Initialization of a Disease Transmission Model

Runvik, Håkan; Medvedev, Alexander; Eriksson, Robin; Engblom, Stefan

doi:10.1016/j.ifacol.2021.04.178

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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] – [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] – [127] . For example, Ghamizi et al [11] , in addition to predicting cases and deaths, also developed a model to search for optimal exit strategies - i.e., best policies to re-open from lockdown, while maintaining low infection rates.…”

Section: The Four Framework and Literature Reviewmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract