Electronics, Information and Biomedical Engineering Department, Politecnico di Milano, 20133 Milan, Italy.
Civil and Environmental Engineering Department, Politecnico di Milano, 20133 Milan, Italy.
Int J Environ Res Public Health. 2022 Jul 25;19(15):9012. doi: 10.3390/ijerph19159012.
The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.
COVID-19 大流行对全球医疗体系构成了前所未有的威胁。为了应对这一紧急情况，人们投入了大量努力，广泛应用了前沿技术，特别是大数据分析和人工智能。在此背景下，本研究提出了一种地理过滤和机器学习（ML）的新组合，用于开发和优化基于紧急医疗服务（EMS）数据的 COVID-19 早期预警系统，以便非常精细地（精细到单个城市）预测疫情爆发。该模型在意大利伦巴第地区实施，表现出了强大的性能，在识别疾病的活跃传播方面的总体准确率达到 80%。进一步对输出结果进行后处理，将该地区分为五个风险等级，从而有效地预测了对 EMS 干预措施的需求。该模型在未来应用中具有很高的潜力，可用于早期发现 COVID-19 或其他类似传染病对医疗体系的影响负担。
