Impact of environmental factors on the spread of dengue fever in Sri Lanka

K. SK, Das S, Ahamed T, Hossain KMA, Al-Emran HM, Hossain MA, Jahid IK. Dengue Virus Serotype 2 Cosmopolitan C Genotype Reemerges With a New Strain in Southwest Region of Bangladesh

In 2023, the dengue virus (DENV) outbreak infected over 0.3 million cases and 1500 deaths in Bangladesh. Our study conducted serotyping and genomic surveillance in four districts of Southwest Bangladesh between September and October 2023. The surveillance data from 2019 to 2023 extracted from the Directorate General of Health Services in Bangladesh indicated a significant increase of Dengue infections in 2023, particularly during September-November. The two-layered hypothesis examination confirmed that, despite endemic months, 2023 dengue outbreak had a higher morbidity rate compared to previous years (2019-2022) in the southwest of Bangladesh. Serotyping using RT-PCR and E gene sequence analysis of 25 randomly selected positive samples reveals that DENV-2 was the dominant serotype circulating in this region during the study period. Genomic analysis (phylogenetic analysis and classical multidimensional scaling [cMDS]) exposed a new strain of DENV-2, classified under Cosmopolitan genotype within C clade, distinct from previous Bangladeshi strains until 2022. This strain, possibly migrating from India, might have emerged during the COVID-19 pandemic years and exhibited higher morbidity rates, thus challenging our existing mitigation strategies. This investigation provides valuable insights for public health interventions and underscores the importance of continuous genomic surveillance in managing dengue outbreaks.

Understanding the factors contributing to dengue virus and chikungunya virus seropositivity and seroconversion among children in Kenya

Dengue virus (DENV) and chikungunya virus (CHIKV) are causes of endemic febrile disease among Kenyan children. The exposure risk to these infections is highly multifactorial and linked to environmental factors and human behavior. We investigated relationships between household, socio-economic, demographic, and behavioral risk factors for DENV and CHIKV seropositivity and seroconversion in four settlements in Kenya. We prospectively followed a pediatric cohort of 3,445 children between 2014-2018. We utilized the Kaplan-Meier curves to describe the temporal patterns of seroconversion among tested participants. We employed logistic regression built using generalized linear mixed models, to identify potential exposure risk factors for DENV and CHIKV seroconversion and seropositivity. Overall, 5.2% children were seropositive for DENV, of which 59% seroconverted during the study period. The seroprevalence for CHIKV was 9.2%, of which 54% seroconverted. The fraction of seroconversions per year in the study cohort was <2% for both viruses. Multivariable analysis indicated that older age and the presence of water containers ((OR: 1.15 [95% CI: 1.10, 1.21]), (OR: 1.50 [95% CI: 1.07, 2.10])) increased the odds of DENV seropositivity, whereas higher wealth (OR: 0.83 [95% CI: 0.73, 0.96]) decreased the odds of DENV seropositivity. Multivariable analysis for CHIKV seropositivity showed older age and the presence of trash in the housing compound to be associated with increased odds of CHIKV seropositivity ((OR: 1.11[95% CI: 1.07, 1.15]), (OR: 1.34 [95% CI: 1.04, 1.73])), while higher wealth decreased the odds of CHIKV seropositivity (OR: 0.74[95% CI: 0.66, 0.83]). A higher wealth index (OR: 0.82 [95% CI: 0.69, 0.97]) decreased the odds of DENV seroconversion, whereas a higher age (OR: 1.08 [95% CI: 1.02, 1.15]) and the presence of water containers in the household (OR: 1.91[95% CI: 1.24, 2.95]) were significantly associated with increased odds of DENV seroconversion. Higher wealth was associated with decreased odds for CHIKV seroconversion (OR: 0.75 [95% CI: 0.66, 0.89]), whereas presence of water containers in the house (OR: 1.57 [95% CI: 1.11, 2.21]) was a risk factor for CHIKV seroconversion. Our study links ongoing CHIKV and DENV exposure to decreased wealth and clean water access, underscoring the need to combat inequity and poverty and further enhance ongoing surveillance for arboviruses in Kenya to decrease disease transmission. The study emphasizes the co-circulation of DENV and CHIKV and calls for strengthening the targeted control strategies of mosquito borne diseases in Kenya including vector control, environmental management, public education, community engagement and personal protection.

Estimating dynamics of dengue disease in Colombo district of Sri Lanka with environmental impact by quantifying the per-capita vector density

Dengue is a vector-borne disease transmitted to humans by vectors of genus Aedes causing a global threat to health, social, and economic sectors in many of the tropical countries including Sri Lanka. In Sri Lanka, the tropical climate, marked by seasonal weather primarily influenced by monsoons, fosters optimal conditions for the virus to spread efficiently. This heightened transmission results in increased per-capita vector density. In this work, we investigate the dynamic influence of environmental conditions on dengue emergence in Colombo district - the geographical region with the highest recorded dengue threat in Sri Lanka. An iterative approach is employed to dynamically estimate dengue cases leveraging the Markov chain Monte Carlo simulations, utilizing the dynamics of four seasons per year influenced by monsoon weather patterns governing in the region. The developed algorithm allows to estimate the risk of dengue outbreaks in 2017 and 2019 with high precision, facilitating accurate forecasts of upcoming disease emergence patterns for better preparedness. The uncertainty quantification not only validated the accuracy of outbreak estimates but also showcased the model's capacity to capture extreme cases and revealed undisclosed external factors such as human mobility and environmental pollution that might affect dengue transmission in the Colombo district of Sri Lanka.

Exploring Dengue Dynamics: A Multi-Scale Analysis of Spatio-Temporal Trends in Ibagué, Colombia

Our study examines how dengue fever incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales in the city of Ibagué, located in the Andean region of Colombia. We used the dengue incidence in Ibagué from 2013 to 2018 to examine the associations with climate, socioeconomic, and demographic factors from the national census and satellite imagery at four levels of local spatial aggregation. We used geographically weighted regression (GWR) to identify the relevant socioeconomic and demographic predictors, and we then integrated them with environmental variables into hierarchical models using integrated nested Laplace approximation (INLA) to analyze the spatio-temporal interactions. Our findings show a significant effect of spatial variables across the different levels of aggregation, including human population density, gas and sewage connection, percentage of woman and children, and percentage of population with a higher education degree. Lagged temporal variables displayed consistent patterns across all levels of spatial aggregation, with higher temperatures and lower precipitation at short lags showing an increase in the relative risk (RR). A comparative evaluation of the models at different levels of aggregation revealed that, while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling, and they highlight the potential for targeted public health interventions based on localized risk factor analyses. Notably, the intermediate levels emerged as the most informative, thereby balancing spatial heterogeneity and case distribution density, as well as providing a robust framework for understanding the spatial determinants of dengue.

Association of cardiovascular disease with severe dengue: A systematic review and meta-analysis

BACKGROUND

Cardiovascular disease (CVD) and dengue are both significant global health concerns, and their intersection presents a growing clinical challenge. Emerging evidence suggests that individuals with pre-existing CVD may face an elevated risk of severe dengue outcomes. The present study aims to perform a systematic review to assess the relationship between CVD and the severity of dengue.

METHODS

We conducted a literature search across multiple databases from inception to November 25, 2023. Primary studies reporting the number of dengue patients with CVD in severe dengue and non-severe dengue groups were included. Quality assessment was performed using the Newcastle-Ottawa Scale, and a meta-analysis was conducted using R software version 4.2 to determine the pooled Relative Risk (RR). The study protocol has been registered in PROSPERO.

RESULTS

Based on data from 5 studies involving 274,576 dengue patients, our meta-analysis revealed a significant association between CVD and an increased risk of severe dengue, with a calculated RR of 2.71 (95  % CI: 1.03 to 7.10). However, substantial heterogeneity was observed among the included studies (I2 = 79  %).

CONCLUSION

The current evidence suggests an association between CVD and severe dengue, emphasizing the importance of closely monitoring individuals with pre-existing cardiovascular disease and providing them with targeted interventions upon dengue diagnosis to mitigate the risk of severe outcomes.

A Maximum Entropy Model of the Distribution of Dengue Serotype in Mexico

Pathogen strain diversity is an important driver of the trajectory of epidemics. The role of bioclimatic factors on the spatial distribution of dengue virus (DENV) serotypes has, however, not been previously studied. Hence, we developed municipality-scale environmental suitability maps for the four dengue virus serotypes using maximum entropy modeling. We fit climatic variables to municipality presence records from 2012 to 2020 in Mexico. Bioclimatic variables were explored for their environmental suitability to different DENV serotypes, and the different distributions were visualized using three cutoff probabilities representing 90%, 95%, and 99% sensitivity. Municipality-level results were then mapped in ArcGIS. The overall accuracy for the predictive models was 0.69, 0.68, 0.75, and 0.72 for DENV-1, DENV-2, DENV-3, and DENV-4, respectively. Important predictors of all DENV serotypes were the growing degree days for December, January, and February, which are an indicator of higher temperatures and the precipitation of the wettest month. The minimum temperature of the coldest month between -5°C and 20°C was found to be suitable for DENV-1 and DENV-2 serotypes. Respectively, above 700-900 mm of rainfall, the suitability for DENV-1 and DENV-2 begins to decline, while higher humidity still favors DENV-3 and DENV-4. The sensitivity concerning the suitability map was developed for Mexico. DENV-1, DENV-2, DENV-3, and DENV-4 serotypes will be found more commonly in the municipalities classified as suitable based on their respective sensitivity of 91%, 90%, 89%, and 85% in Mexico. As the microclimates continue to change, specific bioclimatic indices may be used to monitor potential changes in DENV serotype distribution. The suitability for DENV-1 and DENV-2 is expected to increase in areas with lower minimum temperature ranges, while DENV-3 and DENV-4 will likely increase in areas that experience higher humidity. Ongoing surveillance of municipalities with predicted suitability of 89% and 85% should be expanded to account for the accurate DENV serotype prevalence and association between bioclimatic parameters.

Attitude towards dengue control efforts with the potential of digital technology during COVID-19: partial least squares-structural equation modeling

Background: Dengue fever is still a public health issue in Indonesia, and during the coronavirus disease 2019 (COVID-19) pandemic, integrated digital technology will be required for its control. This study aims to identify critical indicators influencing attitudes towards dengue control related to the potential for implementing digital technology. Methods: This was a cross-sectional survey, with 515 people willing to fill out an online questionnaire. The analysis was conducted using Partial Least Square-Structural Equation Modelling (PLS-SEM). There were 46 indicators used to assess attitudes toward dengue control, which were organized into six variables: the need for digital information systems, perceptions of being threatened with dengue, the benefits of dengue control programs, program constraints, environmental factors and attitudes in dengue control. Results:  The source of information needed for dengue control was mainly through social media. There was a positive relationship between perception of environmental factors to perception of dengue threat, perception of program constraints, perception of program benefits, and perception of digital technology needs. Perception of program benefits and threatened perception of dengue have a positive relationship with perception of digital technology needs. Conclusions: This model showed the variables perception of digital technology and perception of benefits had a positive association with attitude towards dengue control.

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.

Dengue as a Disease Threatening Global Health: A Narrative Review Focusing on Latin America and Brazil

Arboviruses constitute the largest known group of viruses. These viruses are the etiological agents of pathologies known as arboviruses, with dengue being one of the most prevalent. Dengue has resulted in important socioeconomic burdens placed on different countries around the world, including those in Latin America, especially Brazil. Thus, this work intends to carry out a narrative-based review of the literature, conducted using a study of the secondary data developed through a survey of scientific literature databases, and to present the situation of dengue, particularly its distribution in these localities. Our findings from the literature demonstrate the difficulties that managers face in controlling the spread of and planning a response against dengue, pointing to the high cost of the disease for public coffers, rendering the resources that are already limited even scarcer. This can be associated with the different factors that affect the spread of the disease, including ecological, environmental, and social factors. Thus, in order to combat the disease, it is expected that targeted and properly coordinated public policies need to be adopted not only in specific localities, but also globally.

Association Between Hydrological Conditions and Dengue Fever Incidence in Coastal Southeastern China From 2013 to 2019

IMPORTANCE

Dengue fever is a climate-sensitive infectious disease. However, its association with local hydrological conditions and the role of city development remain unclear.

OBJECTIVE

To quantify the association between hydrological conditions and dengue fever incidence in China and to explore the modification role of city development in this association.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study collected data between January 1, 2013, and December 31, 2019, from 54 cities in 4 coastal provinces in southeast China. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated from ambient temperature and precipitation, with SPEI thresholds of 2 for extreme wet conditions and -2 for extreme dry conditions. The SPEI-dengue fever incidence association was examined over a 6-month lag, and the modification roles of 5 city development dimensions were assessed. Data were analyzed in May 2022.

EXPOSURES

City-level monthly temperature, precipitation, SPEI, and annual city development indicators from 2013 to 2019.

MAIN OUTCOMES AND MEASURES

The primary outcome was city-level monthly dengue fever incidence. Spatiotemporal bayesian hierarchal models were used to examine the SPEI-dengue fever incidence association over a 6-month lag period. An interaction term between SPEI and each city development indicator was added into the model to assess the modification role of city development.

RESULTS

Included in the analysis were 70 006 dengue fever cases reported in 54 cities in 4 provinces in China from 2013 to 2019. Overall, a U-shaped cumulative curve was observed, with wet and dry conditions both associated with increased dengue fever risk. The relative risk [RR] peaked at a 1-month lag for extreme wet conditions (1.27; 95% credible interval [CrI], 1.05-1.53) and at a 6-month lag for extreme dry conditions (1.63; 95% CrI, 1.29-2.05). The RRs of extreme wet and dry conditions were greater in areas with limited economic development, health care resources, and income per capita. Extreme dry conditions were higher and prolonged in areas with more green space per capita (RR, 1.84; 95% CrI, 1.37-2.46). Highly urbanized areas had a higher risk of dengue fever after extreme wet conditions (RR, 1.80; 95% CrI, 1.26-2.56), while less urbanized areas had the highest risk of dengue fever in extreme dry conditions (RR, 1.70; 95% CrI, 1.11-2.60).

CONCLUSIONS AND RELEVANCE

Results of this study showed that extreme hydrological conditions were associated with increased dengue fever incidence within a 6-month lag period, with different dimensions of city development playing various modification roles in this association. These findings may help in developing climate change adaptation strategies and public health interventions against dengue fever.

Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review

Dengue fever is a serious and growing public health problem in Latin America and elsewhere, intensified by climate change and human mobility. This paper reviews the approaches to the epidemiological prediction of dengue fever using the One Health perspective, including an analysis of how Machine Learning techniques have been applied to it and focuses on the risk factors for dengue in Latin America to put the broader environmental considerations into a detailed understanding of the small-scale processes as they affect disease incidence. Determining that many factors can act as predictors for dengue outbreaks, a large-scale comparison of different predictors over larger geographic areas than those currently studied is lacking to determine which predictors are the most effective. In addition, it provides insight into techniques of Machine Learning used for future predictive models, as well as general workflow for Machine Learning projects of dengue fever.