Evidence map›Paper›PMID 28584173›Full record

ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2017

Spatial, seasonal and climatic predictive models of Rift Valley fever disease across Africa.

David W Redding, Sonia Tiedt, Gianni Lo Iacono, Bernard Bett, Kate E Jones

Open access · bronzeAbstract read
In one paragraph

Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 3 pooled it
4.2field-weighted citation impact, top 5% 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

48 citing papers in PubMed, 3 syntheses or guidelines pooled it, 74 citations in OpenAlex.

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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

5 authors at 4 institutions in 2 countries.

David W ReddingCentre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, UK dwredding@gmail.com.ORCID http://orcid.org/0000-0001-8615-1798
Sonia TiedtCentre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, UK.ORCID http://orcid.org/0000-0002-1967-6436
Gianni Lo IaconoDepartment of Veterinary Medicine, Disease Dynamics Unit, University of Cambridge, Madingley Road, Cambridge CB3 0ES, UK.ORCID http://orcid.org/0000-0002-6150-2843
Bernard BettInternational Livestock Research Institute, PO Box 30709-00100, Nairobi, Kenya.ORCID http://orcid.org/0000-0001-9376-2941
Kate E JonesCentre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, UK kate.e.jones@ucl.ac.uk.ORCID http://orcid.org/0000-0001-5231-3293
University College London · GBInternational Livestock Research Institute · KEUniversity of Cambridge · GBZoological Society of London · GB

Funding

Medical Research Council MR/R02491X/1
6 · The paper itself

Abstract

Understanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Niño year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make space- and time-sensitive predictions to better direct future surveillance resources.This article is part of the themed issue 'One Health for a changing world: zoonoses, ecosystems and human well-being'.

Indexed as

AfricaAnimalsBayes TheoremDisease OutbreaksKenyaLivestockRift Valley FeverRiskSeasonsSouth AfricaZoonosesAfricaBayesian spatial modelclimatic oscillationsIntegrated Laplace ApproximationsRift Valley feverrisk map

Identifiers

PMID28584173
PMCPMC5468690
OpenAlexW2621550954

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.