Evidence map›Paper›PMID 32310958›Full record

ArticlePLoS computational biology2020

Optimizing the deployment of ultra-low volume and targeted indoor residual spraying for dengue outbreak response.

Sean M Cavany, Guido España, Alun L Lloyd, Lance A Waller, Uriel Kitron, Helvio Astete, William H Elson, Gonzalo M Vazquez-Prokopec, Thomas W Scott, Amy C Morrison and 2 more

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 2 pooled it
6.1field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.

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  4. Research Trends and Collaborative Patterns inInternational journal of environmental research and public health · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 6 institutions in 2 countries.

Sean M CavanyDepartment of Biological Sciences & Eck Institute of Global Health, University of Notre Dame, Notre Dame, Indiana, United States of America.ORCID 0000-0002-2559-797X
Guido EspañaDepartment of Biological Sciences & Eck Institute of Global Health, University of Notre Dame, Notre Dame, Indiana, United States of America.ORCID 0000-0002-9915-8056
Alun L LloydDepartment of Mathematics & Biomathematics Graduate Program, North Carolina State University, Raleigh, North Carolina, United States of America.ORCID 0000-0002-6389-6321
Lance A WallerDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America.ORCID 0000-0001-5002-8886
Uriel KitronDepartment of Environmental Sciences, Emory University, Atlanta, Georgia, United States of America.
Helvio AsteteU.S. Naval Medical Research Unit Six, Lima, Peru.
William H ElsonDepartment of Entomology and Nematology, University of California, Davis, Davis, California, United States of America.ORCID 0000-0002-8630-1378
Gonzalo M Vazquez-ProkopecDepartment of Environmental Sciences, Emory University, Atlanta, Georgia, United States of America.ORCID 0000-0002-0710-270X
Thomas W ScottDepartment of Entomology and Nematology, University of California, Davis, Davis, California, United States of America.ORCID 0000-0003-2947-8123
Amy C MorrisonDepartment of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, Davis, California, United States of America.ORCID 0000-0001-6224-5984
Robert C ReinerInstitute of Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0003-1056-7919
T Alex PerkinsDepartment of Biological Sciences & Eck Institute of Global Health, University of Notre Dame, Notre Dame, Indiana, United States of America.ORCID 0000-0002-7518-4014
Emory University · USUniversity of California, Davis · USUniversity of Notre Dame · USNorth Carolina State University · USUnited States Naval Medical Research Unit SOUTH · PEUniversity of Washington · US

Funding

Surveillance Core (B)P01AI098670 · NIAID · UNIVERSITY OF CALIFORNIA AT DAVIS · PI MORRISON, AMY CELESTE · 2014 to 2018
$7.9M
NIAID NIH HHS P01 AI098670
6 · The paper itself

Abstract

Recent years have seen rising incidence of dengue and large outbreaks of Zika and chikungunya, which are all caused by viruses transmitted by Aedes aegypti mosquitoes. In most settings, the primary intervention against Aedes-transmitted viruses is vector control, such as indoor, ultra-low volume (ULV) spraying. Targeted indoor residual spraying (TIRS) has the potential to more effectively impact Aedes-borne diseases, but its implementation requires careful planning and evaluation. The optimal time to deploy these interventions and their relative epidemiological effects are, however, not well understood. We used an agent-based model of dengue virus transmission calibrated to data from Iquitos, Peru to assess the epidemiological effects of these interventions under differing strategies for deploying them. Specifically, we compared strategies where spray application was initiated when incidence rose above a threshold based on incidence in recent years to strategies where spraying occurred at the same time(s) each year. In the absence of spraying, the model predicted 361,000 infections [inter-quartile range (IQR): 347,000-383,000] in the period 2000-2010. The ULV strategy with the fewest median infections was spraying twice yearly, in March and October, which led to a median of 172,000 infections [IQR: 158,000-183,000], a 52% reduction from baseline. Compared to spraying once yearly in September, the best threshold-based strategy utilizing ULV had fewer median infections (254,000 vs. 261,000), but required more spraying (351 vs. 274 days). For TIRS, the best strategy was threshold-based, which led to the fewest infections of all strategies tested (9,900; [IQR: 8,720-11,400], a 94% reduction), and required fewer days spraying than the equivalent ULV strategy (280). Although spraying twice each year is likely to avert the most infections, our results indicate that a threshold-based strategy can become an alternative to better balance the translation of spraying effort into impact, particularly if used with a residual insecticide.

Indexed as

AedesAnimalsComputational BiologyComputer SimulationDengueDisease OutbreaksHumansIncidenceInsecticidesModels, TheoreticalMosquito ControlMosquito VectorsZika Virus InfectionInsecticides

Identifiers

PMID32310958
PMCPMC7200023
OpenAlexW3016271069

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.