Polydispersity is found to have a significant effect on the potential energy landscape; the average inherent structure energy decreases with polydispersity. Increasing polydispersity at a fixed volume fraction decreases the glass transition temperature and the fragility of glass formation analogous to the antiplasticization seen in some polymeric melts. An interesting temperature dependent crossover of heterogeneity with polydispersity is observed at low temperature due to the faster buildup of dynamic heterogeneity at lower polydispersity.
With more than 140 million people infected globally and 3 million deaths, the COVID 19 pandemic has left a lasting impact. A modern response to a pandemic of such proportions needs to focus on exploiting all available data to inform the response in real-time and allow evidence-based decision-making. The intermittent lockdowns in the last 13 months have created economic adversity to prevent anticipated large-scale mortality and relax the lockdowns have been an attempt at recovering and balancing economic needs and public health realities. This article is a comprehensive case study of the outbreak in the city limits of Pune, Maharashtra, India, to understand the evolution of the disease and transmission dynamics starting from the first case on March 9, 2020. A unique collaborative effort between the Pune Municipal Corporation (PMC), a government entity, and the Pune knowledge Cluster (PKC) allowed us to layout a context for outbreak response and intervention. We report here how access to granular data for a metropolitan city with pockets of very high-density populations will help analyze, in real-time, the dynamics of the pandemic and forecasts for better management and control of SARS-CoV-2. Outbreak data analytics resulted in a real-time data visualization dashboard for accurate information dissemination for public access on the epidemic’s progress. As government agencies craft testing and vaccination policies and implement intervention strategies to mitigate a second wave, our case study underscores the criticality of data quality and analytics to decode community transmission of COVID-19.
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