Evidence map›Paper›PMID 29194440›Full record

ArticlePloS one2017

Attacking the mosquito on multiple fronts: Insights from the Vector Control Optimization Model (VCOM) for malaria elimination.

Samson S Kiware, Nakul Chitnis, Allison Tatarsky, Sean Wu, Héctor Manuel Sánchez Castellanos, Roly Gosling, David Smith, John M Marshall

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Mosquito feeding behavior and how it influences residual malaria transmission across Africa.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Pooled it
  2. EliminatingInsects · 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

8 authors.

Samson S KiwareEnvironmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania.ORCID http://orcid.org/0000-0001-7252-520X
Nakul ChitnisDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.
Allison TatarskyMalaria Elimination Initiative, Global Health Group, University of California San Francisco, San Francisco, California, United States of America.
Sean WuDivisions of Biostatistics and Epidemiology, University of California, Berkeley, California, United States of America.
Héctor Manuel Sánchez CastellanosDivisions of Biostatistics and Epidemiology, University of California, Berkeley, California, United States of America.
Roly GoslingMalaria Elimination Initiative, Global Health Group, University of California San Francisco, San Francisco, California, United States of America.
David SmithDepartment of Global Health, University of Washington, Seattle, United States of America.
John M MarshallDivisions of Biostatistics and Epidemiology, University of California, Berkeley, California, United States of America.

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundDespite great achievements by insecticide-treated nets (ITNs) and indoor residual spraying (IRS) in reducing malaria transmission, it is unlikely these tools will be sufficient to eliminate malaria transmission on their own in many settings today. Fortunately, field experiments indicate that there are many promising vector control interventions that can be used to complement ITNs and/or IRS by targeting a wide range of biological and environmental mosquito resources. The majority of these experiments were performed to test a single vector control intervention in isolation; however, there is growing evidence and consensus that effective vector control with the goal of malaria elimination will require a combination of interventions. METHOD AND

findingsWe have developed a model of mosquito population dynamic to describe the mosquito life and feeding cycles and to optimize the impact of vector control intervention combinations at suppressing mosquito populations. The model simulations were performed for the main three malaria vectors in sub-Saharan Africa, Anopheles gambiae s.s, An. arabiensis and An. funestus. We considered areas having low, moderate and high malaria transmission, corresponding to entomological inoculation rates of 10, 50 and 100 infective bites per person per year, respectively. In all settings, we considered baseline ITN coverage of 50% or 80% in addition to a range of other vector control tools to interrupt malaria transmission. The model was used to sweep through parameters space to select the best optimal intervention packages. Sample model simulations indicate that, starting with ITNs at a coverage of 50% (An. gambiae s.s. and An. funestus) or 80% (An. arabiensis) and adding interventions that do not require human participation (e.g. larviciding at 80% coverage, endectocide treated cattle at 50% coverage and attractive toxic sugar baits at 50% coverage) may be sufficient to suppress all the three species to an extent required to achieve local malaria elimination.

conclusionThe Vector Control Optimization Model (VCOM) is a computational tool to predict the impact of combined vector control interventions at the mosquito population level in a range of eco-epidemiological settings. The model predicts specific combinations of vector control tools to achieve local malaria elimination in a range of eco-epidemiological settings and can assist researchers and program decision-makers on the design of experimental or operational research to test vector control interventions. A corresponding graphical user interface is available for national malaria control programs and other end users.

Indexed as

AnimalsAnophelesEcosystemMalariaMosquito ControlMosquito VectorsSexual Behavior, Animal

Identifiers

PMID29194440
PMCPMC5711017

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.