A Statistical Model for the Spread of SARS-CoV-2 in New Mexico

• Activity Number: 140 - Disease Outbreak and Modeling Applications in Defense and National Security
• Type: Topic Contributed
• Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Defense and National Security
• Author(s): Lyndsay Shand* and Adah Zhang and Alexander Foss and James Derek Tucker and Gabriel Huerta and Audrey McCombs
• Companies: Sandia National Laboratories and Sandia National Laboratories and Sandia National Laboratories and Sandia National Laboratories and Sandia National Laboratories and Sandia National Laboratories
• Keywords: covid-19; space-time modeling; autoregressive; disease modeling

Abstract

Decisions on how to prevent the spread of COVID-19 are being made at a rapid pace based on the most immediately available predictions which are also changing rapidly. Due to the demand for real-time predictions, many modeling efforts are focused on prediction rather than on understanding key driving factors of the virus’ spread and recovery rates. We aim instead to build an inferential model that will highlight influential factors in the COVID-19 outbreak and give critical insight into how a state/nation should better prepare for, prevent and react to the inevitable next pandemic threat. In this work, we present a flexible statistical modeling framework capable of highlighting key influential factors, accounting for underreporting of cases due to testing protocols and asymptomatic, accounting for spatial correlation between counties due to infrastructure and commuting profiles, incorporating well-established disease dynamics and lastly, propagating uncertainty. We demonstrate our method on the 33 New Mexico counties over 22 weeks of data and explore the insights found of the best fitting models.SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.SAND2022-0841A