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Crowd surge at Hindu festival in India leaves 1 dead and many hospitalized
Officials said nearly 100 devotees were hospitalized after the crush, and an investigation is underway into crowd-control measures.
- On Thursday, a crowd crush during the annual Jagannath Rath Yatra in Puri, Odisha, left one person dead and nearly 100 people hospitalized.
- Massive crowds gathered amid continuous rains for the festival, which involves pulling chariots for Lord Jagannath, Lord Balabhadra, and Devi Subhadra. Rituals were delayed beyond schedule.
- Fire and emergency services officer Umashankar Dash reported nearly 100 devotees hospitalized with breathing difficulties. Police official Soumendra Priyadarshi confirmed one death resulted from a heart attack.
- Opposition Biju Janata Dal media coordinator Lenin Mohanty urged the state government to provide medical treatment and assistance to affected families. Officials appealed to devotees to remain calm and follow police guidance.
- Crowd crushes are common at large gatherings in India, where safety regulations are often overlooked. Three people died at this festival last year; a separate stampede at the Maha Kumbh in Uttar Pradesh killed at least 39 people in January last year.
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1 dead, over 30 hospitalised during Puri Rath Yatra crowd crush
One devotee died and more than 30 others were hospitalised after a crowd crush during the annual Rath Yatra of Lord Jagannath in Puri on Thursday, according to The Indian Express. The report said a 45-year-old devotee allegedly fell unconscious due to heavy congestion on the Grand Road and was rushed to the district headquarters hospital, where doctors declared him dead. Hospital sources said the condition of most of the injured devotees was sta…
A sudden overcrowding incident at a popular Hindu festival in the eastern Indian state of Odisha on Thursday has resulted in at least one death and numerous hospitalizations.
Coverage Details
Total News Sources63
Leaning Left15Leaning Right15Center9Last UpdatedBias Distribution39% Left, 38% Right
Bias Distribution
- 39% of the sources lean Left, 38% of the sources lean Right
39% Left
L 39%
C 23%
R 38%
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