the idea model: a single equation approach to the ebola forecasting challenge

Clicks: 74
ID: 218165
2018
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #69 of 69 articles by views in Communications biology

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Mathematical modeling is increasingly accepted as a tool that can inform disease control policy in the face of emerging infectious diseases, such as the 2014–2015 West African Ebola epidemic, but little is known about the relative performance of alternate forecasting approaches. The RAPIDD Ebola Forecasting Challenge (REFC) tested the ability of eight mathematical models to generate useful forecasts in the face of simulated Ebola outbreaks. We used a simple, phenomenological single-equation model (the “IDEA” model), which relies only on case counts, in the REFC. Model fits were performed using a maximum likelihood approach. We found that the model performed reasonably well relative to other more complex approaches, with performance metrics ranked on average 4th or 5th among participating models. IDEA appeared better suited to long- than short-term forecasts, and could be fit using nothing but reported case counts. Several limitations were identified, including difficulty in identifying epidemic peak (even retrospectively), unrealistically precise confidence intervals, and difficulty interpolating daily case counts when using a model scaled to epidemic generation time. More realistic confidence intervals were generated when case counts were assumed to follow a negative binomial, rather than Poisson, distribution. Nonetheless, IDEA represents a simple phenomenological model, easily implemented in widely available software packages that could be used by frontline public health personnel to generate forecasts with accuracy that approximates that which is achieved using more complex methodologies. Keywords: Ebola virus disease, Mathematical modeling, Forecasting
Reference Key
tuite2018epidemicsthe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Ashleigh R. Tuite;David N. Fisman
Journal Communications biology
Year 2018
DOI
DOI not found
URL
http://www.sciencedirect.com/science/article/pii/S1755436516300305
Keywords
Infectious and parasitic diseases

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.