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

Smirnova, Alexandra; Chowell, Gerardo

Developing tools for stable parameter estimation and reliable forecasting of emerging and re-emerging infectious disease epidemics represents a key priority for public health officials and government agencies in their work to prevent and mitigate disease threats. At the early stage of an outbreak, when incidence data are limited and subject to reporting delays, it is often premature to characterize the transition rates of individuals between various disease epidemiological compartments using, for instance, SEIR-type systems of differential equations, which may involve a substantial number of unknown parameters. For the early transmission period, phenomenological models of a logistic type, describing the progression of the epidemic in terms of the cumulative number of reported cases, C, provide a simple alternative. In what follows, we employ the generalized Richards model Smirnova et al., 2017; Turner, Bradley, Kirk, & Pruitt, 1976) dC dt ¼ rC p 1 À C K a ! ; C K ðtÞ ¼ p a þ p 1 a ; t2ð0; TÞ;