We exploit the provincial variability of COVID-19 cases registered in Italy to select the territorial predictors of the pandemic. Absent an established theoretical diffusion model, we apply machine learning to isolate, among 77 potential predictors, those that minimize the out-of-sample prediction error. We first estimate the model considering cumulative cases registered before the containment measures displayed their effects (i.e. at the peak of the epidemic in March 2020), then cases registered between the peak date and when containment measures were relaxed in early June. In the first estimate, the results highlight the dominance of factors related to the intensity and interactions of economic activities. In the second, the relevance of these variables is highly reduced, suggesting mitigation of the pandemic following the lockdown of the economy. Finally, by considering cases at onset of the “second wave”, we confirm that the territorial distribution of the epidemic is associated with economic factors.

我们利用在意大利登记的 COVID-19 病例的省份变异性来选择大流行的地域预测因子。在没有已建立的理论扩散模型的情况下，我们应用机器学习在 77 个潜在预测因子中分离出那些最小化样本外预测误差的因子。我们首先估计该模型，该模型考虑了在遏制措施发挥作用之前（即 2020 年 3 月疫情高峰期）登记的累积病例，然后是高峰日期和 6 月初遏制措施放松时登记的病例。在第一个估计中，结果突出了与经济活动的强度和相互作用相关的因素的主导地位。第二，这些变量的相关性大大降低，建议在经济封锁后缓解大流行。最后，通过考虑“第二波”爆发时的病例，我们确认疫情的地域分布与经济因素有关。