PurposeBlockchain technology, a key feature of the fourth industrial revolution, is receiving widespread attention and exploration around the world. Taking the coronavirus pandemic as an example, the purpose of this study to examine the application of blockchain technology from the perspective of epidemic prevention and control.Design/methodology/approachExploring multiple case studies in the Chinese context at various stages of deployment, this study documents a framework about how some of the major challenges associated with COVID-19 can be alleviated by leveraging blockchain technology.FindingsThe case studies and framework presented herein show that utilization of blockchain acts as an enabler to facilitate the containment of several COVID-19 challenges. These challenges include the following: complications associated with medical data sharing; breaches of patients' data privacy; absence of real-time monitoring tools; counterfeit medical products and non-credible suppliers; fallacious insurance claims; overly long insurance claim processes; misappropriations of funds; and misinformation, rumors and fake news.Originality/valueBlockchain is ushering in a new era of innovation that will lay the foundation for a new paradigm in health care. As there are currently insufficient studies pertaining to real-life case studies of blockchain and COVID-19 interaction, this study adds to the literature on the role of blockchain technology in epidemic control and prevention.
The study examines the seasonal land cover changes (SLCC) and its effects on essential services of haor and non-haor areas of Kishoreganj district. SLCC is a regular event of this district, particularly in haors areas which are large bowl-shaped depressions and seasonal wetlands where water is covered for 6 months in a year. Both quantitative and qualitative approaches were followed in this study. SLCC is illustrated using maximum likelihood classification (MCL) by ArcGIS and integration of other remote sensing softwares (Erdas, Envi and Geomatica for image processing). Data were compiled from both secondary and primary sources. Secondary sources included a detailed review of published and unpublished documents and primary sources included field survey and interview survey like questionnaire survey, focus group interviewing (FGI) and key informants interview (KII). Based on questionnaire survey, bivariate analysis is performed and Pearson's Chi square test is used to investigate whether seasonal effect exists on essential services both in Haor and non-Haor areas. Satellite-based monitoring reveals that, on an average, during dry season waterbodies in haor upazilas is around 6%, but in the wet season it becomes 59% approximately. P value shows that there is a strong association between the effects of SLCC and essential services in haor areas, but no
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