The Framework for the Prediction of the Critical Turning Period for Outbreak of COVID-19 Spread in China based on the iSEIR Model

The goal of this study is to establish a general framework for predicting the so-called critical Turning Period in an infectious disease epidemic such as the COVID-19 outbreak in China early this year. This framework enabled a timely prediction of the turning period when applied to Wuhan COVID-19 epidemic and informed the relevant authority for taking appropriate and timely actions to control the epidemic. It is expected to provide insightful information on turning period for the world's current battle against the COVID-19 pandemic. The underlying mathematical model in our framework is the individual Susceptible-Exposed- Infective-Removed (iSEIR) model, which is a set of differential equations extending the classic SEIR model. We used the observed daily cases of COVID-19 in Wuhan from February 6 to 10, 2020 as the input to the iSEIR model and were able to generate the trajectory of COVID-19 cases dynamics for the following days at midnight of February 10 based on the updated model, from which we predicted that the turning period of CIVID-19 outbreak in Wuhan would arrive within one week after February 14. This prediction turned to be timely and accurate, providing adequate time for the government, hospitals, essential industry sectors and services to meet peak demands and to prepare aftermath planning. Our study also supports the observed effectiveness on flatting the epidemic curve by decisively imposing the Lockdown and Isolation Control Program in Wuhan since January 23, 2020. The Wuhan experience provides an exemplary lesson for the whole world to learn in combating COVID-19.Comment: 24 paages, 9 figures, 10 table

Health Misinformation in Search and Social Media

People increasingly rely on the Internet in order to search for and share health-related information. Indeed, searching for and sharing information about medical treatments are among the most frequent uses of online data. While this is a convenient and fast method to collect information, online sources may contain incorrect information that has the potential to cause harm, especially if people believe what they read without further research or professional medical advice. The goal of this thesis is to address the misinformation problem in two of the most commonly used online services: search engines and social media platforms. We examined how people use these platforms to search for and share health information. To achieve this, we designed controlled laboratory user studies and employed large-scale social media data analysis tools. The solutions proposed in this thesis can be used to build systems that better support people's health-related decisions. The techniques described in this thesis addressed online searching and social media sharing in the following manner. First, with respect to search engines, we aimed to determine the extent to which people can be influenced by search engine results when trying to learn about the efficacy of various medical treatments. We conducted a controlled laboratory study wherein we biased the search results towards either correct or incorrect information. We then asked participants to determine the efficacy of different medical treatments. Results showed that people were significantly influenced both positively and negatively by search results bias. More importantly, when the subjects were exposed to incorrect information, they made more incorrect decisions than when they had no interaction with the search results. Following from this work, we extended the study to gain insights into strategies people use during this decision-making process, via the think-aloud method. We found that, even with verbalization, people were strongly influenced by the search results bias. We also noted that people paid attention to what the majority states, authoritativeness, and content quality when evaluating online content. Understanding the effects of cognitive biases that can arise during online search is a complex undertaking because of the presence of unconscious biases (such as the search results ranking) that the think-aloud method fails to show. Moving to social media, we first proposed a solution to detect and track misinformation in social media. Using Zika as a case study, we developed a tool for tracking misinformation on Twitter. We collected 13 million tweets regarding the Zika outbreak and tracked rumors outlined by the World Health Organization and the Snopes fact-checking website. We incorporated health professionals, crowdsourcing, and machine learning to capture health-related rumors as well as clarification communications. In this way, we illustrated insights that the proposed tools provide into potentially harmful information on social media, allowing public health researchers and practitioners to respond with targeted and timely action. From identifying rumor-bearing tweets, we examined individuals on social media who are posting questionable health-related information, in particular those promoting cancer treatments that have been shown to be ineffective. Specifically, we studied 4,212 Twitter users who have posted about one of 139 ineffective ``treatments'' and compared them to a baseline of users generally interested in cancer. Considering features that capture user attributes, writing style, and sentiment, we built a classifier that is able to identify users prone to propagating such misinformation. This classifier achieved an accuracy of over 90%, providing a potential tool for public health officials to identify such individuals for preventive intervention

Lessons from COVID-19 for future disasters: an opinion paper.

A “Pandemic/Disaster Law” is needed to condense and organize the current dispersed and multiple legislation. The State must exercise a single power and command appropriate to each situation, with national validity. The production of plans for the use of land and real estate as potential centers for health care, shelter or refuge is recommended. There should be specific disaster plans at least for Primary Health Care, Hospitals and Socio-sanitary Centers. The guarantee of the maintenance of communication and supply routes is essential, as well as the guarantee of the autochthonous production of basic goods. The pandemic has highlighted the need to redefine the training plans for physicians who, in their different specialties, have to undertake reforms that allow a more versatile and transversal training. National research must have plans to be able to respond quickly to questions posed by the various crises, using all the nation’s resources and in particular, all the data and capabilities of the health sector. Contingency plans must consider ethical aspects, and meet the needs of patients and families with a humanized approach. In circumstances of catastrophe, conflicts increase and require a bioethical response that allows the best decisions to be made, with the utmost respect for people’s values. Rapid, efficient and truthful communication systems must be contained in a special project for this sector in critic circumstances. Finally, we believe that the creation of National Coordination Centers for major disasters and Public Health can contribute to better face the crises of the future.post-print176 K

VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

Essays on Health-related Disparities

As the most critical conditions of human life and a significant contributor to human capability, health is the fundamental unit for a functioning society. As a construct, health is also inherently multi-dimensional, and to understand and to evaluate whether the infrastructure of a society endows fair "health opportunities" to its people can be an enduring task for both researchers and policy-makers. In this dissertation, I explore this complex and ever more relevant issue of health disparity from different angles using administrative data and extensive exploration of the literature. In particular, I analyse the geographic disparity in quality of care and the potential drivers - differential provider behaviour. Looking at health status, I investigate the disparity of health outcomes due to external economic shocks and found that individuals from economically disadvantaged areas exhibit significantly worse mental health conditions. Given the geographic disparity, I further examine how different sources of information on provider quality affect patient choice and decision to travel for care. Moreover, I survey on how the internet has facilitated the disparity in information and diverging opinions on health. Finally, from a systems perspective, I scrutinise structural characteristics in health care system design that create disparities in benefits and access. My inquiry into the complex phenomenon of health disparity presents a humble contribution to the exiting literature at the intersection of health economics, medical sociology and social epidemiology

Networks of inter-organisational coordination during disease outbreaks

Multi-organisational environment is demonstrating more complexities due the ever-increasing tasks’ complications in modern environments. Disease outbreak coordination is one of these complex tasks that require multi-skilled and multi-jurisdictional agencies to coordinate in dynamic environment. This research discusses theoretical foundations and practical approaches to suggest frameworks to study complex inter-organisational networks in dynamic environments, specifically during disease outbreak. We study coo¬¬rdination as being an interdisciplinary domain, and then uses social network theory to model it. I have surveyed 70 health professionals whom have participated in the swine influenza H1N1 2009 outbreak. I collected both qualitative and quantitative data in order to build a comprehensive understanding of the dynamics of the inter-organisational network that evolved during that outbreak. Then I constructed a performance model by use three main components of the network theory: degree centrality, connectedness and tie strength as the independent variables, and disease outbreak inter-organisational performance as the dependent one. In addition, we study both the formal networks and the informal ones. Formal networks are based on the standard operating structures, and the informal ones emerge based on trust, mutual benefits and relationships. Results suggest that the proposed social network measures have positive effect on coordination performance during the outbreak in both formal and informal networks, except centrality in the formal one. In addition, none of those measures influence performance before the outbreak. Practically, the results suggest that increasing the communication frequency and diversifying the tiers of the inter-organisational links enhance the overall network’s performance in formal coordination. In the informal one, links are created with the intention to improve performance; hence, all suggested network measures improve performance

Networks of inter-organisational coordination during disease outbreaks

Multi-organisational environment is demonstrating more complexities due the ever-increasing tasks’ complications in modern environments. Disease outbreak coordination is one of these complex tasks that require multi-skilled and multi-jurisdictional agencies to coordinate in dynamic environment. This research discusses theoretical foundations and practical approaches to suggest frameworks to study complex inter-organisational networks in dynamic environments, specifically during disease outbreak. We study coo¬¬rdination as being an interdisciplinary domain, and then uses social network theory to model it. I have surveyed 70 health professionals whom have participated in the swine influenza H1N1 2009 outbreak. I collected both qualitative and quantitative data in order to build a comprehensive understanding of the dynamics of the inter-organisational network that evolved during that outbreak. Then I constructed a performance model by use three main components of the network theory: degree centrality, connectedness and tie strength as the independent variables, and disease outbreak inter-organisational performance as the dependent one. In addition, we study both the formal networks and the informal ones. Formal networks are based on the standard operating structures, and the informal ones emerge based on trust, mutual benefits and relationships. Results suggest that the proposed social network measures have positive effect on coordination performance during the outbreak in both formal and informal networks, except centrality in the formal one. In addition, none of those measures influence performance before the outbreak. Practically, the results suggest that increasing the communication frequency and diversifying the tiers of the inter-organisational links enhance the overall network’s performance in formal coordination. In the informal one, links are created with the intention to improve performance; hence, all suggested network measures improve performance

Radioactive Governance: The Politics of Expertise after Fukushima

This dissertation focuses on Japanese public and state responses to the release of radioactive contamination after the 2011 Fukushima nuclear disaster. I argue that the Fukushima nuclear disaster has led to the emergence of new forms of expertise in governing radioactive risks. These include techniques of governance that attempt to normalize peoples relationships with nuclear matter as an everyday concern. They also include decentralized strategies that empower victims of the disaster by providing access to technoscientifc practices of radiation monitoring and delegating radiation protection from the state to the citizens. My findings uncover a major shift in how societies have formerly organized responses to radioactive risks. In the aftermath of nuclear accidents, scholars have criticized central authoritarian decisions, in which state management of radioactive hazards was associated with politics of secrecy, victimhood, or public knowledge deficit. At stake in Fukushima is an increased normalization of citizens relationship with residual radioactivity, which is transformed into an everyday concern, rather than being represented as something exceptional. This is not only done by state experts, but equally via the increased activity of citizen scientists that collectively monitor residual radioactivity. My research is a significant departure from traditional sociocultural works that predominantly focus on micro-scale studies, such as how prior sociocultural factors influence a group understanding of radioactive risks. By highlighting major shifts in the structure of expertise and the regulation of life amidst toxic exposure, my research highlights how the management of contamination risks is evolving in an era where the impacts of modernization represent permanent marks on the planet

The Psychology of Fake News

This volume examines the phenomenon of fake news by bringing together leading experts from different fields within psychology and related areas, and explores what has become a prominent feature of public discourse since the first Brexit referendum and the 2016 US election campaign. Dealing with misinformation is important in many areas of daily life, including politics, the marketplace, health communication, journalism, education, and science. In a general climate where facts and misinformation blur, and are intentionally blurred, this book asks what determines whether people accept and share (mis)information, and what can be done to counter misinformation? All three of these aspects need to be understood in the context of online social networks, which have fundamentally changed the way information is produced, consumed, and transmitted. The contributions within this volume summarize the most up-to-date empirical findings, theories, and applications and discuss cutting-edge ideas and future directions of interventions to counter fake news. Also providing guidance on how to handle misinformation in an age of “alternative facts”, this is a fascinating and vital reading for students and academics in psychology, communication, and political science and for professionals including policy makers and journalists