Risk Analysis of COVID‐19 Infections in Kolkata Metropolitan City: A GIS‐Based Study and Policy Implications
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Abstract
The COVID‐19 pandemic has affected daily lives of people around the world. People have already started to live wearing masks, keeping a safe distance from others, and maintaining a high level of hygiene. This paper deals with an in‐depth analysis of riskness associated with COVID‐19 infections in Kolkata Municipal Corporation (KMC) at the subcity (ward) level. Attempts have been made to identify the areas with high or low risk of infections using GIS‐based geostatistical approach. Cosine Similarity Index has been used to rank different wards of KMC according to the degree of riskness. Four indices were computed to address intervention objectives and to determine “Optimized Prevention Rank” of wards for future policy decisions. The highest risk areas were located in the eastern and western part of the city, to a great extent overlapped with wards containing larger share of population living in slums and/or below poverty level. On the other hand, highly infected areas lie in central Kolkata and in several wards at the eastern and northeastern periphery of the KMC. The “Optimized Prevention Rank” have indicated that the lack of social awareness along with lack of social distancing have contributed to the increasing number of containments of COVID‐19 cases. The rankings of the wards would no doubt provide the policy makers a basis to control further spread of the disease. Since effective antiviral drugs are already in the market, the best application of our research would be in the ensuing vaccination drive against further COVID‐19 infections.
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SciScore for 10.1101/2020.08.31.20185215: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Similarity search tool rank all the wards based on ‘attribute similarity’ in comparison with the worst-case scenario, i.e., a ‘target feature’. Similaritysuggested: (BLAST Similarity Search, RRID:SCR_008419)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical …
SciScore for 10.1101/2020.08.31.20185215: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Similarity search tool rank all the wards based on ‘attribute similarity’ in comparison with the worst-case scenario, i.e., a ‘target feature’. Similaritysuggested: (BLAST Similarity Search, RRID:SCR_008419)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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