Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Ratchathewi, Bangkok, 10400, Thailand.
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA.
BMC Med Res Methodol. 2018 Jul 5;18(1):72. doi: 10.1186/s12874-018-0532-8.
There are an increasing number of geo-coded information streams available which could improve public health surveillance accuracy and efficiency when properly integrated. Specifically, for zoonotic diseases, knowledge of spatial and temporal patterns of animal host distribution can be used to raise awareness of human risk and enhance early prediction accuracy of human incidence.
To this end, we develop a spatiotemporal joint modeling framework to integrate human case data and animal host data to offer a modeling alternative for combining multiple surveillance data streams in a novel way. A case study is provided of spatiotemporal modeling of human tularemia incidence and rodent population data from Finnish health care districts during years 1995-2012.
Spatial and temporal information of rodent abundance was shown to be useful in predicting human cases and in improving tularemia risk estimates in 40 and 75% of health care districts, respectively. The human relative risk estimates' standard deviation with rodent's information incorporated are smaller than those from the model that has only human incidence.
These results support the integration of rodent population variables to reduce the uncertainty of tularemia risk estimates. However, more information on several covariates such as environmental, behavioral, and socio-economic factors can be investigated further to deeper understand the zoonotic relationship.
越来越多的地理编码信息流可以在适当整合后提高公共卫生监测的准确性和效率。具体来说，对于人畜共患疾病，了解动物宿主分布的时空模式可以提高对人类风险的认识，并提高人类发病率的早期预测准确性。
为此，我们开发了一个时空联合建模框架，将人类病例数据和动物宿主数据整合在一起，为以新颖的方式结合多个监测数据流提供了一种建模选择。通过对 1995-2012 年芬兰医疗保健区人类土拉菌病发病率和啮齿动物种群数据的时空建模进行了案例研究。
显示啮齿动物丰度的时空信息可用于预测人类病例，并分别在 40%和 75%的医疗保健区提高土拉菌病风险估计值。将啮齿动物信息纳入后，人类相对风险估计的标准差小于仅使用人类发病率的模型的标准差。
这些结果支持整合啮齿动物种群变量以降低土拉菌病风险估计的不确定性。但是，可以进一步研究更多关于环境、行为和社会经济因素等几个协变量的信息，以更深入地了解人畜共患关系。
