How should managers take into account the propagation of supply chain disruptions and risks (i.e. the ripple effect) when they design their inventory policies? For over 60 years, various extensions and applications to the popular newsvendor model have been suggested, where cost/profit are often the focal objective. We propose a new version of the traditional single-period newsvendor model - the "Rippled Newsvendor" - with supply chain severity (i.e. risk propagation) as the primary objective while taking into account network structure. Our model considers exogenous and endogenous risk(s) of disruption while exploring the tension between under-supply and "wear-and-tear" (i.e system breakdown). To model the intricacies of this trade-off whilst minimizing the potential spread of risk, we leverage a Bayesian Network whereby the conditional probability distributions are functions of the inventory ordering decisions. We use a simulation study to understand the nature of our objective function as well as to gain insight into the potential optimal ordering policies of this new model. Furthermore, the simulation seeks to understand how the various factors in our system impact total risk severity, and if they do so in different ways. Our simulations indicate that local exogenous risk is of greater importance than non-local exogenous risk. Furthermore, we show that the type of risk, as well as the structural characteristics of the supply chain and inventory system, impact risk severity differently. © 2020 Elsevier B.V. All rights reserved.We present details of the EAERE Award for the Best Paper Published in Environmental and Resource Economics During 2019 together with those Highly Commended papers published during this period. © Springer Nature B.V. 2020.During times of public crises, governments must act swiftly to communicate crisis information effectively and efficiently to members of the public; failure to do so will inevitably lead citizens to become fearful, uncertain and anxious in the prevailing conditions. This pioneering study systematically investigates how Chinese central government agencies used social media to promote citizen engagement during the COVID-19 crisis. Using data scraped from 'Healthy China', an official Sina Weibo account of the National Health Commission of China, we examine how citizen engagement relates to a series of theoretically relevant factors, including media richness, dialogic loop, content type and emotional valence. Results show that media richness negatively predicts citizen engagement through government social media, but dialogic loop facilitates engagement. Information relating to the latest news about the crisis and the government's handling of the event positively affects citizen engagement through government social media. Importantly, all relationships were contingent upon the emotional valence of each Weibo post. © 2020 Published by Elsevier Ltd.Present study considers the situation where the removal of population is adopted as a prevention measure for isolating the susceptible population from an infected region to reduce the disease prevalence. To investigate the scenario, a dynamic error based method, Z-type control is applied in an SI type disease model with the aim of achieving a predetermined disease prevalence. The controlled system is designed by introducing a new compartment (the population in an infection-free region) in the uncontrolled system to capture the removal of susceptible population from the infected region to an infection free region. By performing numerical simulations, our study shows that using Z-control mechanism, the removal of susceptible to an infection free region can effectively achieve a predetermined disease prevalence. The removal rates required for achieving a specific reduction in infected population for different levels of disease endemicity are quantified. Furthermore, the global sensitivity analysis (PRCC) is also performed to have a more clear insights on the correlations of the control parameter with the model parameters. © 2019 Elsevier B.V. All rights reserved.Background The emergence of many infectious diseases has been of serious public health implication in the 21st century. Hospital preparedness is a key step in strengthening a country's ability to address any public health emergency of international concern caused by these diseases. In India, because 80% of the health-care utilization happens in the private hospitals, it is of at most importance to assess the preparedness level of these hospitals against emerging infectious diseases. Methods The study was a cross-sectional study, and hospitals which provided consent were included. The estimated participants for the study were 54. Results The results were expressed in a descriptive manner. For the purpose of analysis, the questionnaire was redistributed based on the monitoring and evaluation framework of International Health Regulations and its core capacities.  https://www.selleckchem.com/products/sn-38.html  It was found that there was a need to enhance the preparedness of the hospitals in the response against emerging infectious diseases. There were gaps in the implementation of various plans and protocols for staff training, risk communication, surge capacity, laboratory capacity, and infection control in the hospitals. Conclusion The findings were suggestive of a need for preparedness of the hospitals against the upsurge of emerging infectious diseases. © 2020 Director General, Armed Forces Medical Services. Published by Elsevier, a division of RELX India Pvt. Ltd.Background In India, the SARS-CoV2 COVID-19 epidemic has grown to 1,251 cases and 32 deaths as on 30 Mar 2020. The healthcare impact of the epidemic in India was studied with a stochastic mathematical model. Methods A compartmental SEIR model was developed, in which the flow of individuals through compartments is modeled using a set of differential equations. Different scenarios were modeled with 1000 runs of Monte Carlo simulation each using MATLAB. Hospitalization, ICU requirements and deaths were modeled on SimVoi software. The impact of Non-Pharmacological Interventions (NPI) including social distancing and lockdown on checking the epidemic was estimated. Results Uninterrupted epidemic in India would have resulted in over 364 million cases and 1.56 million deaths with peak by mid-July. As per the model, at growth rate of 1.15, India is likely to reach approximately 3 million cases by 25 May, implying 125,455 (±18,034) hospitalizations, 26,130 (±3,298) ICU admissions and 13,447 (±1,819) deaths. This would overwhelm India's healthcare system.