Data-driven outbreak forecasting with a simple nonlinear growth model

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Recent events have thrown the spotlight on infectious disease outbreak response. We developed a data-driven method, EpiGro, which can be applied to cumulative case reports to estimate the order of magnitude of the duration, peak and ultimate size of an ongoing outbreak. It is based on a surprisingly simple mathematical property of many epidemiological data sets, does not require knowledge or estimation of disease transmission parameters, is robust to noise and to small data sets, and runs quickly due to its mathematical simplicity. Using data from historic and ongoing epidemics, we present the model. We also provide modeling considerations that justify this approach and discuss its limitations. In the absence of other information or in conjunction with other models, EpiGro may be useful to public health responders.

Original languageEnglish (US)
Pages (from-to)19-26
Number of pages8
JournalEpidemics
Volume17
DOIs
StatePublished - Dec 1 2016

Fingerprint

Nonlinear Dynamics
Disease Outbreaks
Growth
Noise
Public Health
Datasets

Keywords

  • Chikungunya virus infection
  • Infectious disease outbreaks
  • Mathematical model
  • Surge capacity

ASJC Scopus subject areas

  • Parasitology
  • Epidemiology
  • Microbiology
  • Public Health, Environmental and Occupational Health
  • Infectious Diseases
  • Virology

Cite this

Data-driven outbreak forecasting with a simple nonlinear growth model. / Lega, Joceline C; Brown, Heidi E.

In: Epidemics, Vol. 17, 01.12.2016, p. 19-26.

Research output: Contribution to journalArticle

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