A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm

Bakushinsky, 1993, Iterative methods for nonlinear operator equations without regularity. New approach, Dokl. Russian Academy of Sciences, 330, 282 Bakushinsky, 2005 Banks, 2009, An inverse problem statistical methodology summary, 249 Cavallini, 1993, Fitting a logistic curve to data, College Mathematics Journal, 24, 247, 10.1080/07468342.1993.11973540 Chowell, 2006, Transmission dynamics of the great influenza pandemic of 1918 in Geneva, Switzerland: Assessing the effects of hypothetical interventions, Journal of Theoretical Biology, 241, 193, 10.1016/j.jtbi.2005.11.026 Chowell, 2016, Using phenomenological models to characterize transmissibility and forecast patterns and final burden of Zika epidemics, PLoS Current Outbreaks, 10.1371/currents.outbreaks.f14b2217c902f453d9320a43a35b9583 Chowell, 2015, The Western Africa ebola virus disease epidemic exhibits both global exponential and local polynomial growth rates, PLoS Current Outbreaks, 7 Nocedal, 2000 Smirnova, 2017, Improving epidemic size prediction through stable reconstruction of disease parameters by reduced iteratively regularized gauss-newton algorithm, Journal of Inverse and Ill-Posed Problems, 10.1515/jiip-2016-0053 Szendroi, 2004, Polynomial epidemics and clustering in contact networks, Proceedings of the Royal Society B: Biological Sciences, 271, S364 Tikhonov, 1995 Tikhonov, 1998 Turner, 1976, A theory of growth, Mathematical Biosciences, 29, 367, 10.1016/0025-5564(76)90112-7 Viboud, 2016, A generalized-growth model to characterize the early ascending phase of infectious disease outbreaks, Epidemics, 15, 27, 10.1016/j.epidem.2016.01.002 World Health Organization, 2016 Yagola, 2010