TY - JOUR
T1 - Forecast and early warning of hand, foot, and mouth disease based on meteorological factors
T2 - Evidence from a multicity study of 11 meteorological geographical divisions in mainland China
AU - Gao, Qi
AU - Liu, Zhidong
AU - Xiang, Jianjun
AU - Tong, Michael
AU - Zhang, Ying
AU - Wang, Shuzi
AU - Zhang, Yiwen
AU - Lu, Liang
AU - Jiang, Baofa
AU - Bi, Peng
N1 - Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2021/1
Y1 - 2021/1
N2 - Background: Hand, foot, and mouth disease (HFMD) is a significant public health issue in China. Early warning and forecasting are one of the most cost-effective ways for HFMD control and prevention. However, relevant research is limited, especially in China with a large population and diverse climatic characteristics. This study aims to identify local specific HFMD epidemic thresholds and construct a weather-based early warning model for HFMD control and prevention across China. Methods: Monthly notified HFMD cases and meteorological data for 22 cities selected from different climate zones from 2014 to 2018 were extracted from the National Notifiable Disease Surveillance System and the Meteorological Data Sharing Service System, respectively. A generalized additive model (GAM) based on meteorological factors was conducted to forecast HFMD epidemics. The receiver operator characteristic curve (ROC) was generated to determine the value of optimal warning threshold. Results: The developed model was solid in forecasting the epidemic of HFMD with all R square (R2) in the 22 cities above 85%, and mean absolute percentage error (MAPE) less than 1%. The warning thresholds varied by cities with the highest threshold observed in Shenzhen (n = 7195) and the lowest threshold in Liaoyang (n = 12). The areas under the curve (AUC) was greater than 0.9 for all regions, indicating a satisfied discriminating ability in epidemics detection. Conclusions: The weather-based HFMD forecasting and early warning model we developed for different climate zones provides needed information on occurrence time and size of HFMD epidemics. An effective early warning system for HFMD could provide sufficient time for local authorities to implement timely interventions to minimize the HFMD morbidity and mortality.
AB - Background: Hand, foot, and mouth disease (HFMD) is a significant public health issue in China. Early warning and forecasting are one of the most cost-effective ways for HFMD control and prevention. However, relevant research is limited, especially in China with a large population and diverse climatic characteristics. This study aims to identify local specific HFMD epidemic thresholds and construct a weather-based early warning model for HFMD control and prevention across China. Methods: Monthly notified HFMD cases and meteorological data for 22 cities selected from different climate zones from 2014 to 2018 were extracted from the National Notifiable Disease Surveillance System and the Meteorological Data Sharing Service System, respectively. A generalized additive model (GAM) based on meteorological factors was conducted to forecast HFMD epidemics. The receiver operator characteristic curve (ROC) was generated to determine the value of optimal warning threshold. Results: The developed model was solid in forecasting the epidemic of HFMD with all R square (R2) in the 22 cities above 85%, and mean absolute percentage error (MAPE) less than 1%. The warning thresholds varied by cities with the highest threshold observed in Shenzhen (n = 7195) and the lowest threshold in Liaoyang (n = 12). The areas under the curve (AUC) was greater than 0.9 for all regions, indicating a satisfied discriminating ability in epidemics detection. Conclusions: The weather-based HFMD forecasting and early warning model we developed for different climate zones provides needed information on occurrence time and size of HFMD epidemics. An effective early warning system for HFMD could provide sufficient time for local authorities to implement timely interventions to minimize the HFMD morbidity and mortality.
KW - Early warning
KW - Forecasting
KW - Generalized additive model
KW - Hand, foot and mouth disease
KW - Threshold
UR - http://www.scopus.com/inward/record.url?scp=85093682389&partnerID=8YFLogxK
U2 - 10.1016/j.envres.2020.110301
DO - 10.1016/j.envres.2020.110301
M3 - Article
SN - 0013-9351
VL - 192
JO - Environmental Research
JF - Environmental Research
M1 - 110301
ER -