As malaria incidence decreases and more countries move towards elimination, maps of malaria risk in low-prevalence areas are increasingly needed. For low-burden areas, disaggregation regression models have been developed to estimate risk at high spatial resolution from routine surveillance reports aggregated by administrative unit polygons. However, in areas with both routine surveillance data and prevalence surveys, models that make use of the spatial information from prevalence point-surveys might make more accurate predictions. Using case studies in Indonesia, Senegal and Madagascar, we compare the out-of-sample mean absolute error for two methods for incorporating point-level, spatial information into disaggregation regression models. The first simply fits a binomial-likelihood, logit-link, Gaussian random field to prevalence point-surveys to create a new covariate. The second is a multi-likelihood model that is fitted jointly to prevalence point-surveys and polygon incidence data. We find that in most cases there is no difference in mean absolute error between models. In only one case, did the new models perform the best. More generally, our results demonstrate that combining these types of data has the potential to reduce absolute error in estimates of malaria incidence but that simpler baseline models should always be fitted as a benchmark.

中文翻译：

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通过在发病率和流行率数据集之间共享空间信息来绘制疟疾地图

随着疟疾发病率下降和更多国家走向消除，越来越需要低流行地区的疟疾风险地图。对于低负担地区，已经开发了分解回归模型，以根据行政单位多边形汇总的常规监测报告以高空间分辨率估计风险。然而，在既有常规监测数据又有流行率调查的地区，利用流行率点调查的空间信息的模型可能会做出更准确的预测。使用印度尼西亚、塞内加尔和马达加斯加的案例研究，我们比较了两种将点级空间信息纳入分解回归模型的方法的样本外平均绝对误差。第一个简单地适合二项式似然，logit-link，高斯随机场到流行点调查以创建新的协变量。第二个是多似然模型，它与流行点调查和多边形发生率数据联合拟合。我们发现在大多数情况下，模型之间的平均绝对误差没有差异。只有一种情况，新模型的表现最好。更一般地说，我们的结果表明，结合这些类型的数据有可能减少疟疾发病率估计中的绝对误差，但应始终将更简单的基线模型作为基准进行拟合。