The proposed theory defines a relative index of epidemic lethality that compares any two configurations in different observation periods, preferably one in the acute and the other in a mild epidemic phase. Raw mortality data represent the input, with no need to recognize the cause of death. Data are categorized according to the victims’ age, which must be renormalized because older people have a greater probability of developing a level of physical decay (human damage), favouring critical pathologies and co-morbidities. The probabilistic dependence of human damage on renormalized age is related to a death criterion considering a virus spread by contagion and our capacity to cure the disease. Remarkably, this is reminiscent of the Weibull theory of the strength of brittle structures containing a population of crack-like defects, in the correlation between the statistical distribution of cracks and the risk of fracture at a prescribed stress level. Age-of-death scaling laws are predicted in accordance with data collected in Italian regions and provinces during the first wave of COVID-19, taken as representative examples to validate the theory. For the prevention of spread and the management of the epidemic, the various parameters of the theory shall be informed on other existing epidemiological models.

中文翻译：

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流行强度的统计理论：在意大利 COVID-19 案例中的应用

所提出的理论定义了一种流行病致死率的相对指数，它比较了不同观察期的任何两种配置，最好是一种处于急性期，另一种处于轻度流行期。原始死亡率数据代表输入，无需识别死因。数据根据受害者的年龄进行分类，必须将其重新归一化，因为老年人更可能出现一定程度的身体衰退（人为损伤），有利于严重的病理和合并症。考虑到病毒通过传染病传播和我们治愈疾病的能力，人类损伤对重新正常化年龄的概率依赖性与死亡标准有关。值得注意的是，这让人想起包含大量裂纹状缺陷的脆性结构强度的威布尔理论，裂纹的统计分布与规定应力水平下的断裂风险之间的相关性。根据第一波 COVID-19 期间在意大利地区和省份收集的数据预测死亡年龄缩放规律，并将其作为验证该理论的代表性示例。为防止传播和管理流行病，该理论的各种参数应参考其他现有的流行病学模型。