Evidence map›Paper›PMID 31404077›Full record

ArticlePLoS neglected tropical diseases2019

Hantavirus host assemblages and human disease in the Atlantic Forest.

Renata L Muylaert, Ricardo Siqueira Bovendorp, Gilberto Sabino-Santos, Paula R Prist, Geruza Leal Melo, Camila de Fátima Priante, David A Wilkinson, Milton Cezar Ribeiro, David T S Hayman

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–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

13 citing papers in PubMed.

  1. Review
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  10. Land-use change and rodent-borne diseases: hazards on the shared socioeconomic pathways.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021
    Article
  11. Article
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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

9 authors.

Renata L MuylaertDepartamento de Ecologia, Universidade Estadual Paulista (UNESP), Rio Claro, Brazil.ORCID 0000-0002-6466-6210
Ricardo Siqueira BovendorpDepartamento de Ecologia, Universidade Estadual Paulista (UNESP), Rio Claro, Brazil.
Gilberto Sabino-SantosCenter for Virology Research, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil.
Paula R PristInstituto de Biociências, Departamento de Ecologia, Universidade de São Paulo (USP), São Paulo, SP, Brazil.
Geruza Leal MeloPrograma de Pós-Graduação em Biodiversidade Animal, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil.
Camila de Fátima PrianteDepartamento de Ecologia, Universidade Estadual Paulista (UNESP), Rio Claro, Brazil.
David A WilkinsonMolecular Epidemiology and Public Health Laboratory, Infectious Disease Research Centre, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand.
Milton Cezar RibeiroDepartamento de Ecologia, Universidade Estadual Paulista (UNESP), Rio Claro, Brazil.
David T S HaymanMolecular Epidemiology and Public Health Laboratory, Infectious Disease Research Centre, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several viruses from the genus Orthohantavirus are known to cause lethal disease in humans. Sigmodontinae rodents are the main hosts responsible for hantavirus transmission in the tropical forests, savannas, and wetlands of South America. These rodents can shed different hantaviruses, such as the lethal and emerging Araraquara orthohantavirus. Factors that drive variation in host populations may influence hantavirus transmission dynamics within and between populations. Landscape structure, and particularly areas with a predominance of agricultural land and forest remnants, is expected to influence the proportion of hantavirus rodent hosts in the Atlantic Forest rodent community. Here, we tested this using 283 Atlantic Forest rodent capture records and geographically weighted models that allow us to test if predictors vary spatially. We also assessed the correspondence between proportions of hantavirus hosts in rodent communities and a human vulnerability to hantavirus infection index across the entire Atlantic Forest biome. We found that hantavirus host proportions were more positively influenced by landscape diversity than by a particular habitat or agricultural matrix type. Local small mammal diversity also positively influenced known pathogenic hantavirus host proportions, indicating that a plasticity to habitat quality may be more important for these hosts than competition with native forest dwelling species. We found a consistent positive effect of sugarcane and tree plantation on the proportion of rodent hosts, whereas defaunation intensity did not correlate with the proportion of hosts of potentially pathogenic hantavirus genotypes in the community, indicating that non-defaunated areas can also be hotspots for hantavirus disease outbreaks. The spatial match between host hotspots and human disease vulnerability was 17%, while coldspots matched 20%. Overall, we discovered strong spatial and land use change influences on hantavirus hosts at the landscape level across the Atlantic Forest. Our findings suggest disease surveillance must be reinforced in the southern and southeastern regions of the biome where the highest predicted hantavirus host proportion and levels of vulnerability spatially match. Importantly, our analyses suggest there may be more complex rodent community dynamics and interactions with human disease than currently hypothesized.

Indexed as

BiodiversityForestsAgricultureAnimalsDisease ReservoirsDisease Transmission, InfectiousEcosystemHantavirus InfectionsHumansOrthohantavirusRodentiaSouth AmericaSpatial Analysis

Identifiers

PMID31404077
PMCPMC6748440

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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.