Evidence map›Paper›PMID 40959797›Full record

ArticleOne health (Amsterdam, Netherlands)2025

Harnessing Socio-Spatial Dynamics for Pandemic Resilience: Evidence from Kozhikode, India.

Shahana Usman Abdulla, Bimal Puthuvayi

Abstract read
In one paragraph

Article in One health (Amsterdam, Netherlands), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Shahana Usman AbdullaDepartment of Architecture & Planning, National Institute of Technology Calicut, Kerala, India.
Bimal PuthuvayiDepartment of Architecture & Planning, National Institute of Technology Calicut, Kerala, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite strict containment measures in India, COVID-19 cases significantly increased between 2020 and 2022, partly due to mass containment strategies based on administrative boundaries, disrupting infected and uninfected populations. This study addresses the gap in research on socio-spatial factors influencing COVID-19 infection by using micro-level data from urban wards of an Indian City, aiming to identify key socio-economic and spatial determinants of disease spread. Based on the data collected from 1194 individuals across 75 wards of Kozhikode City, the study employed a binary logistic regression model to examine how these variables affect COVID-19 test outcomes and a hotspot analysis to analyze the spatial dynamics of infection. The presence of comorbidities (Odds Ratio = 8.61) and the stringency index (Odds Ratio = 1.63) were the most significant factors associated with increased COVID-19 infection risk, highlighting the vulnerability of individuals with chronic health conditions and the complex relationship between government restrictions and case numbers. Residential density, essential job status, proximity to public amenities, and frequency of trips made were also strongly linked to higher infection rates, underscoring the role of socio-spatial factors in virus transmission. Hotspot analysis revealed spatial clustering of infections in urban cores, reinforcing the spatial nature of disease spread and the need for localized, data-driven interventions. The model achieved 86.5 % accuracy, demonstrating its effectiveness in explaining COVID-19 infection using socio-spatial parameters. The findings form a foundation for targeted public health interventions and data-driven strategies to manage infection spread, emphasizing the role of people, activities, and spaces in transmission dynamics.

Indexed as

COVID-19DeterminantsPandemicsRegressionSocio-spatial

Identifiers

PMID40959797
PMCPMC12434689

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.