Data on social and health vulnerability in rural India: A case of covid-19.
Ontology highlight
ABSTRACT:
SUBMITTER: Singh S
PROVIDER: S-EPMC7358164 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The global pandemic has an inherently urban character. The UN-Habitat's publication of a Response Plan for mollification of the SARS-CoV-2 based externalities in the cities of the world testifies to that. This article takes the UN-Habitat report as the premise to carry out an empirical investigation in the four major metro cities of India. The report's concern with the urban character of the pandemic has underlined the role of cities in disease transmission. In that wake, the study demarcates factors at the sub-city level that tend to jeopardize the two mandatory precautionary measures during COVID-19 - Social Distancing and Lockdown. It investigates those factors through a Covid Vulnerability Index. The Index devised with the help of Analytic Hierarchy Process demarcates the low, moderate, high, and very high vulnerable city sub-units. Secondly, UN-Habitat's one of the major action areas is evidence-based knowledge creation through mapping and its analysis. In our study, we do it at a granular scale for arriving at a more nuanced understanding. Thus, in harmony with the UN-habitat's we take the urban seriously and identify the gaps that need to be plugged for the pandemic cities of now and of the future.
| S-EPMC7434393 | biostudies-literature
Project description:The globally fast-spreading novel coronavirus disease (COVID-19) is now testing the abilities of all countries to manage its widespread implications on public health. To effectively contain its impacts, a nation-wide temporary lockdown was enforced in India. The resultant panic buying and stockpiling incidents together with spread of misinformation created a sense of food insecurity at local level. This paper discusses a specific case of Nagpur from the worst affected Maharashtra state of India, wherein the urban-rural food supply chains were reportedly disrupted. Based on formal interviews with local government officials, a month-long timeline of COVID-19 outbreak in Nagpur was studied along with the consequent government initiatives for maintaining public health and food supply. While the city residents were confined to their homes, this study then assessed their perceived food security at household level, along with their "Immediate Concerns" and "Key Information Sources". Through online surveys at two different time intervals, the concerns of "Food and Grocery" were found to be rising, and "Government Apps and Websites" were identified as the most reliable source of information. Based on the research findings, the authors further suggest specific policy recommendations for addressing the immediate and long-term concerns related to food systems in Nagpur.
| S-EPMC7459914 | biostudies-literature
Project description:This study examines the effects of local and nationwide COVID-19 disease control measures on the health and economy of China's rural population. We conducted phone surveys with 726 randomly selected village informants across seven rural Chinese provinces in February 2020. Four villages (0.55%) reported infections, and none reported deaths. Disease control measures had been universally implemented in all sample villages. About 74% of informants reported that villagers with wage-earning jobs outside the village had stopped working due to workplace closures. A higher percentage of rural individuals could not work due to transportation, housing, and other constraints. Local governments had taken measures to reduce the impact of COVID-19. Although schools in all surveyed villages were closed, 71% of village informants reported that students were attending classes online. Overall, measures to control COVID-19 appear to have been successful in limiting disease transmission in rural communities outside the main epidemic area. Rural Chinese citizens, however, have experienced significant economic consequences from the disease control measures.
| S-EPMC8207079 | biostudies-literature
Project description:The unpredictability and unknowns surrounding the ongoing coronavirus disease (COVID-19) pandemic have led to an unprecedented consequence taking a heavy toll on the lives and economies of all countries. There have been efforts to predict COVID-19 case counts (CCC) using epidemiological data and numerical tokens online, which may allow early preventive measures to slow the spread of the disease. In this paper, we use state-of-the-art natural language processing (NLP) algorithms to numerically encode COVID-19 related tweets originated from eight cities in the United States and predict city-specific CCC up to eight days in the future. A city-embedding is proposed to obtain a time series representation of daily tweets posted from a city, which is then used to predict case counts using a custom long-short term memory (LSTM) model. The universal sentence encoder yields the best normalized root mean squared error (NRMSE) 0.090 (0.039), averaged across all cities in predicting CCC six days in the future. The R 2 scores in predicting CCC are more than 0.70 and often over 0.8, which suggests a strong correlation between the actual and our model predicted CCC values. Our analyses show that the NRMSE and R 2 scores are consistently robust across different cities and different numbers of time steps in time series data. Results show that the LSTM model can learn the mapping between the NLP-encoded tweet semantics and the case counts, which infers that social media text can be directly mined to identify the future course of the pandemic.
| S-EPMC9490453 | biostudies-literature
Project description:Complex environmental, economic, and social conditions in the places we live provide strong cues to our longevity, livelihood, and well-being. Although often distinct and evolving relatively independently, health disparity, social vulnerability and environmental justice research and practice intertwine and inform one another. Together, they increasingly provide evidence of how social processes intensify disasters almost predictably giving rise to inequitable disruptions and consequences. The domino and cumulative effects of cascading disasters invariably reveal inequities through differential impacts and recovery opportunities across communities and subgroups of people. Not only do cascading disasters reveal and produce inequitable effects, the cascade itself can emerge out of compounded nested social structures. Drawing on, and integrating, theory and practice from social vulnerability, health inequity, and environmental justice, this paper presents a comprehensive conceptual model of cascading disasters that offers a people-centric lens. The CHASMS conceptual model (Cascading Hazards to disAsters that are Socially constructed eMerging out of Social Vulnerability) interrogates the tension between local communities and the larger structural forces that produce social inequities at multiple levels, capturing how those inequities lead to cascading disasters. We apply the model to COVID-19 as an illustration of how underlying inequities give rise to foreseeable inequitable outcomes, emphasizing the U.S. experience. We offer Kenya and Puerto Rico as examples of cumulative effects and possible cascades when responding to other events in the shadow of COVID-19. COVID-19 has vividly exposed the dynamic, complex, and intense relevance of placing social conditions and structures at the forefront of cascading disaster inquiry and practice. The intensity of social disruption and the continuation of the pandemic will, no doubt, perpetuate and magnify chasms of injustice.
| S-EPMC7467012 | biostudies-literature
Project description:High throughput sequencing is performed on mRNA isolated from whole blood of adult Covid-19 patients, bacterial coinfection with Covid-19 and healthy controls in a South Indian cohort. Samples were collected from individuals at the time of hospitalization or visit to clinic. The Covid-19 samples are categorized by severeity.
2022-02-22 | GSE196822 | GEO
Project description:Coronavirus disease (COVID-19) spread across the globe through the human transmission. The World Health Organization suggested social distancing to curb the community spread. After national social lockdown started in India, air quality improved drastically. This further hypothesized to influence the environment and human health, and this study is positively the first to weigh it using multiple indices. The calculated environmental indices are photochemical ozone creation potential (POCP), acidification potential (AP), and eutrophication potential (EP). The cancer risk, chronic health index (CHI), and acute health index (AHI) were considered to calculate the health risk. The spatial trend change in the air pollution reflecting on these indices are calculated for four Indian megacities Delhi, Bangalore, Hyderabad, and Kolkata. Temporal variation was accounted for monthly (2019 vs 2020), one-week and two-weeks period during the social lockdown. The results showed a significant decrease in environmental and health risk during the lockdown due to a corresponding decrement in air pollution. The decrease in the particulate matter was found to play a vital role in altering the air pollution mediated risks of interest. Delhi showed a maximum difference in POCP and Acute HI by recording a dip of 70.79% and 43.53% respectively in 2020 during lockdown. The maximum reduction in health risk indices was 41%, 31%, 17%, 19% for Delhi, Bangalore, Hyderabad, and Kolkata. Bangalore recorded the maximum decline in EP, Cancer risk, Chronic HI by 66.66%, 58.62%, and 58.76% in 2020 compared to 2019. A maximum fall in AP was seen in Kolkata by 57.23% in 2020 among all cities. The connection between these drop-in indices and the cause of air pollutants were well discussed. This present paper gives more in-depth insights into air pollution's effect on environmental and health parameters by connecting and converging various air pollution aspects into a single scale. This study also enlightens the importance of controlling air pollution to have a better environment and healthy life to attain sustainable development.
| S-EPMC7908889 | biostudies-literature
Project description: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 across human settlements is also important for managing current and future pandemics.
| S-EPMC9613454 | biostudies-literature
Project description:COVID-19 has significantly and unevenly impacted the United States, disproportionately affecting socially vulnerable communities. While epidemiologists and public health officials have suggested social distancing and shelter-in-place orders to halt the spread of this virus, the ability to comply with these guidelines is dependent on neighborhood, household, and individual characteristics related to social vulnerability. We use structural equation modeling and multiple data sources, including anonymized mobile phone location data from SafeGraph, to examine the effects of different social vulnerability and built environment factors on COVID-19 prevalence over two overlapping time periods (March to May and March to November of 2020). We use Chicago, Illinois as a case study and find that zip codes with low educational attainment consistently experienced higher case rates over both periods. Though population density was not significantly related to the prevalence in any period, movement of people made a significant contribution only during the longer time period. This finding highlights the significance of analyzing different timeframes for understanding social vulnerability. Our results suggest social vulnerability played an influential role in COVID-19 prevalence, highlighting the needs to address socioeconomic barriers to pandemic recovery and future pandemic response.
| S-EPMC8459204 | biostudies-literature
Project description:COVID-19 Health Surveillance FVG
| PRJEB46661 | ENA