Advancing the science and application of epidemic forecasting
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1 Advancing the science and application of epidemic forecasting Michael A Johansson The findings and conclusions are those of the author and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
2 all decisions are made on the basis of models. Most models are in our heads. - Jay Forrester Doubt is not a pleasant condition, but certainty is an absurd one. - Voltaire
3 April 2009: Where will H1N1 spread?
4 June 2014: How fast will CHIKV spread? Cauchemez et al., Eurosurveillance, 2014
5 Sept. 2014: What will be the impact of Ebola? WHO Ebola Response Team, NEJM, 2014
6 Dec. 2015: Will this be an early flu season? predict.phiresearchlab.org
7 Arboviruses (Flaviviruses West Nile virus Dengue virus Zika virus Yellow fever virus
8 JANUARY locally-acquired yellow fever cases
9 Forecasting spread
10 A model City A City B
11 5?
12 ?
13 1. Incidence - How many infections are occurring in source locations? 2. Movement - How frequently do infected people travel to unaffected locations? 3. Transmissibility - How likely is transmission in unaffected locations? ? Johansson et al., PLOS One, 2011 Johansson et al., ASTMH, 2012
14 A massive simulation model S V E V I V S R E R I R R R S T (1 E T (1 I T ( 1 R T (1 S T (2 E T (2 I T ( 2 R T (2 S T (J E T (J I T ( J R T (J S V E V I V S R E R I R R R S T (1 E T (1 I T ( 1 R T (1 S T (2 E T (2 I T ( 2 R T (2 S T (J E T (J I T ( J R T (J S V E V I V S R E R I R R R S T (1 E T (1 I T ( 1 R T (1 S T (2 E T (2 I T ( 2 R T (2 S T (J E T (J I T ( J R T (J S V E V I V S R E R I R R R S T (1 E T (1 I T ( 1 R T (1 S T (2 E T (2 I T ( 2 R T (2 S T (J E T (J I T ( J R T (J Johansson et al., PLOS One, 2011 Johansson et al., ASTMH, 2012
15 Massive simulation Branching process S V E V I V S R E R I R R R S T (1 E T (1 I T ( 1 R T (1 S T (2 E T (2 I T ( 2 R T (2 S T (J E T (J I T ( J R T (J S V E V I V S R E R I R R R S T (1 E T (1 I T ( 1 R T (1 S T (2 E T (2 I T ( 2 R T (2 S T (J E T (J I T ( J R T (J IMPORT I, (, 1 (1 sm p im= p s S m M i, s, m D S V E V I V S R E R I R R R S T (1 E T (1 I T ( 1 R T (1 S T (2 E T (2 I T ( 2 R T (2 S T (J E T (J I T ( J R T (J S V E V I V S R E R I R R R S T (1 E T (1 I T ( 1 R T (1 S T (2 E T (2 I T ( 2 R T (2 S T (J E T (J I T ( J R T (J pauto( i = 1 1 pism,, + pism,, e s S m M HM im R MH 0 im, R0, ( e 1 I sm, D Johansson et al., ASTMH, 2012
16 Validation Probability of local transmission Johansson et al., ASTMH, 2012
17 DECEMBER locally-acquired chikungunya cases
18 Data 1. Incidence Asymptomatic Unreported Reporting delay
19 Data 1. Incidence 2. Movement Long-term change Differences for cases Response to epidemic
20 Data 1. Incidence 2. Movement 3. Transmissibility Infectious period Mosquito spp./strain Mosquito abundance Vector competence
21 Uncertainties
22 DECEMBER 2013 DECEMBER 2013 Areas with local transmission
23 DECEMBER 2013 DECEMBER 2013 Air travel routes from areas with local transmission
24 DECEMBER 2013 Probability of at least one infected traveler arriving
25 DECEMBER 2013 Probability of at least one infected traveler arriving Johansson et al., PLOS One,
26 Probability of at least one infected traveler arriving: Local Cases Reported Johansson et al., PLOS One,
27 Probability of at least one local case: Local Cases Reported Johansson et al., PLOS One,
28
29 Local transmission First reported case 16/34 reported the first case within one month of prediction Probability of transmission > 0.5
30
31
32
33 0.5+ Probability of at least one local case & no local cases reported
34 Florida number of infected travelers number of local cases
35
36 Forecasting spread Relatively simple models can capture key invasion components and predict spread. Key uncertainties: Land travel Local transmissibility Future incidence
37 Forecasting dengue epidemics San Juan, Puerto Rico
38 Reported dengue cases (globally Published dengue models Reiner, Perkins, et al., J R Soc Interface, 2013
39 Dengue - San Juan 2010/ / / /2009
40 WK /2011 Dengue - San Juan 2011/ / / /2009
41 WK /2011 Dengue - San Juan 2012/ / / /2009
42 WK /2011 Dengue - San Juan 2012/ / / /2009
43 WK /2011 Dengue - San Juan 2012/ / / /2009
44 WK /2011 Dengue - San Juan 2012/ / / /2009
45 WK /2011 Dengue - San Juan 2012/ / / /2009
46 Dengue forecast error for Mexico Mean Johansson et al., Scientific Reports, 2016
47 Dengue forecast error for Mexico Mean Monthly mean Johansson et al., Scientific Reports, 2016
48 Dengue forecast error for Mexico Temperature Mean Monthly mean Johansson et al., Scientific Reports, 2016
49 Dengue forecast error for Mexico Autocorrelation Temperature Mean Monthly mean Johansson et al., Scientific Reports, 2016
50 Dengue forecast error for Mexico Autocorrelation Temperature Mean Monthly mean Autocorrelation + seasonality Johansson et al., Scientific Reports, 2016
51 A framework for forecasting 1. Establish objectives and targets 2. Identify, acquire, and/or simulate data 3. Formulate models 4. Evaluate predictions 5. Forecast
52 TARGET Key characteristics Decision-oriented Measurable
53 DATA Target Key characteristics Training & testing Future availability Public
54 MODEL Key characteristics Out-of-sample forecasts Estimate uncertainty
55 EVALUATION Make predictions public Key characteristics Compare to a baseline model Assess model confidence
56 Uncertainty is everywhere Uncertainty Forecast Nowcast Surveillance (Hindcast Past Present Future Time
57 It ain t what you don t know that gets you into trouble. It s what you know for sure that just ain t so. - Mark Twain
58 The Upshot's elections model suggests that Hillary Clinton is favored to win the presidency, based on the latest state and national polls. - Josh Katz
59 Log score: S = log(p i NYT 538 HP PW PEC DK Cook Roth Sab. Naïve p log score
60 Evaluation across states NYT 538 HP PW PEC DK Naïve log score skill Log score: S =(1/n log(p i Skill: e S
61 1. The epidemic will peak in week 25.
62 1. The epidemic will peak in week There is an 80% chance that the epidemic will peak in week 25.
63 1. The epidemic will peak in week There is an 80% chance that the epidemic will peak in week There is an 80% chance that the epidemic will peak between week 24 and week 26.
64 Peak week forecast
65 Peak week forecast Observed peak week Log score S = log(p i
66 Peak week forecast Observed peak week Log score(ish S = log(p i-1 + p i + p i+1
67 State of dengue forecasting 1. Lots of models 2. Little sense of appropriateness of models for decision-making 3. No quantitative models being routinely used for decision-making
68 Dengue Forecasting Project June September, 2015 dengueforecasting.noaa.gov predict.phiresearchlab.org INTERAGENCY PPFST Working Group OSTP: Dylan George NOAA: Jesse Bell, Juli Trtanj BARDA: Jason Asher, Matt Clay DOD: Brett Forshey, Vivek Khatri, Aaron Kite-Powell, Caitlin Rivers DATA Puerto Rico Department of Health: Brenda Rivera UC Davis: Tom Scott, Amy Morrison, Steve Stoddard, Chris Barker, Sandy Olkowski PROJECT PARTICIPANTS Breaking Bad Bone Fever: Anna Buzcak Colombia: Jeff Shaman D2DCRC: Nick Lothian Delphi: Roni Rosenfeld ENVADEM: Justin Lessler IC3: Rachel Lowe ICREA: Xavier Rodó Kernel of Truth: Evan Ray RAND: Osonde Osoba RAObots: DJ Rao Arete: Karyn Apfeldorf WVU: Antarpreet Jutla UCSF: Travis Porco UMN: Matteo Convertino VectorAnalytica: Eloy Ortiz VectorBiTE: Leah Johnson
69 TARGETS 1. Peak incidence
70 TARGETS 2. Peak week 1. Peak incidence
71 TARGETS 2. Peak week 1. Peak incidence 3. Total cases
72 DATA Dengue - San Juan, Puerto Rico Dengue - Iquitos, Peru
73 MODELS Reiner, Perkins, et al., J R Soc Interface, 2013
74 Forecasts 16 Teams 3 Targets 2 Locations 8 Years 13 Forecasts per year 9,984 Predictions 256,256 Numbers to evaluate
75 San Juan Week 12, 2012/2013 incidence week
76 San Juan Peak Week Week 12, 2012/2013 Observed peak Observed peak Observed peak
77 San Juan - Peak Incidence Testing Seasons
78 Breaking Bad Bone Fever UCSF Breaking Bad Bone Fever Arete VectorBiTE Delphi
79
80
81 Peak week Peak incidence Total incidence
82 Peak week Peak incidence Total incidence
83 Dengue Forecasting Conclusions Approaches: Ensembles generally had higher performance Climate, serotype, mechanistic, vector remains to be determined Forecasts: Early season forecasting is challenging Big seasons are harder to forecast Some forecast models may be useful for situational awareness
84 A framework for forecasting 1. Establish objectives and targets 2. Identify, acquire, and/or simulate data 3. Formulate models 4. Evaluate predictions 5. Forecast
85 Influenza Forecasting 2016/ Targets Seasonal: onset, peak week, peak height Short-term: 1-, 2-, 3- and 4-weeks ahead 2. Data Outcome: ILINet Input: Anything 3. Models 26 models 4. Evaluation Log score
86 predict.phiresearchlab.org 1 wk ahead 2 wk ahead 3 wk ahead 4 wk ahead
87 Epidemic Prediction Initiative predict.phiresearchlab.org github.com/cdcepi(/zika OBJECTIVES 1. Connect with decision makers 2. Facilitate forecasting projects 3. Open data 4. Deliver forecasts 5. Standardize forecast formats & evaluation
88 Forecasting challenges 2013-today: Seasonal influenza, US (CDC 2015: Seasonal dengue, Puerto Rico & Peru (White House, CDC, NOAA, DOD 2015: Chikungunya epidemics, Americas (DARPA 2015: Simulated Ebola epidemics, West Africa (NIH
89
90 Acknowledgements Centers for Disease Control & Prevention Luis Mier-y-Teran Matt Biggerstaff Craig McGowan Neysarí Arana-Vizcarrondo Brad Biggerstaff Erin Staples Ann Powers Nicki Pesik Nicole Cohen Nancy Gallagher Nina Marano Yoni Haber Andre Berro Forecasting Challenge Participants & Organizers External Collaborators Mauricio Santillana (Harvard Medical School Nicholas Reich (UMASS Amherst Aditi Hota (Harvard University John Brownstein (Harvard Medical School The findings and conclusions are those of the author and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
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