Sakalauskas Leonidas, Dulskis Vytautas, Jankunas Rimas Jonas
Klaipeda University, H. Manto st. 84, Klaipeda, LT-92294, Lithuania.
Health Law Institute, Ulonu st. 5-303, Vilnius, LT-08240, Lithuania.
Heliyon. 2024 May 22;10(11):e31410. doi: 10.1016/j.heliyon.2024.e31410. eCollection 2024 Jun 15.
A scrutiny analysis of the COVID-19 data is required to get insights into effective strategies for pandemic control. However, there is a gap between official data and methods used to assess the effectiveness of the potential measures, which was partly addressed in an editorial-letter-type discussion on the impact of the COVID-19 passport in Lithuania. The therein-applied descriptive statistics method provides only limited evidence, while detailed analysis requires more sensitive and reliable methods. In this regard, this paper advocates a maximum likelihood compartmental modeling approach, which provides the flexibility to raise various hypotheses about infection, recovery, and mortality dynamics and to find the most likely answers given the data. Our paper is based on COVID-19 deaths, which are more reliable and essential than infection cases. It should also be noted that officially collected data are unsuitable for in-depth analyses, including compartmental modeling, as they do not capture important information. Overall, this paper does not aim to solve the underlying problems completely but rather stimulate a discussion.
需要对新冠疫情数据进行详细分析，以深入了解有效的疫情控制策略。然而，官方数据与用于评估潜在措施有效性的方法之间存在差距，这在一篇关于立陶宛新冠疫苗护照影响的编辑信式讨论中得到了部分解决。其中应用的描述性统计方法仅提供了有限的证据，而详细分析需要更敏感和可靠的方法。在这方面，本文提倡采用最大似然分区建模方法，该方法能够灵活地提出关于感染、康复和死亡动态的各种假设，并根据数据找到最可能的答案。我们的论文基于新冠死亡病例，这些数据比感染病例更可靠且更重要。还应注意的是，官方收集的数据不适合进行深入分析，包括分区建模，因为它们没有捕捉到重要信息。总体而言，本文的目的不是完全解决潜在问题，而是激发讨论。
