Evidence map›Paper›PMID 25101786›Full record

ArticlePLoS neglected tropical diseases2014

Patterns of geographic expansion of Aedes aegypti in the Peruvian Amazon.

Sarah Anne Guagliardo, José Luis Barboza, Amy C Morrison, Helvio Astete, Gonzalo Vazquez-Prokopec, Uriel Kitron

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing 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

43 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Highway paving increased dengue transmission in the Peruvian Amazon.medRxiv : the preprint server for health sciences · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Dengue and COVID-19 Co-Circulation in the Peruvian Amazon: A Population-Based Study.The American journal of tropical medicine and hygiene · 2023
    Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. A machine-learning approach to map landscape connectivity inProceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  15. Scaling up of Eco-Bio-Social Strategy to ControlInternational journal of environmental research and public health · 2021
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

6 authors.

Sarah Anne GuagliardoDepartment of Environmental Sciences, Emory University, Atlanta, Georgia, United States of America.
José Luis BarbozaUniversidad Nacional de la Amazonía Peruana, Iquitos, Peru.
Amy C MorrisonDepartment of Entomology, University of California, Davis, Davis, California, United States of America.
Helvio AsteteNaval Medical Research Unit No. 6 (NAMRU-6) Iquitos Laboratory, Iquitos, Peru.
Gonzalo Vazquez-ProkopecDepartment of Environmental Sciences, Emory University, Atlanta, Georgia, United States of America; Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.
Uriel KitronDepartment of Environmental Sciences, Emory University, Atlanta, Georgia, United States of America; Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.

Funding

Measuring Entomological Risk for DengueR01AI069341 · NIAID · UNIVERSITY OF CALIFORNIA AT DAVIS · PI SCOTT, THOMAS WALLACE · 2006 to 2010
$2.3M
Training in Population Biology of Infectious DiseasesT32AI055404 · NIAID · EMORY UNIVERSITY · PI REAL, LESLIE A. · 2004 to 2013
$2.0M
NIAID NIH HHS R01 AI069341NIAID NIH HHS R01 AI069341-01NIAID NIH HHS T32 AI055404NIAID NIH HHS T32 AI55404-10
6 · The paper itself

Abstract

BACKGROUND AND

objectivesIn the Peruvian Amazon, the dengue vector Aedes aegypti is abundant in large urban centers such as Iquitos. In recent years, it has also been found in a number of neighboring rural communities with similar climatic and socioeconomic conditions. To better understand Ae. aegypti spread, we compared characteristics of communities, houses, and containers in infested and uninfested communities.

methodsWe conducted pupal-demographic surveys and deployed ovitraps in 34 communities surrounding the city of Iquitos. Communities surveyed were located along two transects: the Amazon River and a 95 km highway. We calculated entomological indices, mapped Ae. aegypti presence, and developed univariable and multivariable logistic regression models to predict Ae. aegypti presence at the community, household, or container level.

resultsLarge communities closer to Iquitos were more likely to be infested with Ae. aegypti. Within infested communities, houses with Ae. aegypti had more passively-filled containers and were more often infested with other mosquito genera than houses without Ae. aegypti. For containers, large water tanks/drums and containers with solar exposure were more likely to be infested with Ae. aegypti. Maps of Ae. aegypti presence revealed a linear pattern of infestation along the highway, and a scattered pattern along the Amazon River. We also identified the geographical limit of Ae. aegypti expansion along the highway at 19.3 km south of Iquitos.

conclusionIn the Peruvian Amazon, Ae. aegypti geographic spread is driven by human transportation networks along rivers and highways. Our results suggest that urban development and oviposition site availability drive Ae. aegypti colonization along roads. Along rivers, boat traffic is likely to drive long-distance dispersal via unintentional transport of mosquitoes on boats.

Indexed as

AedesAnimalsDengueHumansLogistic ModelsPeruPopulation GrowthRivers

Identifiers

PMID25101786
PMCPMC4125293

What OpenQuestion holds

Textmetadata
LicenceCC0
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.