Abstract
Detecting the period of a disease is of great importance to building information management capacity in disease control and prevention. This paper aims to optimize the disease surveillance process by further identifying the infectious or recovered period of flu cases through social media. Specifically, this paper explores the potential of using public sentiment to detect flu periods at word level. At text level, we constructed a deep learning method to classify the flu period and improve the classification result with sentiment polarity. Three important findings are revealed. Firstly, bloggers in different periods express significantly different sentiments. Blogger sentiments in the recovered period are more positive than in the infectious period when measured by the interclass distance. Secondly, the optimized disease detection process can substantially improve the classification accuracy of flu periods from 0.876 to 0.926. Thirdly, our experimental results confirm that sentiment classification plays a crucial role in accuracy improvement. Precise identification of disease periods enhances the channels for the disease surveillance processes. Therefore, a disease outbreak can be predicted credibly when a larger population is monitored. The research method proposed in our work also provides decision making reference for proactive and effective epidemic control and prevention in real time.
Highlights
The traditional infectious disease detection process is being challenged by potential social media applications [1,2]
Previous studies show that Long Short Term Memory (LSTM) performs relatively well in text processing but has been rarely used for disease weibo analysis. To fill in this gap, this paper aims to investigate the relationship between sentiment polarity and the flu period at the word level and text level based on a weibo dataset
This paper aims to detect the flu period with sentiment polarity at the word and text level based on Sina Weibo data, and it proposes optimization suggestions for optimizing the disease detecting process
Summary
The traditional infectious disease detection process is being challenged by potential social media applications [1,2]. The latest estimates released by the United States Centers for Disease Control and Prevention (US-CDC) revealed the worldwide severity of the illness. According to this authoritative report, the US-CDC estimates that in the period between 1 October 2018 and 4 May 2019, there were approximately 37.4 million to 42.9 million flu infectious in the population population, among which there were from 17.3 to 20.1 million flu-related medical visits [3]. 531,000 to 647,000 people require flu-related hospitalizations, and influenza caused 36,400–61,200 estimated deaths. This estimate is based on more recent data from a larger and more diverse group of countries, including lower middle-income countries, and this estimate excludes deaths from non-respiratory diseases. As evidenced by the current flu season, influenza viruses can rapidly mutate, evading the most current vaccine formulations [4]
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