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How is the State using data to make decisions and slow the spread of COVID-19?

Last Updated: 04/27/2020

Working with a team of leading data scientists and epidemiologists, the State is using several dynamic epidemiology and growth models to predict the spread of the virus and the need for hospital beds, intensive care units, ventilators, and personal protective equipment at a hospital, county, and statewide level.

The models take into account New Jersey's aggressive social distancing policy along with what happened in other countries and tailor those findings to the variations observed at a county level and even hospital level in our state. By using a combination of models, we have been able to achieve accurate predictions for 10-day forecasts, and have also established long-term forecasts.

More specifically, to account for the aggressive social distancing policy in New Jersey, we have developed multiple adaptive growth models using Multivariate Hawkes processes and control theory principles for short-term forecasting -- these models were developed by researchers at New York University and Facebook AI research. For long-term forecasting, the State uses multiple models including dynamic versions of SIR models and SEIR models, and "stay at home" growth variation modeling using transfer learning algorithms developed by researchers at New York University. The State is also continuously monitoring the University of Washington's Institute for Health Metrics and Evaluation (IHME) model as well as the COVID-19 Hospital Impact Model for Epidemics (CHIME), developed by University of Pennsylvania's School of Medicine's Predictive Healthcare Team.

Short-Term Forecasting

  • Adaptive Growth Model with Cross-County Clustering: In this model, developed by Lakshmi Subramanian of the NYU Courant Institute and Srikanth Jagabathula of the NYU Stern School of Business, we directly estimate the growth rate of new confirmed cases at a county level using a learning rate equation commonly used in control theory and gradient descent algorithms. We used exponential smoothing of the parameter inputs to account for county-level spikes, reporting, and testing fluctuations. We use cross-county clustering to learn early growth rate variations in counties with very limited data and smooth the variations across counties.
  • Multivariate Hawkes Process: Another model the State uses for short-term forecasting, developed by Facebook's AI team, trains a multivariate Hawkes process (which is commonly used in financial stock price modeling) to train a county-level model and a collective state-level model.

Both these models are re-trained on a daily basis and provide 7-14 day forecasts.

Long-Term Forecasting

  • Dynamic SIR: The SIR (susceptible-infected-recovered) model is widely used for epidemiology modeling. The CHIME (COVID-19 Hospital Impact Model for Epidemics) tool uses the SIR model, but manually sets the parameters of the SIR model based on doubling time and social distancing. While the CHIME model does leverage a social distancing parameter, the State also uses a dynamic SIR model that adapts the mixing rate (which captures the number of people each infected person comes in contact with) parameter based on the data observed in growth rate variations at a county level to provide an additional long-term model that further captures the impact of the "stay at home" order currently in place in New Jersey.
  • Dynamic SEIR model: Expanding upon our Dynamic SIR model, we trained the parameters of an SEIR (susceptible-exposed-infected-recovered) model in a dynamic manner at a county level based on the growth variations observed in the data at a county and state level. The dynamic SEIR model - developed by Subramanian and Jagabathula - uses the growth variations to adaptively adjust the SEIR parameters, in essence, varying the R0 value of the epidemiology model over time to capture the impact of the "stay at home" order currently in place in New Jersey.
  • "Stay at Home" Growth Variation Modeling using Transfer Learning: Traditional epidemiology models do not capture movement limitation behavior. To account for that, in this model - also developed by Subramanian and Jagabathula - we study the growth-rate variations in other countries after "stay at home" orders have been issues and use transfer learning to determine the appropriate growth variations that best fit the data for New Jersey. This model is especially useful for learning best case, expected case, and worst-case scenarios of peaks.

With the help of this ensemble of forecasts, the State is better able to make difficult decisions on what critical resources to acquire, where to deploy these critical resources, anticipate potential hot spots, and ensure that those who need care can get it when they need it most.