Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pcbi.1009979
Title: Short-term and long-term epidemiological impacts of sustained vector control in various dengue endemic settings: A modelling study
Authors: Sun, Haoyang 
Perkins, Alex
Koo, Joel 
Dickens, Borame L 
Clapham, Hannah E 
Cook, Alex R 
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Mathematical & Computational Biology
Biochemistry & Molecular Biology
AEDES-AEGYPTI
POPULATION-DYNAMICS
ALBOPICTUS
VIREMIA
DISEASE
Issue Date: 1-Apr-2022
Publisher: PUBLIC LIBRARY SCIENCE
Citation: Sun, Haoyang, Perkins, Alex, Koo, Joel, Dickens, Borame L, Clapham, Hannah E, Cook, Alex R (2022-04-01). Short-term and long-term epidemiological impacts of sustained vector control in various dengue endemic settings: A modelling study. PLOS COMPUTATIONAL BIOLOGY 18 (4). ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pcbi.1009979
Abstract: As the most widespread viral infection transmitted by the Aedes mosquitoes, dengue has been estimated to cause 51 million febrile disease cases globally each year. Although sustained vector control remains key to reducing the burden of dengue, current understanding of the key factors that explain the observed variation in the short- and long-term vector control effectiveness across different transmission settings remains limited. We used a detailed individual-based model to simulate dengue transmission with and without sustained vector control over a 30-year time frame, under different transmission scenarios. Vector control effectiveness was derived for different time windows within the 30-year intervention period. We then used the extreme gradient boosting algorithm to predict the effectiveness of vector control given the simulation parameters, and the resulting machine learning model was interpreted using Shapley Additive Explanations. According to our simulation outputs, dengue transmission would be nearly eliminated during the early stage of sustained and intensive vector control, but over time incidence would gradually bounce back to the pre-intervention level unless the intervention is implemented at a very high level of intensity. The time point at which intervention ceases to be effective is strongly influenced not only by the intensity of vector control, but also by the pre-intervention transmission intensity and the individual-level heterogeneity in biting risk. Moreover, the impact of many transmission model parameters on the intervention effectiveness is shown to be modified by the intensity of vector control, as well as to vary over time. Our study has identified some of the critical drivers for the difference in the time-varying effectiveness of sustained vector control across different dengue endemic settings, and the insights obtained will be useful to inform future model-based studies that seek to predict the impact of dengue vector control in their local contexts.
Source Title: PLOS COMPUTATIONAL BIOLOGY
URI: https://scholarbank.nus.edu.sg/handle/10635/230762
ISSN: 1553734X
15537358
DOI: 10.1371/journal.pcbi.1009979
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