A cooperative experience for understanding - COVID-19 ROSA M. CRUJEIRAS
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A cooperative experience for understanding COVID-19 RO S A M . C R UJ E I R A S
How the story began… Health crisis in Wuhan (China)
How the story began… Health crisis in Wuhan (China) 31/01/2020: Italy confirms first cases 22/02/2020: 60 cases in Lombardía 02/03/2020: +2000 cases, 22 deceased 08/03/2020: lockdown in the north 09/03/2020: global lockdown
How the story began… Health crisis in Wuhan (China) 31/01/2020: Italy confirms first cases 22/02/2020: 60 cases in Lombardía 02/03/2020: +2000 cases, 22 deceased 08/03/2020: lockdown in the north 09/03/2020: global lockdown 31/01/2020: Spain confirms first case 29/02/2020: 50 cases 01/03/2020: 83 cases and first restrictions 14/03/2020: lockdown
… and exploded
Science at home… Many people working… ▪Arranging data ▪Visualizing data ▪Fitting models ▪Explaning methods ▪Doing predictions ▪…
CEMat takes the floor… 16/03/2020: an open call for the mathematical community to contribute using our analysis and modelling skills in order to create a better understanding of the COVID-19 health crisis
Mathematical Action against Coronavirus http://matematicas.uclm.es/cemat/covid19/ Collecting links and contributions of the Spanish mathematical community about the virus spread. Promoting discussion in the community using the contributions from researchers and groups and involving a variety of models and techniques. Establishing a Committee of Experts to evaluate the collaborations and report conclusions and suggestions to the authorities.
Mathematical Action against Coronavirus http://matematicas.uclm.es/cemat/covid19/ Collecting links and contributions of the Spanish mathematical community about the virus spread. Promoting discussion in the community using the contributions from researchers and groups and involving a variety of models and techniques. Establishing a Committee of Experts to evaluate the collaborations and report conclusions and suggestions to the authorities.
Mathematical Action against Coronavirus http://matematicas.uclm.es/cemat/covid19/ Collecting links and contributions of the Spanish mathematical community about the virus spread. Promoting discussion in the community using the contributions from researchers and groups and involving a variety of models and techniques. Establishing a Committee of Experts to evaluate the collaborations and report conclusions and suggestions to the authorities.
Mathematical Action against Coronavirus http://matematicas.uclm.es/cemat/covid19/ Collecting links and contributions of the Spanish mathematical community about the virus spread. Promoting discussion in the community using the contributions from researchers and groups and involving a variety of models and techniques. Establishing a Committee of Experts to evaluate the collaborations and report conclusions and suggestions to the authorities.
Mathematical Action against Coronavirus http://matematicas.uclm.es/cemat/covid19/ Collecting links and contributions of the Spanish mathematical community about the virus spread. Promoting discussion in the community using the contributions from researchers and groups and involving a variety of models and techniques. Establishing a Committee of Experts to evaluate the collaborations and report conclusions and suggestions to the authorities.
Short-term predictions The variables to be predicted (1 to 7 days horizon), both at national and regional levels: - Patient requiring intensive cares - Hospitalized cases - Deceased - New cases - Confirmed cases
A cooperative predictor The contributors: - More tan 40 groups have already sent their predictions for any of the variables (at national and/or regional level) - A coordination group, leaded by JA Vilar (UDC)
A cooperative predictor Sharing results https://covid19.citic.udc.es/
A cooperative predictor Methods Simulation Boosting Random forest Time series Branching SIR SEIR Hidden Markov models SEAIDR Spatio-temporal models SIRV Bayesian expert systems SIRM Nonlinear regression Reg. with compositional data Functional regression Dynamic regression Generalized regression models
A cooperative predictor Methods Simulation Boosting Random forest Time series Branching SIR SEIR Hidden Markov models SEAIDR Spatio-temporal models SIRV Bayesian expert systems SIRM Nonlinear regression Reg. with compositional data Functional regression Dynamic regression Generalized regression models: Richards
An example (for the cooperative predictor) https://statgroup-19.blogspot.com/ The data: a cumulative (counting series) Y, observed at days t=1, 2, … The elements in the model: - Lower asymptote: - Upper asymptote: - Slope: - Peak: - Asymmetry: Richards, F. J. (1959). "A Flexible Growth Function for Empirical Use". Journal of Experimental Botany. 10 (2): 290–300
An example (for the cooperative predictor) https://statgroup-19.blogspot.com/ The data: a cumulative (counting series) Y, observed at days t=1, 2, … The elements in the model: - Lower asymptote: b - Upper asymptote: T 1 ൗ = + ( − ) - Slope: h 1 + 10ℎ − - Peak: p - Asymmetry: s Richards, F. J. (1959). "A Flexible Growth Function for Empirical Use". Journal of Experimental Botany. 10 (2): 290–300
An example (for the cooperative predictor) https://jose-ameijeiras.shinyapps.io/StatGroup-19-SP/
An example (for the cooperative predictor) https://jose-ameijeiras.shinyapps.io/StatGroup-19-SP/
A cooperative predictor How does it work? - Patient requiring intensive cares - Hospitalized cases - Deceased - New cases COMBINATION - Confirmed cases Bates and Granger (1969) propose Simulation finding optimal combinations producing Boosting more precise and stable forecasts. Time series Random forest SIR Branching SEIR Hidden Markov models SEAID Spatio-temporal models R Bayesian expert systems SIRV Nonlinear regression SIRM Reg. with compositional data Functional regression Dynamic regression Generalized linear models
A cooperative predictor How does it work? - Patient requiring intensive cares - Hospitalized cases COMBINATION - Deceased CP01. Average - New cases - Confirmed cases CP02. Median CP03. Trimmed mean Simulation CP04. Windsorized mean Boosting CP05. Bates & Granger Time series Random forest SIR Branching SEIR Hidden Markov models CP06. Lowess SEAID R Spatio-temporal models Bayesian expert systems CP07. Loess + Bates & Granger SIRV Nonlinear regression SIRM Reg. with compositional data Functional regression Dynamic regression Generalized regression models
A cooperative predictor How does it work? Best combination and some results for the mean absolute percentage error (MAPE), for all the series and horizons (1 to 7 days) Horizon ICare Hospit. Deceased Confirmed New 1 day CP02 – 4% CP07 – 4% CP02 – 0.2% CP06 – 0.4% CP02 (>20%) 2 days CP02 – 5% CP05 – 5% CP02 – 0.4% CP01 – 0.6% ----- 3 days CP01 – 7% CP01 – 7% CP02 – 0.7% CP01 – 0.9% ----- 4 days CP01 – 7% CP01 – 7% CP02 – 0.9% CP02 – 0.2% ----- 5 days CP01 – 7% CP01 – 7% CP02 – 1.3% CP01 – 0.2% ----- 6 days CP01 – 7% CP03 – 7% CP02 – 2.3% CP01 – 0.3% ----- 7 days CP05 – 5% CP04 – 7% CP01 – 2.7% CP01 – 0.4% -----
But the data… oh! The data! Intensive care and hospitalizations: depending on the region, it may be acumulated or prevalence… (and not even all the time…)
But the data… oh! The data! Intensive care and hospitalizations: depending on the region, it may be acumulated or prevalence… (and not even all the time…)
To think about… All models are wrong, but some are useful. G. Box Garbage in, garbage out…
A take-home message… 1 2 A great opportunity to A global view: better share knowledge also information and fair accross countries… comparisons…
A cooperative experience for understanding COVID-19 RO S A M . C R UJ E I R A S
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