Evaluation of the treatment options for COVID-19 patients using generalized hesitant fuzzy- multi criteria decision making techniques.

The breakout of the pandemic COVID-19 has affected numerous countries and territories worldwide. As COVID-19 specific medicines yet to be invented, at present the treatment is case specific, hence identification and evaluation of different prevalent treatment options based on various criteria and attributes are very important not only from the point of view of present pandemic but also for futuristic pandemic preparedness. The present study focuses on identifying, evaluation and ranking of treatment options using Multi Criteria Decision Making (MCDM). In this regard, the existing literature, doctors and scientist were interviewed to know the current treatment options in vogue and the scale of their importance with respect to the criteria. The criteria taken are side effect, regime cost, treatment duration, plasma stability, plasma turnover, time of suppression, ease of application, drug-drug interaction, compliance, fever, pneumonia, intensive care, organ failure, macrophage activation syndrome, hemophagocytic syndrome, pregnancy, kidney problem, age. This study extended Hesitant Fuzzy Set (HFS) to Generalized Hesitant Fuzzy Sets (GHFS). Generalized Hesitant Pentagonal Fuzzy Number (GHPFN) is developed. The properties of GHPFN are demonstrated. Two types of GHPFN has been described. The GHPFN (2nd type) along with MCDM tool Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been applied to rank the treatment options. The result of the study ranked 'Hydroxychloroquine' as the first alternative followed by, 'Plasma Exchange', 'Tocilizumab', 'Remdesivir' and 'Favipravir'. To check the robustness and steadiness of the proposed methodology, comparative analysis and sensitivity analysis has been conducted.

Evolutionary algorithm based approach for solving transportation problems in normal and pandemic scenario.

In recent times, COVID-19 pandemic has posed certain challenges to transportation companies due to the restrictions imposed by different countries during the lockdown. These restrictions cause delay and/ or reduction in the number of trips of vehicles, especially, to the regions with higher restrictions. In a pandemic scenario, regions are categorized into different groups based on the levels of restrictions imposed on the movement of vehicles based on the number of active cases (i.e., number of people infected by COVID-19), number of deaths, population, number of COVID-19 hospitals, etc. The aim of this study is to formulate and solve a fixed-charge transportation problem (FCTP) during this pandemic scenario and to obtain transportation scheme with minimum transportation cost in minimum number of trips of vehicles moving between regions with higher levels of restrictions. For this, a penalty is imposed in the objective function based on the category of the region(s) where the origin and destination are situated. However, reduction in the number of trips of vehicles may increase the transportation cost to unrealistic bounds and so, to keep the transportation cost within limits, a constraint is imposed on the proposed model. To solve the problem, the Genetic Algorithm (GA) has been modified accordingly. For this purpose, we have designed a new crossover operator and a new mutation operator to handle multiple trips and capacity constraints of vehicles. For numerical illustration, in this study, we have solved five example problems considering three levels of restrictions, for which the datasets are generated artificially. To show the effectiveness of the constraint imposed for reducing the transportation cost, the same example problems are then solved without the constraint and the results are analyzed. A comparison of results with existing algorithms proves that our algorithm is effective. Finally, some future research directions are discussed.

Identification of dominant risk factor involved in spread of COVID-19 using hesitant fuzzy MCDM methodology.

The outburst of the pandemic Coronavirus disease since December 2019, has severely impacted the health and economy worldwide. The epidemic is spreading fast through various means, as the virus is very infectious. Medical science is exploring a vaccine, only symptomatic treatment is possible at the moment. To contain the virus, it is required to categorize the risk factors and rank those in terms of contagion. This study aims to evaluate risk factors involved in the spread of COVID-19 and to rank them. In this work, we applied the methodology namely, Fuzzy Analytic Hierarchy Process (FAHP) to find out the weights and finally Hesitant Fuzzy Sets (HFS) with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to identify the major risk factor. The results showed that "long duration of contact with the infected person" the most significant risk factor, followed by "spread through hospitals and clinic" and "verbal spread". We showed the appliance of the Multi Criteria Decision Making (MCDM) tools in evaluation of the most significant risk factor. Moreover, we conducted sensitivity analysis.