Evidence map›Paper›PMID 35284888›Full record

ArticleCurrent research in parasitology & vector-borne diseases2021

Predicting eastern equine encephalitis spread in North America: An ecological study.

Xin Tang, Luigi Sedda, Heidi E Brown

Open access · goldAbstract read
In one paragraph

Article in Current research in parasitology & vector-borne diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 33% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
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

3 authors at 2 institutions in 2 countries.

Xin TangDepartment of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.
Luigi SeddaLancaster Medical School, Lancaster University, Bailrigg Campus, Lancaster, UK.
Heidi E BrownDepartment of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.
University of Arizona · USLancaster University · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Eastern equine encephalitis (EEE) is a rare but lethal mosquito-borne zoonotic disease. Recent years have seen incursion into new areas of the USA, and in 2019 the highest number of human cases in decades. Due to the low detection rate of EEE, previous studies were unable to quantify large-scale and recent EEE ecological dynamics. We used Bayesian spatial generalized-linear mixed model to quantify the spatiotemporal dynamics of human EEE incidence in the northeastern USA. In addition, we assessed whether equine EEE incidence has predictive power for human cases, independently from other environmental variables. The predictors of the model were selected based on variable importance. Human incidence increased with temperature seasonality, but decreased with summer temperature, summer, fall, and winter precipitation. We also found EEE transmission in equines strongly associated with human infection (OR: 1.57; 95% CI: 1.52-1.60) and latitudes above 41.9°N after 2018. The study designed for sparse dataset described new and known relationships between human and animal EEE and environmental factors, including geographical directionality. Future models must include equine cases as a risk factor when predicting human EEE risks. Future work is still necessary to ascertain the establishment of EEE in northern latitudes and the robustness of the available data.

Indexed as

Bayesian generalized-linear mixed-effects modelHuman and animal Eastern equine encephalitisNortheastern USASpatial analysesWeather

Identifiers

PMID35284888
PMCPMC8906097
OpenAlexW3215148616

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.