Burda Zdzislaw
Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland.
Entropy (Basel). 2020 Oct 30;22(11):1236. doi: 10.3390/e22111236.
We develop an agent-based model to assess the cumulative number of deaths during hypothetical Covid-19-like epidemics for various non-pharmaceutical intervention strategies. The model simulates three interrelated stochastic processes: epidemic spreading, availability of respiratory ventilators and changes in death statistics. We consider local and non-local modes of disease transmission. The first simulates transmission through social contacts in the vicinity of the place of residence while the second through social contacts in public places: schools, hospitals, airports, etc., where many people meet, who live in remote geographic locations. Epidemic spreading is modelled as a discrete-time stochastic process on random geometric networks. We use the Monte-Carlo method in the simulations. The following assumptions are made. The basic reproduction number is R0=2.5 and the infectious period lasts approximately ten days. Infections lead to severe acute respiratory syndrome in about one percent of cases, which are likely to lead to respiratory default and death, unless the patient receives an appropriate medical treatment. The healthcare system capacity is simulated by the availability of respiratory ventilators or intensive care beds. Some parameters of the model, like mortality rates or the number of respiratory ventilators per 100,000 inhabitants, are chosen to simulate the real values for the USA and Poland. In the simulations we compare 'do-nothing' strategy with mitigation strategies based on social distancing and reducing social mixing. We study epidemics in the pre-vacine era, where immunity is obtained only by infection. The model applies only to epidemics for which reinfections are rare and can be neglected. The results of the simulations show that strategies that slow the development of an epidemic too much in the early stages do not significantly reduce the overall number of deaths in the long term, but increase the duration of the epidemic. In particular, a hybrid strategy where lockdown is held for some time and is then completely released, is inefficient.
我们开发了一个基于主体的模型，以评估在各种非药物干预策略下，类似新冠疫情的假设情景中的累计死亡人数。该模型模拟了三个相互关联的随机过程：疫情传播、呼吸呼吸机的可用性以及死亡统计数据的变化。我们考虑了疾病传播的本地和非本地模式。第一种模式模拟通过居住地点附近的社交接触进行传播，而第二种模式模拟通过公共场所（如学校、医院、机场等）的社交接触进行传播，在这些场所，许多来自偏远地理位置的人相聚在一起。疫情传播被建模为随机几何网络上的离散时间随机过程。我们在模拟中使用蒙特卡罗方法。做出了以下假设。基本再生数(R_0 = 2.5)，传染期约为十天。约百分之一的感染病例会导致严重急性呼吸综合征，除非患者接受适当的治疗，否则这些病例很可能导致呼吸衰竭和死亡。医疗系统的能力通过呼吸呼吸机或重症监护病床的可用性来模拟。模型的一些参数，如死亡率或每十万居民的呼吸呼吸机数量，是为了模拟美国和波兰的实际值而选择的。在模拟中，我们将“不采取任何措施”策略与基于社交距离和减少社交接触的缓解策略进行比较。我们研究疫苗接种前时代的疫情，在这个时期，免疫力仅通过感染获得。该模型仅适用于再感染罕见且可忽略不计的疫情。模拟结果表明，在早期阶段过度减缓疫情发展的策略不会显著降低长期的总体死亡人数，反而会增加疫情的持续时间。特别是，一种先实施一段时间封锁然后完全解除的混合策略效率低下。
