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Modelling-of-Impact-of-COVID-19-on-Economical-sectors-at-the-granular-level-in-India

Worked in the Economic Layer(Live Analysis of Economical Effects of COVID)

-The lockdown is leading to an impact of supply an demand across various sectors

-A total of 90 sub-sectors were taken and a model was created that could give the impact on labour, inward logistics, outward logistics, supply, distribution, working capital availability and demand, against each sector

-These sub-sectors were combined into the most prominent sectors, to get a model that can connect different stages of lockdown to economic impact in terms of productivity and GDP of each sector

-Every region has been mapped to specific sectors, with weights given for each sector active in the region

-This allows us to predict the impact of different stages of lockdown on a particular region(ward-level or village-level)

-In the work that is going on right now (that timeline when Phase II of Economic Impact Model was developed), each region's local context based on hyperlocal tagging across multiple parameters would be considered, and that would help in planning specific incentives on a hyperlocal level, with much more accuracy

Granular(Ward level) and District Level Analysis of GDDP change and the change of each sector's contribution of GDDP in India

-Collected data from GDDP Reports, found the production efficiency using the Sectoral maps of the Districts and researched on the factors that could lead to the slowdown in the respective sector of that district

-Developed an Impact model which took into account actors like labour migration rate, Normal Labour availability, Critical Labour Availability, Regular Inward Stock, Critical Inward Stock, Inward Logistic Flow score,Distribution Efficiencyy score and Demand score. These scores were calculated by the analysts' and experts. We used these scores to calculate the Sectoral Impacts of the different zones(Red, Yellow, Amber, Green) by researching and analysis

-Gave weightage to each of the sectors in each zones on the basis of their overall score(The extent of change that a particular sector would observe with a change of GDDP of the district)

Automated the Data Extraction and Cleaning through Google Colab Code

-Analysed the maps using cv library

-Applied the formulaes and specific analysis of the data using switch statements

-Tried to convert the complex tabular data in a pdf to a table in Spreadsheet through automation

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Analysis of COVID on economy at the granular ward level of each district in India

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