While the pathological mechanisms in COVID-19 illness are still poorly understood, it is increasingly clear that high levels of pro-inflammatory mediators play a major role in clinical deterioration in patients with severe disease. Current evidence points to a hyperinflammatory state as the driver of respiratory compromise in severe COVID-19 disease, with a clinical trajectory resembling acute respiratory distress syndrome, but how this ‘runaway train’ inflammatory response emerges and is maintained is not known. Here, we present the first mathematical model of lung hyperinflammation due to SARS-CoV-2 infection. This model is based on a network of purported mechanistic and physiological pathways linking together five distinct biochemical species involved in the inflammatory response. Simulations of our model give rise to distinct qualitative classes of COVID-19 patients: (i) individuals who naturally clear the virus, (ii) asymptomatic carriers and (iii–v) individuals who develop a case of mild, moderate, or severe illness. These findings, supported by a comprehensive sensitivity analysis, point to potential therapeutic interventions to prevent the emergence of hyperinflammation. Specifically, we suggest that early intervention with a locally acting anti-inflammatory agent (such as inhaled corticosteroids) may effectively blockade the pathological hyperinflammatory reaction as it emerges.

尽管人们对 COVID-19 疾病的病理机制仍知之甚少，但越来越清楚的是，高水平的促炎介质在重症患者临床恶化中发挥着重要作用。目前的证据表明，高炎症状态是严重 COVID-19 疾病中呼吸系统损害的驱动因素，其临床轨迹类似于急性呼吸窘迫综合征，但这种“失控列车”炎症反应是如何出现和维持的尚不清楚。在这里，我们提出了第一个由 SARS-CoV-2 感染引起的肺部过度炎症的数学模型。该模型基于所谓的机械和生理途径网络，将参与炎症反应的五种不同的生化物质连接在一起。我们的模型的模拟产生了不同的 COVID-19 患者定性类别：(i) 自然清除病毒的个体，(ii) 无症状携带者和 (iii-v) 出现轻度、中度或重度疾病病例的个体。这些发现得到全面敏感性分析的支持，指出了预防过度炎症出现的潜在治疗干预措施。具体来说，我们建议早期使用局部抗炎剂（例如吸入皮质类固醇）进行干预可以有效阻止病理性高炎症反应的出现。