Geo-mapping of COVID-19 Risk Correlates Across Districts and Parliamentary Constituencies in India
(2020) In Harvard Data Science Review- Abstract
- In the current stage of the COVID-19 pandemic, as countries open up after an extended period of lockdown, it is important to assure the population that their health is not being sacrificed. In this article, we develop a geomapping approach to identify high-risk areas by considering four nonclinical risk correlates for COVID-19. These are population density, percentage of the population that is exposed to crowding in a household, percentage of the population without access to handwashing facilities, and percentage of the population over 65 years of age. We provide an empirical proof-of-concept demonstration for this approach for India at two critical geographic units: districts and parliamentary constituencies, collectively responsible for... (More)
- In the current stage of the COVID-19 pandemic, as countries open up after an extended period of lockdown, it is important to assure the population that their health is not being sacrificed. In this article, we develop a geomapping approach to identify high-risk areas by considering four nonclinical risk correlates for COVID-19. These are population density, percentage of the population that is exposed to crowding in a household, percentage of the population without access to handwashing facilities, and percentage of the population over 65 years of age. We provide an empirical proof-of-concept demonstration for this approach for India at two critical geographic units: districts and parliamentary constituencies, collectively responsible for policy administration and governance. Our findings suggest that the geographies of the four nonclinical risk correlates are largely independent of one another (i.e., at most, there is a small correlation between measures). We avoid applying differential weights to the four measures or combining these measures into a single index, as there is an intrinsic rationale for viewing them separately since they represent mostly independent dimensions of risks that require different responses. Our primary objective was to leverage currently available data to provide decision makers detailed information and geovisualization, identifying areas with potentially differential susceptibilities to COVID-19. The information provided here can be used as a means for further ground verification and, when appropriate, for impact planning and intervention, as well as providing a rationale for eventual efficacy assessment of different nonpharmaceutical interventions. While this exercise is primarily descriptive at this stage, the estimates generated are new, rigorous, and have high relevance for timely policy discussions. We use data from the Demographic and Health Surveys, which have extensive geographic coverage and high level of standardizations, making our highly accessible approach easy to extend to other low- and middle-income countries. We share this conceptualization of geomapping, and all the data and codes used for this exercise, to encourage wider applications and advancements. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/75721ac7-4817-4081-82d9-c29d017b0d72
- author
- Subramanian, S. V. ; Karlsson, Omar LU ; Zhang, Weixing and Kim, Rockli
- publishing date
- 2020-09-21
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Demographic and Health Surveys, public policy, risk correlates, districts, parliamentary constituencies
- in
- Harvard Data Science Review
- issue
- Special Issue 1
- pages
- 35 pages
- ISSN
- 2644-2353
- DOI
- 10.1162/99608f92.68bb12e4
- language
- English
- LU publication?
- no
- id
- 75721ac7-4817-4081-82d9-c29d017b0d72
- date added to LUP
- 2020-09-27 21:03:07
- date last changed
- 2022-04-11 09:27:29
@article{75721ac7-4817-4081-82d9-c29d017b0d72,
abstract = {{In the current stage of the COVID-19 pandemic, as countries open up after an extended period of lockdown, it is important to assure the population that their health is not being sacrificed. In this article, we develop a geomapping approach to identify high-risk areas by considering four nonclinical risk correlates for COVID-19. These are population density, percentage of the population that is exposed to crowding in a household, percentage of the population without access to handwashing facilities, and percentage of the population over 65 years of age. We provide an empirical proof-of-concept demonstration for this approach for India at two critical geographic units: districts and parliamentary constituencies, collectively responsible for policy administration and governance. Our findings suggest that the geographies of the four nonclinical risk correlates are largely independent of one another (i.e., at most, there is a small correlation between measures). We avoid applying differential weights to the four measures or combining these measures into a single index, as there is an intrinsic rationale for viewing them separately since they represent mostly independent dimensions of risks that require different responses. Our primary objective was to leverage currently available data to provide decision makers detailed information and geovisualization, identifying areas with potentially differential susceptibilities to COVID-19. The information provided here can be used as a means for further ground verification and, when appropriate, for impact planning and intervention, as well as providing a rationale for eventual efficacy assessment of different nonpharmaceutical interventions. While this exercise is primarily descriptive at this stage, the estimates generated are new, rigorous, and have high relevance for timely policy discussions. We use data from the Demographic and Health Surveys, which have extensive geographic coverage and high level of standardizations, making our highly accessible approach easy to extend to other low- and middle-income countries. We share this conceptualization of geomapping, and all the data and codes used for this exercise, to encourage wider applications and advancements.}},
author = {{Subramanian, S. V. and Karlsson, Omar and Zhang, Weixing and Kim, Rockli}},
issn = {{2644-2353}},
keywords = {{Demographic and Health Surveys; public policy; risk correlates; districts; parliamentary constituencies}},
language = {{eng}},
month = {{09}},
number = {{Special Issue 1}},
series = {{Harvard Data Science Review}},
title = {{Geo-mapping of COVID-19 Risk Correlates Across Districts and Parliamentary Constituencies in India}},
url = {{http://dx.doi.org/10.1162/99608f92.68bb12e4}},
doi = {{10.1162/99608f92.68bb12e4}},
year = {{2020}},
}