The New England Journal of Statistics in Data Science

In this paper, we build a mechanistic system to understand the relation between a reduction in human mobility and Covid-19 spread dynamics within New York City. To this end, we propose a multivariate compartmental system that jointly models smartphone mobility data and case counts during the first 90 days of the epidemic. Parameter calibration is achieved through the formulation of a general statistical-mechanistic Bayesian hierarchical model. The open-source probabilistic programming language Stan is used for the requisite computation. Through sensitivity analysis and out-of-sample forecasting, we find our simple and interpretable model provides quantifiable evidence for how reductions in human mobility altered early case dynamics in New York City.

The English Premier League is well-known for being not only one of the most popular professional sports leagues in the world, but also one of the toughest competitions to predict. The first purpose of this research was to verify the consistency between goal scoring in the English Premier League and the Poisson process; specifically, the relationships between the number of goals scored in a match and the Poisson distribution, the time between goals throughout the course of a season and the exponential distribution, and the time location of goals during football games and the continuous uniform distribution. We found that the Poisson process and the three probability distributions accurately describe Premier League goal scoring. In addition, Poisson regression was utilized to predict outcomes for a Premier League season, using different sets of season data and with a large number of simulations being involved. We examined and compared various soccer metrics from our simulation results, including an English club’s chances of being the champions, finishing in the top four and bottom three, and relegation points.