Modelling immunity gaps to quantify infection resurgences

Area of Science:

• Epidemiology
• Mathematical Biology
• Public Health

Background:

• Non-pharmaceutical interventions during the COVID-19 pandemic reduced transmission of various respiratory pathogens.
• This led to a potential decrease in population immunity, termed 'immunity debt' or 'immunity gap'.
• Resurgences of pathogens like respiratory syncytial virus (RSV) and influenza were observed globally after restrictions eased.

Purpose of the Study:

• To extend the classic susceptible-immune-susceptible model to explore the 'immunity debt' phenomenon.
• To investigate how waning immunity influences pathogen resurgence after prolonged periods of reduced transmission.
• To inform future public health strategies by predicting disease characteristics prone to resurgence.

Main Methods:

• Developed a modified susceptible-immune-susceptible mathematical model.
• Parametrized the model using respiratory syncytial virus (RSV) antibody data from healthy adults in Aotearoa, New Zealand.
• Conducted a case study analyzing prolonged public health measures (2020-2022) and compared model predictions with hospitalization data.

Main Results:

• The model indicates diseases with very fast waning immunity are less likely to cause significant infection surges post-restrictions.
• Diseases with moderately fast waning immunity, such as RSV, show a higher likelihood of substantial resurgence.
• Model predictions align with observed hospitalization trends in Aotearoa, New Zealand.

Conclusions:

• Mathematical modeling supports the 'immunity debt' hypothesis for certain respiratory pathogens.
• Pathogen characteristics, particularly the rate of immunity waning, are critical factors in predicting post-restriction resurgence.
• Findings can guide public health responses to mitigate future outbreaks of diseases like RSV.