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Real-time prediction of dengue fever in Thailand

Stephen A Lauer
October 26, 2015
Science
0
180

Real-time prediction of dengue fever in Thailand

Dengue fever is an infectious disease with complex transmission dynamics that infects roughly 400 million people annually. Of these cases, nearly 100 million are cases of dengue hemorrhagic fever (DHF), which can lead to debilitating pain, organ shock, and even death. Every year in Thailand, there are on average 50,000 DHF cases throughout the country. The ability to predict when and where these cases occur could guide public health policy decisions that could save lives and efficiently allocate public resources. Working with the Thai Ministry of Public Health, our group has developed an infrastructure for making these predictions in real time. This presentation (which was presented at the 64th Annual Conference of the American Society for Tropical Medicine & Hygiene) summarizes the progress of this project through October of 2015. For an in-depth description of each slide, see my team's blog at http://reichlab.github.io/blog.html

Stephen A Lauer

October 26, 2015
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Transcript

  1. Real-time prediction of dengue fever in Thailand Stephen A Lauer

    Reich Lab, Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst ASTMH :: Philadelphia, PA :: 26 October 2015

  2. Disclosure This project was funded by NIH NIAID grant 1R01AI102939.

    The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to present, or preparation of the presentation. 2/30

  3. Collaborators Reich Lab, UMass Biostatistics Nicholas Reich, Krzysztof Sakrejda, and

    Stephen Lauer Infectious Disease Dynamics working group, Johns Hopkins Bloomberg School of Public Health Justin Lessler, Derek Cummings (now at University of Florida), Hannah Clapham, and Henrik Salje Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health, Thailand Sopon Iamsirithaworn, Paphanij Suangtho, Soawapak Hinjoy, and Suthanun Suthachana 3/30

  4. Dengue in Thailand Average 50,000 DHF cases per year All

    four serotypes co-circulating Geographically diverse 50-50 rural-urban split Dengue patterns vary by province Source: WorldPop 4/30

  5. 1 Developing Real-Time Forecasting Infrastructure 2 2014 Prediction and Evaluation

    3 Real-time 2015 Predictions 4 Future Developments 5/30

  6. Data pipeline cycle Thailand BOE/MoPH JHBSPH/UMass team data file data

    file PostgreSQL database analysis datasets forecast output Developing Real-Time Forecasting Infrastructure 6/30

  7. Developing Real-Time Forecasting Infrastructure 7/30

  8. Samut Songkhram Sing Buri Mae Hong Son Nakhon Nayok Trat

    Ranong Ang Thong Phangnga Satun Amnat Charoen Uthai Thani Chai Nat Mukdahan Krabi Lamphun Phayao Phrae Yala Uttaradit Nan Nong Khai Prachuap Khiri Khan Chumphon Phetchaburi Phatthalung Chanthaburi Nong Bua Lam Phu Yasothon Phuket Tak Loei Prachin Buri Phichit Sa Kaeo Nakhon Phanom Trang Pattani Sukhothai Narathiwat Chachoengsao Saraburi Lampang Lop Buri Ratchaburi Kamphaeng Phet Kanchanaburi Rayong Kalasin Maha Sarakham Suphan Buri Phra Nakhon Si Ayutthaya Samut Sakhon Phitsanulok Phetchabun Sakon Nakhon Nakhon Pathom Chaiyaphum Nakhon Sawan Surat Thani Si Sa Ket Roi Et Surin Chiang Rai Buri Ram Udon Thani Pathum Thani Nonthaburi Nakhon Si Thammarat Songkhla Chon Buri Chiang Mai Khon Kaen Ubon Ratchathani Samut Prakan Nakhon Ratchasima Bangkok Metropolis 1970 1980 1990 2000 2010 0.01 0.1 1 10 100 500 Cases per 100,000 residents Developing Real-Time Forecasting Infrastructure 8/30

  9. Forecasting model estimates reproductive rate Yi,t ∼ Poisson(λi,t · Yi,t−1)

    λi,t = E(Yi,t ) Yi,t−1 ≈ Ri,t Yi,t is the number of cases in province i at time t Ri,t is the reproductive rate for province i at time t Developing Real-Time Forecasting Infrastructure 9/30

  10. Model components log λi,t = f(t) Seasonality + Long-term secular

    trends g(t) + j∈C k∈L αj,k log Yj,t−k + 1 Yj,t−k−1 + 1 Recently observed trends in correlated provinces Developing Real-Time Forecasting Infrastructure 10/30

  11. Predictions using complete data Developing Real-Time Forecasting Infrastructure 11/30

  12. 0 100 200 300 400 01−01 01−29 02−26 03−26 04−23

    05−21 06−18 07−16 08−13 09−10 10−08 11−05 12−03 Total number of dengue cases observed in each biweek in Bangkok Metropolis Assumed observed Unobserved Developing Real-Time Forecasting Infrastructure 12/30

  13. 0 100 200 300 400 01−01 01−29 02−26 03−26 04−23

    05−21 06−18 07−16 08−13 09−10 10−08 11−05 12−03 Total number of dengue cases observed in each biweek in Bangkok Metropolis Assumed observed Unobserved Predicted cases Developing Real-Time Forecasting Infrastructure 13/30

  14. 0 100 200 300 400 01−01 01−29 02−26 03−26 04−23

    05−21 06−18 07−16 08−13 09−10 10−08 11−05 12−03 Total number of dengue cases observed in each biweek in Bangkok Metropolis Assumed observed Unobserved Predicted cases Historical median Developing Real-Time Forecasting Infrastructure 14/30

  15. Forecast Horizon % of provinces where our model makes better

    predictions than a historical medians model 2 weeks 99% 1 month 99% 2 months 88% 3 months 79% 4 months 68% 6 months 56% Developing Real-Time Forecasting Infrastructure 15/30

  16. Predictions using real-time data 2014 Prediction and Evaluation 16/30

  17. 75% 75% 75% 75% 75% 75% 75% 75% 75% 75%

    75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 75% 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95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 95% 0 300 600 900 1200 0 12 20 34 40 60 Reporting delay (in weeks) Number of cases 2014 Prediction and Evaluation 17/30

  18. 0 100 200 300 400 01−01 01−29 02−26 03−26 04−23

    05−21 06−18 07−16 08−13 09−10 10−08 11−05 12−03 Total number of dengue cases observed in each biweek in Bangkok Metropolis Assumed observed Unobserved 2014 Prediction and Evaluation 18/30

  19. 0 100 200 300 400 01−01 01−29 02−26 03−26 04−23

    05−21 06−18 07−16 08−13 09−10 10−08 11−05 12−03 Total number of dengue cases observed in each biweek in Bangkok Metropolis Observed Unobserved 2014 Prediction and Evaluation 19/30

  20. 0 100 200 300 400 01−01 01−29 02−26 03−26 04−23

    05−21 06−18 07−16 08−13 09−10 10−08 11−05 12−03 Total number of dengue cases observed in each biweek in Bangkok Metropolis Assumed complete Assumed incomplete Unobserved 2014 Prediction and Evaluation 20/30

  21. 0 100 200 300 400 01−01 01−29 02−26 03−26 04−23

    05−21 06−18 07−16 08−13 09−10 10−08 11−05 12−03 Total number of dengue cases observed in each biweek in Bangkok Metropolis Assumed complete Assumed incomplete Unobserved Predicted cases 2014 Prediction and Evaluation 21/30

  22. 0 100 200 300 400 01−01 01−29 02−26 03−26 04−23

    05−21 06−18 07−16 08−13 09−10 10−08 11−05 12−03 Total number of dengue cases observed in each biweek in Bangkok Metropolis Assumed complete Assumed incomplete Unobserved Predicted cases Historical median 2014 Prediction and Evaluation 22/30

  23. Best three provinces 0 20 40 60 0 20 40

    60 0 20 40 60 2014−07−16 predictions Surat Thani Sakon Nakhon Si Sa Ket 2014−10−22 predictions case counts 01−Jan 01−Feb 01−Mar 01−Apr 01−May 01−Jun 01−Jul 01−Aug 01−Sep 01−Oct 01−Nov 01−Dec 01−Jan time when cases occurred in 2014 01−Jan 01−Feb 01−Mar 01−Apr 01−May 01−Jun 01−Jul 01−Aug 01−Sep 01−Oct 01−Nov 01−Dec 01−Jan 2014 Prediction and Evaluation 23/30

  24. Middle three provinces 2014−07−16 predictions 2014−10−22 predictions 0 20 40

    60 0 20 40 60 0 20 40 60 case counts Phetchaburi Mae Hong Son Nakhon Pathom 01−Jan 01−Feb 01−Mar 01−Apr 01−May 01−Jun 01−Jul 01−Aug 01−Sep 01−Oct 01−Nov 01−Dec 01−Jan time when cases occurred in 2014 01−Jan 01−Feb 01−Mar 01−Apr 01−May 01−Jun 01−Jul 01−Aug 01−Sep 01−Oct 01−Nov 01−Dec 01−Jan 2014 Prediction and Evaluation 24/30

  25. Worst three provinces 2014−07−16 predictions 2014−10−22 predictions case counts 0

    20 40 60 0 20 40 60 0 20 40 60 01−Jan 01−Feb 01−Mar 01−Apr 01−May 01−Jun 01−Jul 01−Aug 01−Sep 01−Oct 01−Nov 01−Dec 01−Jan time when cases occurred in 2014 Phangnga Trat Nong Bua Lam Phu 01−Jan 01−Feb 01−Mar 01−Apr 01−May 01−Jun 01−Jul 01−Aug 01−Sep 01−Oct 01−Nov 01−Dec 01−Jan time when cases occurred in 2014 2014 Prediction and Evaluation 25/30

  26. Comparison to complete data predictions % of provinces where model

    outperforms a historical medians model Forecast Horizon Complete (2000-2009) “Real-Time” (2014) 2 weeks 99% 68% 1 month 99% 57% 2 months 88% 42% 3 months 79% 39% 4 months 68% 32% 6 months 56% Not forecasted 2014 Prediction and Evaluation 26/30

  27. 0 2000 4000 6000 01−01 01−29 02−26 03−26 04−23 05−21

    06−18 07−16 08−13 09−10 10−08 11−05 12−03 Total number of dengue cases observed in each biweek in Thailand Real-time 2015 Predictions 27/30

  28. Central Achievements We have created an operational real-time prediction infrastructure

    for dengue fever for Thailand using government surveillance data. We have introduced and evaluated a new prediction model for dengue. We have developed a robust and intuitive evaluation framework, that allows us to quickly see the utility of our models at different prediction horizons. In 2016, the Thais are planning to incorporate our predictions into real-time public health decision making. Real-time 2015 Predictions 28/30

  29. Future Developments Incorporate reporting delays into models Account for serotype

    dynamics Nicholas Reich, Poster 1431, Wednesday Incorporate climate information Make predictions at longer time scales i.e. make annual predictions at beginning of season Build an ensemble model consisting of several models Future Developments 29/30

  30. Thank you! Questions? Look for our paper “Infrastructure and methods

    for real-time predictions of the 2014 dengue fever season in Thailand” by Reich et al. Email: slauer at schoolph.umass.edu Website: reichlab.github.io Future Developments 30/30

  31. Future Developments 31/30

  32. Model training Conducted leave-one-year-out cross validation for data from 2000

    to 2009 Used varying sets of lag terms (L) and correlated provinces (C) Recursively predicted 13 biweeks (≈6 months) forward Evaluated using relative mean absolute error (MAE) over historical medians: Relative MAE = MAE of our model MAE of historical medians The best-performing parameters for the model were 3 correlated provinces at 1 previous time step Future Developments 32/30

  33. Evaluating predictions: mean absolute error We use the mean absolute

    error (MAE) to evaluate our predictions: MAE = N i T t |yi,t − ˆ yi,t | N ∗ T The relative MAE provides intuitive, interpretable measures of model utility compared to a simple reference model: relative MAE = MAEforecasting model MAEhistorical medians Future Developments 33/30

  34. Samut Songkhram Sing Buri Mae Hong Son Nakhon Nayok Trat

    Ranong Ang Thong Phangnga Satun Uthai Thani Chai Nat Krabi Lamphun Phayao Phrae Yala Uttaradit Nan Nong Khai Prachuap Khiri Khan Chumphon Phetchaburi Phatthalung Chanthaburi Yasothon Phuket Tak Loei Prachin Buri Phichit Nakhon Phanom Trang Pattani Sukhothai Narathiwat Chachoengsao Saraburi Lampang Lop Buri Ratchaburi Kamphaeng Phet Kanchanaburi Rayong Kalasin Maha Sarakham Suphan Buri Phra Nakhon Si Ayutthaya Samut Sakhon Phitsanulok Phetchabun Sakon Nakhon Nakhon Pathom Chaiyaphum Nakhon Sawan Surat Thani Si Sa Ket Roi Et Surin Chiang Rai Buri Ram Udon Thani Pathum Thani Nonthaburi Nakhon Si Thammarat Songkhla Chon Buri Chiang Mai Khon Kaen Ubon Ratchathani Samut Prakan Nakhon Ratchasima Bangkok Metropolis 0.5 1.0 1.5 2.0 Relative MAE (vs. seasonal baseline) Forecast Horizon 1 2 3 4 6 7 8 13 Future Developments 34/30

  35. Samut Songkhram Sing Buri Mae Hong Son Nakhon Nayok Trat

    Ranong Ang Thong Phangnga Satun Amnat Charoen Uthai Thani Chai Nat Mukdahan Krabi Lamphun Phayao Phrae Yala Uttaradit Nan Nong Khai Prachuap Khiri Khan Chumphon Phetchaburi Phatthalung Chanthaburi Nong Bua Lam Phu Yasothon Phuket Tak Loei Prachin Buri Phichit Sa Kaeo Nakhon Phanom Trang Pattani Sukhothai Narathiwat Chachoengsao Saraburi Lampang Lop Buri Ratchaburi Kamphaeng Phet Kanchanaburi Rayong Kalasin Maha Sarakham Suphan Buri Phra Nakhon Si Ayutthaya Samut Sakhon Phitsanulok Phetchabun Sakon Nakhon Nakhon Pathom Chaiyaphum Nakhon Sawan Surat Thani Si Sa Ket Roi Et Surin Chiang Rai Buri Ram Udon Thani Pathum Thani Nonthaburi Nakhon Si Thammarat Songkhla Chon Buri Chiang Mai Khon Kaen Ubon Ratchathani Samut Prakan Nakhon Ratchasima Bangkok Metropolis 0.1 0.2 0.5 1.0 2.0 5.0 10.0 Relative MAE (vs. seasonal baseline) Forecast Horizon −6 −5 −4 −3 −2 −1 0 1 2 3 Future Developments 35/30