Intelligent Optimization of Diversified Community Prevention of COVID-19 Using Traditional Chinese Medicine

Zheng, Yujun; Yu, Si-Lan; Yang, Junchao; Gan, Tieer; Song, Qin; Yang, Jun; Karataş, Mümtaz

doi:10.1109/mci.2020.3019899

Cited by 14 publications

(8 citation statements)

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“…Metaheuristic optimization approaches have also been applied to solve the optimal control strategies of the pandemic. These strategies range from the implementation of social distancing to reach herd immunity [163] , [164] , to increasing testing and quarantine requirements [165] , and to developing traditional Chinese medicine (TCM) prevention programs [166] . In [165] , the authors developed a nature-inspired model to simulate the distribution process of COVID-19 in different countries and strive to maximize the number of “safe” countries (those that are immune to COVID-19).…”

Section: The Four Framework and Literature Reviewmentioning

confidence: 99%

“…All these actions are designed to control the pandemic and minimize the risk of infected individuals transmitting the virus to others. In [166] , the authors acknowledged the benefits of TCM in treating COVID-19 patients, but realized the need to create diversified prevention and treatment programs for different groups of community residents. TCM is a system of treatment plans or programs that are mainly based on herbal medicine, and one of its principles states that treatment must be individualized, there is no “one-size-fits-all” solution.…”

Section: The Four Framework and Literature Reviewmentioning

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: The Four Framework and Literature Reviewmentioning

confidence: 99%

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

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“…GA No sufficient details on compared metaheuristics and allowed number of function calls. A modified version of this study appeared as Zheng et al ( 2020c ) COVID-19 resource allocations and costs Zheng et al ( 2020b ) Balancing disease prevention and epidemic control Main 2-objective problem: 10,000 ~ 40,000 Transformed 2-objective problem: 200 ~ 600 2 Main 2-objective problem 1. MOEA/D (Zhang and Li, 2007 ) 2.…”

Section: Applications Of Differential Evolution and Particle Swarm Optimization Against Covid-19mentioning

confidence: 99%

Differential evolution and particle swarm optimization against COVID-19

Piotrowski

Piotrowska

2021

Artif Intell Rev

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COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used.

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“…Similar bottlenecks in healthcare resources and their outcomes are also experienced in other hardest hit European countries such as Germany, Italy, United Kingdom, and Turkey. As a response to the tsunami of infected cases, the Chinese government implemented a centralized command and treatment system and strong measures to control sources of infection supported by community prevention (such as traditional medicine implementations) and control mechanisms (Zheng et al 2020 ). At the epicentre of the outbreak, new infectious disease hospitals with larger capacities are built at strategic locations to accommodate more patients.…”

Section: Introductionmentioning

confidence: 99%

A robust multi-objective model for healthcare resource management and location planning during pandemics

2022

Self Cite

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In this study, we consider the problem of healthcare resource management and location planning problem during the early stages of a pandemic/epidemic under demand uncertainty. Our main ambition is to improve the preparedness level and response effectiveness of healthcare authorities in fighting pandemics/epidemics by implementing analytical techniques. Building on lessons from the Chinese experience in the COVID-19 outbreak, we first develop a deterministic multi-objective mixed integer linear program (MILP) which determines the location and size of new pandemic hospitals (strategic level planning), periodic regional health resource re-allocations (tactical level planning) and daily patient-hospital assignments (operational level planning). Taking the forecasted number of cases along a planning horizon as an input, the model minimizes the weighted sum of the number of rejected patients, total travel distance, and installation cost of hospitals subject to real-world constraints and organizational rules. Next, accounting for the uncertainty in the spread speed of the disease, we employ an across scenario robust (ASR) model and reformulate the robust counterpart of the deterministic MILP. The ASR attains relatively more realistic solutions by considering multiple scenarios simultaneously while ensuring a predefined threshold of relative regret for the individual scenarios. Finally, we demonstrate the performance of proposed models on the case of Wuhan, China. Taking the 51 days worth of confirmed COVID-19 case data as an input, we solve both deterministic and robust models and discuss the impact of all three level decisions to the quality and performance of healthcare services during the pandemic. Our case study results show that although it is a challenging task to make strategic level decisions based on uncertain forecasted data, an immediate action can considerably improve the response effectiveness of healthcare authorities. Another important observation is that, the installation times of pandemic hospitals have significant impact on the system performance in fighting with the shortage of beds and facilities.

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Intelligent Optimization of Diversified Community Prevention of COVID-19 Using Traditional Chinese Medicine

Cited by 14 publications

References 43 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

Differential evolution and particle swarm optimization against COVID-19

A robust multi-objective model for healthcare resource management and location planning during pandemics

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