Short Report: Adult Aedes abundance and risk of dengue transmission

Ong, Janet; Aik, Joel; Ng, Lee Ching

doi:10.1371/journal.pntd.0009475

Cited by 19 publications

(23 citation statements)

References 23 publications

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“…When using the percentage of time R t,EpiFilter > 1.0 as the dependent variable, the landed homes were associated with an increased percentage while the vegetation with structure dominated by human management (with tree canopy) was associated with a reduction in percentage (Table 4: β = -0.61%, 95% CI: -1.10%--0.11%). 1 Lags refer to the daily lagged covariate 2 Daily number of reported dengue cases 3 Daily number of reported sporadic dengue cases (i.e. Isolated cases that have no epidemiological link) 4 Daily mean absolute humidity 5 Daily mean temperature 6 Daily mean maximum temperature 7 Daily mean minimum temperature 8 Daily mean rainfall � denotes statistical significance at the 95% level https://doi.org/10.1371/journal.pcbi.1009791.t003…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…Next, several studies have shown that Ae. aegypti abundance is associated with increased transmission [2,45,[53][54], however, due to data availability, we were unable to account for the effects of Ae. aegypti population on dengue transmissibility in our study.…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…Dengue is an arboviral disease that imposes significant health and economic burdens across the globe [1]. It is transmitted by Aedes mosquitoes, primarily the highly urban-adapted vector Aedes aegypti [2]. Increased levels of urbanization, human population density and climate change have led to an expanded geographical coverage of the primary vector Ae.…”

Section: Introductionmentioning

confidence: 99%

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Fine-scale estimation of effective reproduction numbers for dengue surveillance

et al. 2022

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The effective reproduction number Rt is an epidemiological quantity that provides an instantaneous measure of transmission potential of an infectious disease. While dengue is an increasingly important vector-borne disease, few have used Rt as a measure to inform public health operations and policy for dengue. This study demonstrates the utility of Rt for real time dengue surveillance. Using nationally representative, geo-located dengue case data from Singapore over 2010–2020, we estimated Rt by modifying methods from Bayesian (EpiEstim) and filtering (EpiFilter) approaches, at both the national and local levels. We conducted model assessment of Rt from each proposed method and determined exogenous temporal and spatial drivers for Rt in relation to a wide range of environmental and anthropogenic factors. At the national level, both methods achieved satisfactory model performance (R2EpiEstim = 0.95, R2EpiFilter = 0.97), but disparities in performance were large at finer spatial scales when case counts are low (MASE EpiEstim = 1.23, MASEEpiFilter = 0.59). Impervious surfaces and vegetation with structure dominated by human management (without tree canopy) were positively associated with increased transmission intensity. Vegetation with structure dominated by human management (with tree canopy), on the other hand, was associated with lower dengue transmission intensity. We showed that dengue outbreaks were preceded by sustained periods of high transmissibility, demonstrating the potential of Rt as a dengue surveillance tool for detecting large rises in dengue cases. Real time estimation of Rt at the fine scale can assist public health agencies in identifying high transmission risk areas and facilitating localised outbreak preparedness and response.

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Section: Plos Computational Biologymentioning

confidence: 99%

Section: Plos Computational Biologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fine-scale estimation of effective reproduction numbers for dengue surveillance

et al. 2022

Self Cite

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“…However, dengue transmission is affected by a complex interplay of human, climate, mosquito, and virus. Data related to immune population status [ 47 ], population movement [ 48 ], mosquito population [ 49 , 50 ], and cycle of dengue serotypes (DENV 1–4) [ 47 ] should be further collected and used in the deep learning model to improve the prediction accuracy.…”

Section: Discussionmentioning

confidence: 99%

Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling

Gurgel

et al. 2022

Biology

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Timely and accurate forecasts of dengue cases are of great importance for guiding disease prevention strategies, but still face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation capability due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without the application of historical case information. Geospatial big data, cloud computing platforms (e.g., Google Earth Engine, GEE), and emerging deep learning algorithms (e.g., long short term memory, LSTM) provide new opportunities for advancing these efforts. Here, we focused on the dengue epidemics in the urban agglomeration of the Federal District of Brazil (FDB) during 2007–2019. A new framework was proposed using geospatial big data analysis in the Google Earth Engine (GEE) platform and long short term memory (LSTM) modeling for dengue case forecasts over an epidemiological week basis. We first defined a buffer zone around an impervious area as the main area of dengue transmission by considering the impervious area as a human-dominated area and used the maximum distance of the flight range of Aedes aegypti and Aedes albopictus as a buffer distance. Those zones were used as units for further attribution analyses of dengue epidemics by aggregating the pixel values into the zones. The near weekly composite of potential driving factors was generated in GEE using the epidemiological weeks during 2007–2019, from the relevant geospatial data with daily or sub-daily temporal resolution. A multi-step-ahead LSTM model was used, and the time-differenced natural log-transformed dengue cases were used as outcomes. Two modeling scenarios (with and without historical dengue cases) were set to examine the potential of historical information on dengue forecasts. The results indicate that the performance was better when historical dengue cases were used and the 5-weeks-ahead forecast had the best performance, and the peak of a large outbreak in 2019 was accurately forecasted. The proposed framework in this study suggests the potential of the GEE platform, the LSTM algorithm, as well as historical information for dengue risk forecasting, which can easily be extensively applied to other regions or globally for timely and practical dengue forecasts.

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“…The presence of numerous vector species in tourist areas in Botswana, including the invasive Ae. aegypti, a major contributor of dengue infections in the tropics and subtropics globally [71], highlights the need for rigorous mosquito vector surveillance in these areas. Therefore, the use of different trapping techniques in time and space may be essential in exploring a full spectrum of mosquito species diversity across varying seasonal and environmental niches [72].…”

Section: Discussionmentioning

confidence: 99%

Surveillance Studies Reveal Diverse and Potentially Pathogenic-Incriminated Vector Mosquito Species across Major Botswana Touristic Hotspots

Buxton

Nyamukondiwa

Wasserman

et al. 2021

Insects

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Vector mosquitoes contribute significantly to the global burden of diseases in humans, livestock and wildlife. As such, the spatial distribution and abundance of mosquito species and their surveillance cannot be ignored. Here, we surveyed mosquito species across major tourism hotspots in semi-arid Botswana, including, for the first time, the Central Kalahari Game Reserve. Our results reported several mosquito species across seven genera, belonging to Aedes, Anopheles, Culex, Mansonia, Mimomyia, Coquillettidia and Uranotaenia. These results document a significant species inventory that may inform early warning vector-borne disease control systems and likely help manage the risk of emerging and re-emerging mosquito-borne infections.

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Short Report: Adult Aedes abundance and risk of dengue transmission

Cited by 19 publications

References 23 publications

Fine-scale estimation of effective reproduction numbers for dengue surveillance

Fine-scale estimation of effective reproduction numbers for dengue surveillance

Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling

Surveillance Studies Reveal Diverse and Potentially Pathogenic-Incriminated Vector Mosquito Species across Major Botswana Touristic Hotspots

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