A SIR Model with Incomplete Data for the Analysis of Influenza A Spread in Ningbo

Abstract

In this article, the SIR model is used to fit the spread of influenza A in Ningbo, ranging from January 1, 2023 to June 30, 2023. The data that can be collected is the daily newly confirmed cases, but the daily number of infected individuals and recovered individuals is unknown. The initial number of susceptible individuals with exposure risk is also unknown. Based on the incompleteness of the data, the parameter estimation problem of the SIR model is transformed into an optimization problem. The Particle Swarm Optimization algorithm is used to solve the optimization problem. The goodness of fit (R2) of the daily cumulative number of confirmed cases is 0.998. The model can reflect the cycle, peak, and trend of the entire spread of influenza A and is consistent with reality during the post‐COVID‐19 period.