Abstract
A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real‐time monitoring and short‐term forecasting of the main epidemiological indicators within the first outbreak of COVID‐19 in Italy. Accurate short‐term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided.
Highlights
Has been the first European country to be severely hit by the first epidemic wave due to the spread of the SARS-CoV-2 virus
COVID-19 syndrome emerged in northern Italy in February 2020, with a basic reproduction number R0 between 2.5 and 4.1 In its most severe form, COVID-19 has two challenging characteristics:[2] it is highly infectious and, despite having a benign course in the vast majority of patients, it requires hospital admission and even intensive care for about
They can be referred to different time periods; in particular, in the Civil Protection Department (CPD) dataset, daily incidence counts are available for the following indicators:
Summary
Has been the first European country to be severely hit by the first epidemic wave due to the spread of the SARS-CoV-2 virus. We have preferred to follow an alternative approach, which involved direct modeling of the observed counts.[12] This encompasses the use of phenomenological models without detailed mechanistic foundations, but which have the advantage of allowing simple calibrations to the empirical reported data Such approaches are suitable when substantial uncertainty tarnishes the epidemiology of an infectious disease, including the potential contribution of multiple transmission pathways. We further propose different ways of including the effect of exogenous information on the response function of counts, in an extended generalized linear model framework These models have been implemented during the outbreak with the aim of modeling the medium to long term evolution of the epidemic wave. The methods discussed in this article have been implemented in a Shiny app, publicly available at https:// statgroup19.shinyapps.io/StatGroup19-Eng/
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