Pem etaan daerah kerawanan penyakit leptospirosis melalui metode geographically weighted zero inflated poisson regression

Abstract

Mapping leptospirosis vulnerable areas through a geographically weighted zero-inflated poisson regression Purpose:  Gunung Kidul Health Office reported an increase of leptospirosis cases in 2017. There are many zero values in the data count, so the mean and variance values must not be met. Zero-Inflated Poisson regression is used for modeling data counts that are mostly zero. The study aims to map leptospirosis vulnerable areas. Method:  A total of 144 villages were analyzed. The independent variables were percentages in paddy fields, residential land, settlement distance to rivers, population density, soil texture, altitude, and rainfall. The dependent variable was the number of leptospirosis cases in each village from 2011 to 2017. Results:  The average of leptospirosis cases was 0.6 and the variance was 3.4. Observation data with value of zero was 81%. The Geographically Weighted Zero-Inflated Poisson Regression test was better than Zero-Inflated Poisson multivariate regression in mapping of leptospirosis vulnerable areas. The model brought up local variables in the percentage of paddy fields, percentage of residential land, percentage of settlement distance to river, place height, and rainfall and global variables in the form of population density and soil texture (R-Square = 55.9%). This vulnerability modeling was appropriate based on disease distribution and level of vulnerability. Only 5.5% of leptospirosis cases in the area were not vulnerable. Conclusion:  The sentinel leptospirosis surveillance system should be applied in areas prone to early detection of leptospirosis cases.