Becker, NG. Analysis of Infectious Disease Data. New York: Chapman & Hall, 1989, pp. 164.
Liu, Q, et al.
Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model. BMC Infectious Diseases
2011; 11: 218.
Zeger, SL, Irizarry, R, Peng, RD. On time series analysis of public health and biomedical data. Annual Review of Public Health
2006; 27: 57–79.
José, MV, Bishop, RF. Scaling properties and symmetrical patterns in the epidemiology of rotavirus infection. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences
2003; 358: 1625–1641.
Box, GEP, Jenkins, GM. Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day, 1976, pp. 33–35.
Bollerslev, T. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics
1986; 31: 307–327.
Cai, XH. Time series analysis of air pollution CO in California south coast area, with seasonal ARIMA model and VAR model (dissertation). Los Angeles, CA, USA: University of California, 2008, 84 pp.
Ture, M, Kurt, I. Comparison of four different time series methods to forecast hepatitis A virus infection. Expert Systems with Applications
2006; 31: 41–46.
Olsen, LF, Schaffer, WM. Chaos versus noisy periodicity: alternative hypotheses for childhood epidemics. Science
1990; 249: 499–504.
Shumway, RH. Time Series Analysis and its Applications. New York: Springer Press, 2006, pp. 174.
Cai, W, Xu, Z, Baumketner, A. A new FFT-based algorithm to compute Born radii in the generalized Born theory of biomolecule solvation. Journal of Computational Physics
2008; 227: 10162–10177.
Jakoby, B, Vellekoop, MJ. FFT-based analysis of periodic structures in microacoustic devices. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
2000; 47: 651–656.
Dillenseger, JL, Esneault, S. Fast FFT-based bioheat transfer equation computation. Computers in Biology and Medicine
2010; 40: 119–123.
Lui, KJ, Kendal, AP. Impact of influenza epidemics on mortality in the United States from October 1972 to May 1985. American Journal of Public Health
1987; 77: 712–716.
Sumi, A, Kamo, KI. MEM spectral analysis for predicting influenza epidemics in Japan. Environmental Health and Preventive Medicine
2011; 17: 98–108.
Tsay, RS. Analysis of Financial Time Series, 2nd edn. Hoboken, New Jersy: Wiley & Sons, Inc., 2005, pp.146.
Ma, X. Joint estimation of time delay and frequency delay in impulsive noise using fractional lower order statistics. IEEE Transactions on Signal Processing
1996; 44: 2669–2687.
Lin, DD, et al.
Joint estimation of channel response, frequency offset, and phase noise in OFDM. IEEE Transactions on Signal Processing
2006; 54: 3542–3254.
Luo, T, et al.
Time series analysis based by spectral power distribution-maximum entropy method (MEM) [in Chinese]. Chinese Journal of Health Statistics
2010; 5: 477–484.
Peyton, ZP. Probability, Random Variables, and Random Signal Principles, 4th edn. New York: McGraw Hill Companies, Inc., 2001, pp. 25.
Lamden, KH. An outbreak of scarlet fever in a primary school. Archives of Disease in Childhood
2011; 96: 394–397.
Barnett, BO, Frieden, IJ. Streptococcal skin diseases in children. Seminars in Dermatology
1992; 11: 3–10.
Alonso, WJ, et al.
Seasonality of influenza in Brazil: a traveling wave from the Amazon to the subtropics. American Journal of Epidemiology
2007; 165: 1434–1442.
Cochran, WT, et al.
What is the fast Fourier transform?
Proceedings of the IEEE
1967; 55: 1664–1674.
Tsay, RS. Outliers, level shifts, and variance changes in time series. Journal of Forecasting
1988; 7: 1–20.
Chen, C, Liu, LM. Joint estimation of model parameters and outlier effects in time series. Journal of the American Statistical Association
1993; 8: 284–297.
Chang, I, Tiao, GC, Chen, C. Estimation of time series parameters in the presence of outliers. Technometrics
1988; 30: 193–204.
Bracewell, RN. The Fourier Transformation and its Application, 3rd edn. New York: McGraw Hill Companies, Inc., 1999.
Davision, AC, Hinkley, DV. Bootstrap Methods and their Application. Cambridge: Cambridge University Press, 1997, pp. 23.
Breban, R, et al.
Is there any evidence that syphilis epidemics cycle?
Lancet Infectious Diseases
2008; 8: 577–581.
Goh, C, Law, R. Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management
2002; 23: 499–510.
Wang, J, et al.
Epidemiological investigation of scarlet fever in Hefei City, China, from 2004 to 2008. Tropical Doctor
2010; 40: 225–226.
Li, XY, et al.
Correlative study on association between meteorological factors and incidence of scarlet fever in Beijing. Practical Preventive Medicine
2007; 14: 1435–1437.
Qian, HK, et al.
Spatial-temporal scan statistic on scarlet fever cases in Beijing, 2005–2010. Disease Surveillance
2011; 26: 435–238.
Liu, Z, Wang, BX, Wang, SC. The analysis of dynmaics of scarlet fever in China from 2003–2008 [in Chinese]. Journal of public health and preventive medcine
2009; 20: 21–22.
Briko, NI, et al.
Epidemiological pattern of scarlet fever in recent years. Zhurnal mikrobiologii, epidemiologii, i immunobiologii
2003; 5: 67–72.
Hubalek, Z. North Atlantic weather oscillation and human infectious diseases in the Czech Republic, 1951–2003. European Journal of Epidemiology
2005; 20: 263–270.