Statistical estimations for Plasmodium vivax XXXX in South Korea.

@entity1 @entity281 @entity311
OBJECTIVE: To calculate the numbers of @entity281 and prevalence of @entity311 , and to predict future trend of @entity311 incidences in South Korea. METHODS: Weekly incidences of @entity311 for 13 years from the period 2001-2013 in South Korea were analyzed. The back-calculation equations were used with incubation period distributions. The maximum likelihood estimation for Poisson model was also used. The confidence intervals of the estimates were obtained by a bootstrap method. A regression model for time series of @entity311 incidences over 13 years was fitted by the non-linear least squares method, and used to predict futuretrend. RESULTS: The estimated @entity281 curve is narrower and more concentrated in the summer than in the incidence distribution. Infection started around the 19th week and was over around the 41st week. The maximum weekly @entity281 110 was obtained at the 29th week. The prevalence at the first week was around 496 @entity1 , the minimum number was 366 at 22nd week, and the maximum prevalence was 648 at 34th week. Prevalence drops in late spring with @entity1 that falling ill and had had long incubation periods and rose in the summer with new @entity281 . Our future forecast based on the regression model was that an increase at year 2014 compared to 2013 may reach a peak (at maximum about 70 weekly cases) at year 2015, with a decreasing trend after then. CONCLUSIONS: This work shows that back-calculation methods could work well in estimating the @entity281 rates and the prevalence of @entity311 . The obtained results can be useful in establishing an efficient preventive program for @entity281 . The method presented here can be used in other countries where incidence data and incubation period are available.
