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To explore the optimal prediction model for hantavirus hemorrhagic fever with renal syndrome (HFRS) in Hubei province, and to provide a basis for establishing a monitoring and early warning model for HFRS.
Using monthly surveillance data of HFRS incidence in Hubei province from 2005 to 2021, eight single time series models based on exponential smoothing (ETS), seasonal autoregressive integrated moving average (SARIMA) with and without regression variables, a state space model with Box-Cox transformation, ARMA errors, trend, and seasonal components (TBATS), a time series neural network model (NNETAR) with and without regression variables, a linear regression time series model (TSLM), and a cubic spline prediction model (SPLINEF) were used to build 162 models through 1-4 model combinations. The mean absolute percentage error (MAPE) was used as an evaluation index to evaluate the fitting and prediction performance of the models. The comprehensive fitting and prediction performance were evaluated by calculating the mean MAPE of fitting and prediction.
The TSLM model and its combined models had a comprehensive MAPE of more than 100%. Among the other 98 models, the optimal fitting models for single, two, three, and four-model combinations were SPLINEF (11.98%), SARIMA-SPLINEF (15.14%), SARIMA-NNETAR-REG-SPLINEF (16.06%), and SARIMA-TBAT-NNETAR-REG-SPLINEF (17.75%), respectively. The optimal prediction models for single, two, three, and four-model combinations were SARIMA-REG (34.48%), SARIMA-REG-TBATS (22.77%), SARIMA-TBATS-SPLINEF (23.84%), and SARIMA-SARIMA-REG-TBATS-SPLINEF (22.31%), respectively. The optimal fitting and prediction models for single, two, three, and four-model combinations were SPLINEF (24.75%), SARIMA-SPLINEF (22.55%), SARIMA-TBATS-SPLINEF (20.92%), and SARIMA-SARIMA-REG-TBATS-SPLINEF (20.75%), respectively.
Based on the number of models, fitting and prediction accuracy, SARIMA-TBATS-SPLINEF is considered the optimal prediction model and can be used for monitoring and early warning of HFRS in Hubei.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 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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