Statistical Modelling 20 (1) (2020), 58–70

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

David Moriña,
Barcelona Graduate School of Mathematics (BGSMath),
Departament de Matemàtiques, Universitat Autònoma de Barcelona (UAB),
Edici C Campus Bellaterra,
Barcelona,
Spain.
e-mail: david.morina@uab.cat
and

Unit of Infections and Cancer - Information and Interventions (UNIC-I&I),
Catalan Institute of Oncology (ICO)-IDIBELL,
Barcelona,
Spain.


Juan M Leyva-Moral,
Departament d'Infermeria, Facultat de Medicina,
Universitat Autònoma de Barcelona (UAB),
Edifici C Campus Bellaterra,
Barcelona,
Spain.

and

Department d'Infermeria, Facultat de Medicina,
Universitat Autonoma de Barcelona (UAB),
Grups de Recerca d’Àfrica i Amèrica Llatines (GRAAL),
Barcelona,
Spain.


Maria Feijoo-Cid,
Departament d'Infermeria, Facultat de Medicina,
Universitat Autònoma de Barcelona (UAB),
Edifici C Campus Bellaterra,
Barcelona,
Spain.

and

Department d'Infermeria, Facultat de Medicina,
Universitat Autonoma de Barcelona (UAB),
Grups de Recerca d’Àfrica i Amèrica Llatines (GRAAL),
Barcelona,
Spain.


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.

Keywords:

count data; INAR models; intervention analysis; time series.

Downloads:

Example data and code in zipped archive.
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