Modeling of Pneumococcal and Respiratory Syncytial Virus Pneumonia: An Epidemiological Review, with Statistical Inference

Original Abstract

Infectious diseases continue to pose significant public health challenges worldwide, requiring effective prevention and control strategies to mitigate their negative impact. Infectious diseases can be broadly classified into two groups: vaccine-preventable diseases (e.g., measles, polio, influenza, hepatitis B, pneumonia) and vaccine-non-preventable diseases (e.g., HIV/AIDS). Vaccine-preventable disease models are one of the essential tools for understanding infectious disease dynamics, evaluating intervention strategies, and guiding public health policies. In this review article, we explore the recent advancements in modeling two particular vaccine-preventable infectious diseases. Here, we consider both deterministic and stochastic models to comprehensively capture the complexity of disease transmission, vaccine efficacy, and population-level immunity. We highlight the application of these models to the infectious diseases, namely, bacterial and viral pneumonia caused by the bacteria Streptococcus pneumoniae (S. pneumoniae) and the respiratory syncytial virus (RSV). Pneumonia carry a substantial global burden, where modeling has played a crucial role in assessing vaccine impacts and optimizing immunization strategies to minimize the disease burden. By synthesizing recent methodologies and findings, this review provides valuable insights for future research and policy decisions aimed at improving vaccine-preventable disease control for pneumonia caused by S. pneumoniae and RSV.