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Forecast of haemorrhagic fever with renal syndrome and meteorological factors of three cities in Liaoning Province, China, 2005-2019.
Frontiers in Environmental Science ( IF 4.6 ) Pub Date : 2021-06-17 , DOI: 10.3389/fenvs.2021.707960 Wanwan Sun , Zhidong Liu , Qiyong Liu , Wen Li , Liang Lu
Frontiers in Environmental Science ( IF 4.6 ) Pub Date : 2021-06-17 , DOI: 10.3389/fenvs.2021.707960 Wanwan Sun , Zhidong Liu , Qiyong Liu , Wen Li , Liang Lu
ABSTRACT Background: Haemorrhagic fever with renal syndrome (HFRS) is endemic in China, accounting for 90% of HFRS cases worldwide and growing. Therefore, it is urgent to monitor and predict HFRS cases to make control measures more effective. In this study, we applied generalized additive models (GAMs) to Liaoning Province, an area with many HFRS cases. Our aim was to determine whether GAMs could be used to accurately predict HFRS cases and to explore the association between meteorological factors and the incidence of HFRS. Methods: HFRS data from Liaoning was collected from January 2005 to May 2019 and used to construct the GAMs. Generalized cross-validation (GCV) and adjusted R-square (R2) values were used to evaluate the constructed models. The interclass correlation coefficient (ICC) was used as an index to assess the quality of the proposed models. Results: HFRS cases of the previous month and meteorological factors with different lag times were used to construct GAMs for three cities in Liaoning. The three models predicted the number of HFRS cases in the next month. The ICCs of the three models were 0.822, 0.832 and 0.831. Temperature and the number of cases in the previous month had a positive association with HFRS. Conclusion: GAMs applied to HFRS case data are an important tool for HFRS control in China. This study shows that meteorological factors have an effect on the occurrence of HFRS. A mathematical model based on surveillance data could also be used in forecasting. Our study will inform local CDCs and assist them in carrying out more effective measures for HFRS control and prevention through simple modelling and forecasting.
更新日期：2021-06-17
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