Modeling the association between Aedes aegypti ovitrap egg counts, multi-scale remotely sensed environmental data and arboviral cases at Puntarenas, Costa Rica (2017–2018)

Chaves, Luis Fernando; Cordero, José Angel Valerín; Delgado, Gabriela; Aguilar-Avendaño, Carlos; Maynes, Ezequías; Alvarado, José Manuel Gutiérrez; Rojas, Melissa Ramírez; Romero, Luis Mario; Rodríguez, Rodrigo Marín

doi:10.1016/j.crpvbd.2021.100014

Cited by 14 publications

(10 citation statements)

References 116 publications

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“…Considering this premise and the sensitivity of the ovitraps in mosquito egg sampling, this study employed two analytical approaches to investigate the impact of the control activities—particularly from Ae. aegypti [ 33 , 34 , 50 ]. Field collections using ovitraps helped verify the dynamics of the mosquito oviposition weekly following the implementation of control measures.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Assessing the effect of Aedes (Stegomyia) aegypti (Linnaeus, 1762) control based on machine learning for predicting the spatiotemporal distribution of eggs in ovitraps

Piovezan

Azevedo

Faria

et al. 2022

Dialogues in Health

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Section: Discussionmentioning

confidence: 99%

“…The use of ovitraps is an effective way to monitor Ae. aegypti in the urban area [ 50 ]. This method was previously applied and there was spatial correlation between recyclers and Ae.…”

Section: Discussionmentioning

confidence: 99%

Assessing the effect of Aedes (Stegomyia) aegypti (Linnaeus, 1762) control based on machine learning for predicting the spatiotemporal distribution of eggs in ovitraps

Piovezan

Azevedo

Faria

et al. 2022

Dialogues in Health

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“…We assembled a dataset of An. albimanus occurrences with records from collections made largely by the vector control program of the Costa Rican Ministry of Health during surveillance and control activities for vector-borne diseases [ 39 ]. Additional occurrence data were obtained from the Global Biodiversity Information Facility (GBIF— accessed on 20 February 2021) by searching the terms “ Anopheles albimanus ” for species and “Costa Rica” for country.…”

Section: Methodsmentioning

confidence: 99%

Anopheles albimanus (Diptera: Culicidae) Ensemble Distribution Modeling: Applications for Malaria Elimination

Rhodes

Loaiza

Romero

et al. 2022

Insects

Self Cite

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In the absence of entomological information, tools for predicting Anopheles spp. presence can help evaluate the entomological risk of malaria transmission. Here, we illustrate how species distribution models (SDM) could quantify potential dominant vector species presence in malaria elimination settings. We fitted a 250 m resolution ensemble SDM for Anopheles albimanus Wiedemann. The ensemble SDM included predictions based on seven different algorithms, 110 occurrence records and 70 model projections. SDM covariates included nine environmental variables that were selected based on their importance from an original set of 28 layers that included remotely and spatially interpolated locally measured variables for the land surface of Costa Rica. Goodness of fit for the ensemble SDM was very high, with a minimum AUC of 0.79. We used the resulting ensemble SDM to evaluate differences in habitat suitability (HS) between commercial plantations and surrounding landscapes, finding a higher HS in pineapple and oil palm plantations, suggestive of An. albimanus presence, than in surrounding landscapes. The ensemble SDM suggested a low HS for An. albimanus at the presumed epicenter of malaria transmission during 2018–2019 in Costa Rica, yet this vector was likely present at the two main towns also affected by the epidemic. Our results illustrate how ensemble SDMs in malaria elimination settings can provide information that could help to improve vector surveillance and control.

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“…The complexity of dengue transmission has driven many studies to assess its correlation with meteorological and ecological variables [11,12,13,14,15,16,17]. Most of these works evaluated the effects and correlation between dengue cases and climate variables [18,19] and seasonal vegetation dynamics, which may also influence the biology of the vector populations at relatively local scales [20,21,22]. Barrera et al [23] suggested that dense vegetation can promote Ae.…”

Section: Introductionmentioning

confidence: 99%

Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A Wavelet Approach

García¹,

Chou-Chen²,

Barboza³

et al. 2023

Preprint

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Throughout history, prevention and control of dengue transmission have challenged public health authorities worldwide. In the last decades, the interaction of multiple factors, such as environmental and climate variability, has influenced increments in incidence and geographical spread of the virus. In Costa Rica, a country characterized by multiple microclimates separated by short distances, dengue has been endemic since its introduction in 1993. Understanding the role of climatic and environmental factors in the seasonal and inter-annual variability of disease spread is essential to develop effective surveillance and control efforts. In this study, we conducted a wavelet time series analysis of weekly climate, local environmental variables, and dengue cases (2001-2019) from 32 cantons in Costa Rica to identify significant periods (e.g., annual, biannual) in which climate and environmental variables co-varied with dengue cases. Wavelet coherence analysis was used to characterize seasonality, multi-year outbreaks, and relative delays between the time series. Results show that dengue outbreaks occurring every 3 years in cantons located in the country's Central, North, and South Pacific regions were highly coherent with the Oceanic Niño 3.4 and the Tropical North Caribbean Index (TNA). Dengue cases were in phase with El Niño 3.4 and TNA, with El Niño 3.4 ahead of dengue cases by roughly nine months and TNA ahead by less than three months. Annual dengue outbreaks were coherent with local environmental variables (NDWI, EVI, Evapotranspiration, and Precipitation) in most cantons except those located in the Central, South Pacific, and South Caribbean regions of the country. The local environmental variables were in phase with dengue cases and were ahead by around three months.

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Modeling the association between Aedes aegypti ovitrap egg counts, multi-scale remotely sensed environmental data and arboviral cases at Puntarenas, Costa Rica (2017–2018)

Cited by 14 publications

References 116 publications

Assessing the effect of Aedes (Stegomyia) aegypti (Linnaeus, 1762) control based on machine learning for predicting the spatiotemporal distribution of eggs in ovitraps

Assessing the effect of Aedes (Stegomyia) aegypti (Linnaeus, 1762) control based on machine learning for predicting the spatiotemporal distribution of eggs in ovitraps

Anopheles albimanus (Diptera: Culicidae) Ensemble Distribution Modeling: Applications for Malaria Elimination

Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A Wavelet Approach

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