Mining and forecasting of infectious disease transmission data based on smart cities

Abstract

With the rise of a new round of urbanization and the growing public awareness of public health, the traditional infectious disease management system can no longer meet the health management needs of smart cities. Establishing statistical indicators and combined time series analysis and prediction models that reasonably describe the spread of infectious diseases can explore the basic trends of infectious disease transmission and make reasonable predictions. In this paper, the number of patients and deaths in the provinces and cities of tuberculosis from 2004 to 2016 were used. The data of tuberculosis from 2017 to 2019 was predicted by the ARMA model. The model was tested by comparison to prove that the method of mining and prediction was reasonable. The health management department of the smart city formulates policies to provide reference.