Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Dec 15, 2022
Date Accepted: Aug 4, 2023
Influenza epidemic trend surveillance and prediction based on search engine data: a deep learning model study
ABSTRACT
Background:
When emerging of re-emerging infectious outbreak, reported case will not reflect the real epidemic curve, like COVID-19 outbreak in China. Influenza-like Illness (ILI) could take the role of surveillance. Once the correlation between internet search engine and symptomatic surveillance been identified, respiratory infectious like influenza, COVID-19 could develop a supplemented modern intelligence epidemic surveillance method.
Objective:
To find the correlation between the internet search engine and influenza epidemic, providing evidence for supplementing modern intelligence epidemic surveillance methods.
Methods:
We collected the daily proportion of ILI sentinel virological testing results and Baidu query data across the 31 provinces of mainland China from 1 January 2011 to 31 June 2018 with a total of 3,691,865 and 1,563,361 respective samples. We applied Pearson analysis and distributed non-linear lag models to clarify the correlation between selected Baidu search items and the daily influenza-positive rate. We applied a four-in-one deep learning model to identify the contribution of Baidu search information for seasonal influenza trend prediction.
Results:
The traditional disease surveillance systems should be complemented with information from online data sources with advanced technical support. Search engine information can help detect warning signs of influenza outbreaks earlier. However, supplemented search engine information may also reduce the effectiveness of the prediction of modern surveillance. Modern surveillance should be complemented with caution.
Conclusions:
The traditional disease surveillance systems should be complemented with information from online data sources with advanced technical support. Search engine information can detect warning signals of influenza outbreaks earlier, saving time for preparedness. However, it is worth highly noting that supplemented search engine information may also reduce the effectiveness of the prediction that modern surveillance. Modern surveillance should be complemented with caution rather than exaggerating the effect.
Citation
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