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A new COVID-19 vulnerability index to identify districts most at risk in India

WION Web Team
New Delhi, Delhi, IndiaUpdated: May 13, 2020, 05:52 PM IST
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Omicron variant of coronavirus has been spreading unabatedly in India (representative image). Photograph:(ANI)

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A high rate of risk can be due to various socio-economic factors -- low income or education levels, demographic factors, health and hygiene variables and environmental dimensions.

Scientists have developed a COVID-19 vulnerability index to identify where in India people are more at risk of contracting the infection.

A recent study by Swasti, a not-for-profit public health organisation, to this end, showed most of the districts in Bihar, Jharkhand, West Bengal, Odisha, Madhya Pradesh, Chhattisgarh and Gujarat, and adjoining districts in Rajasthan and Maharashtra with high vulnerability scores.

High-vulnerability districts are those where COVID-19 is likely to spread rapidly, while also remaining undetected for longer periods. A high rate of risk can be due to various socio-economic factors -- low income or education levels, demographic factors, health and hygiene variables and environmental dimensions.

Moderate vulnerability is seen in northern districts of Karnataka, Eastern Maharashtra, Telangana, Andhra Pradesh and Eastern districts of Tamil Nadu, showed the findings released on Wednesday.

While the districts of Kerala, Himachal Pradesh, Haryana, Uttarakhand, Punjab, Jammu and Kashmir, and most districts of the north eastern states show relatively low vulnerability scores.

There are 15 indicators that affect the vulnerability of COVID 19 infection -- socio-economic factors like low income or education levels, demographic factors such as population density and urbanization, health and hygiene variables like anemia levels or practicing handwashing, and environmental dimensions including temperature and relative humidity.

According to the study by Swasti, these variables, taken together, could account for 74 per cent of the variation in vulnerability to infection.

The researchers hope that using this and similar analyses, it can be predicted where it is most critical to keep transmission rates low and focus on strengthening health system capacity.