Research Unit, Foundation for Professional Development, Pretoria 0040, South Africa.
Department of Mathematics and Applied Mathematics, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa.
Int J Environ Res Public Health. 2019 Jun 5;16(11):2000. doi: 10.3390/ijerph16112000.
Recent studies have considered the connections between malaria incidence and climate variables using mathematical and statistical models. Some of the statistical models focused on time series approach based on Box-Jenkins methodology or on dynamic model. The latter approach allows for covariates different from its original lagged values, while the Box-Jenkins does not. In real situations, malaria incidence counts may turn up with many zero terms in the time series. Fitting time series model based on the Box-Jenkins approach and ARIMA may be spurious. In this study, a zero-inflated negative binomial regression model was formulated for fitting malaria incidence in Mopani and Vhembe-two of the epidemic district municipalities in Limpopo, South Africa. In particular, a zero-inflated negative binomial regression model was formulated for daily malaria counts as a function of some climate variables, with the aim of identifying the model that best predicts reported malaria cases. Results from this study show that daily rainfall amount and the average temperature at various lags have a significant influence on malaria incidence in the study areas. The significance of zero inflation on the malaria count was examined using the Vuong test and the result shows that zero-inflated negative binomial regression model fits the data better. A dynamical climate-based model was further used to investigate the population dynamics of mosquitoes over the two regions. Findings highlight the significant roles of on malaria transmission over the regions and suggest that vector control activities should be intense to eradicate malaria in Mopani and Vhembe districts. Although has been identified as the major vector over these regions, our findings further suggest the presence of additional vectors transmitting malaria in the study regions. The findings from this study offer insight into climate-malaria incidence linkages over Limpopo province of South Africa.
最近的研究利用数学和统计模型考虑了疟疾发病率与气候变量之间的联系。一些统计模型侧重于基于 Box-Jenkins 方法的时间序列方法或动态模型。后一种方法允许协变量与其原始滞后值不同，而 Box-Jenkins 则不允许。在实际情况下，疟疾发病率计数可能会在时间序列中出现许多零项。基于 Box-Jenkins 方法和 ARIMA 的时间序列模型拟合可能是虚假的。在这项研究中，提出了一个零膨胀负二项回归模型，用于拟合南非林波波省两个流行地区市政的莫潘尼和维姆贝的疟疾发病率。具体来说，提出了一个零膨胀负二项回归模型，用于将每日疟疾计数作为一些气候变量的函数，目的是确定最佳预测报告疟疾病例的模型。这项研究的结果表明，每日降雨量和不同滞后的平均温度对研究地区的疟疾发病率有显著影响。通过 Vuong 检验检查零膨胀对疟疾计数的显著性，结果表明零膨胀负二项回归模型更适合数据。进一步使用动态气候模型研究两个地区的蚊子种群动态。研究结果强调了在这些地区疟疾传播中零的重要作用，并表明应加强病媒控制活动以消灭莫潘尼和维姆贝地区的疟疾。尽管 已被确定为这些地区的主要病媒，但我们的研究结果进一步表明，在研究地区还存在其他传播疟疾的病媒。这项研究的结果为南非林波波省的气候-疟疾发病率关系提供了深入的了解。
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