A Study on a Neural Network Risk Simulation Model Construction for Avian Influenza A (H7N9) Outbreaks in Humans in China during 2013–2017

Abstract

The main purposes of this study were to explore the spatial distribution characteristics of H7N9 human infections during 2013–2017, and to construct a neural network risk simulation model of H7N9 outbreaks in China and evaluate their effects. First, ArcGIS 10.6 was used for spatial autocorrelation analysis, and cluster patterns ofH7N9 outbreaks were analyzed in China during 2013–2017 to detect outbreaks’ hotspots. During the study period, the incidence of H7N9 outbreaks in China was high in the eastern and southeastern coastal areas of China, with a tendency to spread to the central region. Moran’s I values of global spatial autocorrelation of H7N9 outbreaks in China from 2013 to 2017 were 0.080128, 0.073792, 0.138015, 0.139221 and 0.050739, respectively (p < 0.05) indicating a statistically significant positive correlation of the epidemic. Then, SPSS 20.0 was used to analyze the correlation between H7N9 outbreaks in China and population, livestock production, the distance between the case and rivers, poultry farming, poultry market, vegetation index, etc. Statistically significant influencing factors screened out by correlation analysis were population of the city, average vegetation of the city, and the distance between the case and rivers (p < 0.05), which were included in the neural network risk simulation model of H7N9 outbreaks in China. The simulation accuracy of the neural network risk simulation model of H7N9 outbreaks in China from 2013 to 2017 were 85.71%, 91.25%, 91.54%, 90.49% and 92.74%, and the AUC were 0.903, 0.976, 0.967, 0.963 and 0.970, respectively, showing a good simulation effect of H7N9 epidemics in China. The innovation of this study lies in the epidemiological study of H7N9 outbreaks by using a variety of technical means, and the construction of a neural network risk simulation model of H7N9 outbreaks in China. This study could provide valuable references for the prevention and control of H7N9 outbreaks in China.

Predicting pulmonary tuberculosis incidence in China using Baidu search index: an ARIMAX model approach

“…However, it offers simplicity and feasibility in operation and has a wider range of applications. Other models are more susceptible to prediction bias due to missing data and errors in processing [8,28,29]. Additionally, the ARIMA model is suitable for various types of time series data as it can effectively capture long-term trends and periodicity, and it can also incorporate various external variables.…”

“…As early as 2009, researchers discovered the effectiveness of combining search data with disease surveillance and early warning through the development of the Google Flu Trends section [6,7]. Other diseases, such as H7N9, dengue, gonorrhoea, brucellosis, AIDS, and COVID-19, have also been modeled using search engine data [8][9][10]. However, there remains a lack of studies focusing on the prediction model of PTB using Internet search data, which can be widely implemented for disease surveillance and early warning purposes in China.…”