Modelling The Spreading Dynamics Of Infectious Diseases In Complex Social Systems

The outbreak of SARS in 2003, leading to enormous financial loss and casualties, has significantly changed the public health policy in China and boosted the place of biosecurity in the framework of national security strategy. Today, the shadow of SARS has faded away, however the threats from microbes are still persisting. The influenza epidemics of H5N1, H1N1 and H7N9 in the last decade are constant reminders of the challenges of major infectious diseases.The control and prevention for major infectious diseases refers to reducing the susceptible population with vaccines, alleviating the cases and decreasing the mortality with pharmaceuticals and controlling the spread of the epidemics with social-distance intervention measures, such as exposed quarantine and school closure. At the early stage of emerging infectious diseases, the nature of the pathogen is usually even constantly unknown and the research and development of specific vaccine and pharmaceuticals requires a long cycle, which surely couldn’t satisfy the demands of emergency disposal. The public health policymakers have to make a compromise between the intensity of non-pharmaceutical interventions and the effects on economy, which highlights the importance of scientific decision-making and the value of the study of infectious disease dynamics. Modeling infectious diseases is not only the pathway for understanding the evolving and control of the epidemics, but also of great significance to optimizing the control strategies and designing contingency plans for infectious diseases and other biological incidents, such as bioterrorist attacks. It is an international research hotspot in biosecurity and also an important manifestation of national ability in biosecurity.In this thesis, complex social systems are chosen as the fundamental research platform for modelling the spreading dynamics of infectious diseases. Agents in social systems are defined by a set of natural and social attributes similar to the reality and the risk interactions between them are characterized by static and evolving contact networks. A flexible disease dynamic model was constructed to describe the biological characteristics related to the pathogen. Based on the framework above, five aspects of researches are conducted:(1) Topology and formation of complex social contact networks. Topology and statistical characteristics of social networks are summarized and the formation mechanism is analyzed;(2) Dynamic of infectious diseases spreading in structured population. Infection risk model in mesoscale social system is developed; Impacts of contact structure on epidemic dynamics are analyzed; Effects of emerging health behaviors on epidemic dynamics are explored;(3) Effectiveness of immunization strategies. Effectiveness of acquaintance immunization(AI), community-bridge immunization(CBI) and ring vaccination(RV) on controlling the epidemics is comparatively analyzed;(4) Pandemic dynamics outbreaks in large scale population. Notifiable infectious diseases from 2004 to 2014 are analyzed; Spatiotemporal characteristics of epidemics are explored; Influences of long-distance travelers on pandemic dynamics are studies;(5) Computational experiment on assumed imported Ebola epidemics. Computational experiments assuming that imported Ebola epidemic outbreaks in Beijing and Guangzhou are conducted.The primary results and innovations are summarized as follows:(1) An Agent-based model was developed to interpret the evolving mechanism of typical social networks. The utility function in economics was creatively introduced into this model to quantify individual’s satisfaction on its current social activities. Integrated with the turnover effect of the population in social systems, this model successfully reproduced the typical statistical characteristics of social networks, such as degree distribution, small-world effect, network transitivity and community structure. The potential “cutoff” error of questionnaire methods in social network empirical study was also uncovered;(2) The adaptive health behavior model was developed to explore the effects of emerging health behaviors on the spreading dynamics of infectious diseases in social systems. Two primary information sources, which are respectively the global information released by public health departments and the local information perceived by agents, were believed to induce the emergence of health behaviors. Based on current infections and accumulated infections, two different information value definitions were given and were related to the contact transmission rate. The negative correlation between epidemic severity and the emergence of health behaviors was revealed and the inhibition effect of health behaviors on the diffusion of infectious diseases was confirmed;(3) The effectiveness of three stochastic immunization strategies on controlling the epidemics was comparatively analyzed based on realistic social contact networks for the first time. Results show AI, CBI and RV have decreased the coverage of the epidemics compared to a baseline scenario with no control measures, however the effectiveness of AI and CBI are very limited, between which AI outperforms CBI. RV is very effective in controlling the epidemics and its effectiveness significantly outperforms AI and CBI. The sensitivity analysis shows the effectiveness of RV decrease with the decrease of case detection rate and the increase of contact trace escape rate and the contact trace time lag, yet still remarkably outperforms AI and CBI on equal terms. With the increase of the basic reproductive number, the coverage of the epidemics will increase for all these three strategies, however RV still notably outperforms AI and CBI. Even when reaches 6.0, the effectiveness of RV is prominent, implying that RV could be applied to controlling the epidemics with a wide infectivity spectrum;(4) The complete statistical data of notifiable infectious diseases from January 2004 to December 2014 was collected and analyzed. The depicted epidemic trends show also China’s health expenditures have continuously increased in the decade, many infectious diseases still stay uncontrolled, among which some even get worse. A majority of infectious diseases were found to be related to spatiotemporal factors;(5) The large-scale epidemic dynamic model framework was constructed with metapopulation model and for the first time the long-distance travel types was introduced into the epidemic model and studied. The risk computation model with long-distance travelers was developed. The “merging effects” based on long-distance travel types was proposed and found to accelerate and aggravated the epidemics;(6) Based on the summarization of researches on Ebola projection in western Africa, for the first time the spreading dynamics of assumed imported Ebola epidemics in Beijing and Guangzhou were explored in artificial social systems. The result shows the risk of Ebola pandemic in Beijing and Guangzhou is very low with extremely executed prevention and control plan, standard disposal procedure and short free exposure of infectious Ebola cases. The sensitivity analysis shows the uncontrolled exposure duration of Ebola cases greatly dominates final Ebola infections, therefore the public should have elementary case 0R0Ridentification and infection prevention knowledge, ensuring the suspected cases being disposed timely.The thesis analyzed the typical characteristics of complex social systems and developed a social system platform which could be applied to modelling the spreading dynamics of infectious diseases. On the premise of making comprehensive consideration for model reality and computational complexity, agents in social systems were given maximum adaptive decision-making capacity. The biological characteristics of the pathogen and the social system platform was unified by risk computation model and regional human mobility and metapopulation model was introduced to study large-scale epidemic dynamics. A most complete and accessible large-scale epidemic dynamic model framework was proposed, which could be applied to carrying out social computational experiments, thus affording assistant decision-making support to the scientific control and prevention of infectious diseases.