UTLDR: an agent-based framework for modeling infectious diseases and public interventions.

Autor: Rossetti G; KDD Laboratory, ISTI-CNR, Pisa, Italy., Milli L; Department of Computer Science, University of Pisa, Pisa, Italy., Citraro S; Department of Computer Science, University of Pisa, Pisa, Italy., Morini V; KDD Laboratory, ISTI-CNR, Pisa, Italy.
Jazyk: angličtina
Zdroj: Journal of intelligent information systems [J Intell Inf Syst] 2021; Vol. 57 (2), pp. 347-368. Date of Electronic Publication: 2021 Jun 17.
DOI: 10.1007/s10844-021-00649-6
Abstrakt: Due to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were designed with the aim of providing a simple proxy for the study of the potential effects of public interventions (e.g., lockdown, testing, contact tracing). In this work, we introduce UTLDR, a framework that, overcoming such limitations, allows to generate "what if" epidemic scenarios incorporating several public interventions (and their combinations). UTLDR is designed to be easy to use and capable to leverage information provided by stratified populations of agents (e.g., age, gender, geographical allocation, and mobility patterns…). Moreover, the proposed framework is generic and not tailored for a specific epidemic phenomena: it aims to provide a qualitative support to understanding the effects of restrictions, rather than produce forecasts/explanation of specific data-driven phenomena.
Competing Interests: Conflict of InterestsThe authors have no conflicts of interest to declare that are relevant to the content of this article.
(© The Author(s) 2021.)
Databáze: MEDLINE
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