Ying Zhang1, Peng Bi, Janet E Hiller. 1. Discipline of Public Health, School of Population Health and Clinical Practice, The University of Adelaide, Adelaide, SA 5005, Australia. ying.zhang@adelaide.edu.au
Abstract
OBJECTIVES: This study aimed to examine the impact of climate variation on malaria in a temperate region of China. METHODS: A 20-year historical time-series data analysis was conducted to examine the relationship between meteorological variables, including maximum and minimum temperatures, rainfall, humidity, and cases of malaria in Jinan, a temperate city in northern China. Data were retrieved from 1959 and 1979 and analyzed on a monthly basis. Spearman correlation and cross-correlation analyses were performed to identify time lag values between each meteorological variable and the number of malaria cases. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to quantify the relationship between the meteorological variables and malaria cases. RESULTS: The SARIMA models indicate that a 1 degrees C rise in maximum temperature may be related to a 7.7% to 12.7% increase and a 1 degrees C rise in minimum temperature may result in approximately 11.8% to 15.8% increase in the number of malaria cases. A clear association between malaria and other selected weather variables, including rainfall and humidity, has not been detected in this study. CONCLUSIONS: Temperature could play an important role in the transmission of malaria in temperate regions of China. Copyright 2010 Elsevier Ltd. All rights reserved.
OBJECTIVES: This study aimed to examine the impact of climate variation on malaria in a temperate region of China. METHODS: A 20-year historical time-series data analysis was conducted to examine the relationship between meteorological variables, including maximum and minimum temperatures, rainfall, humidity, and cases of malaria in Jinan, a temperate city in northern China. Data were retrieved from 1959 and 1979 and analyzed on a monthly basis. Spearman correlation and cross-correlation analyses were performed to identify time lag values between each meteorological variable and the number of malaria cases. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to quantify the relationship between the meteorological variables and malaria cases. RESULTS: The SARIMA models indicate that a 1 degrees C rise in maximum temperature may be related to a 7.7% to 12.7% increase and a 1 degrees C rise in minimum temperature may result in approximately 11.8% to 15.8% increase in the number of malaria cases. A clear association between malaria and other selected weather variables, including rainfall and humidity, has not been detected in this study. CONCLUSIONS: Temperature could play an important role in the transmission of malaria in temperate regions of China. Copyright 2010 Elsevier Ltd. All rights reserved.
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