Evidence map›Paper›PMID 31600194›Full record

ArticlePLoS computational biology2019

Consensus and uncertainty in the geographic range of Aedes aegypti and Aedes albopictus in the contiguous United States: Multi-model assessment and synthesis.

Andrew J Monaghan, Rebecca J Eisen, Lars Eisen, Janet McAllister, Harry M Savage, John-Paul Mutebi, Michael A Johansson

Abstract read
In one paragraph

Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

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

  1. Pooled it
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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

7 authors.

Andrew J MonaghanResearch Computing, University of Colorado Boulder, Boulder, Colorado, United States of America.
Rebecca J EisenDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado, United States of America.
Lars EisenDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado, United States of America.
Janet McAllisterDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado, United States of America.
Harry M SavageDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado, United States of America.
John-Paul MutebiDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado, United States of America.
Michael A JohanssonDivision of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, United States of America.ORCID 0000-0002-5090-7722

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aedes (Stegomyia) aegypti (L.) and Ae. (Stegomyia) albopictus (Skuse) mosquitoes can transmit dengue, chikungunya, yellow fever, and Zika viruses. Limited surveillance has led to uncertainty regarding the geographic ranges of these vectors globally, and particularly in regions at the present-day margins of habitat suitability such as the contiguous United States. Empirical habitat suitability models based on environmental conditions can augment surveillance gaps to describe the estimated potential species ranges, but model accuracy is unclear. We identified previously published regional and global habitat suitability models for Ae. aegypti (n = 6) and Ae. albopictus (n = 8) for which adequate information was available to reproduce the models for the contiguous U.S. Using a training subset of recently updated county-level surveillance records of Ae. aegypti and Ae. albopictus and records of counties conducting surveillance, we constructed accuracy-weighted, probabilistic ensemble models from these base models. To assess accuracy and uncertainty we compared individual and ensemble model predictions of species presence or absence to both training and testing data. The ensemble models were among the most accurate and also provided calibrated probabilities of presence for each species. The quantitative probabilistic framework enabled identification of areas with high uncertainty and model bias across the U.S. where improved models or additional data could be most beneficial. The results may be of immediate utility for counties considering surveillance and control programs for Ae. aegypti and Ae. albopictus. Moreover, the assessment framework can drive future efforts to provide validated quantitative estimates to support these programs at local, national, and international scales.

Indexed as

AedesAnimalsArbovirus InfectionsConsensusDemographyModels, StatisticalMosquito VectorsUncertaintyUnited States

Identifiers

PMID31600194
PMCPMC6786520

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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.