The pandemic transition is a hallmark of current epidemiological models, predicting a continuous shift from a healthy to a pandemic state, whose critical point is driven by the parameters of the disease, e.g., its infection, recovery or mortality rates. These parameters, characterizing the disease cycle, are tuned by the biological characteristics of the pathogen, capturing its natural time-scales, often considered independent of the state of the spread itself. If, however, the disease gains a population-wide impact, its prevalence may exceed the health-care system capacity, resulting in sub-optimal treatment, and hence a potential feedback mechanism, in which the disease cycle is no longer decoupled from the state of the spread. Such dependence was demonstrated during the spread of COVID-19, for instance, where hard-hit places showed elevated mortality rates, likely due to an over-stressed health-care system. We therefore introduce an infection-reduced recovery mechanism, linking an individual’s rate of recovery to the prevalence of the disease. The outcome, we show, may have dramatic consequences on the observed patterns of spread. For instance, under rather broad conditions, the pandemic transition becomes discontinuous, exhibiting an abrupt shift from a healthy to a pandemic state. In some cases the disease reaches population-wide coverage even below the classically predicted critical transition point. We also observe a potential multi-stability and hysteresis, capturing an irreversible pandemic transition, in which overcoming the disease requires us to quench infection rates significantly below the critical threshold. These findings not only provide hints on the current difficulties to contain COVID-19, but more broadly, they set the bar for sustaining a stably functioning treatment capacity in the face of population-wide demand.

大流行的转变是当前流行病学模型的标志，它预测从健康状态到大流行状态的持续转变，其临界点由疾病的参数（例如其感染，恢复或死亡率）驱动。这些参数（表征疾病周期）由病原体的生物学特征调节，捕获其自然时标，通常被认为与传播本身的状态无关。但是，如果该疾病在整个人群中产生影响，则其流行程度可能会超出医疗保健系统的能力，从而导致治疗效果欠佳，从而形成潜在的反馈机制，从而不再使疾病周期与国家脱钩的传播。例如，在COVID-19传播期间就证明了这种依赖性，在受灾最严重的地方，死亡率可能上升，这可能是由于医疗体系压力太大。因此，我们引入了减少感染的恢复机制，将个体的恢复率与疾病的流行联系在一起。我们表明，结果可能会对观察到的传播方式产生重大影响。例如，在相当宽泛的条件下，大流行过渡变得不连续，表现出从健康状态向大流行状态的突然转变。在某些情况下，该疾病甚至在经典预测的临界转变点以下也能覆盖整个人群。我们还观察到潜在的多重稳定性和滞后性，捕获了不可逆的大流行过渡，在这种情况下，要想克服这种疾病，就需要我们将感染率大大降低到临界阈值以下。