Log In Sign Up

Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories

by   Gian Marco Visani, et al.

We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic in order to inform decision-makers of future demand and assess the societal value of possible interventions. For broad applicability, we focus on the common yet challenging scenario where patient-level data for a region of interest are not available. Instead, given daily admissions counts, we model aggregated counts of observed resource use, such as the number of patients in the general ward, in the intensive care unit, or on a ventilator. In order to explain how individual patient trajectories produce these counts, we propose an aggregate count explicit-duration hidden Markov model, nicknamed the ACED-HMM, with an interpretable, compact parameterization. We develop an Approximate Bayesian Computation approach that draws samples from the posterior distribution over the model's transition and duration parameters given aggregate counts from a specific location, thus adapting the model to a region or individual hospital site of interest. Samples from this posterior can then be used to produce future forecasts of any counts of interest. Using data from the United States and the United Kingdom, we show our mechanistic approach provides competitive probabilistic forecasts for the future even as the dynamics of the pandemic shift. Furthermore, we show how our model provides insight about recovery probabilities or length of stay distributions, and we suggest its potential to answer challenging what-if questions about the societal value of possible interventions.


page 1

page 2

page 3

page 4


Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach

We consider the problem of forecasting the daily number of hospitalized ...

Excess registered deaths in England and Wales during the COVID-19 pandemic, March 2020 to May 2020

Official counts of COVID-19 deaths have been criticized for potentially ...

EpiBeds: Data informed modelling of the COVID-19 hospital burden in England

The first year of the COVID-19 pandemic put considerable strain on the n...

Surveillance of COVID-19 Pandemic using Hidden Markov Model

COVID-19 pandemic has brought the whole world to a stand-still over the ...

Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus

A major difficulty in the analysis of propagation of the coronavirus is ...