Evidence map›Paper›PMID 31056044›Full record

ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2019

Assessing the potential impact of vector-borne disease transmission following heavy rainfall events: a mathematical framework.

G Chowell, K Mizumoto, J M Banda, S Poccia, C Perrings

Abstract read
In one paragraph

Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Spatial and Temporal Variability of Visceral Leishmaniasis in Colombia, 2007 to 2018.The American journal of tropical medicine and hygiene · 2021
    Article
  11. Social Media Mining Toolkit (SMMT).Genomics & informatics · 2020
    Article
  12. Review
  13. Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
    Article
  14. Review
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.

G Chowell1 Department of Population Health Sciences, School of Public Health, Georgia State University , Atlanta, GA 30303 , USA.
K Mizumoto1 Department of Population Health Sciences, School of Public Health, Georgia State University , Atlanta, GA 30303 , USA.
J M Banda2 Computer Science Department, Georgia State University , Atlanta, GA 30303 , USA.
S Poccia3 Computer Science Department, University of Turin , 10124 Turin, Italy.
C Perrings4 School of Life Sciences, Arizona State University , Tempe, AZ 85281 , USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting the impact of natural disasters such as hurricanes on the transmission dynamics of infectious diseases poses significant challenges. In this paper, we put forward a simple modelling framework to investigate the impact of heavy rainfall events (HREs) on mosquito-borne disease transmission in temperate areas of the world such as the southern coastal areas of the USA. In particular, we explore the impact of the timing of HREs relative to the transmission season via analyses that test the sensitivity of HRE-induced epidemics to variation in the effects of rainfall on the dynamics of mosquito breeding capacity, and the intensity and temporal profile of human population displacement patterns. The recent Hurricane Harvey in Texas motivates the simulations reported. Overall, we find that the impact of vector-borne disease transmission is likely to be greater the earlier the HREs occur in the transmission season. Simulations based on data for Hurricane Harvey suggest that the limited impact it had on vector-borne disease transmission was in part because of when it occurred (late August) relative to the local transmission season, and in part because of the mitigating effect of the displacement of people. We also highlight key data gaps related to models of vector-borne disease transmission in the context of natural disasters. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Indexed as

RainAnimalsClimateClimate ChangeCulicidaeCyclonic StormsFemaleHumansMaleModels, TheoreticalSeasonsTexasVector Borne Diseasesclimate changeheavy rainfall eventmathematical modelmosquito-borne diseasetransmission dynamicsvector-borne disease

Identifiers

PMID31056044
PMCPMC6553605

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.