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Prediction of Infectious Disease Spread using
Twitter:
A Case of Influenza
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Hideo Hirose, Liangliang Wang
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The 2012 IEEE 5th International Symposium
on Parallel Architectures, Algorithms and Programming (PAAP'12), pp.100-105, December
17-20, 2012, Taipei, Taiwan
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Nowadays,
detecting the disaster phenomena and predicting the final stage
become very important in the risk analysis view-point.
The statistical methods provide accurate estimates of parameters
when the data are completely given. However, when the data are incomplete,
the accuracy of the estimates becomes poor. Therefore, statistical
methods are weak in predicting the future trends.
The SIR methods, for infectious disease spread prediction, using
the differential equations can sometimes provide accurate estimates
for the final stage.
These methods, however, require some inspection time, which means
the delay of analysis at least one week or so when we want to predict
the future trends. To detect the disasters and to predict the future
trends much earlier, we can use the social network system (SNS).
In this paper, we have proposed a method to predict the future trend
of influenza by using Twitter. We have analyzed the possibility of
building a regression model by combining Twitter messages and CDC's
Influenza-Like Illness (ILI) data, and we have found that the multiple
linear regression model with ridge regularization outperforms the
single linear regression model and other un-regularized least squared
methods. The model of multiple linear regression with ridge can notably
improve the prediction accuracy. |
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| Twitter;
early detection; influenza; infectious disease; logistic
regression; ridge; ILI; AIC; SNS; truncated data.
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Times Cited in Web of Science:
Times Cited in Google Scholar: 8
Cited in Books:
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