Infectious diseases pose a persistent challenge to public health worldwide. Recent global health crises, such as the COVID-19 pandemic and Ebola outbreaks, have underscored the vital role of infectious disease modeling in guiding public health policy and response. Infectious disease modeling is a critical tool for society, informing risk mitigation measures, prompting timely interventions, and aiding preparedness for healthcare delivery systems. This article synthesizes the current landscape of infectious disease modeling, emphasizing the integration of statistical methods in understanding and predicting the spread of infectious diseases. We begin by examining the historical context and the foundational models that have shaped the field, such as the SIR (susceptible, infectious, recovered) and SEIR (susceptible, exposed, infectious, recovered) models. Subsequently, we delve into the methodological innovations that have arisen, including stochastic modeling, network-based approaches, and the use of big data analytics. We also explore the integration of machine learning techniques in enhancing model accuracy and responsiveness. The review identifies the challenges of parameter estimation, model validation, and the incorporation of real-time data streams. Moreover, we discuss the ethical implications of modeling, such as privacy concerns and the communication of risk. The article concludes by discussing future directions for research, highlighting the need for data integration and interdisciplinary collaboration for advancing infectious disease modeling.

中文翻译：

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传染病建模


传染病对全球公共卫生构成持续挑战。最近的全球健康危机，例如 COVID-19 大流行和埃博拉疫情，凸显了传染病建模在指导公共卫生政策和应对方面的重要作用。传染病建模是社会的重要工具，可为风险缓解措施提供信息，促进及时干预，并有助于为医疗保健提供系统做好准备。本文综合了传染病建模的现状，强调在理解和预测传染病传播方面整合统计方法。我们首先研究历史背景和塑造该领域的基础模型，例如 SIR（易感、传染、康复）和 SEIR（易感、暴露、传染、康复）模型。随后，我们深入研究了已经出现的方法创新，包括随机建模、基于网络的方法和大数据分析的使用。我们还探讨了机器学习技术在提高模型准确性和响应能力方面的集成。该综述确定了参数估计、模型验证和实时数据流整合的挑战。此外，我们还讨论了建模的道德影响，例如隐私问题和风险沟通。文章最后讨论了未来的研究方向，强调了数据整合和跨学科合作的必要性，以推进传染病建模。