Future Climate Data from RCP 4.5 and Occurrence of Malaria in Korea

Jaewon Kwak, Huiseong Noh, Soojun Kim, Vijay P. Singh, Seung Jin Hong, Duckgil Kim, Keonhaeng Lee, Narae Kang and Hung Soo Kim
Additional contact information
Jaewon Kwak: Hydroclimatic Statistical Research Group, Centre Eau Terre Environnement, INRS, Québec, QC G1K 9A9, Canada
Huiseong Noh: Department of Civil Engineering, Inha University, Incheon 402-751, Korea
Soojun Kim: Columbia Water Center, Earth Institute, Columbia University, New York, NY 10027, USA
Vijay P. Singh: Department of Biological and Agricultural Engineering, Zachry Department of Civil Engineering, Texas A & M University, College Station, TX 77843, USA
Seung Jin Hong: Department of Civil Engineering, Inha University, Incheon 402-751, Korea
Duckgil Kim: Water Environment Research Department, Water Quality Assessment Research Division, National Institute of Environmental Research, Incheon 404-708, Korea
Keonhaeng Lee: Water Resources Research Division, Water Resources and Environment Research Department, Korea Institute of Civil Engineering and Building Technology, Goyang-si, Gyeonggi-do 411-712, Korea
Narae Kang: Department of Civil Engineering, Inha University, Incheon 402-751, Korea
Hung Soo Kim: Department of Civil Engineering, Inha University, Incheon 402-751, Korea

IJERPH, 2014, vol. 11, issue 10, 1-19

Abstract: Since its reappearance at the Military Demarcation Line in 1993, malaria has been occurring annually in Korea. Malaria is regarded as a third grade nationally notifiable disease susceptible to climate change. The objective of this study is to quantify the effect of climatic factors on the occurrence of malaria in Korea and construct a malaria occurrence model for predicting the future trend of malaria under the influence of climate change. Using data from 2001–2011, the effect of time lag between malaria occurrence and mean temperature, relative humidity and total precipitation was investigated using spectral analysis. Also, a principal component regression model was constructed, considering multicollinearity. Future climate data, generated from RCP 4.5 climate change scenario and CNCM3 climate model, was applied to the constructed regression model to simulate future malaria occurrence and analyze the trend of occurrence. Results show an increase in the occurrence of malaria and the shortening of annual time of occurrence in the future.

Keywords: malaria; climate change; PCA-regression analysis; climate variable (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2014
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