A Scoping Review (To 31 August 2025) of Compartmental Epidemic Models for H1N1 and H3N2 Seasonal Influenza

Anselina Sok Mian Goh¹, Sharmila Thirumalaikumar², Kannan Kowsalya² and Maurice Han Tong Ling¹*
¹School of Health and Life Sciences, Teesside University, United Kingdom
²School of Health Sciences, Management Development Institute of Singapore, Republic of Singapore
*Corresponding Author: Maurice Han Tong Ling, School of Health Sciences, Management Development Institute of Singapore, Republic of Singapore.

Published: September 30, 2025

DOI: 10.55162/MCMS.09.317

View Pdf

Abstract  

H1N1 and H3N2 Influenza remains a persistent threat to global health, with annual seasonal outbreaks causing a sizeable number to be severely ill and dead. To address this public health issue, mathematical modelling, particularly epidemic compartmental models are used to understand disease dynamics and guide public health policy. However, there are no systematic review to-date examining the epidemiological models for H1N1 and H3N2. Hence, this study is a scoping review of epidemic compartmental models of H1N1 and H3N2 seasonal influenza strains, using studies indexed in PubMed to 31 August 2025, inclusive. Of the 370 studies obtained, 81 studies are included and analysed to identify and characterize common trends in viral strains, methodological approach, thematic applications, and structural complexity. The results show the focus on the H1N1 viral strain (83.9% of studies), which reflects the impact of the 2009 H1N1 pandemic to the global and scientific community. Simultaneously, 13.6% of research studies is focused on both H1N1 and H3N2 strains, indicating an interest towards understanding complex multi-strain interactions. From a methodological perspective, Ordinary Differential Equations (ODEs) continue to be the leading framework (80.3%), due to their ability to provide clear evaluation of population-level trends and effectiveness of interventions. However, the deliberate use of Stochastic Differential Equations (SDEs) and other models demonstrates a versatile approach to include uncertainty and subtle dynamics. Thematic analysis reveals a dual emphasis in the research field: a sizeable portion of studies is centred on Intervention and Policy Assessment (37.0%), whereas another key theme is Core Epidemiological Dynamics (33.3%), highlighting the need to translate theoretical knowledge into practical policy. Additionally, increased model complexity (i.e., more compartments) is directly correlated to detailed, policy-focused insights. The overall trend shows a shift from generic theoretical frameworks to advanced, "fit-for-purpose" methodologies that deliver prompt and reliable insights to support public health policies.