tetano
Editor, Senior Moderator
Public Health
. 2024 Mar 25:230:122-127.
doi: 10.1016/j.puhe.2024.02.025. Online ahead of print. Convolutional LSTM-LSTM model for predicting the daily number of influenza patients in South Korea using satellite images
H-J Lee[SUP] 1 [/SUP], S-K Mun[SUP] 2 [/SUP], M Chang[SUP] 3 [/SUP]
Affiliations
Objectives: Influenza affects a considerable proportion of the global population each year, and meteorological conditions may have a significant impact on its transmission. In this study, we aimed to develop a prediction model for the number of influenza patients at the national level using satellite images and provide a basis for predicting influenza through satellite image data.
Study design: We developed an influenza incidence prediction model using satellite images and influenza patient data.
Methods: We collected satellite images and daily influenza patient data from July 2014 to June 2019 and developed a convolutional long short-term memory (LSTM)-LSTM neural network model. The model with the lowest average of mean absolute error (MAE) was selected.
Results: The final model showed a high correlation between the predicted and actual number of influenza patients, with an average MAE of 5.9010 per million population. The model performed best with a 2-week time sequence.
Conclusions: We developed a national-level prediction model using satellite images to predict influenza incidence. The model offers the advantage of nationwide analysis. These results may reduce the burden of influenza by enabling timely public health interventions.
Keywords: Influenza; Meteorological factor; Neural network model; Public health; Satellite imagery.
. 2024 Mar 25:230:122-127.
doi: 10.1016/j.puhe.2024.02.025. Online ahead of print. Convolutional LSTM-LSTM model for predicting the daily number of influenza patients in South Korea using satellite images
H-J Lee[SUP] 1 [/SUP], S-K Mun[SUP] 2 [/SUP], M Chang[SUP] 3 [/SUP]
Affiliations
- PMID: 38531234
- DOI: 10.1016/j.puhe.2024.02.025
Objectives: Influenza affects a considerable proportion of the global population each year, and meteorological conditions may have a significant impact on its transmission. In this study, we aimed to develop a prediction model for the number of influenza patients at the national level using satellite images and provide a basis for predicting influenza through satellite image data.
Study design: We developed an influenza incidence prediction model using satellite images and influenza patient data.
Methods: We collected satellite images and daily influenza patient data from July 2014 to June 2019 and developed a convolutional long short-term memory (LSTM)-LSTM neural network model. The model with the lowest average of mean absolute error (MAE) was selected.
Results: The final model showed a high correlation between the predicted and actual number of influenza patients, with an average MAE of 5.9010 per million population. The model performed best with a 2-week time sequence.
Conclusions: We developed a national-level prediction model using satellite images to predict influenza incidence. The model offers the advantage of nationwide analysis. These results may reduce the burden of influenza by enabling timely public health interventions.
Keywords: Influenza; Meteorological factor; Neural network model; Public health; Satellite imagery.