Intervention analysis for low-count time series with applications in public health

Abstract

It is common in many fields to be interested in the evaluation of the impact of an intervention over a particular phenomenon. In the context of classical time series analysis, a possible choice might be intervention analysis, but there is no analogous methodology developed for low-count time series. In this article, we propose a modified INAR model that allows us to quantify the effect of an intervention, and is also capable of taking into account possible trends or seasonal behaviour. Several examples of application in different real and simulated contexts will also be discussed.