Abstract
The COVID-19 pandemic confronts governments and their health systems with great challenges for disease management. In many countries, hospitalization and in particular ICU occupancy is the primary measure for policy makers to decide on possible non-pharmaceutical interventions. In this paper a combined methodology for the prediction of COVID-19 case numbers, case-specific hospitalization and ICU admission rates as well as hospital and ICU occupancies is proposed. To this end, we employ differential flatness to provide estimates of the states of an epidemiological compartmental model and estimates of the unknown exogenous inputs driving its nonlinear dynamics. A main advantage of this method is that it requires the reported infection cases as the only data source. As vaccination rates and case-specific ICU rates are both strongly age-dependent, specifically an age-structured compartmental model is proposed to estimate and predict the spread of the epidemic across different age groups. By utilizing these predictions, case-specific hospitalization and case-specific ICU rates are subsequently estimated using deconvolution techniques. In an analysis of various countries we demonstrate how the methodology is able to produce real-time state estimates and hospital/ICU occupancy predictions for several weeks thus providing a sound basis for policy makers.
Highlights
The ongoing fight against recurring epidemic waves of the SARS-CoV-2 pandemic is a complex undertaking that requires several key directions of action such as: (i) social distancing and personal protective equipment, (ii) testing for infection, (iii) quarantine of infected people and (iv) vaccination.Management of healthcare systems which are treating the actual COVID-19 patients remains a central issue during the ongoing pandemic
In an analysis of various countries we demonstrate how the methodology is able to produce real-time state estimates and hospital/intensive care units (ICUs) occupancy predictions for several weeks providing a sound basis for policy makers
The necessity to avoid overloading of the healthcare system is imperative as the level and quality of medical care and mortality are directly related to the available capacities
Summary
The ongoing fight against recurring epidemic waves of the SARS-CoV-2 pandemic is a complex undertaking that requires several key directions of action such as: (i) social distancing and personal protective equipment, (ii) testing for infection, (iii) quarantine of infected people and (iv) vaccination. It explicitly accounts for time lags between infection and hospitalization as well as for the distribution of the length of stay To allow for this approach to make predictions depending on different future evolutions of infections, an age-structured compartmental model is proposed along with a methodology that provides estimates of the states of that model and the otherwise unknown exogenous inputs driving its dynamics requiring only the reported infection data. In the method presented in this paper both case-specific admission rates are treated as variable over time and a methodology is proposed to estimate them in real-time using deconvolution techniques To this end, only the reported active cases along with hospital and ICU occupancy data are required. The estimates of the epidemiological states obtained from the proposed segregated modelling approach provide the basis for estimating the hospital and ICU occupancy
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