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Updated: Feb 24, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

LSTM-Based Recurrent Neural Network Predicts Influenza-Like-Illness in Variable Climate Zones.

Alfred Amendolara¹, Christopher Gowans¹, Joshua Barton¹

¹Department of Biomedical Science, Noorda College of Osteopathic Medicine, Provo, Utah, USA.

Immunity, Inflammation and Disease

|February 23, 2026

Summary

This summary is machine-generated.

Seasonal climate patterns, not absolute weather, drive influenza-like-illness (ILI) trends. Recurrent neural networks showed consistent flu prediction across diverse regions, highlighting the importance of seasonal timing over specific climate variables.

Keywords:

LSTM influenza modeling neural network

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Last Updated: Feb 24, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

Epidemiology
Climatology
Data Science

Background:

Influenza virus causes annual epidemics globally, posing a significant public health burden.
Understanding seasonal variations in flu transmission is crucial, especially with the ongoing impact of SARS-CoV-2.
Mechanisms driving seasonal flu burden across different climate zones remain incompletely understood.

Purpose of the Study:

To investigate the influence of distinct climate regions on seasonal influenza-like-illness (ILI) trends.
To analyze the predictive power of various climate variables on flu seasonality.
To explore the efficacy of machine learning models in forecasting flu trends across different ecological zones.

Main Methods:

Utilized a long short-term memory (LSTM) recurrent neural network to predict ILI trends.
Collected weekly ILI data from the CDC and weather data (temperature, humidity, wind speed, etc.) from Visual Crossing.
Trained and evaluated models using data from three distinct climate regions: Hawaii, Vermont, and Nevada.

Main Results:

All regions exhibited strong flu seasonality, with Hawaii showing the highest absolute ILI values.
Temperature showed a moderate negative correlation with ILI across all regions.
Solar radiation and UV index had moderate correlations in Vermont and Nevada, but weak correlations in Hawaii. Cross-regional model performance was equivalent to baseline predictions.

Conclusions:

Climate variables demonstrated weak to moderate predictive power for ILI trends.
LSTM models performed uniformly across regions, suggesting seasonal patterns are key drivers of ILI.
Relative seasonal changes in climate appear more influential on flu trends than absolute climate variables.