Multistakeholder participation in disaster management—the case of the covid-19 pandemic

The coronavirus disease 2019 (COVID-19) pandemic is affecting society’s health, economy, environment and development. COVID-19 has claimed many lives across the globe and severely impacted the livelihood of a considerable section of the world’s population. We are still in the process of finding optimal and effective solutions to control the pandemic and minimise its negative im-pacts. In the process of developing effective strategies to combat COVID-19, different countries have adapted diverse policies, strategies and activities and yet there are no universal or comprehensive solutions to the problem. In this context, this paper brings out a conceptual model of multistake-holder participation governance as an effective model to fight against COVID-19. Accordingly, the current study conducted a scientific review by examining multi-stakeholder disaster response strategies, particularly in relation to COVID-19. The study then presents a conceptual framework for multistakeholder participation governance as one of the effective models to fight against COVID-19. Subsequently, the article offers strategies for rebuilding the economy and healthcare system through multi-stakeholder participation, and gives policy directions/decisions based on evidence to save lives and protect livelihoods. The current study also provides evidence about multidimensional approaches and multi-diplomatic mechanisms during the COVID-19 crisis, in order to examine dimensions of multi-stakeholder participation in disaster management and to document innovative, collaborative strategic directions across the globe. The current research findings highlight the need for global collaboration by working together to put an end to this pandemic situation through the application of a Multi-Stakeholder Spatial Decision Support System (MS-SDSS)

Appraisal of climate change and cyclone trends in Indian coastal states: a systematic approach towards climate action

Abstract: Indian coastal regions have often been affected by frequent climate-induced natural disasters such as cyclones, floods, droughts and other related hazards in recent decades. Existing literature was not sufficient to fully understand these event trends from diverse perspectives in a systematised manner at current scenarios. Therefore, a systematic approach has been employed to assess the climate change and cyclone trends of nine Indian coastal states by using various geographical information system (GIS) tools for 2006–2020. The results showed that 61 cyclones occurred in nine coastal states from 2006 to 2020; the highest numbers were recorded in Odisha (20), West Bengal (14) and Andhra Pradesh (11). Accordingly, these three coastal states emerged as the most vulnerable for high-intensity cyclones. The results also identified that the highest average temperature (29.3 °C) was recorded at Tamil Nadu and Gujarat, and the lowest temperature (26.7 °C) was recorded in West Bengal and Odisha. Most of the coastal states showed fluctuations in temperatures during the study period. At the same time, Kerala and Karnataka states recorded the highest average rainfall (2341 mm and 2261 mm) and highest relative humidity (78.11% and 76.57%). Conversely, the Gujarat and West Bengal states recorded the lowest relative humidity at 59.65% and 70.78%. Based on these results, the current study generated GIS vulnerability maps for climate change and cyclone activity, allowing one to rank each state’s vulnerability. Cumulatively, these results and maps assist in understanding the driving mechanisms of climate change, cyclones and will contribute towards more effective and efficient sustainable disaster management in the future

A Multi-Data Geospatial Approach for Understanding Flood Risk in the Coastal Plains of Tamil Nadu, India

The coastal plains of Tamil Nadu, India, are prone to floods, the most common disaster experienced in this region almost every year. This research aims to identify flood risks in the coastal plain region of Tamil Nadu, delineated through a watershed approach with 5020 micro-administrative units covering an area of about 26,000 sq. km. A comprehensive flood risk assessment covering hazard, vulnerability, and exposure parameters was carried out using multiple datasets derived from field surveys, satellite data, and secondary data sources. The flood hazard layer was prepared on a probability scale (0−1) with the help of Sentinel-1 Synthetic Aperture Radar data coupled with GIS-based water rise modelling using Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM) and reports of the District Disaster Management Plans of 13 coastal districts. In addition, the National Resources Conservation Service-Curve Number (NRCS-CN) method was adopted to estimate surface runoff potential for identifying low probability flood-prone regions. The vulnerability and exposure of the population to flood hazards were determined using census and household data-based indicators. The different categories of built-up areas were delineated and intersected with the flood hazard layer to estimate elements at flood risk. An exhaustive field survey was conducted at 514 locations of the study area, targeting deprived communities of all major settlements to validate the flood hazard layer and understand the public perceptions. The amalgamation of results shows that very high flood risk prevails in the northern parts of coastal Tamil Nadu, especially the stretch between Chennai and Cuddalore. In addition, to provide baseline datasets for the first time at micro-administrative units for the entire coastal plains of Tamil Nadu, the study offers a pragmatic methodology for determining location-specific flood risks for policy interventions

Spatial epidemiology of acute respiratory infections in children under 5 years and associated risk factors in India: District-level analysis of health, household, and environmental datasets

BackgroundIn India, acute respiratory infections (ARIs) are a leading cause of mortality in children under 5 years. Mapping the hotspots of ARIs and the associated risk factors can help understand their association at the district level across India.MethodsData on ARIs in children under 5 years and household variables (unclean fuel, improved sanitation, mean maternal BMI, mean household size, mean number of children, median months of breastfeeding the children, percentage of poor households, diarrhea in children, low birth weight, tobacco use, and immunization status of children) were obtained from the National Family Health Survey-4. Surface and ground-monitored PM2.5 and PM10 datasets were collected from the Global Estimates and National Ambient Air Quality Monitoring Programme. Population density and illiteracy data were extracted from the Census of India. The geographic information system was used for mapping, and ARI hotspots were identified using the Getis-Ord Gi* spatial statistic. The quasi-Poisson regression model was used to estimate the association between ARI and household, children, maternal, environmental, and demographic factors.ResultsAcute respiratory infections hotspots were predominantly seen in the north Indian states/UTs of Uttar Pradesh, Bihar, Delhi, Haryana, Punjab, and Chandigarh, and also in the border districts of Uttarakhand, Himachal Pradesh, and Jammu and Kashmir. There is a substantial overlap among PM2.5, PM10, population density, tobacco smoking, and unclean fuel use with hotspots of ARI. The quasi-Poisson regression analysis showed that PM2.5, illiteracy levels, diarrhea in children, and maternal body mass index were associated with ARI.ConclusionTo decrease ARI in children, urgent interventions are required to reduce the levels of PM2.5 and PM10 (major environmental pollutants) in the hotspot districts. Furthermore, improving sanitation, literacy levels, using clean cooking fuel, and curbing indoor smoking may minimize the risk of ARI in children

Comparison and Evaluation of Dimensionality Reduction Techniques for Hyperspectral Data Analysis

Hyperspectral datasets provide explicit ground covers with hundreds of bands. Filtering contiguous hyperspectral datasets potentially discriminates surface features. Therefore, in this study, a number of spectral bands are minimized without losing original information through a process known as dimensionality reduction (DR). Redundant bands portray the fact that neighboring bands are highly correlated, sharing similar information. The benefits of utilizing dimensionality reduction include the ability to slacken the complexity of data during processing and transform original data to remove the correlation among bands. In this paper, two DR methods, principal component analysis (PCA) and minimum noise fraction (MNF), are applied to the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) dataset of Kalaburagi for discussion

Assessment on the Potential of Multispectral and Hyperspectral Datasets for Land Use/Land Cover Classification

Land use/land cover (LULC) is a significant factor which plays a vital role in defining an urban ecosystem. Interpretations of LULC are eased in recent times by utilizing hyperspectral and multispectral datasets obtained from various platforms. An attempt is made to comparatively assess the potentiality of AVIRIS NG with Sentinel 2 data through applied classification techniques for Kalaburagi urban sphere. Spectral responses of both datasets were analyzed to derive reflectance spectra. A standard supervised classification algorithm associated with dimensionality reduction techniques is applied. For performance evaluation, results are validated to check which dataset outperforms well and provides better accuracy

A comprehensive study on preparedness, impacts, response and recovery from tropical severe cyclonic storm ‘GAJA’: lessons for the future

Tropical cyclones are very destructive natural hazards that often result in casualties, economic loss, and environmental degradation, especially in the coastal communities. Gaja, categorized as a severe cyclone struck the southeast coast of India on November 16, 2018 and devastated thousands of people in the six districts of the State of Tamil Nadu, India; two years since, many are still reeling from its aftermath. In this study, to assess the 1) preparedness of the community, 2) physio-socio-economic-environment impacts, and 3) post-phase response and recovery, various methodologies were adopted. A total of 18 villages were selected based on the land track of the cyclone for field visits and questionnaire survey. Field data, satellite image processing, and review of report indicated that more than 3/4th of the population, ~ 90 percent of the built-up areas, ~ 40 percent of the vegetation and agricultural crops were severely affected by the cyclone. Even though the community were warned about the cyclone trajectories, only 3/4th of them took precautionary measures to mitigate the effects. The State government has played a major role in evacuating people to safe shelters, bringing back communication to the affected areas, and has also provided some sort of compensation to the victims. In addition to the efforts of the government, volunteers have also offered immediate relief to the affected communities. However, even after one year of the cyclone, only less than 20 percent of the community have completely recovered from the devastation. The results of this study will help comprehend the different phases of cyclone management and formulate better cyclone hazard management plans in the future

Human telomerase is directly regulated by non-telomeric TRF2-G-quadruplex interaction

Human telomerase reverse transcriptase (hTERT) remains suppressed in most normal somatic cells. Resulting erosion of telomeres leads eventually to replicative senescence. Reactivation of hTERT maintains telomeres and triggers progression of >90% of cancers. However, any direct causal link between telomeres and telomerase regulation remains unclear. Here, we show that the telomere-repeat-binding-factor 2 (TRF2) binds hTERT promoter G-quadruplexes and recruits the polycomb-repressor EZH2/PRC2 complex. This is causal for H3K27 trimethylation at the hTERT promoter and represses hTERT in cancer as well as normal cells. Two highly recurrent hTERT promoter mutations found in many cancers, including ∼83% glioblastoma multiforme, that are known to destabilize hTERT promoter G-quadruplexes, showed loss of TRF2 binding in patient-derived primary glioblastoma multiforme cells. Ligand-induced G-quadruplex stabilization restored TRF2 binding, H3K27-trimethylation, and hTERT re-suppression. These results uncover a mechanism of hTERT regulation through a telomeric factor, implicating telomere-telomerase molecular links important in neoplastic transformation, aging, and regenerative therapy