A simple model for the analysis of epidemics based on hospitalization data

Area of Science:

• Epidemiology
• Mathematical Modeling
• Infectious Disease Dynamics

Background:

• Accurate disease surveillance is crucial, but often hampered by underreported cases.
• Existing models may require extensive data, limiting application in resource-limited settings or during novel outbreaks.
• Estimating key epidemiological parameters like the reproductive number is vital for public health interventions.

Purpose of the Study:

• To develop and validate a parsimonious epidemiological model for analyzing disease outbreaks with suspected underreporting.
• To assess the structural and practical identifiability of the proposed model.
• To apply the model to estimate COVID-19 dynamics during the initial surge and Omicron wave in Belgium.

Main Methods:

• Development of a minimal-parameter epidemiological model.
• Analytical and numerical investigation of model identifiability.
• Parameter fitting using hospitalization data only.
• Estimation of initial epidemiological class sizes as part of the fitting process.
• Validation using two distinct datasets.

Main Results:

• The model demonstrated high structural and practical identifiability.
• Analysis indicated significant underestimation of actual COVID-19 cases by reported figures.
• Estimated basic reproductive number (R0) values aligned with findings from other studies.
• The model successfully characterized the initial surge and Omicron wave dynamics in Belgium.

Conclusions:

• The proposed minimal-parameter model effectively estimates disease dynamics using solely hospitalization data, even with substantial underreporting.
• This approach enhances confidence in epidemiological parameter estimations, including the basic reproductive number (R0) and effective reproductive number (Re).
• The methodology is adaptable for studying various infectious diseases where confirmed case data is unreliable or scarce.