Big Data-Planetary Health approach for evaluating the Brazilian Dengue Control Program.

OBJECTIVE: This study aims to integrate the concepts of planetary health and big data into the Donabedian model to evaluate the Brazilian dengue control program in the state of São Paulo. METHODS: Data science methods were used to integrate and analyze dengue-related data, adding context to the structure and outcome components of the Donabedian model. This data, considering the period from 2010 to 2019, was collected from sources such as Department of Informatics of the Unified Health System (DATASUS), the Brazilian Institute of Geography and Statistics (IBGE), WorldClim, and MapBiomas. These data were integrated into a Data Warehouse. K-means algorithm was used to identify groups with similar contexts. Then, statistical analyses and spatial visualizations of the groups were performed, considering socioeconomic and demographic variables, soil, health structure, and dengue cases. OUTCOMES: Using climate variables, the K-means algorithm identified four groups of municipalities with similar characteristics. The comparison of their indicators revealed certain patterns in the municipalities with the worst performance in terms of dengue case outcomes. Although presenting better economic conditions, these municipalities held a lower average number of community healthcare agents and basic health units per inhabitant. Thus, economic conditions did not reflect better health structure among the three studied indicators. Another characteristic of these municipalities is urbanization. The worst performing municipalities presented a higher rate of urban population and human activity related to urbanization. CONCLUSIONS: This methodology identified important deficiencies in the implementation of the dengue control program in the state of São Paulo. The integration of several databases and the use of Data Science methods allowed the evaluation of the program on a large scale, considering the context in which activities are conducted. These data can be used by the public administration to plan actions and invest according to the deficiencies of each location.

Spatial distribution of arboviruses and its association with a social development index and the waste disposal in São Luís, state of Maranhão, Brazil, 2015 to 2019.

OBJECTIVE: To detect spatial and spatiotemporal clusters of urban arboviruses and to investigate whether the social development index (SDI) and irregular waste disposal are related to the coefficient of urban arboviruses detection in São Luís, state of Maranhão, Brazil. METHODS: The confirmed cases of Dengue, Zika and Chikungunya in São Luís, from 2015 to 2019, were georeferenced to the census tract of residence. The Bayesian Conditional Autoregressive regression model was used to identify the association between SDI and irregular waste disposal sites and the coefficient of urban arboviruses detection. RESULTS: The spatial pattern of arboviruses pointed to the predominance of a low-incidence cluster, except 2016. For the years 2015, 2016, 2017, and 2019, an increase of one unit of waste disposal site increased the coefficient of arboviruses detection in 1.25, 1.09, 1.23, and 1.13 cases of arboviruses per 100 thousand inhabitants, respectively. The SDI was not associated with the coefficient of arboviruses detection. CONCLUSION: In São Luís, spatiotemporal risk clusters for the occurrence of arboviruses and a positive association between the coefficient of arbovirus detection and sites of irregular waste disposal were identified.

Tiger prowling: Distribution modelling for northward-expanding Aedes albopictus (Diptera: Culicidae) in Japan.

The Asian tiger mosquito, Aedes albopictus, is a significant public health concern owing to its expanding habitat and vector competence. Disease outbreaks attributed to this species have been reported in areas under its invasion, and its northward expansion in Japan has caused concern because of the potential for dengue virus infection in newly populated areas. Accurate prediction of Ae. albopictus distribution is crucial to prevent the spread of the disease. However, limited studies have focused on the prediction of Ae. albopictus distribution in Japan. Herein, we used the random forest model, a machine learning approach, to predict the current and potential future habitat ranges of Ae. albopictus in Japan. The model revealed that these mosquitoes prefer urban areas over forests in Japan on the current map. Under predictions for the future, the species will expand its range to the surrounding areas and eventually reach many areas of northeastern Kanto, Tohoku District, and Hokkaido, with a few variations in different scenarios. However, the affected human population is predicted to decrease owing to the declining birth rate. Anthropogenic and climatic factors contribute to range expansion, and urban size and population have profound impacts. This prediction map can guide responses to the introduction of this species in new areas, advance the spatial knowledge of diseases vectored by it, and mitigate the possible disease burden. To our knowledge, this is the first distribution-modelling prediction for Ae. albopictus with a focus on Japan.

Dengue Virus and Zika Virus Seroprevalence in the South Pacific Populations of the Cook Islands and Vanuatu.

Arboviral diseases are serious threats to global health with increasing prevalence and potentially severe complications. Significant arthropod-borne viruses are the dengue viruses (DENV 1-4), the Zika virus (ZIKV), and the chikungunya virus (CHIKV). Among the areas most affected is the South Pacific Region (SPR). Here, arboviruses not only cause a high local burden of disease, but the region has also proven to contribute to their global spread. Outpatient serum samples collected between 08/2016 and 04/2017 on three islands of the island states of Vanuatu and the Cook Islands were tested for anti-DENV- and anti-ZIKV-specific antibodies (IgG) using enzyme-linked immunosorbent assays (ELISA). ELISA test results showed 89% of all test sera from the Cook Islands and 85% of the Vanuatu samples to be positive for anti-DENV-specific antibodies. Anti-ZIKV antibodies were identified in 66% and 52%, respectively, of the test populations. Statistically significant differences in standardized immunity levels were found only at the intranational level. Our results show that in both the Cook Islands and Vanuatu, residents were exposed to significant Flavivirus transmission. Compared to other seroprevalence studies, the marked difference between ZIKV immunity levels and previously published CHIKV seroprevalence rates in our study populations is surprising. We propose the timing of ZIKV and CHIKV emergence in relation to recurrent DENV outbreaks and the impact of seasonality as explanatory external factors for this observation. Our data add to the knowledge of arboviral epidemics in the SPR and contribute to a better understanding of virus spread, including external conditions with potential influence on outbreak dynamics. These data may support preventive and rapid response measures in the affected areas, travel-related risk assessment, and infection identification in locals and returning travelers.

Cytokine profiles in dengue fever and dengue hemorrhagic fever: A study from Indonesia.

Recent studies have demonstrated that cytokine dysregulation has a critical role in the pathogenesis of dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). The aim of this study was to investigate the association between tumor necrosis factor (TNF- α), interleukin 6 (IL-6), interleukin 10 (IL-10), and interleukin 17 (IL-17) with infection status, and severity of dengue. A prospective cross-sectional study was conducted at three hospitals in Gianyar regency and Denpasar municipality, Bali, Indonesia, from June to December 2022. Sixty-four dengue infected patients were involved. Patients' serum was tested for dengue infection using NS1 antigen rapid test, dengue virus immunoglobulin M (IgM) and immunoglobulin G (IgG) test, and reverse transcription polymerase chain reaction (RT-PCR). Cytokine levels (TNF-α, IL-6, IL-10, and IL-17) were measured using enzyme-linked immunosorbent assay (ELISA). Infection status was determined by combining serological and RT-PCR results, categorizing patients into primary and secondary infections. The present study found that DF patients had lower TNF-α, IL-6, and IL-17 but higher IL-10 levels compared to DHF patients (p<0.001). Elevated TNF-α, IL-6, and IL-17 levels were higher in secondary infection, while IL-10 level was higher in primary infection (p<0.001). In conclusion, cytokines play a crucial role in the interplay between cytokine dysregulation and dengue infection dynamics.

Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning.

Previous dengue epidemiological analyses have been limited in spatiotemporal extent or covariate dimensions, the latter neglecting the multifactorial nature of dengue. These constraints, caused by rigid and traditional statistical tools which collapse amidst 'Big Data', prompt interpretable machine-learning (iML) approaches. Predicting dengue incidence and mortality in the Philippines, a data-limited yet high-burden country, the mlr3 universe of R packages was used to build and optimize ML models based on remotely sensed provincial and dekadal 3 NDVI and 9 rainfall features from 2016 to 2020. Between two tasks, models differ across four random forest-based learners and two clustering strategies. Among 16 candidates, rfsrc-year-case and ranger-year-death significantly perform best for predicting dengue incidence and mortality, respectively. Therefore, temporal clustering yields the best models, reflective of dengue seasonality. The two best models were subjected to tripartite global exploratory model analyses, which encompass model-agnostic post-hoc methods such as Permutation Feature Importance (PFI) and Accumulated Local Effects (ALE). PFI reveals that the models differ in their important explanatory aspect, rainfall for rfsrc-year-case and NDVI for ranger-year-death, among which long-term average (lta) features are most relevant. Trend-wise, ALE reveals that average incidence predictions are positively associated with 'Rain.lta', reflective of dengue cases peaking during the wet season. In contrast, those for mortality are negatively associated with 'NDVI.lta', reflective of urban spaces driving dengue-related deaths. By technologically addressing the challenges of the human-animal-ecosystem interface, this study adheres to the One Digital Health paradigm operationalized under Sustainable Development Goals (SDGs). Leveraging data digitization and predictive modeling for epidemiological research paves SDG 3, which prioritizes holistic health and well-being.

Evaluation of clinical and laboratory characteristics of dengue viral infection and risk factors of dengue hemorrhagic fever: a multi-center retrospective analysis.

BACKGROUND: Dengue Viral Infection (DVI) has become endemic in Pakistan since the first major outbreak in Karachi in 1996. Despite aggressive measures taken by relevant authorities, Pakistan has been dealing with a worsening dengue crisis for the past two decades. DHF is severe form of dengue infection which is linked with significant morbidity and mortality. Early identification of severe dengue infections can reduce the morbidity and mortality. In this context we planned current study in which we find out the different factors related with DHF as well as clinical laboratory features of DHF and compare them to DF so that patients can be best evaluated for DHF and managed accordingly at admission. METHODS: Retrospective study conducted over a period of 6 years (2013-2018) in two tertiary care hospitals in Pakistan. Data were collected by using a pre-structured data collection form. Data were statistically analyzed to determine the clinical and laboratory characteristics of DVI and risk factors of dengue hemorrhagic fever (DHF). RESULTS: A total 512 dengue cases (34.05 ± 15.08 years; Male 69.53%) were reviewed. Most common clinical manifestations of DVI were fever (99.60%), headache (89.1%), chills (86.5%), rigors (86.5%), myalgia (72.3%). Less common clinical manifestations were vomiting (52.5%), arthralgia (50.2%) and skin rashes (47.5%). Furthermore, nasal bleeding (44.1%), gum bleeding (32.6%), pleural effusion (13.9%) and hematuria (13.1%) were more profound clinical presentations among DHF patients. Mortality rate was 1.5% in this study. Logistic regression analysis indicated that delayed hospitalization (OR: 2.30) and diabetes mellitus (OR:2.71), shortness of breath (OR:2.21), association with risk groups i.e., living near stagnant water, travelling to endemic areas, living in endemic regions (OR:1.95), and presence of warning signs (OR:2.18) were identified as risk factors of DHF. Statistically we found that there is strong association of diabetes mellitus (DM) with DHF while the patient suffering from DM individually had higher odds (2.71) of developing DHF than patients without disease. CONCLUSIONS: The current study demonstrated that the clinical and laboratory profiles of DF and DHF are significantly distinct. Significant predictors of DHF were advanced age, diabetes mellitus, ascites, pleural effusion, thick gallbladder and delayed hospitalization. The identification of these factors at early stage provides opportunities for the clinicians to identify high risk patients and to reduce dengue-related morbidity and mortality.

Sentinel Enhanced Dengue Surveillance System - Puerto Rico, 2012-2022.

Problem/Condition: Dengue is the most prevalent mosquitoborne viral illness worldwide and is endemic in Puerto Rico. Dengue's clinical spectrum can range from mild, undifferentiated febrile illness to hemorrhagic manifestations, shock, multiorgan failure, and death in severe cases. The disease presentation is nonspecific; therefore, various other illnesses (e.g., arboviral and respiratory pathogens) can cause similar clinical symptoms. Enhanced surveillance is necessary to determine disease prevalence, to characterize the epidemiology of severe disease, and to evaluate diagnostic and treatment practices to improve patient outcomes. The Sentinel Enhanced Dengue Surveillance System (SEDSS) was established to monitor trends of dengue and dengue-like acute febrile illnesses (AFIs), characterize the clinical course of disease, and serve as an early warning system for viral infections with epidemic potential. Reporting Period: May 2012-December 2022. Description of System: SEDSS conducts enhanced surveillance for dengue and other relevant AFIs in Puerto Rico. This report includes aggregated data collected from May 2012 through December 2022. SEDSS was launched in May 2012 with patients with AFIs from five health care facilities enrolled. The facilities included two emergency departments in tertiary acute care hospitals in the San Juan-Caguas-Guaynabo metropolitan area and Ponce, two secondary acute care hospitals in Carolina and Guayama, and one outpatient acute care clinic in Ponce. Patients arriving at any SEDSS site were eligible for enrollment if they reported having fever within the past 7 days. During the Zika epidemic (June 2016-June 2018), patients were eligible for enrollment if they had either rash and conjunctivitis, rash and arthralgia, or fever. Eligibility was expanded in April 2020 to include reported cough or shortness of breath within the past 14 days. Blood, urine, nasopharyngeal, and oropharyngeal specimens were collected at enrollment from all participants who consented. Diagnostic testing for dengue virus (DENV) serotypes 1-4, chikungunya virus, Zika virus, influenza A and B viruses, SARS-CoV-2, and five other respiratory viruses was performed by the CDC laboratory in San Juan. Results: During May 2012-December 2022, a total of 43,608 participants with diagnosed AFI were enrolled in SEDSS; a majority of participants (45.0%) were from Ponce. During the surveillance period, there were 1,432 confirmed or probable cases of dengue, 2,293 confirmed or probable cases of chikungunya, and 1,918 confirmed or probable cases of Zika. The epidemic curves of the three arboviruses indicate dengue is endemic; outbreaks of chikungunya and Zika were sporadic, with case counts peaking in late 2014 and 2016, respectively. The majority of commonly identified respiratory pathogens were influenza A virus (3,756), SARS-CoV-2 (1,586), human adenovirus (1,550), respiratory syncytial virus (1,489), influenza B virus (1,430), and human parainfluenza virus type 1 or 3 (1,401). A total of 5,502 participants had confirmed or probable arbovirus infection, 11,922 had confirmed respiratory virus infection, and 26,503 had AFI without any of the arboviruses or respiratory viruses examined. Interpretation: Dengue is endemic in Puerto Rico; however, incidence rates varied widely during the reporting period, with the last notable outbreak occurring during 2012-2013. DENV-1 was the predominant virus during the surveillance period; sporadic cases of DENV-4 also were reported. Puerto Rico experienced large outbreaks of chikungunya that peaked in 2014 and of Zika that peaked in 2016; few cases of both viruses have been reported since. Influenza A and respiratory syncytial virus seasonality patterns are distinct, with respiratory syncytial virus incidence typically reaching its annual peak a few weeks before influenza A. The emergence of SARS-CoV-2 led to a reduction in the circulation of other acute respiratory viruses. Public Health Action: SEDSS is the only site-based enhanced surveillance system designed to gather information on AFI cases in Puerto Rico. This report illustrates that SEDSS can be adapted to detect dengue, Zika, chikungunya, COVID-19, and influenza outbreaks, along with other seasonal acute respiratory viruses, underscoring the importance of recognizing signs and symptoms of relevant diseases and understanding transmission dynamics among these viruses. This report also describes fluctuations in disease incidence, highlighting the value of active surveillance, testing for a panel of acute respiratory viruses, and the importance of flexible and responsive surveillance systems in addressing evolving public health challenges. Various vector control strategies and vaccines are being considered or implemented in Puerto Rico, and data from ongoing trials and SEDSS might be integrated to better understand epidemiologic factors underlying transmission and risk mitigation approaches. Data from SEDSS might guide sampling strategies and implementation of future trials to prevent arbovirus transmission, particularly during the expansion of SEDSS throughout the island to improve geographic representation.

First clinical reports of acute hantavirus and dengue infections among pregnant women in the Caribbean.

BACKGROUND: Hantavirus and dengue virus infections lead to diseases causing economic and public health concerns. Acute hantavirus infections can lead to similar clinical haemorrhagic signs as other endemic diseases including dengue and leptospirosis. METHODS: Using a retrospective case analysis of pregnant dengue and hantavirus disease patients with clinical reports and compatible clinical laboratory information during pregnancy, we report the first evidence of dengue and hantavirus infections and a case of dual dengue and hantavirus infection among pregnant women in the Caribbean. Laboratory testing by enzyme-linked immunosorbent assay (ELISA) and non-structural protein 1 (NS1) for DENV and for hantavirus infection pseudotype focus reduction neutralisation tests (pFRNT), ELISA and immunochromatographic (ICG) strips. RESULTS: Four pregnant cases with acute DENV infections were identified; however, only one out of the four cases (25%) had a detailed medical record to permit abstraction of clinical data. Six hantavirus infected pregnant cases were identified with gestation periods ranged from 36 to 39 weeks; none of the reported patients exhibited previous pregnancy complications prior to hospitalisation and infection. Acute liver damage was observed in three of the six cases (AST readings) who were subsequently diagnosed with hepatitis in pregnancy and variable clinical outcomes were observed with term and pre-term deliveries. CONCLUSIONS: Whilst hantavirus infection in pregnancy is rare, consideration should be given to differential diagnosis with fever, kidney involvement, liver involvement, haemorrhagic symptoms and thrombocytopenia in endemic areas with clinically similar diseases such as dengue and leptospirosis.HighlightsFirst recorded case of hantavirus and dengue co-infection in a pregnant woman.First detailed report of clinical hantavirus infection in pregnant women in the Caribbean.First published report of clinical dengue infection in pregnant woman in the Caribbean.Possible complications of pregnancy following hantavirus infection.Pre-term birth and low birth weights.Clinical course of hantavirus infection in a Caribbean population.

Dynamics of emergence and genetic diversity of dengue virus in Reunion Island from 2012 to 2022.

BACKGROUND: Dengue is a major public health concern in Reunion Island, marked by recurrent epidemics, including successive outbreaks of dengue virus serotypes 1 and 2 (DENV1 and DENV2) with over 70,000 cases confirmed since 2017. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we used Oxford Nanopore NGS technology for sequencing virologically-confirmed samples and clinical isolates collected between 2012 and 2022 to investigate the molecular epidemiology and evolution of DENV in Reunion Island. Here, we generated and analyzed a total of 499 DENV1, 360 DENV2, and 18 DENV3 sequences. By phylogenetic analysis, we show that different genotypes and variants of DENV have circulated in the past decade that likely originated from Seychelles, Mayotte and Southeast Asia and highly affected areas in Asia and Africa. CONCLUSIONS/SIGNIFICANCE: DENV sequences from Reunion Island exhibit a high genetic diversity which suggests regular introductions of new viral lineages from various Indian Ocean islands. The insights from our phylogenetic analysis may inform local health authorities about the endemicity of DENV variants circulating in Reunion Island and may improve dengue management and surveillance. This work emphasizes the importance of strong local coordination and collaboration to inform public health stakeholders in Reunion Island, neighboring areas, and mainland France.

Introduction of New Dengue Virus Lineages of Multiple Serotypes after COVID-19 Pandemic, Nicaragua, 2022.

Major dengue epidemics throughout Nicaragua's history have been dominated by 1 of 4 dengue virus serotypes (DENV-1-4). To examine serotypes during the dengue epidemic in Nicaragua in 2022, we performed real-time genomic surveillance in-country and documented cocirculation of all 4 serotypes. We observed a shift toward co-dominance of DENV-1 and DENV-4 over previously dominant DENV-2. By analyzing 135 new full-length DENV sequences, we found that introductions underlay the resurgence: DENV-1 clustered with viruses from Ecuador in 2014 rather than those previously seen in Nicaragua; DENV-3, which last circulated locally in 2014, grouped instead with Southeast Asia strains expanding into Florida and Cuba in 2022; and new DENV-4 strains clustered within a South America lineage spreading to Florida in 2022. In contrast, DENV-2 persisted from the formerly dominant Nicaragua clade. We posit that the resurgence emerged from travel after the COVID-19 pandemic and that the resultant intensifying hyperendemicity could affect future dengue immunity and severity.

Discrepancies in dengue burden estimates: a comparative analysis of reported cases and global burden of disease study, 2010-2019.

BACKGROUND: Dengue is a significant mosquito-borne disease. Several studies have utilized estimates from the Global Burden of Disease (GBD) study to assess the global, regional or national burden of dengue over time. However, our recent investigation suggests that GBD's estimates for dengue cases in Taiwan are unrealistically high. The current study extends the scope to compare reported dengue cases with GBD estimates across 30 high-burden countries and territories, aiming to assess the accuracy and interpretability of the GBD's dengue estimates. METHODS: Data for this study were sourced from the GBD 2019 study and various national and international databases documenting reported dengue cases. The analysis targeted the top 30 countries and territories with the highest 10-year average of reported cases from 2010 to 2019. Discrepancies were quantified by computing absolute differences and ratios between the 10-year average of reported cases and GBD estimates. Coefficients of variation (CV) and estimated annual percentage changes (EAPCs) were calculated to assess variations and trends in the two data sources. RESULTS: Significant discrepancies were noted between reported data and GBD estimates in the number of dengue cases, incidence rates, and EAPCs. GBD estimates were substantially higher than reported cases for many entities, with the most notable differences found in China (570.0-fold), India (303.0-fold), Bangladesh (115.4-fold), Taiwan (85.5-fold) and Indonesia (23.2-fold). Furthermore, the GBD's estimates did not accurately reflect the extensive yearly fluctuations in dengue outbreaks, particularly in non-endemic regions such as Taiwan, China and Argentina, as evidenced by high CVs. CONCLUSIONS: This study reveals substantial discrepancies between GBD estimates and reported dengue cases, underscoring the imperative for comprehensive analysis in areas with pronounced disparities. The failure of GBD estimates to represent the considerable annual fluctuations in dengue outbreaks highlights the critical need for improvement in disease burden estimation methodologies for dengue.

Long-term effects of climate factors on dengue fever over a 40-year period.

BACKGROUND: Dengue fever stands as one of the most extensively disseminated mosquito-borne infectious diseases worldwide. While numerous studies have investigated its influencing factors, a gap remains in long-term analysis, impeding the identification of temporal patterns, periodicity in transmission, and the development of effective prevention and control strategies. Thus, we aim to analyze the periodicity of dengue fever incidence and explore the association between various climate factors and the disease over an extended time series. METHODS: By utilizing monthly dengue fever cases and climate data spanning four decades (1978-2018) in Guangdong province, China, we employed wavelet analysis to detect dengue fever periodicity and analyze the time-lag relationship with climate factors. Additionally, Geodetector q statistic was employed to quantify the explanatory power of each climate factor and assess interaction effects. RESULTS: Our findings revealed a prolonged transmission period of dengue fever over the 40-year period, transitioning from August to November in the 1970s to nearly year-round in the 2010s. Moreover, we observed lags of 1.5, 3.5, and 3 months between dengue fever and temperature, relative humidity, and precipitation, respectively. The explanatory power of precipitation, temperature, relative humidity, and the Oceanic Niño Index (ONI) on dengue fever was determined to be 18.19%, 12.04%, 11.37%, and 5.17%, respectively. Dengue fever exhibited susceptibility to various climate factors, with notable nonlinear enhancement arising from the interaction of any two variables. Notably, the interaction between precipitation and humidity yielded the most significant effect, accounting for an explanatory power of 75.32%. CONCLUSIONS: Consequently, future prevention and control strategies for dengue fever should take into account these climate changes and formulate corresponding measures accordingly. In regions experiencing the onset of high temperatures, humidity, and precipitation, it is imperative to initiate mosquito prevention and control measures within a specific window period of 1.5 months.

Human movement and environmental barriers shape the emergence of dengue.

Understanding how emerging infectious diseases spread within and between countries is essential to contain future pandemics. Spread to new areas requires connectivity between one or more sources and a suitable local environment, but how these two factors interact at different stages of disease emergence remains largely unknown. Further, no analytical framework exists to examine their roles. Here we develop a dynamic modelling approach for infectious diseases that explicitly models both connectivity via human movement and environmental suitability interactions. We apply it to better understand recently observed (1995-2019) patterns as well as predict past unobserved (1983-2000) and future (2020-2039) spread of dengue in Mexico and Brazil. We find that these models can accurately reconstruct long-term spread pathways, determine historical origins, and identify specific routes of invasion. We find early dengue invasion is more heavily influenced by environmental factors, resulting in patchy non-contiguous spread, while short and long-distance connectivity becomes more important in later stages. Our results have immediate practical applications for forecasting and containing the spread of dengue and emergence of new serotypes. Given current and future trends in human mobility, climate, and zoonotic spillover, understanding the interplay between connectivity and environmental suitability will be increasingly necessary to contain emerging and re-emerging pathogens.

Identifying villages and breeding habitats for dengue transmission in Thailand: insights from long-term larval surveys.

BACKGROUND: In Thailand, the Department of Disease Control (DDC) regularly performs visual larval surveys throughout the country to monitor dengue fever outbreaks. Since 2016, the DDC switched from a paper-based to a digital-based larval survey process. The significant amount of larval survey data collected digitally presents a valuable opportunity to precisely identify the villages and breeding habitats that are vulnerable to dengue transmission. METHODS: The study used digitally collected larval survey data from 2017 to 2019. It employed larval indices to evaluate the risk of dengue transmission in villages based on seasonal, regional, and categorical perspectives. Furthermore, the study comprehensively scrutinized each container category by employing different measures to determine its breeding preference ratio. RESULTS: The result showed that villages with a very high-risk of dengue transmission were present year-round in all regions, with the highest proportion during the rainy season. The Southern region had more high-risk villages during the winter season due to rainfall. Slums and residential communities were more vulnerable to dengue than commercial areas. All container categories could potentially serve as breeding habitats for dengue-carrying mosquitoes, with abandoned containers being the most significant breeding sites. CONCLUSIONS: The risk of dengue transmission was present year-round throughout Thailand. This underscores the importance of community and government initiatives, along with sustained public awareness campaigns and active community engagement, to efficiently and permanently eradicate mosquito breeding habitats. It should be noted that larval indices may not strongly correlate with dengue cases, as indicated by the preliminary analysis. However, they offer valuable insights into potential breeding sites for targeted preventive measures.