tetano
Editor, Senior Moderator
Immun Inflamm Dis
. 2026 Feb;14(2):e70367.
doi: 10.1002/iid3.70367.
LSTM-Based Recurrent Neural Network Predicts Influenza-Like-Illness in Variable Climate Zones
Alfred Amendolara[SUP] 1 [/SUP], Christopher Gowans[SUP] 1 [/SUP], Joshua Barton[SUP] 1 [/SUP], Andrew Payne[SUP] 1 [/SUP], David Sant[SUP] 1 [/SUP]
Affiliations
Background: Influenza virus is responsible for a recurrent, yearly epidemic in most temperate regions of the world. Flu has been responsible for a high disease burden in recent years, despite the confounding presence of SARS-CoV-2. However, the mechanisms behind seasonal variance in flu burden are not well understood. This study seeks to expand understanding of the impact of variable climate regions on seasonal flu trends. To that end, three climate regions have been selected. Each region represents a different ecological zone and provides different weather patterns.
Methods: A long short-term memory (LSTM)-based recurrent neural network was used to predict influenza-like-illness trends for three separate locations: Hawaii, Vermont, and Nevada. Flu data were gathered from the Center for Disease Control as weekly influenza-like-illness (ILI) percentages. Weather data were collected from Visual Crossing and included temperature, wind speed, UV index, solar radiation, precipitation, and humidity. Data were prepared and the model was trained as described previously.
Results: All three regions showed strong seasonality of flu trends with Hawaii having the largest absolute ILI values. Temperature showed a moderate negative correlation with ILI in all three regions (Vermont = -0.54, Nevada = -0.56, Hawaii = -0.44). Humidity was moderately correlated in Nevada (0.47) and weakly correlated with ILI in Hawaii (0.22). Vermont ILI did not correlate with humidity. Precipitation and wind speed were weakly correlated in all three regions. Solar radiation and UV index showed moderate correlation in Vermont (-0.33, -0.36) and Nevada (-0.53, -0.55), but only a weak correlation in Hawaii (-0.15, -0.18). When trained on the complete data sets, baseline model performances for all three datasets at +1 week were equivalent. Models trained on one region and used to predict cross-regional data performed uniformly and equivalent to baseline.
Conclusions: Results indicate that climate variables were weak to moderate predictors in all regions. Initial modeling attempts revealed uniform performance in all regions. Despite strong climate differences, cross-regional LSTM models performed comparably, suggesting that seasonal patterns, rather than absolute climate variables, drive ILI trends. Additionally, this data suggests that absolute climate changes may not be influential as relative seasonal changes.
Keywords: LSTM; influenza; modeling; neural network.
. 2026 Feb;14(2):e70367.
doi: 10.1002/iid3.70367.
LSTM-Based Recurrent Neural Network Predicts Influenza-Like-Illness in Variable Climate Zones
Alfred Amendolara[SUP] 1 [/SUP], Christopher Gowans[SUP] 1 [/SUP], Joshua Barton[SUP] 1 [/SUP], Andrew Payne[SUP] 1 [/SUP], David Sant[SUP] 1 [/SUP]
Affiliations
- PMID: 41728750
- PMCID: PMC12928072
- DOI: 10.1002/iid3.70367
Background: Influenza virus is responsible for a recurrent, yearly epidemic in most temperate regions of the world. Flu has been responsible for a high disease burden in recent years, despite the confounding presence of SARS-CoV-2. However, the mechanisms behind seasonal variance in flu burden are not well understood. This study seeks to expand understanding of the impact of variable climate regions on seasonal flu trends. To that end, three climate regions have been selected. Each region represents a different ecological zone and provides different weather patterns.
Methods: A long short-term memory (LSTM)-based recurrent neural network was used to predict influenza-like-illness trends for three separate locations: Hawaii, Vermont, and Nevada. Flu data were gathered from the Center for Disease Control as weekly influenza-like-illness (ILI) percentages. Weather data were collected from Visual Crossing and included temperature, wind speed, UV index, solar radiation, precipitation, and humidity. Data were prepared and the model was trained as described previously.
Results: All three regions showed strong seasonality of flu trends with Hawaii having the largest absolute ILI values. Temperature showed a moderate negative correlation with ILI in all three regions (Vermont = -0.54, Nevada = -0.56, Hawaii = -0.44). Humidity was moderately correlated in Nevada (0.47) and weakly correlated with ILI in Hawaii (0.22). Vermont ILI did not correlate with humidity. Precipitation and wind speed were weakly correlated in all three regions. Solar radiation and UV index showed moderate correlation in Vermont (-0.33, -0.36) and Nevada (-0.53, -0.55), but only a weak correlation in Hawaii (-0.15, -0.18). When trained on the complete data sets, baseline model performances for all three datasets at +1 week were equivalent. Models trained on one region and used to predict cross-regional data performed uniformly and equivalent to baseline.
Conclusions: Results indicate that climate variables were weak to moderate predictors in all regions. Initial modeling attempts revealed uniform performance in all regions. Despite strong climate differences, cross-regional LSTM models performed comparably, suggesting that seasonal patterns, rather than absolute climate variables, drive ILI trends. Additionally, this data suggests that absolute climate changes may not be influential as relative seasonal changes.
Keywords: LSTM; influenza; modeling; neural network.