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Predicting mortality for Covid-19 in the US using the delayed elasticity method
Luis Ángel Hierro1, Antonio J Garzón1, Pedro Atienza-Montero2
1Department of Economics and Economic History, University of Seville, Avda. Ramón y Cajal, 1, 41018, Seville, Spain.
Scientific Reports
|December 1, 2020
Summary
This study presents a method to predict COVID-19 deaths, enabling health authorities to plan resources effectively. By forecasting clinical needs days in advance, this approach aims to reduce mortality during the pandemic.
Area of Science:
- Epidemiology
- Health Systems Management
- Econometrics
Background:
- The COVID-19 pandemic has placed immense strain on national health systems due to its high transmissibility and significant clinical demands.
- Effective resource allocation is critical for managing the overwhelming impact of the pandemic on healthcare infrastructure.
Purpose of the Study:
- To develop a predictive model for COVID-19 mortality to aid health authorities in resource planning.
- To establish a leading indicator for clinical needs, allowing for proactive management of the pandemic's impact.
Main Methods:
- Ordinary Least Squares (OLS) regression was utilized for econometric estimation.
- Forecast performance was evaluated using Root Mean Square Error (RMSE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE).
- The best lagged predictor for dependent variables (deaths and clinical needs) was identified using these performance measures.
Main Results:
- The study successfully identified a method for predicting COVID-19 deaths.
- The developed model serves as a leading indicator for anticipating clinical needs.
Conclusions:
- Advance forecasting of clinical needs and deaths can significantly improve governmental response to health crises like COVID-19.
- Proactive resource planning based on accurate predictions can mitigate the gap between healthcare demands and available resources, ultimately reducing pandemic-related mortality.
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