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Abstract
BackgroundThere 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.MethodsTo 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.ResultsSpatial 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.ConclusionsThese 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.
Highlights
There are an increasing number of geo-coded information streams available which could improve public health surveillance accuracy and efficiency when properly integrated
Data sources A complete description of the rodent population and human tularemia incidence data is available from earlier reports [6]
The Rbestimates under all the models are approximately or less than 1.005 which indicates the chains converge to a posterior distribution
Summary
There are an increasing number of geo-coded information streams available which could improve public health surveillance accuracy and efficiency when properly integrated. 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. Integration of data and analyses, whether of population or health related variables, has been suggested to improve zoonoses surveillance accuracy and efficiency [1, 2]. Integration appears more feasible for endemic zoonoses, and for those with domesticated animals as source, given the likely greater availability of animal health data. For zoonoses with a non-domesticated animal source (e.g. sylvatic yellow fever, tularemia), availability of animal health data is likely to be a limiting factor towards integration and alternative animal data sources must be sought
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