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Norovirus and Twitter - GSS data analysis competition 2015
- 1. Using Twitter to predict Norovirus outbreaks David Millson on behalf of Callum Staff
- 2. THE PROJECT
- 3. © 2015 Food Standards Agency Predicting and reducing Norovirus • Tweets discussing Norovirus and its symptoms were first identified as a proxy indicator for Norovirus cases through an MSc project • Outbreaks are predicted using rises in tweets about Norovirus symptoms (diarrhoea and vomiting) • Predictions are used to inform FSA and NHS Choices interventions • Interventions can prevent outbreaks from getting out of control by encouraging sufferers to stay at home and avoid passing the bug on
- 4. © 2015 Food Standards Agency Citizen Need / Business Need • Citizen Need: • More timely surveillance = quicker reactions = more cases prevented • Lower burden on economy and public • Business Need: • To understand the predictive power of social media data • Demonstrate to Government the value of including social media analysis in surveillance strategy
- 5. © 2015 Food Standards Agency FSA and social media analysis – from little acorns • Cabinet Office approached the FSA to pilot a joint project with Ipsos MORI using machine learning to categorise Tweets • Led in producing cross-government guidance on social media research • Set up a review and innovation group to bring the expertise of industry and academia to Government social media research • Designed and presented workshop on using social media in policy and analysis
- 7. © 2015 Food Standards Agency Crowd-sourcing the keywords
- 8. © 2015 Food Standards Agency Excluding bad keywords
- 9. © 2015 Food Standards Agency Do people really tweet when they have Norovirus?
- 10. © 2015 Food Standards Agency The trade-off between usefulness and rigour • We can rigorously predict Norovirus cases three weeks after they happen • To be useful to Communications, we need to predict them three weeks before they happen Tweets Community cases
- 11. © 2015 Food Standards Agency The trade-off between usefulness and rigour • We can rigorously predict Norovirus cases three weeks after they happen • To be useful to Communications, we need to predict them three weeks before they happen Tweets Community cases
- 12. © 2015 Food Standards Agency Calibrating the Cut Off Value
- 13. © 2015 Food Standards Agency Calibrating the Cut Off Value 0.35
- 14. © 2015 Food Standards Agency Calibrating the Cut Off Value 0.30
- 15. © 2015 Food Standards Agency Calibrating the Cut Off Value 0.25
- 16. © 2015 Food Standards Agency Calibrating the Cut Off Value 0.20
- 17. © 2015 Food Standards Agency Calibrating the Cut Off Value 0.25
- 19. © 2015 Food Standards Agency Outbreak predicted Outbreak reduced (hopefully)
- 20. VALUE FOR MONEY
- 21. © 2015 Food Standards Agency Value for Money • Cost of the project • One analyst working approx. one day a week for 2/3 year ~ £2,500 • NHS Choices spend/external research brings total to approx. £20,000 • Cost of Norovirus • Estimated 2.8 million cases in the UK a year at a cost of £120 million WOULD NEED TO PREVENT JUST 500 CASES A YEAR, OR 0.02% OF THE TOTAL, TO BE PROVIDING VALUE FOR MONEY
- 22. NEXT STEPS
- 23. © 2015 Food Standards Agency Spatial Mapping
- 24. © 2015 Food Standards Agency Summary • A marriage of “supply” and “demand”: • Twitter identified as a measure of Norovirus, providing information much more rapidly than lab reports • A need to roll out public information on Norovirus at the right time to make the biggest impact • A gateway project to demonstrate the value of social media analysis • Low cost and therefore low risk, with potentially high rewards
Editor's Notes
- Staff members were asked on the FSA’s Yammer network (a closed social media network) to share their ideas of words that might be used when discussing norovirus. The results were used in a keyword search to build the dataset of tweets.
- As well as keywords that we want to include, there are keywords that often go hand-in-hand with them and indicate that the subject of the tweet was unrelated to Norovirus. We also crowd-sourced these exclusions, as well as looking out for common terms that indicated a “red herring”
- Assuming that lab reports are themselves a good indicator of Norovirus cases in the community, this chart demonstrates that tweets including “sickness bug” and related terms are a similarly good indicator, reproducing not just the seasonality but features such as the double peak in the winter of 2012/13 and the relative heights of the peaks. For this graph, both lab reports and volumes are smoothed by averaging over seven week periods.
- In order to be a predictive tool, we need to identify the characteristics of the tweets curve at a time prior to the peak of the cases. We therefore look at a lagged set of data where we are comparing tweets to “future” cases.
- In order to be a predictive tool, we need to identify the characteristics of the tweets curve at a time prior to the peak of the cases. We therefore look at a lagged set of data where we are comparing tweets to “future” cases.
- The predictive model, whether it be logistic regression or naïve Bayes, will give us a value that we need to convert into a one or a zero. We need to choose a cutoff value that suits our needs for interventions. This could be done by minimising false positives, maximising true positives, or some other method. The outcome we want it a method that gives an early warning. We are willing to accept false positives in spring and summer on the basis that these can be eliminated by inspection.
- The predictive model, whether it be logistic regression or naïve Bayes, will give us a value that we need to convert into a one or a zero. We need to choose a cutoff value that suits our needs for interventions. This could be done by minimising false positives, maximising true positives, or some other method. The outcome we want it a method that gives an early warning. We are willing to accept false positives in spring and summer on the basis that these can be eliminated by inspection.
- The predictive model, whether it be logistic regression or naïve Bayes, will give us a value that we need to convert into a one or a zero. We need to choose a cutoff value that suits our needs for interventions. This could be done by minimising false positives, maximising true positives, or some other method. The outcome we want it a method that gives an early warning. We are willing to accept false positives in spring and summer on the basis that these can be eliminated by inspection.
- The predictive model, whether it be logistic regression or naïve Bayes, will give us a value that we need to convert into a one or a zero. We need to choose a cutoff value that suits our needs for interventions. This could be done by minimising false positives, maximising true positives, or some other method. The outcome we want it a method that gives an early warning. We are willing to accept false positives in spring and summer on the basis that these can be eliminated by inspection.
- The predictive model, whether it be logistic regression or naïve Bayes, will give us a value that we need to convert into a one or a zero. We need to choose a cutoff value that suits our needs for interventions. This could be done by minimising false positives, maximising true positives, or some other method. The outcome we want it a method that gives an early warning. We are willing to accept false positives in spring and summer on the basis that these can be eliminated by inspection.
- The predictive model, whether it be logistic regression or naïve Bayes, will give us a value that we need to convert into a one or a zero. We need to choose a cutoff value that suits our needs for interventions. This could be done by minimising false positives, maximising true positives, or some other method. The outcome we want it a method that gives an early warning. We are willing to accept false positives in spring and summer on the basis that these can be eliminated by inspection.
- NHS Choices have created infographics to influence people’s behaviour when they or their children catch Norovirus. These will be released when the model predicts an outbreak (these images are not yet finalised, and may still be subject to change).
- Epidemiology and cost of nosocomial gastroenteritis, Avon, England, 2002-2003. Lopman BA1, Reacher MH, Vipond IB, Hill D, Perry C, Halladay T, Brown DW, Edmunds WJ, Sarangi J. http://www.ncbi.nlm.nih.gov/pubmed/15504271 Longitudinal study of infectious intestinal disease in the UK (IID2 study): incidence in the community and presenting to general practice Open Access Clarence C Tam1, Laura C Rodrigues1, Laura Viviani1, Julie P Dodds2, Meirion R Evans3, Paul R Hunter4, Jim J Gray5, Louise H Letley2, Greta Rait2, David S Tompkins6, Sarah J O'Brien7 On behalf of the IID2 Study Executive Committee* http://gut.bmj.com/content/early/2011/06/26/gut.2011.238386.short?q=w_gut_ahead_tab
- Some tweets are geotagged, and most carry some location information such as the address of the user. This could potentially be used to map Norovirus outbreaks as they occur, and target interventions even more effectively.