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      The making of India's COVID-19 disaster: A disaster risk management (DRM) assemblage analysis

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          Abstract

          This article analyses the suite of policies and measures enacted by the Indian Union Government in response to the COVID-19 pandemic through apparatuses of disaster management. We focus on the period from the onset of the pandemic in early 2020, until mid-2021. This holistic review adopts a Disaster Risk Management (DRM) Assemblage conceptual approach to make sense of how the COVID-19 disaster was made possible and importantly how it was responded to, managed, exacerbated, and experienced as it continued to emerge. This approach is grounded in literature from critical disaster studies and geography. The analysis also draws on a wide range of other disciplines, ranging from epidemiology to anthropology and political science, as well as grey literature, newspaper reports, and official policy documents. The article is structured into three sections that investigate in turn and at different junctures the role of governmentality and disaster politics; scientific knowledge and expert advice, and socially and spatially differentiated disaster vulnerabilities in shaping the COVID-19 disaster in India. We put forward two main arguments on the basis of the literature reviewed. One is that both the impacts of the virus spread and the lockdown-responses to it affected already marginalised groups disproportionately. The other is that managing the COVID-19 pandemic through disaster management assemblage/apparatuses served to extend centralised executive authority in India. These two processes are demonstrated to be continuations and extensions of pre-pandemic trends. We conclude that evidence of a paradigm shift in India's approach to disaster management remains thin on the ground.

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          A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)

          COVID-19 has prompted unprecedented government action around the world. We introduce the Oxford COVID-19 Government Response Tracker (OxCGRT), a dataset that addresses the need for continuously updated, readily usable and comparable information on policy measures. From 1 January 2020, the data capture government policies related to closure and containment, health and economic policy for more than 180 countries, plus several countries' subnational jurisdictions. Policy responses are recorded on ordinal or continuous scales for 19 policy areas, capturing variation in degree of response. We present two motivating applications of the data, highlighting patterns in the timing of policy adoption and subsequent policy easing and reimposition, and illustrating how the data can be combined with behavioural and epidemiological indicators. This database enables researchers and policymakers to explore the empirical effects of policy responses on the spread of COVID-19 cases and deaths, as well as on economic and social welfare.
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            Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21

            (2022)
            Background Mortality statistics are fundamental to public health decision making. Mortality varies by time and location, and its measurement is affected by well known biases that have been exacerbated during the COVID-19 pandemic. This paper aims to estimate excess mortality from the COVID-19 pandemic in 191 countries and territories, and 252 subnational units for selected countries, from Jan 1, 2020, to Dec 31, 2021. Methods All-cause mortality reports were collected for 74 countries and territories and 266 subnational locations (including 31 locations in low-income and middle-income countries) that had reported either weekly or monthly deaths from all causes during the pandemic in 2020 and 2021, and for up to 11 year previously. In addition, we obtained excess mortality data for 12 states in India. Excess mortality over time was calculated as observed mortality, after excluding data from periods affected by late registration and anomalies such as heat waves, minus expected mortality. Six models were used to estimate expected mortality; final estimates of expected mortality were based on an ensemble of these models. Ensemble weights were based on root mean squared errors derived from an out-of-sample predictive validity test. As mortality records are incomplete worldwide, we built a statistical model that predicted the excess mortality rate for locations and periods where all-cause mortality data were not available. We used least absolute shrinkage and selection operator (LASSO) regression as a variable selection mechanism and selected 15 covariates, including both covariates pertaining to the COVID-19 pandemic, such as seroprevalence, and to background population health metrics, such as the Healthcare Access and Quality Index, with direction of effects on excess mortality concordant with a meta-analysis by the US Centers for Disease Control and Prevention. With the selected best model, we ran a prediction process using 100 draws for each covariate and 100 draws of estimated coefficients and residuals, estimated from the regressions run at the draw level using draw-level input data on both excess mortality and covariates. Mean values and 95% uncertainty intervals were then generated at national, regional, and global levels. Out-of-sample predictive validity testing was done on the basis of our final model specification. Findings Although reported COVID-19 deaths between Jan 1, 2020, and Dec 31, 2021, totalled 5·94 million worldwide, we estimate that 18·2 million (95% uncertainty interval 17·1–19·6) people died worldwide because of the COVID-19 pandemic (as measured by excess mortality) over that period. The global all-age rate of excess mortality due to the COVID-19 pandemic was 120·3 deaths (113·1–129·3) per 100 000 of the population, and excess mortality rate exceeded 300 deaths per 100 000 of the population in 21 countries. The number of excess deaths due to COVID-19 was largest in the regions of south Asia, north Africa and the Middle East, and eastern Europe. At the country level, the highest numbers of cumulative excess deaths due to COVID-19 were estimated in India (4·07 million [3·71–4·36]), the USA (1·13 million [1·08–1·18]), Russia (1·07 million [1·06–1·08]), Mexico (798 000 [741 000–867 000]), Brazil (792 000 [730 000–847 000]), Indonesia (736 000 [594 000–955 000]), and Pakistan (664 000 [498 000–847 000]). Among these countries, the excess mortality rate was highest in Russia (374·6 deaths [369·7–378·4] per 100 000) and Mexico (325·1 [301·6–353·3] per 100 000), and was similar in Brazil (186·9 [172·2–199·8] per 100 000) and the USA (179·3 [170·7–187·5] per 100 000). Interpretation The full impact of the pandemic has been much greater than what is indicated by reported deaths due to COVID-19 alone. Strengthening death registration systems around the world, long understood to be crucial to global public health strategy, is necessary for improved monitoring of this pandemic and future pandemics. In addition, further research is warranted to help distinguish the proportion of excess mortality that was directly caused by SARS-CoV-2 infection and the changes in causes of death as an indirect consequence of the pandemic. Funding Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom
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              Methodological nationalism and beyond: nation-state building, migration and the social sciences

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                Author and article information

                Journal
                Int J Disaster Risk Reduct
                Int J Disaster Risk Reduct
                International Journal of Disaster Risk Reduction
                Published by Elsevier Ltd.
                2212-4209
                12 June 2023
                12 June 2023
                : 103797
                Affiliations
                [a ]United Nations University – Institute for Environment and Human Security, UN Campus, Platz der Vereinten Nationen 1, D-53113, Bonn, Germany
                [b ]School of Social Sciences, Oxford Brookes University, Gibbs Building, Oxford Brookes Headington Campus, Headington Road, Oxford, OX3 0BP, UK
                [c ]Global Health Section, Department of Public Health & Copenhagen Centre for Disaster Research, University of Copenhagen, CSS, Øster Farimagsgade 5, 1014, København K, Denmark
                [d ]African Centre for Disaster Studies, North-West University, Private Bag X6001, Potchefstroom, North West Province, 2520, South Africa
                [e ]Institute for Risk and Disaster Reduction, University College London, London, UK
                Author notes
                []Corresponding author. United Nations University – Institute for Environment and Human Security, UN Campus, Platz der Vereinten Nationen 1, D-53113, Bonn, Germany.
                Article
                S2212-4209(23)00277-7 103797
                10.1016/j.ijdrr.2023.103797
                10259166
                e652828d-fb7f-4311-b009-cb13fdebf00e
                © 2023 Published by Elsevier Ltd.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 5 July 2022
                : 31 May 2023
                : 10 June 2023
                Categories
                Article

                india,covid-19,disaster risk management,disaster risk governance,assemblage theory,mobility

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