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

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*Int. J. Environ. Res. Public Health*

**2014**,

*11*(10), 10587-10605; https://doi.org/10.3390/ijerph111010587

## Abstract

**:**

## 1. Introduction

## 2. Malaria Occurrence in Korea

#### 2.1. Trend of Malaria Occurrence

#### 2.2. Data Collection

**Figure 1.**(

**a**) Time series of each variable; (

**b**) scatter diagram of mean temperature and malaria; (

**c**) scatter diagram of relative humidity and malaria; (

**d**) scatter diagram of monthly precipitation and malaria.

## 3. Methodology

#### 3.1. Spectral Analysis

#### 3.2. Brock-Dechert-Scheinkman(BDS) Statistic

#### 3.3. Principal Components Regression

^{th}, and $\beta $ : coefficients matrix of independent variables, $\epsilon $ : error term.

## 4. Modeling of Malaria and Climate Variables

#### 4.1. Nonlinear Regression Analysis

**Figure 2.**(

**a**) Spectrogram of malaria; (

**b**) coherency of Temp. and malaria; (

**c**) coherency of humidity and malaria; (

**d**) coherency of precipitation and malaria.

Test method | Test statistic | 95% C. I. | Randomness Check | |
---|---|---|---|---|

Run Test | −7.3261 | [−1.96, +1.96] | X | |

Anderson | 0.1741 | [−1.65. +1.65] | O | |

Spearman | 0.5760 | [−1.96, +1.96] | O | |

Turning Point | −11.9868 | [−1.96, +1.96] | X | |

BDS(2) | 10.5150 | [−1.96, +1.96] | X | |

BDS(3) | 9.7895 | [−1.96, +1.96] | X | |

BDS(4) | 9.2964 | [−1.96, +1.96] | X | |

BDS(5) | 8.9231 | [−1.96, +1.96] | X |

**Figure 3.**Malaria simulation (2001–2008) (

**a**) Malaria simulation by multiple regression; (

**b**) malaria simulation by PCA-regression.

^{−19}, and statistical significance did exist (see Figure 3a). However, according to Figure 1a, it is highly likely that there is coherency between data, and if this is the case, regression model drawn from regression analysis may be under the influence of multicollinearity. Thus, analysis was conducted based on malaria occurrences and each of the Pearson, Kendall and Spearman correlation coefficients; it revealed that each variable had a high correlation, from 0.52 between average temperature and humidity to 0.78 between average temperature and malaria (see Figure 4). Thus, there is clearly a strong correlation between each climate variable and there could be an error when building a regression model for malaria through multiple regression analysis.

#### 4.2. PCA-Regression Analysis

^{−24}, thus statistical significance was present (See Figure 3b). Also, since each principal component is independent, the regression model can be established without the multicollinearity problem. Thus, principal components regression analysis is quite plausible for simulation of malaria occurrences.

#### 4.3. Validation of Malaria Model

## 5. Malaria Occurrence under Climate Change

#### 5.1. Climate Change Scenario

Senarios | Description | CO density (ppm) |
---|---|---|

RCP 2.6 | Peak in radiative forcing at ~3 W/m before 2100 year and then decline | 490 |

RCP 4.5 | Stabilization without overshoot pathway to ~4.5 W/m at stabilization after 2100 year | 650 |

RCP 6.0 | Stabilization without overshoot pathway to ~6 W/m at stabilization after 2100 year | 850 |

RCP 8.5 | Rising radiative forcing pathway leading to 8.5 W/m by 2100 year | 1370 |

#### 5.2. Future Malaria Simulation and Analysis

**Figure 10.**Malaria occurrence boxplot for each period 2001–2011 year (

**b**) 2011–2039 year (

**c**) 2040–2069 year (

**d**) 2070–2100 year.

## 6. Conclusions

- Correlation between malaria occurrence and monthly average temperature, relative humidity and precipitation data is analyzed with time lag effect between malaria occurrence and climate variables using spectral analysis between each variable. A strong coherency between each climate variable data is clear, thus regression model is analyzed under the influence of multicollinearity. To resolve this issue, principal component regression analysis based on PCA is used to establish a regression model. Using the regression model, malaria infection occurrences from 2009–2011 are tested and coefficient of determination ${R}^{2}$ is 0.852, NRSE is 0.117 and RE is 0.026, which clearly accounts for malaria infection.
- By applying climate data between 2011 and 2100 using the RCP 4.5 climate change scenario and the CNCM3 climate model to the regression model, future malaria occurrence is simulated. Analysis of simulated data shows the malaria occurrence trend in general will gradually increase. Also, in the future, the occurrence period will diminish and it shows an increase of malaria occurrence before the rainy season in summer; thus, adaptation in the malaria occurrence response plan of Korea is needed.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**MDPI and ACS Style**

Kwak, J.; Noh, H.; Kim, S.; Singh, V.P.; Hong, S.J.; Kim, D.; Lee, K.; Kang, N.; Kim, H.S.
Future Climate Data from RCP 4.5 and Occurrence of Malaria in Korea. *Int. J. Environ. Res. Public Health* **2014**, *11*, 10587-10605.
https://doi.org/10.3390/ijerph111010587

**AMA Style**

Kwak J, Noh H, Kim S, Singh VP, Hong SJ, Kim D, Lee K, Kang N, Kim HS.
Future Climate Data from RCP 4.5 and Occurrence of Malaria in Korea. *International Journal of Environmental Research and Public Health*. 2014; 11(10):10587-10605.
https://doi.org/10.3390/ijerph111010587

**Chicago/Turabian Style**

Kwak, Jaewon, Huiseong Noh, Soojun Kim, Vijay P. Singh, Seung Jin Hong, Duckgil Kim, Keonhaeng Lee, Narae Kang, and Hung Soo Kim.
2014. "Future Climate Data from RCP 4.5 and Occurrence of Malaria in Korea" *International Journal of Environmental Research and Public Health* 11, no. 10: 10587-10605.
https://doi.org/10.3390/ijerph111010587