Qiwei Li1, Tejasv Bedi1, Christoph U Lehmann2,3,4, Guanghua Xiao3,4, Yang Xie3,4. 1. Department of Mathematical Sciences, The University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, USA. 2. Department of Pediatrics, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA. 3. Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA. 4. Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Abstract
BACKGROUND: Forecasting of COVID-19 cases daily and weekly has been one of the challenges posed to governments and the health sector globally. To facilitate informed public health decisions, the concerned parties rely on short-term daily projections generated via predictive modeling. We calibrate stochastic variants of growth models and the standard susceptible-infectious-removed model into 1 Bayesian framework to evaluate and compare their short-term forecasts. RESULTS: We implement rolling-origin cross-validation to compare the short-term forecasting performance of the stochastic epidemiological models and an autoregressive moving average model across 20 countries that had the most confirmed COVID-19 cases as of August 22, 2020. CONCLUSION: None of the models proved to be a gold standard across all regions, while all outperformed the autoregressive moving average model in terms of the accuracy of forecast and interpretability.
BACKGROUND: Forecasting of COVID-19 cases daily and weekly has been one of the challenges posed to governments and the health sector globally. To facilitate informed public health decisions, the concerned parties rely on short-term daily projections generated via predictive modeling. We calibrate stochastic variants of growth models and the standard susceptible-infectious-removed model into 1 Bayesian framework to evaluate and compare their short-term forecasts. RESULTS: We implement rolling-origin cross-validation to compare the short-term forecasting performance of the stochastic epidemiological models and an autoregressive moving average model across 20 countries that had the most confirmed COVID-19 cases as of August 22, 2020. CONCLUSION: None of the models proved to be a gold standard across all regions, while all outperformed the autoregressive moving average model in terms of the accuracy of forecast and interpretability.
Authors: Joshua S Weitz; Stephen J Beckett; Ashley R Coenen; David Demory; Marian Dominguez-Mirazo; Jonathan Dushoff; Chung-Yin Leung; Guanlin Li; Andreea Măgălie; Sang Woo Park; Rogelio Rodriguez-Gonzalez; Shashwat Shivam; Conan Y Zhao Journal: Nat Med Date: 2020-05-07 Impact factor: 53.440
Authors: Kristen Nixon; Sonia Jindal; Felix Parker; Nicholas G Reich; Kimia Ghobadi; Elizabeth C Lee; Shaun Truelove; Lauren Gardner Journal: Lancet Digit Health Date: 2022-10