School of Forest Resources and Conservation, University of Florida, Gainesville, USA.
Emerging Pathogens Institute, University of Florida, Gainesville, USA.
Malar J. 2019 Mar 15;18(1):81. doi: 10.1186/s12936-019-2703-4.
Bayesian methods have been used to generate country-level and global maps of malaria prevalence. With increasing availability of detailed malaria surveillance data, these methodologies can also be used to identify fine-scale heterogeneity of malaria parasitaemia for operational prevention and control of malaria.
In this article, a Bayesian geostatistical model was applied to six malaria parasitaemia surveys conducted during rainy and dry seasons between November 2010 and 2013 to characterize the micro-scale spatial heterogeneity of malaria risk in northern Ghana.
The geostatistical model showed substantial spatial heterogeneity, with malaria parasite prevalence varying between 19 and 90%, and revealing a northeast to southwest gradient of predicted risk. The spatial distribution of prevalence was heavily influenced by two modest urban centres, with a substantially lower prevalence in urban centres compared to rural areas. Although strong seasonal variations were observed, spatial malaria prevalence patterns did not change substantially from year to year. Furthermore, independent surveillance data suggested that the model had a relatively good predictive performance when extrapolated to a neighbouring district.
This high variability in malaria prevalence is striking, given that this small area (approximately 30 km × 40 km) was purportedly homogeneous based on country-level spatial analysis, suggesting that fine-scale parasitaemia data might be critical to guide district-level programmatic efforts to prevent and control malaria. Extrapolations results suggest that fine-scale parasitaemia data can be useful for spatial predictions in neighbouring unsampled districts and does not have to be collected every year to aid district-level operations, helping to alleviate concerns regarding the cost of fine-scale data collection.
贝叶斯方法已被用于生成疟疾流行的国家和全球地图。随着详细疟疾监测数据的可用性不断增加，这些方法还可用于确定疟疾寄生虫血症的细粒度异质性，以便对疟疾进行操作预防和控制。
在本文中，应用贝叶斯地质统计模型对 2010 年 11 月至 2013 年期间在雨季和旱季进行的六次疟疾寄生虫血症调查进行了分析，以描述加纳北部疟疾风险的微观空间异质性。
地质统计模型显示出显著的空间异质性，疟疾寄生虫患病率在 19%至 90%之间变化，并显示出从东北到西南的预测风险梯度。患病率的空间分布受到两个适度城市中心的强烈影响，城市中心的患病率明显低于农村地区。尽管观察到强烈的季节性变化，但从年到年，疟疾流行模式的空间分布并没有发生重大变化。此外，独立的监测数据表明，当该模型外推到邻近的区时，具有相对较好的预测性能。
鉴于该小面积（约 30 公里×40 公里）根据国家一级的空间分析据称是同质的，因此疟疾流行率的这种高度可变性引人注目，这表明细粒度的寄生虫血症数据可能对指导区一级的规划工作以预防和控制疟疾至关重要。外推结果表明，细粒度的寄生虫血症数据可用于邻近未采样区的空间预测，并且不必每年收集以帮助区一级的运作，有助于减轻对细粒度数据收集成本的担忧。
