title: Data-driven outbreak forecasting with a simple nonlinear growth model.
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.
authors: University of Arizona, Tucson, AZ, USA. Electronic address: lega@math.arizona.edu.
authors: Joceline
authors: J
authors: Lega
authors: University of Arizona, Tucson, AZ, USA.
authors: Heidi E
authors: HE
authors: Brown
mesh_terms: D004196:Disease Outbreaks; D058872:Epidemics; D005544:Forecasting; D006801:Humans
keywords: Chikungunya virus infection; Infectious disease outbreaks; Mathematical model; Surge capacity