Background The dense social contact networks and high mobility in congested urban areas facilitate the rapid transmission of infectious diseases. Typical mechanistic epidemiological models are either based on uniform mixing with ad-hoc contact processes or need real-time or archived population mobility data to simulate the social networks. However, the rapid and global transmission of the novel coronavirus (SARS-CoV-2) has led to unprecedented lockdowns at global and regional scales, leaving the archived datasets to limited use. Findings While it is often hypothesized that population density is a significant driver in disease propagation, the disparate disease trajectories and infection rates exhibited by the different cities with comparable densities require a high-resolution description of the disease and its drivers. In this study, we explore the impact of creation of containment zones on travel patterns within the city. Further, we use a dynamical network-based infectious disease model to understand the key drivers of disease spread at sub-kilometer scales demonstrated in the city of Ahmedabad, India, which has been classified as a SARS-CoV-2 hotspot. We find that in addition to the contact network and population density, road connectivity patterns and ease of transit are strongly correlated with the rate of transmission of the disease. Given the limited access to real-time traffic data during lockdowns, we generate road connectivity networks using open-source imageries and travel patterns from open-source surveys and government reports. Within the proposed framework, we then analyze the relative merits of social distancing, enforced lockdowns, and enhanced testing and quarantining mitigating the disease spread. Scope Our results suggest that the declaration of micro-containment zones within the city with high road network density combined with enhanced testing can help in containing the outbreaks until clinical interventions become available.

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Maintaining food surplus for growing populace while reducing agriculture’s environmental impact poses significant challenge. Intensive agricultural practices fueled green revolutions in nations, which helped them achieve self-sufficiency. However, the fertilizer-intensive agricultural practices and exploitative trade systems eventually created a legacy of unsustainable agrarian systems. To understand the environmental consequences of the national cereal trade, we map Nitrogen, Phosphorous, and Potassium transferred through interstate wheat and rice trade in India over the decade. We quantify the fate of leftover nutrient surplus across the traded network. Nation’s food bowls, while contributing significantly towards national food demand, are becoming pollution-rich by sustaining 1.19 TgNyr-1, and 0.35 TgPyr-1 surplus that accounts for 61% of total surplus arising from trade transfer, indicating a growing disparity in agricultural nutrient budget. Given India’s role in global food security, identifying the nation’s environmental vulnerability can help to design appropriate policy interventions for sustainable development.

<p>Catastrophic flood leads to a major disaster in developing countries. It loses a life and significant valuable properties, therefore it assessment is a prime requirement to identify the risk and vulnerable area in a flood-prone region. Many hydrodynamic models are providing a solution to identify the flood inundation area, flood arrival time, and velocity of flow in flood susceptible area, however, due to the low resolution of DEM, it can’t assess the actual flooding condition. To overcome this limitation, the present study describes the creation of high resolution (3 cm gridded) DEM for Dhanera city, Rel river catchment in Gujarat where it was affected by the catastrophic flood in the year of 2015 and 2017. Phantom 4 Pro RTK, DGPS and Pix4 software are used for creation of high-resolution DEM. The entire 10 km<sup>2</sup> area of Dhanera city is divided 4 blocks and each block is mapped by Phantom 4 pro-RTK Unmanned Aerial Vehicle (UAV) at 80 % image overlaps. A total of 9222 images are captured and post-processed using a Pix 4 software. Ground Control Points were marked for rectification in the geo-location of aerial images using DGPS (RTK). The aerial images collected during the survey have a spatial resolution of 3 cm with geo-location. The data collected is put for post-processing using Pix4D mapper software. 3D classified point cloud, DTM and DSM of 3 cm spatial resolution, orthomosaic of 3 cm spatial resolution are produced after the processing. Generated High-resolution DEM (DTM & DSM) will be utilized for hydrodynamic modeling to produce a precise flood inundation maps. </p><p> </p><p>Acknowledgement: The corresponding author is thankful to the ORSP, PDPU, and SAC-ISRO, SARITA program for providing the research grant to execute the work. (Grant no: ORSP/R&D/SRP/2019/MPDP/007; SAC/EPSA/GHCAG/LHD/SARITA/01/19)</p>

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