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Identification and prioritization of strategies to tackle COVID-19 outbreak: A group-BWM based MCDM approach.

ABSTRACT: The world is reeling in the midst of the novel coronavirus pandemic with fear of rising toll due to the deadly virus. Decision making during a pandemic outbreak has numerous challenges. Covid19 has become a challenging problem for organizations, countries and the world at large. It is even more complicated when governments and medical care communities are changing their priorities based on the growing challenges and level of effectiveness of measures taken in other countries. In this study, a potential application of a well-known MCDM method called the Group Best-Worst Method is presented to overcome such challenges and draw the strategies to handle COVID19 outbreak. The methodology is applied to rank the 10 identified strategies based on their relative importance provided by multiple groups of stakeholder. These strategies focus on social distancing, medical care, essential commodities, financial support to poor people, public awareness, overall impact of COVID19, digital surveillance of infected or doubtful people, maintaining the economy of the country, and an effect on industries. Furthermore, the local and global weights along with ranking order of strategies are obtained. A sensitivity analysis has also been done to show the change in global weights and ranking order of strategies.

SUBMITTER: Ahmad N

PROVIDER: S-EPMC8252723 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundControlling the COVID-19 outbreak in Brazil is a challenge due to the population's size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources.ObjectiveThe purpose of this study is to effectively prioritize patients who are symptomatic for testing to assist early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies.MethodsRaw data from 55,676 Brazilians were preprocessed, and the chi-square test was used to confirm the relevance of the following features: gender, health professional, fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache. Classification models were implemented relying on preprocessed data sets; supervised learning; and the algorithms multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). The models' performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances.ResultsGender, fever, and dyspnea were among the highest-ranked features used by the classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model.ConclusionsThe DT classification model can effectively (with a mean accuracy≥89.12%) assist COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a patient who is symptomatic for COVID-19 testing.

Project description:34,354,966 active cases and 460,787 deaths because of COVID-19 pandemic were recorded on November 06, 2021, in India. To end this ongoing global COVID-19 pandemic, there is an urgent need to implement multiple population-wide policies like social distancing, testing more people and contact tracing. To predict the course of the pandemic and come up with a strategy to control it effectively, a compartmental model has been established. The following six stages of infection are taken into consideration: susceptible (S), asymptomatic infected (A), clinically ill or symptomatic infected (I), quarantine (Q), isolation (J) and recovered (R), collectively termed as SAIQJR. The qualitative behavior of the model and the stability of biologically realistic equilibrium points are investigated in terms of the basic reproduction number. We performed sensitivity analysis with respect to the basic reproduction number and obtained that the disease transmission rate has an impact in mitigating the spread of diseases. Moreover, considering the non-pharmaceutical and pharmaceutical intervention strategies as control functions, an optimal control problem is implemented to mitigate the disease fatality. To reduce the infected individuals and to minimize the cost of the controls, an objective functional has been constructed and solved with the aid of Pontryagin's maximum principle. The implementation of optimal control strategy at the start of a pandemic tends to decrease the intensity of epidemic peaks, spreading the maximal impact of an epidemic over an extended time period. Extensive numerical simulations show that the implementation of intervention strategy has an impact in controlling the transmission dynamics of COVID-19 epidemic. Further, our numerical solutions exhibit that the combination of three controls are more influential when compared with the combination of two controls as well as single control. Therefore, the implementation of all the three control strategies may help to mitigate novel coronavirus disease transmission at this present epidemic scenario.Supplementary informationThe online version supplementary material available at 10.1007/s11071-022-07235-7.

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Identification and prioritization of strategies to tackle COVID-19 outbreak: A group-BWM based MCDM approach.

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