Epidemic forecasting based on mobility patterns: an approach and experimental evaluation on COVID-19 Data

被引：0
作者：
Maria Pia Canino
Eugenio Cesario
Andrea Vinci
Shabnam Zarin
机构：
[1] University of Calabria,
[2] ICAR-CNR,undefined
[3] Monmouth University,undefined
来源：
Social Network Analysis and Mining | 2022年 / 12卷
关键词：
COVID-19; Epidemic forecasting; Predictive models;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
During an epidemic, decision-makers in public health need accurate predictions of the future case numbers, in order to control the spread of new cases and allow efficient resource planning for hospital needs and capacities. In particular, considering that infectious diseases are spread through human-human transmissions, the analysis of spatio-temporal mobility data can play a fundamental role to enable epidemic forecasting. This paper presents the design and implementation of a predictive approach, based on spatial analysis and regressive models, to discover spatio-temporal predictive epidemic patterns from mobility and infection data. The experimental evaluation, performed on mobility and COVID-19 data collected in the city of Chicago, is aimed to assess the effectiveness of the approach in a real-world scenario. 
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