Evidence map›Paper›PMID 27744095›Full record

ArticleEpidemics2016

Estimating infectious disease transmission distances using the overall distribution of cases.

Henrik Salje, Derek A T Cummings, Justin Lessler

Abstract read
In one paragraph

Article in Epidemics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Spatial scale of tuberculosis transmission in Lima, Peru.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. 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.

Henrik SaljeDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA; Mathematical Modeling of Infectious Diseases Unit, Institut Pasteur, Paris, France; CNRS, URA3012, Paris 75015, France; Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris 75015, France. Electronic address: hsalje@jhu.edu.
Derek A T CummingsDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA; Mathematical Modeling of Infectious Diseases Unit, Institut Pasteur, Paris, France; Department of Biology, University of Florida, Gainesville, FL, USA; Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
Justin LesslerDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA; Department of Biology, University of Florida, Gainesville, FL, USA.

Funding

Methods for Reducing Spatial Uncertainty and Bias in Disease SurveillanceR01AI102939 · NIAID · JOHNS HOPKINS UNIVERSITY · PI LESSLER, JUSTIN · 2013 to 2017
$4.6M
NIAID NIH HHS R01 AI102939
6 · The paper itself

Abstract

The average spatial distance between transmission-linked cases is a fundamental property of infectious disease dispersal. However, the distance between a case and their infector is rarely measurable. Contact-tracing investigations are resource intensive or even impossible, particularly when only a subset of cases are detected. Here, we developed an approach that uses onset dates, the generation time distribution and location information to estimate the mean transmission distance. We tested our method using outbreak simulations. We then applied it to the 2001 foot-and-mouth outbreak in Cumbria, UK, and compared our results to contact-tracing activities. In simulations with a true mean distance of 106m, the average mean distance estimated was 109m when cases were fully observed (95% range of 71-142). Estimates remained consistent with the true mean distance when only five percent of cases were observed, (average estimate of 128m, 95% range 87-165). Estimates were robust to spatial heterogeneity in the underlying population. We estimated that both the mean and the standard deviation of the transmission distance during the 2001 foot-and-mouth outbreak was 8.9km (95% CI: 8.4km-9.7km). Contact-tracing activities found similar values of 6.3km (5.2km-7.4km) and 11.2km (9.5km-12.8km), respectively. We were also able to capture the drop in mean transmission distance over the course of the outbreak. Our approach is applicable across diseases, robust to under-reporting and can inform interventions and surveillance.

Indexed as

Disease OutbreaksAnimalsContact TracingDisease Transmission, InfectiousFoot-and-Mouth DiseaseHumansUnited KingdomEpidemiologyFoot-and-mouth diseaseInfectious diseaseOutbreaksTransmission distance

Identifiers

PMID27744095
PMCPMC5159225

What OpenQuestion holds

Textmetadata
LicenceTDM
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.