The effectiveness of policies to control a human influenza pandemic : a literature review

The studies reviewed in this paper indicate that with adequate preparedness planning and execution it is possible to contain pandemic influenza outbreaks where they occur, for viral strains of moderate infectiousness. For viral strains of higher infectiousness, containment may be difficult, but it may be possible to mitigate the effects of the spread of pandemic influenza within a country and/or internationally with a combination of policies suited to the origins and nature of the initial outbreak. These results indicate the likelihood of containment success in'frontline risk'countries, given specific resource availability and level of infectiousness; as well as mitigation success in'secondary'risk countries, given the assumption of inevitable international transmission through air travel networks. However, from the analysis of the modeling results on interventions in the U.S. and U.K. after a global pandemic starts, there is a basis for arguing that the emphasis in the secondary risk countries could shift from mitigation towards containment. This follows since a mitigation-focused strategy in such developed countries presupposes that initial outbreak containment in these countries will necessarily fail. This is paradoxical if containment success at similar infectiousness of the virus is likely in developing countries with lower public health resources, based on results using similar modeling methodologies. Such a shift in emphasis could have major implications for global risk management for diseases of international concern such as pandemic influenza or a SARS-like disease.Avian Flu,Disease Control&Prevention,Health Monitoring&Evaluation,Population Policies,HIV AIDS

Spatial and Temporal Dynamics of Influenza

Despite the significant amount of research conducted on the epidemiology of seasonal influenza, the patterns in the annual oscillations of influenza epidemics have not been fully described or understood. Furthermore, the current understanding of the intrinsic properties of influenza epidemics is limited by the geographic scales used to evaluate the data. Analyses conducted at larger spatial scales may potentially conceal local trends in disease structure which may reveal the effect of population structure or environmental factors on disease spread. By using influenza incidence data from the Commonwealth of Pennsylvania and United States influenza mortality data, this dissertation characterizes seasonal influenza epidemics, evaluates factors that drive local influenza epidemics, and provides an initial assessment in how administrative borders influence surveillance for local and regional influenza epidemics.Evidence of spatial heterogeneity existed in the distribution of influenza epidemics for Pennsylvania counties resulting in a cluster of elevated incidence in the South Central region of the state that persisted during the entire study period (2003-2009). Lower monthly precipitation levels during the influenza season (OR = 0.52, p = 0.0319), fewer residents over age 64 (OR = 0.27, p = 0.01) and fewer residents with more than a high school education (OR = 0.76, p = 0.0148) were significantly associated with membership in this cluster. In addition, significant synchrony in the timing of epidemics existed across the entire state and decayed with distance (regional correlation r = 62%). Synchrony as a function of population size displayed evidence of hierarchical spread with more synchronized epidemics occurring among the most populated counties. A gravity model describing movement between two populations was the best predictor of influenza spread suggesting that non-routine and leisure travel drive local epidemics. Within the United States, clusters of epidemic synchronization existed, most notably in densely populated regions where connectivity is stronger. Observation of county and state epidemic clusters highlights the importance and necessity of correctly identifying the ontologic unit of epidemicity for influenza and other diseases. Recognition of the appropriate geographic unit to implement effective surveillance and prevention methods can strengthen the public health response and minimize inefficient mechanisms

When Infodemic Meets Epidemic: a Systematic Literature Review

Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts. Social media offer significant amounts of data that can be leveraged for bio-surveillance. They also provide a platform to quickly and efficiently reach a sizeable percentage of the population, hence their potential impact on various aspects of epidemic mitigation. The general objective of this systematic literature review is to provide a methodical overview of the integration of social media in different epidemic-related contexts. Three research questions were conceptualized for this review, resulting in over 10000 publications collected in the first PRISMA stage, 129 of which were selected for inclusion. A thematic method-oriented synthesis was undertaken and identified 5 main themes related to social media enabled epidemic surveillance, misinformation management, and mental health. Findings uncover a need for more robust applications of the lessons learned from epidemic post-mortem documentation. A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. Harnessing the full potential of social media in epidemic related tasks requires streamlining the results of epidemic forecasting, public opinion understanding and misinformation propagation, all while keeping abreast of potential mental health implications. Pro-active prevention has thus become vital for epidemic curtailment and containment

Data-Centric Epidemic Forecasting: A Survey

The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While forecasting epidemic progression is frequently conceptualized as being analogous to weather forecasting, however it has some key differences and remains a non-trivial task. The spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics, weather and environmental conditions. Research interest has been fueled by the increased availability of rich data sources capturing previously unobservable facets and also due to initiatives from government public health and funding agencies. This has resulted, in particular, in a spate of work on 'data-centered' solutions which have shown potential in enhancing our forecasting capabilities by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. This survey delves into various data-driven methodological and practical advancements and introduces a conceptual framework to navigate through them. First, we enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting, capturing various factors like symptomatic online surveys, retail and commerce, mobility, genomics data and more. Next, we discuss methods and modeling paradigms focusing on the recent data-driven statistical and deep-learning based methods as well as on the novel class of hybrid models that combine domain knowledge of mechanistic models with the effectiveness and flexibility of statistical approaches. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems including decision-making informed by forecasts. Finally, we highlight some challenges and open problems found across the forecasting pipeline.Comment: 67 pages, 12 figure

Spreading News: The Coverage Of Epidemics By American Newspapers And Its Effects On Audiences - A Crisis Communication Approach

Launched in 2002 in response to inadequate communications during the anthrax attacks and in preparations to the threats posed by H5N1, the Centers for Disease Control and Prevention (CDC)’s Crisis and Emergency Risk Communication (CERC) framework provides health professionals with trainings, tools, and resources to help them communicate effectively during emergencies and public health crises. Since that time, the framework has been used by the organization during outbreaks of infectious diseases. A core argument of CERC is that lack of certainty, efficacy, and trust serve as barriers to compliance with and support in CDC during an outbreak. According to CERC, providing the public with information about health and social risks, as well as information about ways individuals and organizations may ameliorate threats, could counter these perceptions, improve communications, and eventually save lives. However, the dissemination of the organization’s crisis messages depends largely on the mass media coverage. Understanding the news media’s agenda, priorities and role during outbreaks is essential for improving the cooperation between CDC and journalists. However, CERC provides little information about the actual behavior of journalists during crises, as reflected in news coverage of past outbreaks. This work aims to fill that gap in our understanding of the routinization of news during epidemics and its impact on audiences by systematically analyzing the coverage of epidemics in leading newspapers and using experiments to test its effects. This study analyzed 5,006 articles from leading American newspapers covering three epidemics: H1N1, Ebola, and Zika. Using a mixed method of automated and manual content analysis, it identified three distinct themes used to cover the diseases; pandemic, scientific, and social. Next, manual content analysis was conducted to assess the prevalence of information components theorized by CERC to increase certainty, efficacy and trust- information about medical/health risks, social/economic disruptions, and potential individual and organizational responses to ameliorate risks and reduce harm. Analysis of the themes based on CERC principles demonstrated substantial discrepancies between what CDC aims to communicate during epidemics and what the media actually disseminated to the public. An experiment (n = 321) found that exposure to articles representing the themes affected perceptions of certainty, efficacy, and trust, that in turn were associated with intentions to comply with CDC. The experiment also demonstrated the ability of coverage that follows CERC principles more closely to reduce harmful perceptions that were associated with behavioral intentions in target audiences. Implications for public health organizations and communicators are discussed, including ways to improve cooperation with journalists and the use of alternative direct-channels for filling gaps in news media coverage

Combating Fake News: A Gravity Well Simulation to Model Echo Chamber Formation In Social Media

Fake news has become a serious concern as distributing misinformation has become easier and more impactful. A solution is critically required. One solution is to ban fake news, but that approach could create more problems than it solves, and would also be problematic from the beginning, as it must first be identified to be banned. We initially propose a method to automatically recognize suspected fake news, and to provide news consumers with more information as to its veracity. We suggest that fake news is comprised of two components: premises and misleading content. Fake news can be condensed down to a collection of premises, which may or may not be true, and to various forms of misleading material, including biased arguments and language, misdirection, and manipulation. Misleading content can then be exposed. While valuable, this framework’s utility may be limited by artificial intelligence, which can be used to alter fake news strategies at a rate exceeding the ability to update the framework. Therefore, we propose a model for identifying echo chambers, which are widely reported to be havens for fake news producers and consumers. We simulate a social media interest group as a gravity well, through which we identify the online groups postured to become echo chambers, and thus a source for fake news consumption and replication. This echo chamber model is supported by three pillars related to the social media group: technology employed, topic explored, and confirmation bias of group members. The model is validated by modeling and analyzing 19 subreddits on the Reddit social media platform. Contributions include a working definition for fake news, a framework for recognizing fake news, a generic model for social media echo chambers including three pillars central to echo chamber formation, and a gravity well simulation for social media groups, implemented for 19 subreddits

GIS in Healthcare

The landscape of healthcare is dynamic, gradually becoming more complicated with factors beyond simple supply and demand. Similar to the diversity of social, political and economic contexts, the practical utilization of healthcare resources also varies around the world. However, the spatial components of these contexts, along with aspects of supply and demand, can reveal a common theme among these factors. This book presents advancements in GIS applications that reveal the complexity of and solutions for a dynamic healthcare landscape

Statistical Modeling of Influenza-Like-Illness in Montana using Spatial and Temporal Methods

Studying air pollution and public health has been a historically important question in science. It has long been hypothesized that severe air pollution conditions lead to negative implications in basic human health. Primarily, areas thats are prone to severe degrees of human pollution are the focus of such studies. Such research relating to less populated areas are scarce, and this scarcity raises the question of how such pollution dynamics (human-made and natural) influence human health in more rural areas. The aim of this study is to explore this hole in research; in particular we explore possible links between air pollution and Influenza-like-illness in Montana. We begin with a discussion of our starting hypotheses, the data we have accumulated to test these hypotheses, and some exploratory analysis of these data. The body of this research is based on modeling of the natural factors that influence influenza dynamics in general and how these factors apply in the state of Montana. Here, we will explore different modeling approaches and how to apply them to the given data. To conclude this research, a summary is provided and the implications this has for the state of Montana

The impact of the intensity of media use on potential tourists’ risk perception and travel protective behavioral intentions in COVID-19

Introduction: In light of the COVID-19 pandemic, there is an increased need for potential travelers to gather information about their trips to mitigate perceived risks. This study aims to understand the relationship between the intensity of media use (both new and traditional), epidemic risk perception, and tourism protection behavior intention among potential tourists. Methods: A total of 491 valid questionnaires were collected in Shanghai, China. Factor analysis, path analysis, and effect analysis were conducted using SPSS and AMOS to examine the impact of different media types on epidemic risk perception and tourism protection behavior. Results: The findings indicate a positive association between new media use intensity and epidemic risk perception, as well as an intention to adopt safety-conscious tourism behaviors. In contrast, traditional media usage is inversely associated with risk perception but has no significant influence on protective behavior. The results also highlight the role of demographic factors, such as age, education level, occupation, and income, in modulating the relationship between media usage and risk perception. Discussion: The contrasting effects of new and traditional media suggest the need for a tailored approach in epidemic communication strategies. Public health officials should leverage new media to enhance risk perception and safety-oriented behaviors, while recognizing the role of traditional media in managing lower risk perceptions and assuaging panic. The study emphasizes the importance of personalized messaging based on demographic disparities in media usage and perception. The mediating role of risk perception in shaping protective behaviors offers insights for promoting adherence to safety protocols. Conclusion: This study contributes to a comprehensive understanding of media influences during health crises, emphasizing the responsibility of media platforms in transmitting accurate information. The findings call for a nuanced approach to epidemic communication, considering the strengths and weaknesses of different media types. Segmented and personalized messaging strategies can cater to demographic variations in media usage and perception. Enhancing risk perception through tailored messaging can promote protective behaviors and effectively manage public sentiment during health crises