1. |
Diagana M, Preux PM, Dumas M.Japanese encephalitis revisited. J Neurol Sci 2007; 262(1-2):165-70. doi: 10.1016/j.jns.2007.06.041.
|
2. |
Ghimire S, Dhakal S.Japanese encephalitis: challenges and intervention opportunities in Nepal. Vet World 2015, 8(1):61-5. doi: 10.14202/vetworld.2015.61-65.
|
3. |
Misra UK, Kalita J.Overview: Japanese encephalitis. Prog Neurobiol 2010; 91(2):108-20. doi: 10.1016/j.pneurobio.2010.01.008.
|
4. |
Jasik KP, Okła H, Słodki J, Rozwadowska B, Słodki A, Rupik W.Congenital tick borne diseases: is this an alternative route of transmission of tick-borne pathogens in mammals? Vector Borne Zoonotic Dis 2015; 15(11):637-44. doi: 10.1089/vbz.2015.1815.
|
5. |
Ai J, Xie Z, Liu G, Chen Z, Yang Y, Li Y, et al.Etiology and prognosis of acute viral encephalitis and meningitis in Chinese children: a multicentre prospective study. BMC Infect Dis 2017; 17(1):494. doi: 10.1186/s12879-017-2572-9.
|
6. |
Feng H, Duan G, Zhang R, Zhang W.Time series analysis of hand-foot-mouth disease hospitalization in Zhengzhou: establishment of forecasting models using climate variables as predictors. PLoS One 2014; 9(1):e87916. doi: 10.1371/journal.pone.0087916.eCollection2014.
|
7. |
Cappelle J, Duong V, Pring L, Kong L, Yakovleff M, Prasetyo DB, et al.Intensive circulation of Japanese Encephalitis virus in peri-urban Sentinel Pigs near Phnom Penh, Cambodia. PLoS Negl Trop Dis 2016; 10(12):e0005149. doi: 10.1371/journal.pntd.0005149.
|
8. |
Luo L, Luo L, Zhang X, He X.Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Serv Res 2017; 17(1):469. doi: 10.1186/s12913-017-2407-9.
|
9. |
Bozkurt ÖÖ, Biricik G, Tayşi ZC.Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market. PLoS One 2017; 12(4): e0175915. doi: 10.1371/journal.pone.0175915.
|
10. |
Sharafi M, Ghaem H, Tabatabaee HR, Faramarzi H.Forecasting the number of zoonotic cutaneous leishmaniasis cases in south of Fars province, Iran using seasonal ARIMA time series method. Asian Pac J Trop Med 2017; 10(1):79-86. doi: 10.1016/j.apjtm.2016.12.007.
|
11. |
Yousefzadeh-Chabok S, Ranjbar-Taklimie F, Malekpouri R, Razzaghi A.A time series model for assessing the trend and forecasting the road traffic accident mortality. Arch Trauma Res 2016; 5(3):e36570. doi: 10.5812/atr.36570.
|
12. |
Azeez A, Obaromi D, Odeyemi A, Ndege J, Muntabayi R. Seasonality and trend forecasting of tuberculosis prevalence data in Eastern Cape, South Africa, using a hybrid model. Int J Environ Res Public Health 2016; 13(8). pii: E757. doi: 10.3390/ijerph13080757.
|
13. |
Zhang G, Huang S, Duan Q, Shu W, Hou Y, Zhu S, et al.Application of a hybrid model for predicting the incidence of tuberculosis in Hubei, China. PLoS One 2013; 8(11): e80969. doi: 10.1371/journal.pone.0080969.
|
14. |
Kam HJ, Sung JO, Park RW.Prediction of daily patient numbers for a regional emergency medical center using time series analysis. Healthc Inform Res 2010; 16(3): 158-65. doi: 10.4258/hir.2010.16.3.158.
|
15. |
Guo Chen, Amy K. Glasmeier, Min Zhang, et al.Urbanization and income inequality in post-reform China: a causal analysis based on time series data. PLoS One 2016; 11(7):e0158826. doi: 10.1371/journal.pone.0158826.
|
16. |
Box GE, Jenkins GM.Time series analysis: forecasting and control. editors. Oakland: Holden-Day; 1976. p. 300-33.
|
17. |
Ren X, Fu S, Dai P, Wang H, Li Y, Li X, et al.Pigsties near dwellings as a potential risk factor for the prevalence of Japanese encephalitis virus in adult in Shanxi, China. Infect Dis Poverty 2017; 6(1):100. doi: 10.1186/s40249-017-0312-4.
|
18. |
Yu L, Zhou L, Tan L, Jiang H, Wang Y, Wei S, et al.Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China. PLoS One 2014; 9(6):e98241. doi: 10.1371/journal.pone.0098241.
|
19 |
IHS EViews EViews 7. [cited 2016 Nov 1]. Available from: .
|
20. |
Wang X, Li SH, Zhu L, Nian QG, Yuan S, Gao Q, et al.Near-atomic structure of Japanese encephalitis virus reveals critical determinants of virulence and stability. Nat Commun 2017; 8(1):14. doi: 10.1038/s41467-017-00024-6.
|
21. |
Parmar KS, Bhardwaj R.Statistical, time series, and fractal analysis of full stretch of river Yamuna (India) for water quality management. Environ Sci Pollut Res Int 2015; 22(1):397-414. doi: 10.1007/s11356-014-3346-1.
|
22. |
Heffelfinger JD, Li X, Batmunkh N, Grabovac V, Diorditsa S, Liyanage JB, et al.Japanese Encephalitis Surveillance and Immunization-Asia and Western Pacific Regions, 2016. MMWR Morb Mortal Wkly Rep 2017; 66(22):579-83. doi: 10.15585/mmwr.mm6622a3.
|