Adaptive COVID-19 Forecasting via Bayesian Optimization

Bannur, Nayana; Maheshwari, Harsh; Jain, Sansiddh; Shetty, Shreyas; Merugu, Srujana; Raval, Alpan

doi:10.1101/2020.10.19.20215293

Cited by 6 publications

(4 citation statements)

References 6 publications

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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%

“…Examples include optimizing prediction models to quantify future COVID-19 cases [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24], optimizing the allocation of critical hospital supplies to treat COVID-19 patients [25][26][27][28][29][30][31][32], and developing decision support tools to guide data-driven mitigation strategies to combat the spread of the virus [33][34][35][36][37]. While numerous studies have tried to tackle these challenges, the COVID-19 pandemic continues to expose the flaws and shortcomings of current systemic policies, many of which were unanticipated and unprepared for.…”

Section: Introductionmentioning

confidence: 99%

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Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

et al. 2021

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This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

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Section: B Predictionmentioning

confidence: 99%

Section: B Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

et al. 2021

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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%

Bayesian Optimization for auto-tuning GPU kernels

Willemsen¹,

Nieuwpoort²,

Werkhoven³

2021

Preprint

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Finding optimal parameter configurations for tunable GPU kernels is a non-trivial exercise for large search spaces, even when automated. This poses an optimization task on a nonconvex search space, using an expensive to evaluate function with unknown derivative. These characteristics make a good candidate for Bayesian Optimization, which has not been applied to this problem before.However, the application of Bayesian Optimization to this problem is challenging. We demonstrate how to deal with the rough, discrete, constrained search spaces, containing invalid configurations. We introduce a novel contextual variance exploration factor, as well as new acquisition functions with improved scalability, combined with an informed acquisition function selection mechanism.By comparing the performance of our Bayesian Optimization implementation on various test cases to the existing search strategies in Kernel Tuner, as well as other Bayesian Optimization implementations, we demonstrate that our search strategies generalize well and consistently outperform other search strategies by a wide margin.

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Artificial Intelligence and Data Science in the Detection, Diagnosis, and Control of COVID-19: A Systematic Mapping Study

Tintín

Flórez

2021

Computational Science and Its Applications – ICCSA 2021

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Adaptive COVID-19 Forecasting via Bayesian Optimization

Cited by 6 publications

References 6 publications

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

Bayesian Optimization for auto-tuning GPU kernels

Artificial Intelligence and Data Science in the Detection, Diagnosis, and Control of COVID-19: A Systematic Mapping Study

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