Chapter 8 - Big Data Applications in Health Sciences and Epidemiology

Pandemics such as H1N1 influenza are global outbreaks of infectious disease. Human behavior, social contact networks, and pandemics are closely intertwined. The ordinary behavior and daily activities of individuals create varied and dense social interactions that are characteristic of modern urban societies. They provide a perfect fabric for rapid, uncontrolled disease propagation. Individuals' changing behaviors in response to public policies and their evolving perception of how an infectious disease outbreak is unfolding can dramatically alter normal social interactions. Effective planning and response strategies must take these complicated interactions into account. Recent quantitative changes in high performance computing and networking have created new opportunities for collecting, integrating, analyzing and accessing information related to such large social contact networks and epidemic outbreaks. The paper will describe our efforts to build a Cyber Infrastructure for EPIdemics (CIEPI) -- a high performance computing oriented decision-support environment to support planning and response in the event of epidemics.

Pandemics such as H1N1 influenza are global outbreaks of infectious disease. Human behavior, social contact networks, and pandemics are closely intertwined. The ordinary behavior and daily activities of individuals create varied and dense social interactions that are characteristic of modern urban societies. They provide a perfect fabric for rapid, uncontrolled disease propagation. Individuals' changing behaviors in response to public policies and their evolving perception of how an infectious disease outbreak is unfolding can dramatically alter normal social interactions. Effective planning and response strategies must take these complicated interactions into account. Recent quantitative changes in high performance computing and networking have created new opportunities for collecting, integrating, analyzing and accessing information related to such large social contact networks and epidemic outbreaks. The paper will describe our efforts to build a Cyber Infrastructure for EPIdemics (CIEPI) -- a high performance computing oriented decision-support environment to support planning and response in the event of epidemics.

Big data applications and analytics are vital in proposing ultimate strategic decisions. The existing literature emphasizes that big data applications and analytics can empower those who apply Big Data Analytics during the COVID-19 pandemic. This paper reviews the existing literature specializing in big data applications pre and peri-COVID-19. A comparison between Pre and Peri of the pandemic for using Big Data applications is presented. The comparison is expanded to four highly recognized industry fields: Healthcare, Education, Transportation, and Banking. A discussion on the effectiveness of the four major types of data analytics across the mentioned industries is highlighted. Hence, this paper provides an illustrative description of the importance of big data applications in the era of COVID-19, as well as aligning the applications to their relevant big data analytics models. This review paper concludes that applying the ultimate big data applications and their associated data analytics models can harness the significant limitations faced by organizations during one of the most fateful pandemics worldwide. Future work will conduct a systematic literature review and a comparative analysis of the existing Big Data Systems and models. Moreover, future work will investigate the critical challenges of Big Data Analytics and applications during the COVID-19 pandemic.

Social support is an essential part in suppressing the outbreak of serious infectious diseases. With the development of network science, how social support affects the dynamics of disease spreading on complex networks has now been more and more attracting and important topics. In this paper, we propose an epidemic model that considers the resource support from social neighbors of infected individuals based on social-contact multiplex networks. A bias parameter is introduced in the model to regulate the resource contribution strategy. Through extensive simulations, we find on both uncorrelated and correlated multiplex networks, there is always an optimal resource contribution strategy that can suppress the disease spreading to the maximum extent. Interestingly, the strategies of social support on social subnetwork has a so called double-edged sword effect on the dynamics of epidemic spreading. When disease transmission rate is relatively small, the nodes with small degrees in the social subnetwork should contributed more resources to suppress the disease spreading. While when transmission rate is large, the nodes with large degrees in the subnetworks should contributed more resources. When considering the inter-layer degree correlated, there is double edged sword effect of inter-layer degree correlation on dynamics of epidemic spreading.

Despite intensive studies on epidemic spreading in metapopulation networks, the infection process is normally assumed to be a Markovian process without considering time-dependent infectivity. In this paper, a reaction-diffusion SIRS epidemic model with non-Markovian infection processes is constructed on metapopulation networks, where the infection rate function associated with the sojourn time in the infected state is obtained by constructing the generation time distribution of the emerging infection process. Meanwhile, the recurrent mobility patterns are incorporated by the metapopulation model. By Lyapunov stability analysis, the epidemic outbreak condition is derived theoretically. The results show that the non-Markovian infection characteristics can significantly alter the transient behavior of epidemic spreading under the same basic reproduction number. Furthermore, with the increase in the average time for an infected individual to generate secondary cases in the non-Markovian process, the impact of mobility rate on transient epidemic spreading behavior will be reduced. Finally, mobility can have a suppressing effect on epidemic spreading dynamics when the population distribution exhibits significant asymmetry. The results can sharpen our understanding of the role of real-world non-Markovian infection processes in epidemic outbreaks.

Individual and Collective Behavior in Public Health Epidemiology

Impact of hopping characteristics of inter-layer commuters on epidemic spreading in multilayer networks

Effects of heterogeneous self-protection awareness on resource-epidemic coevolution dynamics

Chapter 7 - Public Transportation Big Data Mining and Analysis

Extensive real-data indicate that human motion exhibits novel patterns and has a significant impact on the epidemic spreading process. The research on the influence of human motion patterns on epidemic spreading dynamics still lacks a systematic study in network science. Based on an agent-based model, this paper simulates the spread of the disease in the gathered population by combining the susceptible-infected-susceptible epidemic process with human motion patterns, described by moving speed and gathering preference. Our simulation results show that the emergence of a hysteresis loop is observed in the system when the moving speed is slow, particularly when humans prefer to gather; that is, the epidemic prevalence of the systems depends on the fraction of initial seeds. Regardless of the gathering preference, the hysteresis loop disappears when the population moves fast. In addition, our study demonstrates that there is an optimal moving speed for the gathered population, at which the epidemic prevalence reaches its maximum value.

Cloud computing and big data: Technologies and applications

Development model based on visual image big data applied to art management

The dynamics of epidemic spreading on signed networks

Models for complex epidemic spreading are an essential tool for predicting both local and global effects of epidemic outbreaks. The ongoing development of the COVID-19 pandemic has shown that many classic compartmental models, like SIR, SIS, SEIR considering homogeneous mixing of the population may lead to over-simplified estimations of outbreak duration, amplitude and dynamics (e.g., waves). The issue addressed in this paper focuses on the importance of considering the social organization into geo-spatially organized communities (i.e., the size, position, and density of cities, towns, settlements) which have a profound impact on shaping the dynamics of epidemics. We introduce a novel geo-spatial population model (GPM) which can be tailored to reproduce a similar heterogeneous individuals’ organization to that of real-world communities in chosen countries. We highlight the important differences between a homogeneous model and GPM in their capability to estimate epidemic outbreak dynamics (e.g., waves), duration and overall coverage using a dataset of the world’s nations. Results show that community size and density play an important role in the predictability and controllability of epidemics. Specifically, small and dense community systems can either remain completely isolated, or show rapid bursts of epidemic dynamics; larger systems lengthen the epidemic size and duration proportionally with their number of communities.

In today’s era, with the wide application of artificial intelligence AI, the application of media is gradually approaching to intelligence, the application of big data in the era of smart media has gradually become an indispensable part of social life. The era of big data has promoted the development and progress of the society and facilitated people’s life. Accordingly, new media such as short video “we media” have sprung up like mushrooms and entered the public’s vision. Through the application of big data in the era of smart media, this paper analyzes the development trend of short video we media in social life, analyzes the influence of the era of big data on short video, and further reflects the general advantages of the era of smart media from the discussion of short video we media in the era of intelligent big data. This paper discusses some entertainment and convenience created by the application of the short video “we media” in the era of smart media for People’s Daily life. Based on the problems and challenges encountered in the application of smart media in some fields, it puts forward specific plans for the security protection of people’s personal information in the era of big data. The purpose of this paper is to try to reveal the application of smart media and big data to short video in this era and the corresponding research. Based on the above discussion, this paper combines big data analysis and relevant theoretical knowledge in the field of news media, combines intelligence with “we media”, and studies the value of “we media” short videos to the social development in the era of smart media. This article research results show that the wide application of wisdom media era of big data is a trend of rapid development of today’s society, in this trend, a short video from the development of the media heat continues to increase, not only make the communication between people more close, and accelerated the development of the modern intelligent society and optimize the traditional mode of transmission medium.