Understanding spatial determinants, i.e., social, infrastructural, and environmental features of a place, which shape infectious disease is critically important for public health. We present an exploration of the spatial determinants of reported COVID-19 incidence across India's 641 urban and rural districts, comparing two waves (2020-2021). Three key results emerge using three COVID-19 incidence metrics: cumulative incidence proportion (aggregate risk), cumulative temporal incidence rate, and severity ratio. First, in the same district, characteristics of COVID-19 incidences are similar across waves, with the second wave over four times more severe than the first. Second, after controlling for state-level effects, urbanization (urban population share), living standards, and population age emerge as positive determinants of both risk and rates across waves. Third, keeping all else constant, lower shares of workers working from home correlate with greater infection risk during the second wave. While much attention has focused on intra-urban disease spread, our findings suggest that understanding spatial determinants human settlements is also important for managing current and future pandemics.
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http://dx.doi.org/10.1038/s42949-022-00071-z | DOI Listing |
J Pathol
January 2025
The Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, Australia.
Spatial transcriptomics (ST) offers enormous potential to decipher the biological and pathological heterogeneity in precious archival cancer tissues. Traditionally, these tissues have rarely been used and only examined at a low throughput, most commonly by histopathological staining. ST adds thousands of times as many molecular features to histopathological images, but critical technical issues and limitations require more assessment of how ST performs on fixed archival tissues.
View Article and Find Full Text PDFZool Res
January 2025
School of Basic Medicine, Institute of Brain Science and Disease, Shandong Provincial Key Laboratory of Pathogenesis and Prevention of Brain Diseases, Qingdao University, Qingdao, Shandong, 266071, China. E-mail:
Iron is the most abundant transition metal in the brain and is essential for brain development and neuronal function; however, its abnormal accumulation is also implicated in various neurological disorders. The olfactory bulb (OB), an early target in neurodegenerative diseases, acts as a gateway for environmental toxins and contains diverse neuronal populations with distinct roles. This study explored the cell-specific vulnerability to iron in the OB using a mouse model of intranasal administration of ferric ammonium citrate (FAC).
View Article and Find Full Text PDFIJID Reg
March 2025
Institute of Geography & Heidelberg Centre for Environment, Heidelberg University, Heidelberg, Germany.
Objectives: Little is known about the complex interplay between urban structure and health in rapidly urbanizing cities in Nigeria.
Methods: The study broadly used very high-resolution satellite imagery and gathered primary data. With the aid of the very high-resolution imagery and identified neighborhoods, two neighborhoods each were sampled based on their classified urban structure characteristics.
Front Public Health
January 2025
School of Business Administration, Zhongnan University of Economics and Law, Wuhan, China.
Background: Exploring the coordinated relationship between urban-rural integration and air quality has significant implications for promoting urban-rural development, preventing air pollution and ensuring residents' health. This study takes Yangtze River middle reaches city cluster as a case study, calculates the levels of urban-rural integration and air quality development, analyzes their coupled coordination relationship and driving factors, and explores the path of coordinated development.
Methods: This study constructs a coupling coordination degree model to analyze the relationship between the urban-rural integration development level and air quality development level.
Front Public Health
January 2025
School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, China.
The accuracy of spatial clustering detection is crucial for public health policy development and identifying etiological clues. Circular and flexibly-shaped scan statistics are widely used for disease cluster detection, but differences in results arise mainly due to parameter sensitivity and variations in the scanning window shapes. This study aims to analyze the impact of parameter settings on the results of these methods and compare their performance in disease clustering detection.
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