Assessing The Spread Of Infectious Diseases Risk Based On The Spatial-temporal Trajectory Data Analysis

Inner-city epidemic outbreaks tend to occur in the population and intensive area, how to better reflect the risk of transmission of infectious diseases in the geospatial science for prevention and control and prevention of infectious diseases has important significance. Big Data herein by reference methods targeted to high risk of transmitting infectious diseases location prediction has been verified taxi trajectory data for the study area of Manhattan Island, New York City, the experimental data provided to Crawdad company.Starting from the propagation velocity of infectious diseases, the use of buffer analysis and analysis method, calculate the maximum speed taxi traverse each period of the study area, known as the spread of infectious disease risk coefficient. The spread of infectious diseases as a risk coefficient of the risk of propagation velocity threshold, through the changes of the consecutive day period of analysis, but also in the time series for the best analysis interval is determined to arrive at different times of day data differences and sensitivity of different data types.The risk of spread of infectious diseases mentioned threshold speed, we need to discuss the impact of spatial density propagation velocity. Both spatial density of the main factors restricting the speed of transmission of infectious diseases, but also a direct reflection of the intensity of the mobile population, according to city transportation accessibility and theoretical knowledge of complex networks constructed for taxi transport nodes temporal trajectory data fully connected network, area of the node and the number of nodes in the network node is determined by the characteristic length, obtained solving the actual location of the spatial density formula. The spatial density of population movement as factors determining whether they have a high risk of transmission of infectious diseases were analyzed.Taxi spatial density shows a mobile population density of space-time, average speed indicates the speed of movement of people. The use of statistical methods to establish propagation velocity and density of the feature space variation of the propagation velocity and density of the risk factor and Zipf’s Law reflects the spread of infectious diseases from the known, the spatial density values obtained at high risk of infectious diseases, obtained high risk of infectious diseases correspondent node area. Data generated by the high-risk node derived prediction model to predict the accuracy of most of the period in more than 80%.