Statistical estimations for Plasmodium vivax malaria in South Korea
10.1016/S1995-7645(14)60310-2
- Author:
Youngsaeng LEE
1
;
Jeong-Soo PARK
1
;
Hyeongap JANG
2
;
Jeong Ae RHEE
3
Author Information
1. Department of Statistics, Chonnam National University
2. JW LEE Center for Global Medicine, College of Medicine, Seoul National University
3. Department of Preventive Medicine, Chonnam National University
- Publication Type:Journal Article
- Keywords:
Back calculation;
Incidence;
Incubation period;
Infection;
Poisson model;
Prevalence;
Regression model
- From:
Asian Pacific Journal of Tropical Medicine
2015;8(3):169-175
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To calculate the numbers of weekly infections and prevalence of malaria, and to predict future trend of malaria incidences in South Korea. Methods: Weekly incidences of malaria 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 malaria incidences over 13 years was fitted by the non-linear least squares method, and used to predict futuretrend. Results: The estimated infection 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 infection 110 was obtained at the 29th week. The prevalence at the first week was around 496 persons, the minimum number was 366 at 22nd week, and the maximum prevalence was 648 at 34th week. Prevalence drops in late spring with people that falling ill and had had long incubation periods and rose in the summer with new infections. 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 infection rates and the prevalence of malaria. The obtained results can be useful in establishing an efficient preventive program for malaria infection. The method presented here can be used in other countries where incidence data and incubation period are available.
