Nesteruk Igor
Institute of Hydromechanics, National Academy of Sciences of Ukraine, Kyiv, Ukraine.
Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine.
Infect Dis Model. 2023 Jul 8;8(3):806-821. doi: 10.1016/j.idm.2023.06.003. eCollection 2023 Sep.
The challenges humanity is facing due to the Covid-19 pandemic require timely and accurate forecasting of the dynamics of various epidemics to minimize the negative consequences for public health and the economy. One can use a variety of well-known and new mathematical models, taking into account a huge number of factors. However, complex models contain a large number of unknown parameters, the values of which must be determined using a limited number of observations, e.g., the daily datasets for the accumulated number of cases. Successful experience in modeling the COVID-19 pandemic has shown that it is possible to apply the simplest SIR model, which contains 4 unknown parameters. Application of the original algorithm of the model parameter identification for the first waves of the COVID-19 pandemic in China, South Korea, Austria, Italy, Germany, France, Spain has shown its high accuracy in predicting their duration and number of diseases. To simulate different epidemic waves and take into account the incompleteness of statistical data, the generalized SIR model and algorithms for determining the values of its parameters were proposed. The interference of the previous waves, changes in testing levels, quarantine or social behavior require constant monitoring of the epidemic dynamics and performing SIR simulations as often as possible with the use of a user-friendly interface. Such tool will allow predicting the dynamics of any epidemic using the data on the number of diseases over a limited period (e.g., 14 days). It will be possible to predict the daily number of new cases for the country as a whole or for its separate region, to estimate the number of carriers of the infection and the probability of facing such a carrier, as well as to estimate the number of deaths. Results of three SIR simulations of the COVID-19 epidemic wave in Japan in the summer of 2022 are presented and discussed. The predicted accumulated and daily numbers of cases agree with the results of observations, especially for the simulation based on the datasets corresponding to the period from July 3 to July 16, 2022. A user-friendly interface also has to ensure an opportunity to compare the epidemic dynamics in different countries/regions and in different years in order to estimate the impact of vaccination levels, quarantine restrictions, social behavior, etc. on the numbers of new infections, death, and mortality rates. As example, the comparison of the COVID-19 pandemic dynamics in Japan in the summer of 2020, 2021 and 2022 is presented. The high level of vaccinations achieved in the summer of 2022 did not save Japan from a powerful pandemic wave. The daily numbers of cases were about ten times higher than in the corresponding period of 2021. Nevertheless, the death per case ratio in 2022 was much lower than in 2020.
新冠疫情给人类带来的挑战要求及时、准确地预测各类疫情的动态,以尽量减少对公众健康和经济的负面影响。可以使用各种知名的和新的数学模型,并考虑大量因素。然而,复杂模型包含大量未知参数,其值必须通过有限数量的观测来确定,例如每日累计病例数的数据集。对新冠疫情进行建模的成功经验表明,可以应用包含4个未知参数的最简单的SIR模型。在中国、韩国、奥地利、意大利、德国、法国、西班牙对新冠疫情第一波应用该模型参数识别的原始算法,已显示出其在预测疫情持续时间和疾病数量方面的高精度。为了模拟不同的疫情波并考虑统计数据的不完整性,提出了广义SIR模型及其参数值确定算法。先前疫情波的干扰、检测水平的变化、检疫或社会行为需要持续监测疫情动态,并尽可能频繁地使用用户友好界面进行SIR模拟。这样的工具将允许使用有限时间段(例如14天)内的疾病数量数据来预测任何疫情的动态。有可能预测整个国家或其单独地区的每日新增病例数,估计感染携带者的数量以及遇到此类携带者的概率,以及估计死亡人数。给出并讨论了对2022年夏季日本新冠疫情波的三次SIR模拟结果。预测的累计病例数和每日病例数与观测结果相符,特别是基于2022年7月3日至7月16日期间数据集的模拟。用户友好界面还必须确保有机会比较不同国家/地区以及不同年份的疫情动态,以便估计疫苗接种水平、检疫限制、社会行为等对新增感染数、死亡数和死亡率的影响。例如,给出了2020年、2021年和2022年夏季日本新冠疫情动态的比较。2022年夏季实现的高疫苗接种水平并未使日本免受强大疫情波的影响。每日病例数比2021年同期高出约十倍。然而,2022年的病例死亡率远低于2020年。