Abstract
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 = -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 (-0.5263, -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 acceptable and uniform performance in all regions. When cross-regional predictions were made, performance remained uniform across all regions, implying that climate patterns may be more important than absolute climate values. Additionally, this data suggests that climate may not be as influential on flu trends as population-level human factors.