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Research on pertussis incidence prediction in Urumqi based on ARIMA and LSTM models
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Chu-yao XIAO1, Ting-ting LI1, Ruo-nan FU1, Yu YIN2, Ying ZOU2, Pei-sheng WANG2
Modern Preventive Medicine | 2024, 51(21) : 3877 - 3883
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Modern Preventive Medicine | 2024, 51(21): 3877-3883
• Epidemiology and Statistical Methods •
Research on pertussis incidence prediction in Urumqi based on ARIMA and LSTM models
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Chu-yao XIAO1, Ting-ting LI1, Ruo-nan FU1, Yu YIN2, Ying ZOU2, Pei-sheng WANG2
Affiliations
  • School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang 830011, China
Published: 2024-11-10 doi: 10.20043/j.cnki.MPM.202406461
Outline
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Objective

To analyze the application of the ARIMA and LSTM models in predicting pertussis incidence in Urumqi, providing a basis for assessing the epidemic trend of pertussis.

Methods

Monthly reported incidence data of pertussis in Urumqi from 2011 to 2021 were used to establish ARIMA and LSTM models. The incidence data from 2022 to 2023 were utilized to validate the predictive performance of the two models. The models’ performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), and the incidence of pertussis in 2024 was predicted.

Results

The incidence of pertussis in Urumqi from 2011 to 2023 showed an upward trend with seasonal variations. Additionally, a high incidence state of pertussis began in August 2023. Both the ARIMA and LSTM models demonstrated good fitting, although there were discrepancies in their predictions for July to December 2023. The overall predictive performance of the LSTM model (RMSE=32.34, MAE=11.41) was superior to that of the ARIMA model (RMSE=42.81, MAE=14.34). The LSTM model, which showed better validation results, predicted a continued increase in pertussis incidence for 2024.

Conclusion

The LSTM model provides a more accurate prediction of the pertussis incidence trend in Urumqi, offering valuable insights for monitoring and controlling the epidemic of pertussis.

Pertussis  /  ARIMA model  /  LSTM neural network model  /  Prediction
Chu-yao XIAO, Ting-ting LI, Ruo-nan FU, Yu YIN, Ying ZOU, Pei-sheng WANG. Research on pertussis incidence prediction in Urumqi based on ARIMA and LSTM models[J]. Modern Preventive Medicine, 2024 , 51 (21) : 3877 -3883 . DOI: 10.20043/j.cnki.MPM.202406461
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Year 2024 volume 51 Issue 21
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Article Info
doi: 10.20043/j.cnki.MPM.202406461
  • Receive Date：2024-06-24
  • Online Date：2026-03-20
  • Published：2024-11-10
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History
  • Received：2024-06-24
Funding
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    School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang 830011, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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