TY - JOUR
T1 - Data-driven outbreak forecasting with a simple nonlinear growth model
AU - Lega, Joceline
AU - Brown, Heidi E.
N1 - Funding Information:
We are grateful to Luigi Sedda for useful feedback on a previous version of this manuscript. Research reported in this publication was supported in part by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number K01AI101224 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© 2016 The Authors
PY - 2016/12/1
Y1 - 2016/12/1
N2 - 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.
AB - 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.
KW - Chikungunya virus infection
KW - Infectious disease outbreaks
KW - Mathematical model
KW - Surge capacity
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U2 - 10.1016/j.epidem.2016.10.002
DO - 10.1016/j.epidem.2016.10.002
M3 - Article
C2 - 27770752
AN - SCOPUS:84992504861
VL - 17
SP - 19
EP - 26
JO - Epidemics
JF - Epidemics
SN - 1755-4365
ER -