Cabezas Heriberto, Štefančić Hrvoje
University of Miskolc, Miskolc, Hungary.
Catholic University of Croatia, Zagreb, Croatia.
Heliyon. 2024 Feb 20;10(4):e26707. doi: 10.1016/j.heliyon.2024.e26707. eCollection 2024 Feb 29.
We explored the application of Fisher information to the study of pandemics and illustrated the insights that can be gained using the COVID-19 pandemic, as a test case. To do so, we applied the Fisher information theory previously applied to periodic systems, to non-periodic dynamic systems. The resulting mathematical machinery was then used to compute the Fisher information measure, as the amount of information extracted from the time series for COVID-19 confirmed infections and deaths. The analysis was performed for the World as a whole and five nation-states: India, USA, Japan, Germany, and Chile. Several insights resulted from the study: (1) the information content of the time series varied widely for different time periods, over the course of the pandemic, (2) it is advisable not to fit model parameters or make policy decisions based on data from time periods with low Fisher information, (3) the most information about a wave of infections comes towards the end of the wave where the time series data has the most information about the dynamics of the pandemic, and (4) the quality of the time series data significantly affects the Fisher information value, and, therefore, what can be learned from studying the time series.
我们探讨了费希尔信息在大流行研究中的应用，并以新冠疫情作为案例，阐述了从中可获得的见解。为此，我们将先前应用于周期系统的费希尔信息理论应用于非周期动态系统。然后，利用由此产生的数学机制来计算费希尔信息量度，即从新冠确诊感染和死亡时间序列中提取的信息量。分析针对全球以及五个民族国家进行：印度、美国、日本、德国和智利。该研究得出了几点见解：（1）在疫情期间，时间序列的信息含量在不同时间段差异很大；（2）不宜根据费希尔信息较低时间段的数据来拟合模型参数或做出政策决策；（3）关于感染波的大部分信息出现在波的末尾，此时时间序列数据包含有关大流行动态的最多信息；（4）时间序列数据的质量会显著影响费希尔信息值，进而影响从研究时间序列中所能学到的内容。
