Adéoti Olaiya Mathilde, Agbla Schadrac, Diop Aliou, Glèlè Kakaï Romain
Laboratoire de Biomathématiques et d'Estimations Forestières, University of Abomey-Calavi, Cotonou, Benin.
Department of Health Data Science, University of Liverpool, Liverpool United Kingdom.
Infect Dis Model. 2024 Sep 18;10(1):110-128. doi: 10.1016/j.idm.2024.09.001. eCollection 2025 Mar.
The level of surveillance and preparedness against epidemics varies across countries, resulting in different responses to outbreaks. When conducting an in-depth analysis of microinfection dynamics, one must account for the substantial heterogeneity across countries. However, many commonly used statistical model specifications lack the flexibility needed for sound and accurate analysis and prediction in such contexts. Nonlinear mixed effects models (NLMMs) constitute a specific statistical tool that can overcome these significant challenges. While compartmental models are well-established in infectious disease modeling and have seen significant advancements, Nonlinear Mixed Models (NLMMs) offer a flexible approach for handling heterogeneous and unbalanced repeated measures data, often with less computational effort than some individual-level compartmental modeling techniques. This study provides an overview of their current use and offers a solid foundation for developing guidelines that may help improve their implementation in real-world situations. Relevant scientific databases in the Access initiative programs were used to search for papers dealing with key aspects of NLMMs in infectious disease modeling (IDM). From an initial list of 3641 papers, 124 were finally included and used for this systematic and critical review spanning the last two decades, following the PRISMA guidelines. NLMMs have evolved rapidly in the last decade, especially in IDM, with most publications dating from 2017 to 2021 (83.33%). The routine use of normality assumption appeared inappropriate for IDM, leading to a wealth of literature on NLMMs with non-normal errors and random effects under various estimation methods. We noticed that NLMMs have attracted much attention for the latest known epidemics worldwide (COVID-19, Ebola, Dengue and Lassa) with the robustness and reliability of relaxed propositions of the normality assumption. A case study of the application of COVID-19 data helped to highlight NLMMs' performance in modeling infectious diseases. Out of this study, estimation methods, assumptions, and random terms specification in NLMMs are key aspects requiring particular attention for their application in IDM.
各国针对流行病的监测和防范水平各不相同，这导致对疫情的应对措施也有所差异。在对微观感染动态进行深入分析时，必须考虑到各国之间存在的巨大异质性。然而，许多常用的统计模型规格缺乏在这种情况下进行合理准确分析和预测所需的灵活性。非线性混合效应模型（NLMMs）是一种特定的统计工具，能够克服这些重大挑战。虽然 compartments 模型在传染病建模中已得到广泛应用并取得了显著进展，但非线性混合模型（NLMMs）提供了一种灵活的方法来处理异质和不平衡的重复测量数据，其计算量通常比一些个体层面的 compartments 建模技术要少。本研究概述了它们目前的使用情况，并为制定可能有助于在实际情况中改进其应用的指南奠定了坚实基础。利用 Access 倡议项目中的相关科学数据库，搜索了涉及传染病建模（IDM）中 NLMMs 关键方面的论文。从最初的 3641 篇论文列表中，最终纳入了 124 篇，并按照 PRISMA 指南用于此次跨越过去二十年的系统和批判性综述。在过去十年中，NLMMs 发展迅速，尤其是在 IDM 领域，大多数出版物的日期为 2017 年至 2021 年（83.33%）。常态假设的常规使用在 IDM 中似乎并不合适，并导致了大量关于具有非正态误差和各种估计方法下随机效应的 NLMMs 的文献。我们注意到，由于常态假设的宽松命题具有稳健性和可靠性,NLMMs 在全球范围内最新已知的流行病（COVID -19、埃博拉、登革热和拉沙热）中受到了广泛关注。一项关于 COVID -19 数据应用的案例研究有助于突出 NLMMs 在传染病建模中的表现。在这项研究中，NLMMs 中的估计方法、假设和随机项规格是其在 IDM 中应用时需要特别关注的关键方面。
