2020
DOI: 10.1101/2020.10.19.20215293
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Adaptive COVID-19 Forecasting via Bayesian Optimization

Abstract: Accurate forecasts of infections for localized regions are valuable for policy making and medical capacity planning. Existing compartmental and agent-based models for epidemiological forecasting employ static parameter choices and cannot be readily contextualized, while adaptive solutions focus primarily on the reproduction number. In the current work, we propose a novel model-agnostic Bayesian optimization approach for learning model parameters from observed data that generalizes to multiple application-spec… Show more

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Cited by 6 publications
(4 citation statements)
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“…Regarding the optimization of COVID-19 prediction models, three main approaches have been reported in the literature. The first uses the SEIR (Susceptible -Exposed -Infectious -Recovered) model (or its derivatives) as its basis and applies machine learning and optimization methods to determine the epidemiological parameters of the model [6][7][8][9][10][11][12][13][95][96][97][98][99][100][101][102][103][104][105][106]. The second approach uses a population-based model to simulate the transmission of the virus [14,15].…”
Section: B Predictionmentioning
confidence: 99%
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“…Regarding the optimization of COVID-19 prediction models, three main approaches have been reported in the literature. The first uses the SEIR (Susceptible -Exposed -Infectious -Recovered) model (or its derivatives) as its basis and applies machine learning and optimization methods to determine the epidemiological parameters of the model [6][7][8][9][10][11][12][13][95][96][97][98][99][100][101][102][103][104][105][106]. The second approach uses a population-based model to simulate the transmission of the virus [14,15].…”
Section: B Predictionmentioning
confidence: 99%
“…Due to the novelty of the virus, its epidemiological parameters are unknown, so the SEIR model is fitted to historical COVID-19 data, and the resulting estimated parameters are used to predict future cases. Bayesian optimization [6], metaheuristics (e.g., particle swarm optimization, stochastic fractal search) [7-10, 104, 108-114], neural networks [11,115,116], and nonlinear curve-fitting based optimization methods [12,13,[117][118][119] are some of the most popular approaches used to fit the model to the data and estimate the epidemiological parameters of the model, such as the reproduction number. In addition to forecasting COVID-19 cases, some studies considered additional aspects, such as the effect of different non-pharmaceutical intervention policies (social distancing and lockdown) and re-opening plans [101,114,[120][121][122][123][124][125][126][127].…”
Section: B Predictionmentioning
confidence: 99%
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“…From chocolate chip cookies [15] to COVID [16]: Bayesian Optimization has been applied in a wide range of subjects, including auto-tuning. To grasp the current state of BO in GPU auto-tuning, this section briefly explores related applications of BO in auto-tuning.…”
Section: Related Workmentioning
confidence: 99%