The COVID-19 epidemic hit Italy particularly hard, yielding the implementation of strict national lockdown rules. Previous modelling studies at the national level overlooked the fact that Italy is divided into administrative regions which can independently oversee their own share of the Italian National Health Service. Here, we show that heterogeneity between regions is essential to understand the spread of the epidemic and to design effective strategies to control the disease. We model Italy as a network of regions and parameterize the model of each region on real data spanning over two months from the initial outbreak. We confirm the effectiveness at the regional level of the national lockdown strategy and propose coordinated regional interventions to prevent future national lockdowns, while avoiding saturation of the regional health systems and mitigating impact on costs. Our study and methodology can be easily extended to other levels of granularity to support policy- and decision-makers. An ongoing global debate concerns effective and sustainable lockdown release strategies in the current pandemic. Here, the authors implement a network model at healthcare-relevant spatial scale to show that coordinated local strategies can be effective in containing further resurgence of the disease.
【저자키워드】 viral infection, Applied mathematics, Computational models, 【초록키워드】 pandemic, lockdown, Intervention, Local, Italy, heterogeneity, Spread, Region, outbreak, implementation, Effectiveness, methodology, health system, COVID-19 epidemic, National Health Service, Support, lockdowns, National, granularity, Prevent, Italian, effective, the epidemic, initial, the disease, effective strategy, yielding, 【제목키워드】 Italy, COVID-19 epidemic, alleviate,