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A machine learning framework to quantify and assess the impact of COVID-19 on the power sector: An Indian context

ABSTRACT: As the COVID-19 continues to disrupt the global norms, there is the requirement of modeling frameworks to accurately assess and quantify the impact of the pandemic on the electricity sector and its emissions. In this study, we devise machine learning models to estimate the pandemic induced reduction in electricity consumption based on weather, econometrics, and social-distancing parameters for seven major Indian states. As per our baseline electricity consumption model, we find that the electricity consumption dropped by 15–33% in 2020 (March-May) during the complete lockdown phase, followed by 6–13% (June-August) during the unlock phases and gradually reached the norms by September 2020. As a result, the net CO2 emissions from power generation in 2020 dropped by 7% and 5% compared to 2018 and 2019 respectively. Amidst the ongoing second wave since mid-April 2021, we projected the electricity consumption across states from May-August by accounting for two scenarios. Under the reference and worst-case scenarios, the electricity consumption approximates 106% and 96% of the non-pandemic situation, respectively. The modeling framework developed in this study is purely data-orientated, cross-deployable across spatio-temporal scales and can serve as a valuable tool to inform current and future energy policies amidst and post COVID-19.

SUBMITTER: Suvarna M

PROVIDER: S-EPMC8653530 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo present a content analysis and method for applying a social determinants of health (SDOH) analytical framework to legislation.Data sources and study settingSecondary data include 215 sections of the American Rescue Plan Act of 2021 (ARPA) and related information from federal government websites (e.g., press releases, notices of funding announcements, and funding tables).Study designResearchers conducted a qualitative content analysis of legislative text, recording all sections, appropriations, allocations, and administrators. Using an SDOH analytical framework defined by Healthy People 2030, researchers coded each section, appropriation, and allocation within the legislation.Data collection/extraction methodsResearchers reviewed all ARPA sections, appropriations, and allocations separately, resulting in 328 entries. Descriptive characteristics were calculated using Tableau and Microsoft Excel. Researchers coded each appropriation or allocation using definitions and key words presented in the SDOH analytical framework.Principal findingsApplying an SDOH analytical framework to the legislation's funding amounts reveals an overlap of investment opportunities that cross-sector initiatives can leverage. This overlap is seen primarily in two ways: (1) specific allocations and appropriations that can be used to meet multiple SDOH goals and (2) federal administrators receiving money that can be categorized according to multiple SDOHs. For example, approximately 99% of tracked ARPA funds can be used to support one or more SDOHs. Thirty-five appropriations or allocations can support programs categorized for more than one SDOH category. Eight departments received funds that could be designated for two or more SDOHs. All five SDOH categories can potentially receive funding from 3 to 11 federal administrators.ConclusionsUsing an SDOH analytical framework is an innovative approach to conceptualizing and synthesizing the contents of complex legislation. This approach demonstrates funding patterns across SDOH that can encourage cross-sector collaborations. Future content analysis of legislation can employ this SDOH framework to demonstrate cross-sector initiative funding opportunities.

Project description:Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings.To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy.This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months.Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication.The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease's Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy.Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics.

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A machine learning framework to quantify and assess the impact of COVID-19 on the power sector: An Indian context

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