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• W4367185436 abstract "This study serves to ascertain trends of space and time for Japanese encephalitis (JE) transmission at the township-level and develop an innovative time series predictive model to predict the geographical spread of JE in Gansu Province, China.We collected weekly data on JE from 2005 to 2019 at the township-level. Kriging interpolation maps were used to visualize the trend of the epidemic spread of JE, and linear regression models were used to calculate the monthly changes in minimum longitude and maximum latitude of emerging towns with JE to assess the speed of the epidemic's spread to the northwest. Additionally, we utilized a time series Seasonal Autoregressive Integrated Moving Average (SARIMA) model to dynamically predict the ongoing weekly number of JE emerging townships.The Kriging difference map revealed a significant trend of JE spread towards the northwest. Our regression model indicated that the rate of decrease in minimum longitude was approximately 0.64 km per month, while the rate of increase in maximum latitude was approximately 1.00 km per month. Furthermore, the SARIMA pattern (2,0,0)(2,0,1)52 exhibited a better goodness-of-fit for predicting JE transmission, with an overall agreement of 93.27% to 94.23%.Our study highlights the expansion of JE cases towards the northwest of Gansu, indicating the need for ongoing surveillance and control efforts. The use of the SARIMA model provides a valuable tool for predicting the trend of JE spatial dispersion, thereby improving early warning systems. Our findings suggest that the number of emerging townships can be used to predict the trend of JE spatial dispersion, providing crucial insights for future research on JE incidence." @default.
• W4367185436 created "2023-04-28" @default.
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• W4367185436 date "2023-06-01" @default.
• W4367185436 modified "2023-10-16" @default.
• W4367185436 title "Japanese encephalitis transmission trends in Gansu, China: A time series predictive model based on spatial dispersion" @default.
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• W4367185436 doi "https://doi.org/10.1016/j.onehlt.2023.100554" @default.
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