A simplified math approach to predict ICU beds and mortality rate for hospital emergency planning under Covid-19 pandemic. (2nd September 2020) - Manca, Davide; Caldiroli, Dario; Storti, Enrico
Highlights: Simple mathematics for medical doctors and decision-makers under Covid-19 emergency. Regression models describe the evolution of intensive care patients and fatalities. Proposed models allow predicting both short- and long-term horizon scenarios. Key values and dates of the Covid-19 phenomenon can be predicted easily. An Excel file is available in the supplement material for dynamic forecasts. Abstract: The different stages of Covid-19 pandemic can be described by two key-variables: ICU patients and deaths in hospitals. We propose simple models that can be used by medical doctors and decision makers to predict the trends on both short-term and long-term horizons. Daily updates of the models with real data allow forecasting some key indicators for decision-making (an Excel file in the Supplemental material allows computing them). These are beds allocation, residence time, doubling time, rate of renewal, maximum daily rate of change (positive/negative), halfway points, maximum plateaus, asymptotic conditions, and dates and time intervals when some key thresholds are overtaken. Doubling time of ICU beds for Covid-19 emergency can be as low as 2–3 days at the outbreak of the pandemic. The models allow identifying the possible departure of the phenomenon from the predicted trend and thus can play the role of early warning systems and describe further outbreaks. Graphical abstract: Image, graphical abstract