Data Analytics Epidemic Modelling and Human Dynamics Approaches for Pandemic Outbreak

A pandemic is a disease that spreads across countries or continents. It affects more people and takes more lives than an epidemic. Examples are Influenza A, HIV-1, Ebola, SARS, pneumonic plague. Currently, the ongoing COVID-19 pandemic is one of the major health emergencies in decades that has affected almost every country in the world. As of 23 October 2020, it has caused an outbreak with more than 40 million confirmed cases, and more than 1 million reported deaths globally. Also, as of 23 October 2020, the reproduction number (R) and growth rate of coronavirus (COVID-19) in the UK range is 1.2-1.4. Due to the unavailability of an effective treatment (or vaccine) and insufficient evidence regarding the transmission mechanism of the epidemic, the world population is currently in a vulnerable position. This chapter explores data analytics epidemic modelling and human dynamics approaches for pandemic outbreaks.

Ensuring Food Supply and Security During Localised Lockdowns

A pandemic crisis inevitably puts great pressure on different aspects of societal and commercial infrastructure. Paths for information and goods designed and optimised for stable conditions may fail to meet the needs of emergency situations, whether suddenly imposed or planned. This chapter discusses the effects of the 2020 pandemic on food supply chains. These issues are considered as problems of information sharing and systemic behaviour with implications for both people and technology. Based on work in Wolverhampton, UK, the effect of the 2020 lockdown period on local businesses and charities is considered. In response to these observations, the design and development of Lupe, a prototype application to support the distribution and trading of food during periods of lockdown, is described. The aim of the system is to integrate the needs of consumers, businesses, and third sector organisations. The use of blockchain technology in the Lupe system to provide appropriate functionality and security for data is explored. Initial evaluations of the prototype by stakeholders are also included.

Towards Combating Pandemic-Related Misinformation in Social Media

Conventional preventive measures during pandemics include social distancing and lockdown. Such measures in the time of social media brought about a new set of challenges – vulnerability to the toxic impact of online misinformation is high. A case in point is COVID-19. As the virus propagates, so does the associated misinformation and fake news about it leading to an infodemic. Since the outbreak, there has been a surge of studies investigating various aspects of the pandemic. Of interest to this chapter are studies centering on datasets from online social media platforms where the bulk of the public discourse happens. The main goal is to support the fight against negative infodemic by (1) contributing a diverse set of curated relevant datasets; (2) offering relevant areas to study using the datasets; and (3) demonstrating how relevant datasets, strategies, and state-of-the-art IT tools can be leveraged in managing the pandemic.

Privacy-Preserving Pandemic Monitoring

The recent COVID-19 pandemic has presented a significant challenge for health organisations around the world in providing treatment and ensuring public health safety. While this has highlighted the importance of data sharing amongst them, it has also highlighted the importance of ensuring patient data privacy in doing so. This chapter explores the different techniques which facilitate this, along with their overall implementations. It first provides an overview of pandemic monitoring and the privacy implications associated with it. It then explores the different privacy-preserving approaches that have been used in existing research. It also explores the strengths as well as their limitations, along with possible areas for future research.

Overview of IoT and Machine Learning for E-Healthcare in Pandemics and Health Crises

The outbreak of COVID-19 has severely affected the healthcare infrastructure. The limitations of conventional healthcare urge the use of the digital technologies to lessen the overall load on the healthcare infrastructure and assist healthcare workers/staff. This chapter focuses on digital technologies to enable smart healthcare solutions to sustain and improve health services. The chapter focuses on two main driving technologies (internet of things [IoT] and artificial intelligence [AI]), pioneering automation and digitalization of healthcare. The enabling technologies possess the potential to transform the healthcare with emergence of new and novel research directions to realize and address healthcare needs. Therefore, it is essential to focus on key driving and complementing technologies to establish multidisciplinary research solutions with cross-platform design coupled with translational learning to unlock the potentials of next generation healthcare.

The Importance of Big Data Metadata in Crisis Management

Big data have the potential to change the way responders make sense of crisis situations, respond, and make decisions concerning the crises. At the same time, however, the explosion to the amount of crisis-related data can create an information overload to the crisis responders, and a challenge for their efficient management and utilisation. Crisis big data streams for epidemics may lack, for instance, key demographic identifiers such as age and sex, or may underrepresent certain age groups as well as residents of developing countries. Relevant metadata information needs therefore to be obtained and validated in order to trust make predictions and decisions based on the big data set. Crisis-related big data must be meaningful to the responders in order to form the basis of sound decisions. The aim of the chapter is to review all issues pertaining to the use of metadata for big data in emergency/crisis management situations.

Pandemic Management With Social Media Analytics

Social media analytics appears as one of recently developing disciplines that helps understand public perception, reaction, and emerging developments. Particularly, pandemics are one of overwhelming phenomena that push public concerns and necessitate serious management. It turned to be a useful tool to understand the thoughts, concerns, needs, expectations of public and individuals, and supports public authorities to take measures for handling pandemics. It can also be used to predict the spread of the virus, spread parameters, and to estimate the number of cases in the future. In this chapter, recent literature on use of social media analytics in pandemic management is overviewed covering all relevant studies on various aspects of pandemic management. It also introduces social media data sources, software, and tools used in the studies, methodologies, and AI techniques including how the results of the analysis are used in pandemic management. Consequently, the chapter drives conclusions out of findings and results of relevant analysis.

Deep Learning Approaches in Pandemic and Disaster Management

A quick decision-making process in response and management of epidemics has been the most common approach, as accurate and relevant decisions have been demonstrated to have beneficial impacts on life preservation as well as on global and local economies. However, any disaster or epidemic is rarely represented by a set of single and linear parameters, as they often exhibit highly complex and chaotic behaviours, where interconnected unknowns rapidly evolve. As a consequence, any such decision-making approach must be computationally robust and able to process large amounts of data, whilst evaluating the potential outcomes based on specific decisions in real time.

A Review of Technologies in Emergency Medicine and Sophisticated Collective Decision-Making in the Era of the Fight Against the Pandemic

For last decade, one of the most popular ideas in healthcare services increased computational power. In this global health crisis, several new diseases have emerged in different geographical areas with pathogens including Ebola, zika, and coronavirus. The authors promise emergency technologies to prevent the concerningly rapid spread of coronavirus disease in the era on fight against pandemic. However, a digital revolution and sophisticated clinical decision tools play a key role in the support of system in public health globally. Emphasis is on an innovation introduction by various advance technologies to achieve with a various issue linked to this viral pandemic. Regardless, future research could continue to explore how different innovative technologies and support systems are helping in the fight against pandemics such as coronavirus.

Strategies for Upskilling in Data Science After the COVID 19 Pandemic

The World Health Organization (WHO) declared COVID-19, an infectious disease caused by the virus SARS-CoV-2, as a pandemic in March 2020. More than 2.8 million people tested positive at the time of publication. Infections are exponentially increasing, and immense attempts are being made to tackle the epidemic. In this chapter, the authors aim to systematize data science works and evaluate the fast-growing community of recent studies. They also analyze public datasets and repositories that can be used to map COVID-19 dissemination and mitigation strategies. As part of that, they suggest a library review of the papers produced in this short period of time. Finally, they emphasize typical issues and pitfalls found in the surveyed works on the basis of these observations. Data science, narrowly established, will play a critical role in the global COVID-19 pandemic response. This chapter addresses the implications of data science for policymakers and strategists and allows them to resolve the threats, possibilities, and pitfalls inherent in using data science for tackling the COVID-19 pandemic.