Trend and prediction of daily incidence of hand, foot, and mouth disease in Shenzhen, 2011 - 2023 with projections to 2024: a Prophet model approach

ObjectiveTo evaluate the performance of the Prophet model in predicting the daily incidence of hand, foot, and mouth disease (HFMD) in Shenzhen city, to analyze the impact of the COVID-19 pandemic, public holidays, and school vacations (summer/winter) on HFMD predictions, and to provide new insights...

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Main Authors: Wenhai LU, Lixia SONG, Huawei XIONG, Zhigao CHEN, Yan LU, Yanpeng CHENG, Zhen ZHANG
Format: Article
Language:zho
Published: Editorial Office of Chinese Journal of Public Health 2025-05-01
Series:Zhongguo gonggong weisheng
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Online Access:https://www.zgggws.com/article/doi/10.11847/zgggws1146131
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author Wenhai LU
Lixia SONG
Huawei XIONG
Zhigao CHEN
Yan LU
Yanpeng CHENG
Zhen ZHANG
author_facet Wenhai LU
Lixia SONG
Huawei XIONG
Zhigao CHEN
Yan LU
Yanpeng CHENG
Zhen ZHANG
author_sort Wenhai LU
collection DOAJ
description ObjectiveTo evaluate the performance of the Prophet model in predicting the daily incidence of hand, foot, and mouth disease (HFMD) in Shenzhen city, to analyze the impact of the COVID-19 pandemic, public holidays, and school vacations (summer/winter) on HFMD predictions, and to provide new insights for HFMD surveillance and early warning systems. MethodsUsing daily incidence rate data of hand, foot, and mouth disease (HFMD) in Shenzhen city from 2011 to 2023 as a training set, we constructed different Prophet models based on two factors: (1) whether data from the COVID-19 epidemic period were included, and (2) whether holiday effects were adjusted. These models were then used to predict the daily incidence rate from January to July 2024, and the predictions were compared with the actual observations. Model performance was evaluated using four metrics: mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and symmetric mean absolute percentage error (SMAPE). ResultsCompared to the Prophet model trained on data during the COVID-19 pandemic, the baseline Prophet model (without pandemic data) achieved reductions in daily average MSE, MAE, RMSE, and SMAPE of 45.64%, 27.63%, 25.89%, and 7.97%, respectively. After controlling for holiday effects, the MAE, RMSE, and SMAPE values remained unchanged. When the prediction horizon exceeded 4 months, the daily MSE decreased by 3.81%. For predictions within 4 months, the model yielded MSE, MAE, RMSE, and SMAPE values (with 95% confidence intervals) of 0.42 (0.33, 0.90), 0.51 (0.45, 0.75), 0.65 (0.57, 0.95), and 0.42 (0.24, 0.44), respectively. In contrast, the predictions at 7 months showed higher errors: MSE 1.78 (0.39, 29.61), MAE 0.94 (0.48, 3.05), RMSE 1.33 (0.63, 5.44), and SMAPE 0.40 (0.33, 0.43). ConclusionsDuring the COVID-19 pandemic (2020 - 2022), the daily HFMD incidence rate significantly affected the performance of the Prophet model. While controlling for holidays and school vacations (winter/summer) had a limited effect on short-term predictions, it improved the model′s accuracy for long-term predictions.
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spelling doaj-art-4db88e80902c45bdba6433235b0b44e82025-08-20T02:35:49ZzhoEditorial Office of Chinese Journal of Public HealthZhongguo gonggong weisheng1001-05802025-05-0141558559110.11847/zgggws11461311146131Trend and prediction of daily incidence of hand, foot, and mouth disease in Shenzhen, 2011 - 2023 with projections to 2024: a Prophet model approachWenhai LU0Lixia SONG1Huawei XIONG2Zhigao CHEN3Yan LU4Yanpeng CHENG5Zhen ZHANG6Department of Infectious Disease Control and Prevention, Pingdi Street Public Health Service Center, Longgang District, Shenzhen 518117, ChinaDepartment of Infectious Disease Control and Prevention, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, ChinaDepartment of Infectious Disease Control and Prevention, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, ChinaDepartment of Infectious Disease Control and Prevention, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, ChinaDepartment of Infectious Disease Control and Prevention, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, ChinaDepartment of Infectious Disease Control and Prevention, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, ChinaDepartment of Infectious Disease Control and Prevention, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, ChinaObjectiveTo evaluate the performance of the Prophet model in predicting the daily incidence of hand, foot, and mouth disease (HFMD) in Shenzhen city, to analyze the impact of the COVID-19 pandemic, public holidays, and school vacations (summer/winter) on HFMD predictions, and to provide new insights for HFMD surveillance and early warning systems. MethodsUsing daily incidence rate data of hand, foot, and mouth disease (HFMD) in Shenzhen city from 2011 to 2023 as a training set, we constructed different Prophet models based on two factors: (1) whether data from the COVID-19 epidemic period were included, and (2) whether holiday effects were adjusted. These models were then used to predict the daily incidence rate from January to July 2024, and the predictions were compared with the actual observations. Model performance was evaluated using four metrics: mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and symmetric mean absolute percentage error (SMAPE). ResultsCompared to the Prophet model trained on data during the COVID-19 pandemic, the baseline Prophet model (without pandemic data) achieved reductions in daily average MSE, MAE, RMSE, and SMAPE of 45.64%, 27.63%, 25.89%, and 7.97%, respectively. After controlling for holiday effects, the MAE, RMSE, and SMAPE values remained unchanged. When the prediction horizon exceeded 4 months, the daily MSE decreased by 3.81%. For predictions within 4 months, the model yielded MSE, MAE, RMSE, and SMAPE values (with 95% confidence intervals) of 0.42 (0.33, 0.90), 0.51 (0.45, 0.75), 0.65 (0.57, 0.95), and 0.42 (0.24, 0.44), respectively. In contrast, the predictions at 7 months showed higher errors: MSE 1.78 (0.39, 29.61), MAE 0.94 (0.48, 3.05), RMSE 1.33 (0.63, 5.44), and SMAPE 0.40 (0.33, 0.43). ConclusionsDuring the COVID-19 pandemic (2020 - 2022), the daily HFMD incidence rate significantly affected the performance of the Prophet model. While controlling for holidays and school vacations (winter/summer) had a limited effect on short-term predictions, it improved the model′s accuracy for long-term predictions.https://www.zgggws.com/article/doi/10.11847/zgggws1146131hand, foot, and mouth diseaseprophet modeltime series analysispredictionshenzhen city
spellingShingle Wenhai LU
Lixia SONG
Huawei XIONG
Zhigao CHEN
Yan LU
Yanpeng CHENG
Zhen ZHANG
Trend and prediction of daily incidence of hand, foot, and mouth disease in Shenzhen, 2011 - 2023 with projections to 2024: a Prophet model approach
Zhongguo gonggong weisheng
hand, foot, and mouth disease
prophet model
time series analysis
prediction
shenzhen city
title Trend and prediction of daily incidence of hand, foot, and mouth disease in Shenzhen, 2011 - 2023 with projections to 2024: a Prophet model approach
title_full Trend and prediction of daily incidence of hand, foot, and mouth disease in Shenzhen, 2011 - 2023 with projections to 2024: a Prophet model approach
title_fullStr Trend and prediction of daily incidence of hand, foot, and mouth disease in Shenzhen, 2011 - 2023 with projections to 2024: a Prophet model approach
title_full_unstemmed Trend and prediction of daily incidence of hand, foot, and mouth disease in Shenzhen, 2011 - 2023 with projections to 2024: a Prophet model approach
title_short Trend and prediction of daily incidence of hand, foot, and mouth disease in Shenzhen, 2011 - 2023 with projections to 2024: a Prophet model approach
title_sort trend and prediction of daily incidence of hand foot and mouth disease in shenzhen 2011 2023 with projections to 2024 a prophet model approach
topic hand, foot, and mouth disease
prophet model
time series analysis
prediction
shenzhen city
url https://www.zgggws.com/article/doi/10.11847/zgggws1146131
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