15 June 2020: General Recognition of COVID Forecast Model Failures

Forecasting for COVID-19 has failed

This analysis on the International Institute of Forecasters blog examines the multiple failures in public policy that have resulted from following poor forecasting models. The following passage is directly applicable to what happened in New York, but certainly applies in other states that aped its policies:

Experienced modelers drew early on parallels between COVID-19 and the Spanish flu [2] that caused >50 million deaths with mean age at death being 28. We all lament the current loss of life. However, as of June 8, total fatalities are ~410,000 with median age ~80 and typically multiple comorbidities.

Predictions for hospital and ICU bed requirements were also entirely misinforming. Public leaders trusted models (sometimes even black boxes without disclosed methodology) inferring massively overwhelmed health care capacity (Table 1) [3]. However, eventually very few hospitals were stressed, for a couple of weeks. Most hospitals maintained largely empty wards, waiting for tsunamis that never came. The general population was locked and placed in horror-alert to save the health system from collapsing. Tragically, many health systems faced major adverse consequences, not by COVID-19 cases overload, but for very different reasons. Patients with heart attacks avoided visiting hospitals for care [4], important treatments (e.g. for cancer) were unjustifiably delayed [5], mental health suffered [6]. With damaged operations, many hospitals started losing personnel, reducing capacity to face future crises (e.g. a second wave). With massive new unemployment, more people may lose health insurance. The prospects of starvation and of lack of control for other infectious diseases (like tuberculosis, malaria, and childhood communicable diseases for which vaccination is hindered by the COVID-19 measures) are dire [7,8].

We think it will take years to fully learn the lessons the flawed coronavirus models have taught.