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The term "herd immunity" is widely used but carries a variety of meanings. Some authors use it to describe the proportion immune among individuals in a population. Others use it with reference to a particular threshold proportion of immune individuals that should lead to a decline in incidence of infection. Still others use it to refer to a pattern of immunity that should protect a population from invasion of a new infection. A common implication of the term is that the risk of infection among susceptible individuals in a population is reduced by the presence and proximity of immune individuals (this is sometimes referred to as "indirect protection" or a "herd effect"). We provide brief historical, epidemiologic, theoretical, and pragmatic public health perspectives on this concept.

Objectives: The present study is aimed at estimating patient ow dynamical parameters and requirement of hospital beds. Secondly, the effects of age and gender on parameters were evaluated. Patients and Methods: In this retrospective cohort study, 987 COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India). The survival analysis was carried out from 29 Feb to 19 May 2020 for two hazards-'Hazard 1' was hospital discharge and 'Hazard 2' was hospital death. The starting point for survival analysis of the two hazards was considered to be hospital admission. The survival curves were estimated and additional effects of age and gender were evaluated using Cox proportional hazard regression analysis. Results: The Kaplan Meier estimates of lengths of hospital stay (Median =10 days, IQR =10 days) and median survival rate (more than 60 days due to large amount of censored data) were obtained. The Cox Model for 'Hazard 1' showed no signi cant effect of age and gender on duration of hospital stay. Similarly, the Cox Model 2 showed no signi cant difference of gender on survival rate. The case fatality rate 8.1 % , recovery rate 78.8% , mortality rate 0.10 per 100 person-days and hospital admission rate 0.35 per 10 5 person-days were estimated. Conclusion : The study estimates hospital bed requirement based on patient ow dynamic parameters. Furthermore, study concludes that average length of hospital stay were similar for patients of both genders and all age groups.

Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases. Public-health need Digital tool or technology Example of use Refs. Digital epidemiological surveillance Machine learning Web-based epidemic intelligence tools and online syndromic surveillance Web-based epidemic intelligence tools: 20-23,25 Based on social media or online search data: 30-33 Survey apps and websites Symptom reporting 37,38,48,49 Data extraction and visualization Data dashboard 39-45 Rapid case identification Connected diagnostic device Point-of-care diagnosis 58 Sensors including wearables Febrile symptoms checking 51-53 Machine learning Medical image analysis 65,66

More than 60% of human infectious diseases are caused by pathogens shared with wild or domestic animals. Zoonotic disease organisms include those that are endemic in human populations or enzootic in animal populations with frequent cross-species transmission to people. Some of these diseases have only emerged recently. Together, these organisms are responsible for a substantial burden of disease, with endemic and enzootic zoonoses causing about a billion cases of illness in people and millions of deaths every year. Emerging zoonoses are a growing threat to global health and have caused hundreds of billions of US dollars of economic damage in the past 20 years. We aimed to review how zoonotic diseases result from natural pathogen ecology, and how other circumstances, such as animal production, extraction of natural resources, and antimicrobial application change the dynamics of disease exposure to human beings. In view of present anthropogenic trends, a more eff ective approach to zoonotic disease prevention and control will require a broad view of medicine that emphasises evidence-based decision making and integrates ecological and evolutionary principles of animal, human, and environmental factors. This broad view is essential for the successful development of policies and practices that reduce probability of future zoonotic emergence, targeted surveillance and strategic prevention, and engagement of partners outside the medical community to help improve health outcomes and reduce disease threats.

The COVID-19 pandemic is an unprecedented global crisis. Many countries have implemented restrictions on population movement to slow the spread of severe acute respiratory syndrome coronavirus 2 and prevent health systems from becoming overwhelmed; some have instituted full or partial lockdowns. However, lockdowns and other extreme restrictions cannot be sustained for the long term in the hope that there will be an effective vaccine or treatment for COVID-19. Governments worldwide now face the common challenge of easing lockdowns and restrictions while balancing various health, social, and economic concerns. To facilitate cross-country learning, this Health Policy paper uses an adapted framework to examine the approaches taken by nine high-income countries and regions that have started to ease COVID-19 restrictions: five in the Asia Pacific region (ie, Hong Kong [Special Administrative Region], Japan, New Zealand, Singapore, and South Korea) and four in Europe (ie, Germany, Norway, Spain, and the UK). This comparative analysis presents important lessons to be learnt from the experiences of these countries and regions. Although the future of the virus is unknown at present, countries should continue to share their experiences, shield populations who are at risk, and suppress transmission to save lives.

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