چکیده (انگلیسی):

A better characterization of the early growth dynamics of an epidemic is needed to dissectthe important drivers of disease transmission, refine existing transmission models, and improve diseaseforecasts.Materials and methods: We introduce a 2-parameter generalized-growth model to characterize theascending phase of an outbreak and capture epidemic profiles ranging from sub-exponential to exponen-tial growth. We test the model against empirical outbreak data representing a variety of viral pathogensin historic and contemporary populations, and provide simulations highlighting the importance of sub-exponential growth for forecasting purposes.Results: We applied the generalized-growth model to 20 infectious disease outbreaks representing arange of transmission routes. We uncovered epidemic profiles ranging from very slow growth (p = 0.14for the Ebola outbreak in Bomi, Liberia (2014)) to near exponential (p > 0.9 for the smallpox outbreak inKhulna (1972), and the 1918 pandemic influenza in San Francisco). The foot-and-mouth disease outbreakin Uruguay displayed a profile of slower growth while the growth pattern of the HIV/AIDS epidemic inJapan was approximately linear. The West African Ebola epidemic provided a unique opportunity toexplore how growth profiles vary by geography; analysis of the largest district-level outbreaks revealedsubstantial growth variations (mean p = 0.59, range: 0.14–0.97). The districts of Margibi in Liberia andBombali and Bo in Sierra Leone had near-exponential growth, while the districts of Bomi in Liberia andKenema in Sierra Leone displayed near constant incidences.Conclusions: Our findings reveal significant variation in epidemic growth patterns across different infec-tious disease outbreaks and highlights that sub-exponential growth is a common phenomenon, especiallyfor pathogens that are not airborne. Sub-exponential growth profiles may result from heterogeneity incontact structures or risk groups, reactive behavior changes, or the early onset of interventions strate-gies, and consideration of “deceleration parameters” may be useful to refine existing mathematicaltransmission models and improve disease forecasts.© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/).