Abstract

Public health preparedness and response are critical components of effective public health systems, especially in the face of emerging infectious diseases, natural disasters, and other public health emergencies. Predictive models play a pivotal role in enhancing these efforts by providing data-driven insights that can guide decision-making processes. This review article explores the development of predictive models for public health preparedness and response, focusing on recent innovations, methodologies, and applications. This review article examines the development and application of predictive models in this domain, emphasizing recent developments and emerging technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). These models are essential for predicting disease outbreaks, optimizing resource allocation, and formulating effective intervention strategies. Moreover, these emerging technologies are paving the way for personalized predictive models that consider individual health data in public health contexts. Personalized models can account for specific genetic, environmental, and lifestyle factors, leading to more accurate predictions and tailored interventions. These advancements also facilitate the creation of adaptive models that continuously learn from new data, making them highly responsive to changing public health landscapes. The shift towards personalization in predictive modelling marks a significant evolution from traditional one-size-fits-all approaches, potentially leading to more effective and equitable public health strategies. The article also highlights the challenges and future directions in this rapidly evolving field.