A model to forecast regional demand for COVID-19 related hospital beds

Johannes Opsahl Ferstad; Angela Jessica Gu; Raymond Ye Lee; Isha Thapa; Andrew Y Shin; Joshua A Salomon; Peter Glynn; Nigam H Shah; Arnold Milstein; Kevin Schulman; David Scheinker

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.26.20044842 doi: medRxiv preprint There are several limitations to the model. First, projections related to the spread of a pandemic at this early stage are plagued with uncertainty about critical model parameters, e.g., the doubling time and the true number of cases in the population. To account for this, the model was designed to produce projections given current assumptions about parameters (with the option to update those variables as better data become available). Second, although estimates of disease propagation follow first-order evidence, important input parameters may not have been considered in the model which may affect hospitalization rates and bed availability. For example, assumptions on shared healthcare resources (such as the use of pediatric beds for adult hospitalization overflow) are not modifiable by users but only by those maintaining the model. Finally, the model estimates the effect of one or the summation of more than one intervention (by modification of the doubling time) but does not ascribe the individual impact of sequential interventions.