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Introduction: Countries develop their EQ-5D-5L value sets using the EuroQol Valuation Technology (EQ-VT) protocol. This study aims to assess if extension in the conventional EQ-VT design can lead to development of value sets with improved precision., Methods: A cross-sectional survey was undertaken in a representative sample of 3,548 adult respondents, selected from 5 different states of India using a multistage stratified random sampling technique. A novel extended EQ-VT design was created that included 18 blocks of 10 health states, comprising 150 unique health states and 135 observations per health state. In addition to the standard EQ-VT design, which is based on 86 health states and 100 observations per health state, 3 extended designs were assessed for their predictive performance. The extended designs were created by 1) increasing the number of observations per health state in the design, 2) increasing the number of health states in the design, and 3) implementing both 1) and 2) at the same time. Subsamples of the data set were created for separate designs. The root mean squared error (RMSE) and mean absolute error (MAE) were used to measure the predictive accuracy of the conventional and extended designs., Results: The average RMSE and MAE for the standard EQ-VT design were 0.055 and 0.041, respectively, for the 150 health states. All 3 types of design extensions showed lower RMSE and MAE values as compared with the standard design and hence yielded better predictive performance. RMSE and MAE were lowest (0.051 and 0.039, respectively) for the designs that use a greater number of health states. Extending the design with inclusion of more health states was shown to improve the predictive performance even when the sample size was fixed at 1,000., Conclusion: Although the standard EQ-VT design performs well, its prediction accuracy can be further improved by extending its design. The addition of more health states in EQ-VT is more beneficial than increasing the number of observations per health state., Highlights: The EQ-5D-5L value sets are developed using the standardized EuroQol Valuation Technology (EQ-VT) protocol. This is the first study to empirically assess how much can be gained from extending the standard EQ-VT design in terms of sample size and/or health states. It not only presents useful insights into the performance of the standard design of the EQ-VT but also tests the potential extensions in the standard EQ-VT design in terms of increasing the health states to be directly valued as well as the number of observations recorded to predict the utility value of each of these health states.The study demonstrates that the standard EQ-VT design performs good, and an extension in the design of the standard EQ-VT can lead to further improvement in its performance. The addition of more health states in EQ-VT is more beneficial than increasing the number of observations per health state. Extending the design with inclusion of more health states marginally improves the predictive performance even when the sample size was fixed at 1,000.The findings of the study will streamline the systematic process for generating precise EQ-5D-5L value sets, thus facilitating the conduct of credible, transparent, and robust outcome valuation in health technology assessments.

The range of effectiveness of the novel corona virus, known as COVID-19, has been continuously spread worldwide with the severity of associated disease and effective variation in the rate of contact. This paper investigates the COVID-19 virus dynamics among the human population with the prediction of the size of epidemic and spreading time. Corona virus disease was first diagnosed on January 30, 2020 in India. From January 30, 2020 to April 21, 2020, the number of patients was continuously increased. In this scientific work, our main objective is to estimate the effectiveness of various preventive tools adopted for COVID-19. The COVID-19 dynamics is formulated in which the parameters of interactions between people, contact tracing, and average latent time are included. Experimental data are collected from April 15, 2020 to April 21, 2020 in India to investigate this virus dynamics. The Genocchi collocation technique is applied to investigate the proposed fractional mathematical model numerically via Caputo-Fabrizio fractional derivative. The effect of presence of various COVID parameters e.g. quarantine time is also presented in the work. The accuracy and efficiency of the outputs of the present work are demonstrated through the pictorial presentation by comparing it to known statistical data. The real data for COVID-19 in India is compared with the numerical results obtained from the concerned COVID-19 model. From our results, to control the expansion of this virus, various prevention measures must be adapted such as self-quarantine, social distancing, and lockdown procedures., (Copyright © 2022 Elsevier Ltd. All rights reserved.)