Changes of China’s Status in the Global System and Its Influencing Factors:A Multiple Contact Networks Perspective

Clarifying China’s position in the global system is an important logical basis for developing national diplomacy.Although much research has been done on China’s development status,most studies have been based on country comparisons or institutional en-vironment.In today’s networked era in which the global economy,trade,personnel,and information are closely connected,studies on China’s global position and its status changes and influencing factors in multiple contact networks are still insufficient.In this study,from the perspective of diverse global contact networks,we constructed economic,cultural,and political influence indices to explore the changes and influencing factors on China’s status in the global system from 2005 to 2018.The results show that during the study period,China’s global influence in the fields of economic ties,cultural exchanges,and political contacts increased significantly,but its influ-ence in the fields of cultural exchanges and political contacts lagged far economic ties.The pattern of China’s economic influence on various economies around the world has shown a transformation from an‘upright pyramid’to an‘inverted pyramid’structure.The proportion of these economies in low-influence zones has decreased from more than 60%in 2005 to less than 20%in 2018.China’s cultural and political influence on various economies around the world has increased significantly;however,for the former,the percentage of high-influence areas is still less than 20%,whereas for the latter the percentage of these economies in medium-and high-influence areas is still less than 50%.Analyses such as a scatter plot matrix show that geographical proximity,economic globalization,close cooperation with developing countries,and a proactive and peaceful foreign policy are important factors in improving China’s status in the diverse global network system.

Structural network characteristics affect epidemic severity and prediction in social contact networks

Understanding and mitigating epidemic spread in complex networks requires the measurement of structural network properties associated with epidemic risk.Classic measures of epidemic thresholds like the basic reproduction number(R0)have been adapted to account for the structure of social contact networks but still may be unable to capture epidemic potential relative to more recent measures based on spectral graph properties.Here,we explore the ability of R0 and the spectral radius of the social contact network to estimate epidemic susceptibility.To do so,we simulate epidemics on a series of constructed(small world,scale-free,and random networks)and a collection of over 700 empirical biological social contact networks.Further,we explore how other network properties are related to these two epidemic estimators(R0 and spectral radius)and mean infection prevalence in simulated epidemics.Overall,we find that network properties strongly influence epidemic dynamics and the subsequent utility of R0 and spectral radius as indicators of epidemic risk.

Epidemic threshold influenced by non-pharmaceutical interventions in residential university environments

The control of highly contagious disease spreading in campuses is a critical challenge.In residential universities,students attend classes according to a curriculum schedule,and mainly pack into classrooms,dining halls and dorms.They move from one place to another.To simulate such environments,we propose an agent-based susceptible–infected–recovered model with time-varying heterogeneous contact networks.In close environments,maintaining physical distancing is the most widely recommended and encouraged non-pharmaceutical intervention.It can be easily realized by using larger classrooms,adopting staggered dining hours,decreasing the number of students per dorm and so on.Their real-world influence remains uncertain.With numerical simulations,we obtain epidemic thresholds.The effect of such countermeasures on reducing the number of disease cases is also quantitatively evaluated.

Using the contact network model and Metropolis-Hastings sampling to reconstruct the COVID-19 spread on the “Diamond Princess”

Traditional compartmental models such as SIR(susceptible,infected,recovered)assume that the epidemic transmits in a homogeneous population,but the real contact patterns in epidemics are heterogeneous.Employing a more realistic model that considers heterogeneous contact is consequently necessary.Here,we use a contact network to reconstruct unprotected,protected contact,and airborne spread to simulate the two-stages outbreak of COVID-19(coronavirus disease 2019)on the‘‘Diamond Princess"cruise ship.We employ Bayesian inference and Metropolis-Hastings sampling to estimate the model parameters and quantify the uncertainties by the ensemble simulation technique.During the early epidemic with intensive social contacts,the results reveal that the average transmissibility t was 0.026 and the basic reproductive number R0 was 6.94,triple that in the WHO report,indicating that all people would be infected in one month.The t and R0 decreased to 0.0007 and 0.2 when quarantine was implemented.The reconstruction suggests that diluting the airborne virus concentration in closed settings is useful in addition to isolation,and high-risk susceptible should follow rigorous prevention measures in case exposed.This study can provide useful implications for control and prevention measures for the other cruise ships and closed settings.

Reopen schools safely:simulating COVID-19 transmission on campus with a contact network agent-based model

As the COVID-19 vaccination has been quickly rolling out around the globe,the evaluation of the effects of vaccinating populations for the safe reopening of schools has become a focal point for educators,decision-makers,and the general public.Within this context,we develop a contact network agent-based model(CN-ABM)to simulate on-campus disease transmission scenarios.The CN-ABM establishes contact networks for agents based on their daily activity patterns,evaluates the agents’health status change in different activity environments,and then simulates the epidemic curve.By applying the model to a real-world campus environment,we identify how different community risk levels,teaching modalities,and vaccination rates would shape the epidemic curve.The results show that without vaccination,retaining under 50%of on-campus students can largely flatten the curve,and having 25%on-campus students can achieve the best result(peak value<1%).With vaccination,having a maximum of 75%on-campus students and at least a 45%vaccination rate can suppress the curve,and a 65%vaccination rate can achieve the best result.The developed CN-ABM can be employed to assist local government and school officials with developing proactive intervention strategies to safely reopen schools.

The dynamics of the risk perception on a social network and its effect on disease dynamics

The perceived infection risk changes individual behaviors,which further affects the disease dynamics.This perception is influenced by social communication,including surveying their social network neighbors about the fraction of infected neighbors and averaging their neighbors’perception of the risk.We model the interaction of disease dynamics and risk perception on a two-layer random network that combines a social network layer with a contact network layer.We found that if information spreads much faster than disease,then all individuals converge on the true prevalence of the disease.On the other hand,if the two dynamics have comparable speeds,the risk perception still converges to a value uniformly on the network.However,the perception lags behind the true prevalence and has a lower peak value.We also study the behavior change caused by the perception of infection risk.This behavior change may affect the disease dynamics by reducing the transmission rate along the edges of the contact network or by breaking edges and isolating the infectious individuals.The effects on the basic reproduction number,the peak size,and the final size are studied.We found that these two effects give the same basic reproduction number.We find edge-breaking has a larger effect on reducing the final size,while reducing the transmission rate has a larger effect on reducing the peak size,which is true for both scale-free and Poisson networks.

Network and equation-based models in epidemiology

Network and equation-based （EB） models are two prominent methods used in the study of epidemics. While EB models use a global approach to model aggregate population, net- work models focus on the behavior of individuals in the population. The two approaches have been used in several areas of research, including finance, computer science, social science and epidemiology. In this study, epidemiology is used to contrast EB models with network models. The methods are based on the assumptions and properties of compartmental models. In EB models we solve a system of ordinary differential equations and in network models we simulate the spread of epidemics on contact networks using bond percolation. We examine the impact of network structures on the spread of infection by considering various networks, including Poisson, Erd3s R6nyi, Scale-free, and Watts- Strogatz small-world networks, and discuss how control measures can make use of the network structures. In addition, we simulate EB assumptions on Watts-Strogatz net- works to determine when the results are similar to that of EB models. As a case study, we use data from the 1918 Spanish flu pandemic and that from measles outbreak to validate our results.