Centre for Emerging Zoonotic and Parasitic Diseases, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg 2131, South Africa.
Wits Research Institute for Malaria, School of Pathology, University of the Witwatersrand, Johannesburg 2050, South Africa.
Int J Environ Res Public Health. 2024 Apr 28;21(5):558. doi: 10.3390/ijerph21050558.
It is widely accepted that climate affects the mosquito life history traits; however, its precise role in determining mosquito distribution and population dynamics is not fully understood. This study aimed to investigate the influence of various climatic factors on the temporal distribution of populations in Mamfene, South Africa between 2014 and 2019. Time series analysis, wavelet analysis, cross-correlation analysis, and regression model combined with the autoregressive integrated moving average (ARIMA) model were utilized to assess the relationship between climatic factors and population density. In total 3826 adult collected was used for the analysis. ARIMA (0, 1, 2) (0, 0, 1) models closely described the trends observed in population density and distribution. The wavelet coherence and time-lagged correlation analysis showed positive correlations between population density and temperature (r = 0.537 ), humidity (r = 0.495) and rainfall (r = 0.298) whilst wind showed negative correlations (r = -0.466). The regression model showed that temperature ( = 0.00119), rainfall ( = 0.0436), and humidity ( = 0.0441) as significant predictors for forecasting abundance. The extended ARIMA model (AIC = 102.08) was a better fit for predicting abundance compared to the basic model. still remains the predominant malaria vector in the study area and climate variables were found to have varying effects on the distribution and abundance of . This necessitates other complementary vector control strategies such as the Sterile Insect Technique (SIT) which involves releasing sterile males into the environment to reduce mosquito populations. This requires timely mosquito and climate information to precisely target releases and enhance the effectiveness of the program, consequently reducing the malaria risk.
人们普遍认为气候会影响蚊子的生活史特征；然而，其在确定蚊子的分布和种群动态方面的确切作用尚未完全了解。本研究旨在调查各种气候因素对南非 Mamfene 地区 2014 年至 2019 年期间种群时间分布的影响。采用时间序列分析、小波分析、交叉相关分析和回归模型结合自回归综合移动平均 (ARIMA) 模型来评估气候因素与 种群密度之间的关系。共分析了采集到的 3826 只成年 。ARIMA（0，1，2）（0，0，1）模型很好地描述了 种群密度和分布的趋势。小波相干和时间滞后相关分析表明， 种群密度与温度（r = 0.537）、湿度（r = 0.495）和降雨量（r = 0.298）呈正相关，而与风速呈负相关（r = -0.466）。回归模型表明，温度（ = 0.00119）、降雨量（ = 0.0436）和湿度（ = 0.0441）是预测 丰度的重要预测因子。与基本模型相比，扩展的 ARIMA 模型（AIC = 102.08）更适合预测 丰度。 仍然是研究区域内主要的疟疾传播媒介，气候变量对 的分布和丰度有不同的影响。这需要其他补充性的蚊虫控制策略，如释放不育雄蚊到环境中以减少蚊虫数量的不育昆虫技术（SIT）。这需要及时的蚊虫和气候信息来精确瞄准释放，并提高该计划的有效性，从而降低疟疾风险。
