Developing Machine Learning Prediction Model for Daily Influenza Reported Cases Using Multichannel Surveillance Data - A City, Hubei Province, China, 2023-2025.

ABSTRACT:

Introduction

Public health surveillance is crucial for decision-making. Given the significant threat of influenza to public health, developing predictive models using multichannel surveillance systems is imperative.

Methods

Data were collected from multichannel surveillance systems, including hospitals, search engines, and climatological and air pollutant surveillance systems, in a southern Chinese city from January 2023 to January 2025. Spearman's correlation analysis assessed the relationships between variables and reported influenza cases. Several machine learning models were used to predict trends in reported cases.

Results

Correlation analysis showed that all four surveillance systems were related to influenza, with 27 variables correlated with daily reported cas

SUBMITTER: Zhang X 

PROVIDER: S-EPMC12620572 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature