Department of Mathematics and Informatics (DMI), Eduardo Mondlane University, Maputo, Mozambique.
Malar J. 2011 Jul 13;10:189. doi: 10.1186/1475-2875-10-189.
The objective of this study is to analyze the spatial and temporal patterns of malaria incidence as to determine the means by which climatic factors such as temperature, rainfall and humidity affect its distribution in Maputo province, Mozambique.
This study presents a model of malaria that evolves in space and time in Maputo province-Mozambique, over a ten years period (1999-2008). The model incorporates malaria cases and their relation to environmental variables. Due to incompleteness of climatic data, a multiple imputation technique is employed. Additionally, the whole province is interpolated through a Gaussian process. This method overcomes the misalignment problem of environmental variables (available at meteorological stations--points) and malaria cases (available as aggregates for every district--area). Markov Chain Monte Carlo (MCMC) methods are used to obtain posterior inference and Deviance Information Criteria (DIC) to perform model comparison.
A Bayesian model with interaction terms was found to be the best fitted model. Malaria incidence was associated to humidity and maximum temperature. Malaria risk increased with maximum temperature over 28 °C (relative risk (RR) of 0.0060 and 95% Bayesian credible interval (CI) of 0.00033-0.0095) and humidity (relative risk (RR) of 0.00741 and 95% Bayesian CI 0.005141-0.0093). The results would suggest that additional non-climatic factors including socio-economic status, elevation, etc. also influence malaria transmission in Mozambique.
These results demonstrate the potential of climate predictors particularly, humidity and maximum temperature in explaining malaria incidence risk for the studied period in Maputo province. Smoothed maps obtained as monthly average of malaria incidence allowed to visualize months of initial and peak transmission. They also illustrate a variation on malaria incidence risk that might not be related to climatic factors. However, these factors are still determinant for malaria transmission and intensity in the region.
本研究旨在分析疟疾发病率的时空分布模式，以确定温度、降雨和湿度等气候因素如何影响莫桑比克马普托省的疟疾分布。
本研究提出了一个在莫桑比克马普托省（1999-2008 年）时空演变的疟疾模型。该模型纳入了疟疾病例及其与环境变量的关系。由于气候数据不完整，采用了多重插补技术。此外，通过高斯过程对整个省份进行插值。该方法克服了环境变量（在气象站可用——点）和疟疾病例（在每个地区可用——区域）的不对齐问题。马尔可夫链蒙特卡罗（MCMC）方法用于获得后验推断，偏差信息准则（DIC）用于进行模型比较。
发现具有交互项的贝叶斯模型是最佳拟合模型。疟疾发病率与湿度和最高温度有关。最高温度超过 28°C 时（相对风险（RR）为 0.0060，95%贝叶斯可信区间（CI）为 0.00033-0.0095）和湿度（RR 为 0.00741，95%贝叶斯 CI 为 0.005141-0.0093）时，疟疾风险增加。结果表明，包括社会经济地位、海拔等在内的其他非气候因素也会影响莫桑比克的疟疾传播。
这些结果表明，气候预测因子，特别是湿度和最高温度，在解释马普托省研究期间的疟疾发病率风险方面具有潜力。作为疟疾发病率月平均值获得的平滑地图允许可视化初始和峰值传播的月份。它们还说明了与气候因素无关的疟疾发病率风险变化。然而，这些因素仍然是该地区疟疾传播和强度的决定因素。
