman's rank correlation coefficient between climatic variables and Dengue incidence indicated that no substantial relationship exists between daily Dengue cases and wind speed, temperature, and surface pressure (Spearman's rho; r = -0.007, p > 0.05; r = 0.085, p > 0.05; and r = -0.086, p > 0.05, respectively). Still, a significant relationship exists between daily Dengue cases and dew point, relative humidity, and rainfall (r = 0.158, p < 0.05; r = 0.175, p < 0.05; and r = 0.138, p < 0.05, respectively). Using the ARIMAX and GA models, the relationship for Dengue cases with wind speed is -666.50 [95% CI: -1711.86 to 378.86] and -953.05 [-2403.46 to 497.36], respectively. A similar negative relation between Dengue cases and wind speed was also determined in the GLM model (IRR = 0.98). Dew point and surface pressure also represented a negative correlation in both ARIMAX and GA models, respectively, but the GLM model showed a positive association. Additionally, temperature and relative humidity showed a positive correlation with Dengue cases (105.71 and 57.39, respectively, in the ARIMAX, 633.86, and 200.03 in the GA model). In contrast, both temperature and relative humidity showed negative relation with Dengue cases in the GLM model. In the Poisson regression model, windspeed has a substantial significant negative connection with Dengue cases in all seasons. Temperature and rainfall are significantly and positively associated with Dengue cases in all seasons. The association between meteorological factors and recent outbreak data is the first study where we are aware of the use of maximum time series models in Bangladesh. Taking comprehensive measures against DENV outbreaks in the future can be possible through these findings, which can help fellow researchers and policymakers.