Application of Time and Space Big Data Analysis Technology in Forecasting and Early Warning of Wild Animal Infectious Diseases

  • Jianbo Li

Abstract

The purpose of this paper is to use the time and space big data analysis technology to realize the prediction and warning of wildlife infectious diseases. This paper establishes time-space big data based on network and spatial information technology to provide new data acquisition channels and advanced data processing methods for wildlife infectious disease prediction and early warning. It can break through the limitations of traditional forecasting methods and achieve the purpose of fast, timely and dynamic prediction, and effectively improve the efficiency and effectiveness of epidemic prevention and control. Combined with the principle of big data technology, aiming at the time and space information contained in the network when the epidemic situation of wild animal infectious diseases occurs, using time and space semantic correlation information to acquire technology, time and space information processing and storage technology, and text-space dynamic analysis technology based on natural semantics. To construct a multi-perspective, multi-level, and in-depth three-dimensional access to infectious disease epidemic information based on ubiquitous networks, complementing the direct reporting system and providing a new technology and means for improving the predictive, early warning and prevention and control capabilities of wild animal infectious disease epidemics. Through the analysis of the overall and individual trends of wild animal infectious diseases and the outliers, it shows that the proposed system can comprehensively consider the multi-dimensional spatiotemporal characteristics of infectious disease data, which can effectively help users to explore the time and space pattern of infectious disease transmission and quickly search for infectious disease outbreak time nodes and spatial distribution transfer events for better prevention, control and analysis.

Published
2019-08-04