Temporal SARS-CoV-2 Severity Estimates
estimating and predicting the true severity of SARS-CoV-2
Created Mar 30, 2023 - Last updated: Mar 30, 2023
Severity of the SARS-CoV-2 has always been a matter of relative evaluation. At the onset of the pandemic, the virus was dubbed the 2019 novel coronavirus (2019-nCoV), as it was fairly novel to our immune system. Its impact was profound, with staggering mortality rates, hospitalizations, intensive care admissions, and frightening intubations, all of which prompted widespread pandemic restrictions across the globe. The healthcare system was caught off guard, grappling with shortages of personnel, equipment, and therapeutics.
To this day, gauging the severity of COVID-19 remains a relative issue. However, it has become increasingly challenging to do so, given that: (1) the virus has undergone multiple mutations and continues to evolve, (2) our immune system has evolved and continues to change as a result of vaccine immunizations and prior infections, and (3) the healthcare systems are better equipped and more experienced in handling the disease. The global populace has also changed, with an estimated 7 million deaths from COVID-19 worldwide.
Reports indicate that the more recent variants may have decreased pathogenicity owing to altered cell tropism, despite heightened immune evasion. The severity of viruses cannot be determined solely on the basis of their inherent biological traits, and doing so is a sluggish process that necessitates virus isolation and animal modeling. Precise evaluations of severity must consider biological characteristics of the host environment, the healthcare system’s ability to combat the antigen, and the measure used to assess severity.
We have developed a system that uses incidence-level data from over 200 thousand patients who receive care at Mass General Brigham (MGB) to measure, track, and predict severity of SARS-CoV-2 over time.
The observed and predicted temporal severity profiles of SARS-CoV-2 in Massachusetts
We use time-series forecasting models to predict outcome-based true severity the next 3 months.
Our predictions of the month of February were almost perfect for hospitalization. We predict a spike in hospitalization risks in the next 3 months.
We overestimated mortality in February. This is potentially due to a spike in January, which confused our models. Our estimates for the next 3 months show similar trends as the past few months.