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Liu Wei

Scan statistics is one of the most widely used method for detecting and locating the clusters of disease or health-related events through the space-time dimension. Although the Specific software of SatScan is available for free and easier to use graphical user interface (GUI) interface, the click way and the resulting text format have became obstacles in biosurveillance since automated or reproduction operation and the fast communicate information tool appeared. With the power of R software and rsatscan package, we extended the visualization results to become a faster, more effective communication and motivation tool.

Objective:

The purpose of this article was to provide static and interact mapping for the results' SaTscan with R package thereby reduce the gap between decision-makers and researchers.

Submitted by elamb on

Influenza is a highly contagious, acute respiratory disease that causes periodic seasonal epidemics and global pandemics[1]. Yunnan Province is characterized by poverty, multi-ethnic, and cross-border movement, which maybe be susceptible of influenza (Fig-1). Finding from spatial patter of ILI will promote to control and prevent the respiratory diseases epidemic

Objective

The purpose of the study was to determine spatial clustering of the spread of influenza like illness (ILI) epidemic in Yunnan province, China with the aim of producing useful information for prevention and control measures.

 

Submitted by Magou on

The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In order to achieve this goal, the primary foundation is using those big surveillance data for understanding and controlling the spatiotemporal variability of disease through populations. Typically, public health’s surveillance system would generate data with the big data characteristics of high volume, velocity, and variety. One common question of big data analysis is most of the data have the multilevel or hierarchy structure, in other word the big data are non-independent. Traditional multilevel or hierarchical model can only deal with 2 or 3 hierarchical data structure, which bound health big data further research for modeling, forecast and early-warning in the public health surveillance, in particular involving complex spatial and temporal variability of Infectious Diseases in the reality. 

Objective

The purpose of this article was to quantitative analyses the spatial variability and temporal variability of influenza like illness (ILI) by a three-level Poisson model, which means to explain the spatial and temporal level effects by introducing the random effects. 

Submitted by Magou on
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