标题： 基于异构数据源的疫情预测模型研究：一项为期两年的新冠疫情研究 显示英文标题

标题： Investigating Forecasting Models for Pandemic Infections Using Heterogeneous Data Sources: A 2-year Study with COVID-19

摘要： 2019年12月出现的新冠疫情造成了广泛的健康、经济和社会中断。 快速的全球传播使医疗系统不堪重负，导致高感染率、住院和死亡人数增加。 为了减少传播，政府实施了诸如封锁和旅行限制等非药物干预措施。 虽然这些措施在控制传播方面有效，但也带来了显著的经济和社会挑战。 尽管世界卫生组织于2023年5月宣布新冠疫情不再构成全球卫生紧急事件，但其影响仍然存在，继续塑造公共卫生策略。 疫情期间收集的大量数据为了解疾病动态、传播和干预措施的有效性提供了宝贵的见解。 利用这些见解可以改进预测模型，提高对未来爆发的准备和应对能力，同时减轻其社会和经济影响。 本文介绍了一项关于塞浦路斯新冠疫情预测的大规模案例研究，使用了两年的数据集，该数据集整合了流行病学数据、疫苗接种记录、政策措施和天气状况。 我们分析了感染趋势，评估了预测性能，并研究了外部因素对疾病动态的影响。 获得的见解有助于改进疫情准备和应对策略。 显示英文摘要

摘要： Emerging in December 2019, the COVID-19 pandemic caused widespread health, economic, and social disruptions. Rapid global transmission overwhelmed healthcare systems, resulting in high infection rates, hospitalisations, and fatalities. To minimise the spread, governments implemented several non-pharmaceutical interventions like lockdowns and travel restrictions. While effective in controlling transmission, these measures also posed significant economic and societal challenges. Although the WHO declared COVID-19 no longer a global health emergency in May 2023, its impact persists, shaping public health strategies. The vast amount of data collected during the pandemic offers valuable insights into disease dynamics, transmission, and intervention effectiveness. Leveraging these insights can improve forecasting models, enhancing preparedness and response to future outbreaks while mitigating their social and economic impact. This paper presents a large-scale case study on COVID-19 forecasting in Cyprus, utilising a two-year dataset that integrates epidemiological data, vaccination records, policy measures, and weather conditions. We analyse infection trends, assess forecasting performance, and examine the influence of external factors on disease dynamics. The insights gained contribute to improved pandemic preparedness and response strategies.