Araujo Navas Andrea L, Hamm Nicholas A S, Soares Magalhães Ricardo J, Stein Alfred
Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, AE, Enschede, The Netherlands.
UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Gatton QLD, Australia.
PLoS Negl Trop Dis. 2016 Dec 22;10(12):e0005208. doi: 10.1371/journal.pntd.0005208. eCollection 2016 Dec.
Spatial modelling of STH and schistosomiasis epidemiology is now commonplace. Spatial epidemiological studies help inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration; however, limited attention has been given to propagated uncertainties, their interpretation, and consequences for the mapped values. Using currently published literature on the spatial epidemiology of helminth infections we identified: (1) the main uncertainty sources, their definition and quantification and (2) how uncertainty is informative for STH programme managers and scientists working in this domain.
METHODOLOGY/PRINCIPAL FINDINGS: We performed a systematic literature search using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol. We searched Web of Knowledge and PubMed using a combination of uncertainty, geographic and disease terms. A total of 73 papers fulfilled the inclusion criteria for the systematic review. Only 9% of the studies did not address any element of uncertainty, while 91% of studies quantified uncertainty in the predicted morbidity indicators and 23% of studies mapped it. In addition, 57% of the studies quantified uncertainty in the regression coefficients but only 7% incorporated it in the regression response variable (morbidity indicator). Fifty percent of the studies discussed uncertainty in the covariates but did not quantify it. Uncertainty was mostly defined as precision, and quantified using credible intervals by means of Bayesian approaches.
CONCLUSION/SIGNIFICANCE: None of the studies considered adequately all sources of uncertainties. We highlighted the need for uncertainty in the morbidity indicator and predictor variable to be incorporated into the modelling framework. Study design and spatial support require further attention and uncertainty associated with Earth observation data should be quantified. Finally, more attention should be given to mapping and interpreting uncertainty, since they are relevant to inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration.
土壤传播性蠕虫病（STH）和血吸虫病流行病学的空间建模如今已很常见。空间流行病学研究有助于为有关高危人群数量以及需要进行群体药物给药的地理区域的决策提供信息；然而，对于传播性不确定性、其解释以及对地图值的影响却关注有限。利用目前已发表的关于蠕虫感染空间流行病学的文献，我们确定了：（1）主要的不确定性来源、其定义和量化，以及（2）不确定性如何为从事该领域工作的STH项目管理人员和科学家提供信息。
方法/主要发现：我们使用系统评价和Meta分析的首选报告项目（PRISMA）方案进行了系统的文献检索。我们结合使用不确定性、地理和疾病术语在Web of Knowledge和PubMed上进行搜索。共有73篇论文符合系统评价的纳入标准。只有9%的研究未涉及任何不确定性因素，而91%的研究对预测发病率指标中的不确定性进行了量化，23%的研究绘制了不确定性地图。此外，57%的研究对回归系数中的不确定性进行了量化，但只有7%的研究将其纳入回归响应变量（发病率指标）。50%的研究讨论了协变量中的不确定性，但未对其进行量化。不确定性大多被定义为精度，并通过贝叶斯方法使用可信区间进行量化。
结论/意义：没有一项研究充分考虑了所有不确定性来源。我们强调需要将发病率指标和预测变量中的不确定性纳入建模框架。研究设计和空间支持需要进一步关注，与地球观测数据相关的不确定性应予以量化。最后，应更加关注不确定性的绘制和解释，因为它们对于为有关高危人群数量以及需要进行群体药物给药的地理区域的决策提供信息至关重要。
