Evidence map›Paper›PMID 29348656›Full record

ArticleScientific reports2018

Quantifying the Risk and Cost of Active Monitoring for Infectious Diseases.

Nicholas G Reich, Justin Lessler, Jay K Varma, Neil M Vora

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
2.3field-weighted citation impact, top 11% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. 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

4 authors at 3 institutions in 1 country.

Nicholas G ReichUniversity of Massachusetts-Amherst, Department of Biostatistics and Epidemiology, Amherst, MA, 01003, USA. nick@schoolph.umass.edu.ORCID http://orcid.org/0000-0003-3503-9899
Justin LesslerJohns Hopkins Bloomberg School of Public Health, Department of Epidemiology, Baltimore, MD, 21205, USA.
Jay K VarmaNew York City Department of Health and Mental Hygiene, New York, NY, 11101, USA.
Neil M VoraNew York City Department of Health and Mental Hygiene, New York, NY, 11101, USA.
Centers for Disease Control and Prevention · USJohns Hopkins University · USUniversity of Massachusetts Amherst · US

Funding

Statistical methods for real-time forecasts of infectious disease: expanding dynamic time-series and machine learning approaches for pandemic scenariosR35GM119582 · NIGMS · UNIVERSITY OF MASSACHUSETTS AMHERST · PI Nicholas G Reich · 2016 to 2026
$4.8M
NIGMS NIH HHS R35 GM119582
6 · The paper itself

Abstract

During outbreaks of deadly emerging pathogens (e.g., Ebola, MERS-CoV) and bioterror threats (e.g., smallpox), actively monitoring potentially infected individuals aims to limit disease transmission and morbidity. Guidance issued by CDC on active monitoring was a cornerstone of its response to the West Africa Ebola outbreak. There are limited data on how to balance the costs and performance of this important public health activity. We present a framework that estimates the risks and costs of specific durations of active monitoring for pathogens of significant public health concern. We analyze data from New York City's Ebola active monitoring program over a 16-month period in 2014-2016. For monitored individuals, we identified unique durations of active monitoring that minimize expected costs for those at "low (but not zero) risk" and "some or high risk": 21 and 31 days, respectively. Extending our analysis to smallpox and MERS-CoV, we found that the optimal length of active monitoring relative to the median incubation period was reduced compared to Ebola due to less variable incubation periods. Active monitoring can save lives but is expensive. Resources can be most effectively allocated by using exposure-risk categories to modify the duration or intensity of active monitoring.

Indexed as

Costs and Cost AnalysisEpidemiological MonitoringCommunicable DiseasesHumansInfectious Disease Incubation PeriodModels, TheoreticalPopulation SurveillanceRisk

Identifiers

PMID29348656
PMCPMC5773605
OpenAlexW2950776296

What OpenQuestion holds

Textmetadata
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.