COVID-19 in Spain and India: Comparing Policy Implications by Analyzing Epidemiological and Social Media Data

The COVID-19 pandemic has forced public health experts to develop contingent policies to stem the spread of infection, including measures such as partial/complete lockdowns. The effectiveness of these policies has varied with geography, population distribution, and effectiveness in implementation. Consequently, some nations (e.g., Taiwan, Haiti) have been more successful than others (e.g., United States) in curbing the outbreak. A data-driven investigation into effective public health policies of a country would allow public health experts in other nations to decide future courses of action to control the outbreaks of disease and epidemics. We chose Spain and India to present our analysis on regions that were similar in terms of certain factors: (1) population density, (2) unemployment rate, (3) tourism, and (4) quality of living. We posit that citizen ideology obtainable from twitter conversations can provide insights into conformity to policy and suitably reflect on future case predictions. A milestone when the curves show the number of new cases diverging from each other is used to define a time period to extract policy-related tweets while the concepts from a causality network of policy-dependent sub-events are used to generate concept clouds. The number of new cases is predicted using sentiment scores in a regression model. We see that the new case predictions reflects twitter sentiment, meaningfully tied to a trigger sub-event that enables policy-related findings for Spain and India to be effectively compared

Does Fecal Microbiota Transplant Have a Role in Treating Recurrent Clostridioides difficile Infection in Rural Hospitals?

Clostridioides difficile infection possesses a significant economical burden, specifically in the inpatient and rural settings. Fecal Microbiota Transplant has been used for treatment of recurrent Clostridioides difficile but its utility is limited by current guidelines and resources. We conducted a retrospective chart review to evaluate the financial benefit of using Fecal Microbiota Transplant after first recurrence of Clostridioides difficile infection. We found that while its use was restricted, on average Fecal Microbiota Transplant can save $11,603.49 per patient. In conclusion, our study shows that using Fecal Microbiota Transplant could prove to be economically beneficial in treating recurrent CDI in rural hospitals

Development of decadal (1985–1995–2005) land use and land cover database for India

India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study

Horizon-scale tests of gravity theories and fundamental physics from the Event Horizon Telescope image of Sagittarius A∗^*

Horizon-scale images of black holes (BHs) and their shadows have opened an unprecedented window onto tests of gravity and fundamental physics in the strong-field regime. We consider a wide range of well-motivated deviations from classical General Relativity (GR) BH solutions, and constrain them using the Event Horizon Telescope (EHT) observations of Sagittarius A$^*$ (Sgr A$^*$), connecting the size of the bright ring of emission to that of the underlying BH shadow and exploiting high-precision measurements of Sgr A$^*$'s mass-to-distance ratio. The scenarios we consider, and whose fundamental parameters we constrain, include various regular BHs, string-inspired space-times, violations of the no-hair theorem driven by additional fields, alternative theories of gravity, novel fundamental physics frameworks, and BH mimickers including well-motivated wormhole and naked singularity space-times. We demonstrate that the EHT image of Sgr A$^*$ places particularly stringent constraints on models predicting a shadow size larger than that of a Schwarzschild BH of a given mass, with the resulting limits in some cases surpassing cosmological ones. Our results are among the first tests of fundamental physics from the shadow of Sgr A$^*$ and, while the latter appears to be in excellent agreement with the predictions of GR, we have shown that a number of well motivated alternative scenarios, including BH mimickers, are far from being ruled out at present.Comment: 82 pages, 47 figures, 50+ models tested. v3: fixed a few figures, clarified several points, included various analytical expressions for shadow sizes within the different models, added a few references, included a summary table (Table II). Version accepted for publication in Classical and Quantum Gravit

Science with the Daksha High Energy Transients Mission

We present the science case for the proposed Daksha high energy transients mission. Daksha will comprise of two satellites covering the entire sky from 1~keV to $>1$~MeV. The primary objectives of the mission are to discover and characterize electromagnetic counterparts to gravitational wave source; and to study Gamma Ray Bursts (GRBs). Daksha is a versatile all-sky monitor that can address a wide variety of science cases. With its broadband spectral response, high sensitivity, and continuous all-sky coverage, it will discover fainter and rarer sources than any other existing or proposed mission. Daksha can make key strides in GRB research with polarization studies, prompt soft spectroscopy, and fine time-resolved spectral studies. Daksha will provide continuous monitoring of X-ray pulsars. It will detect magnetar outbursts and high energy counterparts to Fast Radio Bursts. Using Earth occultation to measure source fluxes, the two satellites together will obtain daily flux measurements of bright hard X-ray sources including active galactic nuclei, X-ray binaries, and slow transients like Novae. Correlation studies between the two satellites can be used to probe primordial black holes through lensing. Daksha will have a set of detectors continuously pointing towards the Sun, providing excellent hard X-ray monitoring data. Closer to home, the high sensitivity and time resolution of Daksha can be leveraged for the characterization of Terrestrial Gamma-ray Flashes.Comment: 19 pages, 7 figures. Submitted to ApJ. More details about the mission at https://www.dakshasat.in

C-peptide and metabolic outcomes in trials of disease modifying therapy in new-onset type 1 diabetes: an individual participant meta-analysis

Background Metabolic outcomes in type 1 diabetes remain suboptimal. Disease modifying therapy to prevent β-cell loss presents an alternative treatment framework but the effect on metabolic outcomes is unclear. We, therefore, aimed to define the relationship between insulin C-peptide as a marker of β-cell function and metabolic outcomes in new-onset type 1 diabetes. Methods 21 trials of disease-modifying interventions within 100 days of type 1 diabetes diagnosis comprising 1315 adults (ie, those 18 years and older) and 1396 children (ie, those younger than 18 years) were combined. Endpoints assessed were stimulated area under the curve C-peptide, HbA1c, insulin use, hypoglycaemic events, and composite scores (such as insulin dose adjusted A1c, total daily insulin, U/kg per day, and BETA-2 score). Positive studies were defined as those meeting their primary endpoint. Differences in outcomes between active and control groups were assessed using the Wilcoxon rank test. Findings 6 months after treatment, a 24·8% greater C-peptide preservation in positive studies was associated with a 0·55% lower HbA1c (p<0·0001), with differences being detectable as early as 3 months. Cross-sectional analysis, combining positive and negative studies, was consistent with this proportionality: a 55% improvement in C-peptide preservation was associated with 0·64% lower HbA1c (p<0·0001). Higher initial C-peptide levels and greater preservation were associated with greater improvement in HbA1c. For HbA1c, IDAAC, and BETA-2 score, sample size predictions indicated that 2–3 times as many participants per group would be required to show a difference at 6 months as compared with C-peptide. Detecting a reduction in hypoglycaemia was affected by reporting methods. Interpretation Interventions that preserve β-cell function are effective at improving metabolic outcomes in new-onset type 1 diabetes, confirming their potential as adjuncts to insulin. We have shown that improvements in HbA1c are directly proportional to the degree of C-peptide preservation, quantifying this relationship, and supporting the use of C-peptides as a surrogate endpoint in clinical trials

Hydrological Modeling with Respect to Impact of Land-Use and Land-Cover Change on the Runoff Dynamics in Godavari River Basin Using the HEC-HMS Model

Hydrological modeling and the hydrological response to land-use/land-cover changes induced by human activities have gained enormous research interest over the last few decades. The study presented here analyzes the spatial and qualitative changes in the rainfall–runoff that have resulted from the land-cover changes between 1985–2014 in the Godavari River Basin using the Hydrologic Engineering Centre-Hydrologic Modeling System(HEC-HMS) model and remote sensing—GIS (geographic information system) techniques. The purpose of this paper is to analyze the dynamics of land-use/land-cover (LULC) changes for the years 1985, 1995, 2005, and 2014 for the Godavari Basin. The findings reveal an increase of 0.64% of built-up land, a decrease of 0.92% in shrubland, and an increase of 0.56% in waterbodies between 1985–2014. The LULC change detection results between the years 1985–2014 indicated a drastic change in the cropland, forest, built-up land, and water bodies among all of the other classes. The urbanization and agricultural activities are the major reasons for the increase of cropland, built-up land, and water bodies, at the expense of decreases in shrubland and forest. The study had an overall classification accuracy of 92% and an overall Kappa coefficient of 0.9. The HEC-HMS model is used to simulate the hydrology of the Godavari Basin. The analyses carried out were mainly focussed on the impact of LULC changes on the streamflow pattern. The surface runoff was simulated for the year 2014 to quantify the changes that have taken place due to changes in LULC. The observed and the simulated peak streamflow was found to be the same i.e., 56,780 m3/s on 9 September 2014. In the validation part, the linear regression method was used to correlate the observed and simulated streamflow data at the prominent gauge station of the Badrachalam outlet for the Godavari River Basin and give a correlation coefficient value of 0.83. It was found that the HEC-HMS model is compatible and works better for the rainfall–runoff modeling, as it takes into account the various parameters that are influencing the process. The hydrological modeling that was carried out using the HEC-HMS model has brought out the significant impact of LULCC on rainfall–runoff at the Pranhita sub-basinscale, indicating the model’s ability to successfully accommodate all of the environmental and landscape variables. The study indicates that deforestation at the cost of urbanization and cropland expansions leads to decreases in the overall evapotranspiration (ET) and infiltration, with an increase in runoff. The results of the study show that the integration of remote sensing, GIS, and the hydrological model (HEC-HMS) can solve hydrological problems in a river basin

Biodiversity Characterization at Landscape level using Geospatial Model

Biodiversity is generally considered at the species level although conservation of biodiversity requires management at higher level of organization, particularly at the landscape scale. It is difficult to manage for each threatened species individually. Alternatively, management can focus on the ecosystems that contain these species, and on the landscapes containing the ecosystems. The information on the biodiversity characteristics such as species richness and their spatial distribution, economic and the ethno-botanical importance is of great significance to any nation. Considering the importance of Indias bioresources a national level initiative was undertaken to map vegetation and to demarcate the bio-rich areas at landscape level using remote sensing and extensive field sample. Nationwide project on the biodiversity characterization at landscape level, was carried out between 1998 and 2010 to characterize and map the flowering plants richness in the natural (forests, grasslands, scrub etc.) and man-made (forest plantations) vegetation formations. The spatial database on vegetation types generated using wet and dry season satellite imagery and ancillary data such as topographic maps and the species richness through field inventory were used to generate the spatially-explicit species distribution maps and statistics. Spatial Landscape Model (SPLAM) has been developed for landscape analysis and spatial data integration. The present study is first attempt which resulted in spatial database on vegetation types, porosity, patchiness, interspersion, juxtaposition, fragmentation, disturbance regimes, ecosystem uniqueness, terrain complexity and the species richness for biodiversity conservation. The field sampling involved 19,876 geo-referenced 0.04 ha plots across India and 7215 plant species. The geospatially-tagged species database, created in the project, provides information on the endemic, rare, endangered, threatened and medicinally/economically important species. The database, disseminated to large number of organizations has found extensive applications in policy planning, operational management, biodiversity conservation, bio-prospecting and the climate change studies.Pages: 3321-332

Flood risk assessment using multi-criteria analysis: a case study from Kopili River Basin, Assam, India

A multi-criteria analysis (MCA) approach to describe the effective utilization of geospatial techniques for disaster risk reduction at village level in Kopili River Basin (KRB) of Assam State, India is presented. The KRB is chronically flood affected due to seasonal monsoon and rise in water levels of Kopili River. Based on the MCA approach using flood hazard layer derived from the spatio-multi-temporal historic satellite data-sets (comprising of sensors from RISAT-1 SAR, Radarsat SAR and IRS AWiFS), socio-economic data (based on five census variables), infrastructure (road network) and land use vulnerabilities (cropped and uncropped areas), flood risk zones are derived. Our study elucidates that 24,837 ha of crop area spread across 95 villages in the KRB falls in high risk zone, about 39,209 ha distributed in 150 villages falls under moderate-high risk zones and remaining area spread over 162 villages is more or less unaffected. The proposed approach can be applied elsewhere in other river basins to estimate the flood risk so as to mitigate the disaster risk posed by the floods