Bosman M, Cordon Y, Duran-Sala M, Gabbanelli L, García-Pérez C, Jordan X, Manera M, Masjuan P, Medina A, Mir Ll M, Oròs A, Vitagliano V
Institut de Física d'Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain.
DIME, University of Genova, via all'Opera Pia 15, 16145, Genova, Italy.
Sci Rep. 2024 Dec 30;14(1):31858. doi: 10.1038/s41598-024-83238-1.
During the COVID-19 pandemic, effective public policy interventions have been crucial in combating virus transmission, sparking extensive debate on crisis management strategies and emphasizing the necessity for reliable models to inform governmental decisions, particularly at the local level. Leveraging disaggregated socio-demographic microdata, including social determinants, age-specific strata, and mobility patterns, we design a comprehensive network model of Catalonia's population and, through numerical simulation, assess its response to the outbreak of COVID-19 over the two-year period 2020-21. Our findings underscore the critical importance of timely implementation of broad non-pharmaceutical measures and effective vaccination campaigns in curbing virus spread; in addition, the identification of high-risk groups and their corresponding maps of connections within the network paves the way for tailored and more impactful interventions.
在新冠疫情期间，有效的公共政策干预对于抗击病毒传播至关重要，引发了关于危机管理策略的广泛辩论，并强调了可靠模型对于为政府决策（尤其是地方层面的决策）提供信息的必要性。利用包括社会决定因素、特定年龄层和流动模式在内的细分社会人口微观数据，我们设计了加泰罗尼亚人口的综合网络模型，并通过数值模拟评估了该模型在2020年至2021年这两年期间对新冠疫情爆发的反应。我们的研究结果强调了及时实施广泛的非药物措施和有效的疫苗接种运动在遏制病毒传播方面的至关重要性；此外，识别高风险群体及其在网络中的相应联系图为量身定制且更具影响力的干预措施铺平了道路。
