Utility of entomological indices for predicting transmission of dengue virus: secondary analysis of data from the Camino Verde trial in Mexico and Nicaragua

Morales-Pérez, Arcadio; Nava-Aguilera, Elizabeth; Hernández-Álvarez, Carlos; Alvarado-Castro, Víctor Manuel; Arosteguí, Jorge; Legorreta-Soberanis, José; Flores-Moreno, Miguel; Morales-Nava, Liliana; Harris, Eva; Ledogar, Robert J.; Andersson, Neil; Cockcroft, Anne

doi:10.1371/journal.pntd.0008768

Cited by 8 publications

(9 citation statements)

References 43 publications

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“…Chadee 10 used retrospective data from Trinidad and showed that PI and CI were significantly associated with dengue incidences. Morales-Perez et al 11 assessed HI, BI, CI and PI at household and cluster levels of dengue transmission and found that BI was the only weak predictor of dengue infections. The results of these studies show that the indices can potentially be used as variables to predict dengue transmission rates.…”

Section: Introductionmentioning

confidence: 99%

“…Roster et al 20 found that DT ensemble models with epidemiological and meteorological variables gave more robust results in predicting monthly dengue cases in Brazil. Nevertheless, most studies have not adequately considered such appropriate disease variables in modelling 21 , focusing mainly on meteorological data 11 – 20 , or used only vector indices to develop a logistic regression model 22 . Moreover, other ML algorithms that was demonstrated robust in prediction, such as XG Boost, AdaBoost and Random Forest have not been investigated for their ability to predict dengue transmission.…”

Section: Introductionmentioning

confidence: 99%

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Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data

Ong,

Isawasan,

Ngesom

et al. 2023

Sci Rep

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Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. In this study, we use vector indices and meteorological data as predictors to develop the ML models. We trained and validated seven ML algorithms, including an ensemble ML method, and compared their performance using the receiver operating characteristic (ROC) with the area under the curve (AUC), accuracy and F1 score. Our results show that an ensemble ML such as XG Boost, AdaBoost and Random Forest perform better than the logistics regression, Naïve Bayens, decision tree, and support vector machine (SVM), with XGBoost having the highest AUC, accuracy and F1 score. Analysis of the importance of the variables showed that the container index was the least important. By removing this variable, the ML models improved their performance by at least 6% in AUC and F1 score. Our result provides a framework for future studies on the use of predictive models in the development of an early warning system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data

Ong,

Isawasan,

Ngesom

et al. 2023

Sci Rep

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“…In general, the procedure should be simple, cost-effective, and easy to apply 15 . Entomological indicators might be used for investigating mosquito epidemiology 16 . Laboratory testing is necessary for species identification, and epidemiological studies are conducted using four indices: the House (premise) index (HI), the Container index (CI), the Breteau index (BI), and the Trap index (TI) 16 .…”

Section: Introductionmentioning

confidence: 99%

“…Entomological indicators might be used for investigating mosquito epidemiology 16 . Laboratory testing is necessary for species identification, and epidemiological studies are conducted using four indices: the House (premise) index (HI), the Container index (CI), the Breteau index (BI), and the Trap index (TI) 16 . However, a larval survey can only provide information on contact frequency, and determining the number of larvae is not possible.…”

Section: Introductionmentioning

confidence: 99%

The Evaluation of the Aedes Albopictus Entomological Index Based on the Topography to Compute the Essential Requirements for Dengue Transmission Surveillance in Nakhon Si Thammarat, Thailand

Rattanachoo¹,

Promprao

Sintupachee³

2022

MJPHM

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An entomological index is widely used for determining dengue transmission and mosquito populations. In this study, we determined the association between Aedes albopictus mosquito counts in three areas with different topographical features from six districts in Nakhon Si Thammarat and infected patients reported by the Ministry of Public Health's Department of Disease Control, using entomological indices, such as the sticky pad index and the sweep net index. The sampling region was selected from the top six districts of the province based on the number of reported dengue cases per 100,000 people. The regions were Khanom, Mueang, Pak Phanang, Phrom Khiri, Thung Song, and Thung Yai. They were divided into three categories of geographical features, including city, beach, and mountain. The mosquitos were collected by walking in a star form inside a 5 m diameter circle. The mosquitoes were separated into groups based on their species, and the percentile of the index was computed. We found that the number of mosquitoes was the highest in Pak Phanang and lowest in Khanom. The two indices and the number of A. albopictus collected from the six sampling locations were not significantly different (t = 1.31, p > 0.05). The coastline region had the highest number of mosquitoes and was substantially different from the other two regions (cosmopolitan and mountain) (F = 4.30; p < 0.05). By performing Pearson's correlation analysis, we found a negative correlation between dengue cases and the entomological index (r = -0.472; N = 36). Similarly, the number of patients showed a negative correlation with relative humidity (r = -0.355) but a positive correlation with temperature (r = 0.064). The epidemic pattern shifted throughout the year, and the number of cases then increased 2-4 weeks after the insect was found.

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“…Furthermore, studies that attempt to look for associations between these entomological indices and risk of infection often have mixed results (23)(24)(25)(26).…”

Section: Introductionmentioning

confidence: 99%

Determinants of exposure to Aedes mosquitoes: a comprehensive geospatial analysis in peri-urban Cambodia

Parker

Medina

Bohl

et al. 2022

Preprint

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Aedes mosquitoes are some of the most important and globally expansive vectors of disease. Public health efforts are largely focused on prevention of human-vector contact. A range of entomological indices are used to measure risk of disease, though with conflicting results (i.e. larval or adult abundance does not always predict risk of disease). There is a growing interest in the development and use of biomarkers for exposure to mosquito saliva, including for Aedes spp, as a proxy for disease risk. In this study, we conduct a comprehensive geostatistical analysis of exposure to Aedes mosquito bites among a pediatric cohort in a peri-urban setting endemic to dengue, Zika, and chikungunya viruses. We use demographic, household, and environmental variables (the flooding index (NFI), land type, and proximity to a river) in a Bayesian geostatistical model to predict areas of exposure to Aedes aegypti bites. We found that hotspots of exposure to Ae. aegypti salivary gland extract (SGE) were relatively small (< 500m and sometimes < 250m) and stable across the two-year study period. Age was negatively associated with antibody responses to Ae. aegypti SGE. Those living in agricultural settings had lower antibody responses than those living in urban settings, whereas those living near recent surface water accumulation were more likely to have higher antibody responses. Finally, we incorporated measures of larval and adult density in our geostatistical models and found that they did not show associations with antibody responses to Ae. aegypti SGE after controlling for other covariates in the model. Our results indicate that targeted house- or neighborhood-focused interventions may be appropriate for vector control in this setting. Further, demographic and environmental factors more capably predicted exposure to Ae. aegypti mosquitoes than commonly used entomological indices.

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Utility of entomological indices for predicting transmission of dengue virus: secondary analysis of data from the Camino Verde trial in Mexico and Nicaragua

Cited by 8 publications

References 43 publications

Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data

Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data

The Evaluation of the Aedes Albopictus Entomological Index Based on the Topography to Compute the Essential Requirements for Dengue Transmission Surveillance in Nakhon Si Thammarat, Thailand

Determinants of exposure to Aedes mosquitoes: a comprehensive geospatial analysis in peri-urban Cambodia

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