Biological and (Bio)Medical Complexity

We study a spreading pattern of epidemics over a complex network. In susceptible-infected (SI) model, it has been well known that the initial growth of infected nodes follows the exponential form as a function of time t with a time scale described by degree statistics, and that so does the final growth with an infection rate as a time scale. We put focus on the dynamics in the intermediate range except for both the early- and late- time regime and examine on the complex networks governed by degree distribution P_d(k)^{-γ}.

The Social network, understood as the structure of influence between individuals, is of major interest in the study of public opinion formation. However, it is impossible to know the complete social network at the population level. Sociological studies typically analyse the relation of opinion with the population socio-demographic variables, such as age, gender, ethnicity, religion or income. However, correlations between individuals are often treated as confounds rather than primitives.

Understanding complex networked systems is key to solving some of the most vexing problems confronting humankind, from discovering how thoughts and behaviors arise from dynamic brain connections, to preventing the spread of disease. However, a critical gap remains in understanding how the structure of networks affects the dynamics of complex systems, which we have been addressing with methods to compute and remove redundancy from them.

Most existing works on spreading processes assume that the propagating object, i.e., a virus or a piece of information, is transferred across the nodes without going through any modification. However, in real-life spreading processes, pathogens often evolve in response to changing environments and medical interventions, and information is often modified by individuals before being forwarded.

Griffiths phase (GP) generated by the heterogeneities on modular networks has
recently been suggested to provide a mechanism, rid of fine parameter tuning,
to explain the critical behavior of the brain. One conjectured requirement was
that the network of modules must be hierarchically organized and possess finite
topological dimension. We investigate the dynamical behavior of an activity
spreading model evolving in heterogeneous random networks with highly modular
structure, organized non-hierarchically. We observe that loosely coupled

Health-care systems can be represented as complex, multi-layered networks. Demographic shifts, like population aging and urbanization, together with increasing financial pressures exert an ever-growing amount of stress on the system and its ability delivery of appropriate care. Given these tendencies, that often result in a reduction of the density of primary care providers in a specific region, how much can the stress increase before the health-care system loses its capacity to deliver adequate care to the population? How does one measure the systemic risk in health care?

Healthcare records contain rich unstructured data in different forms. Free text constitutes a large portion of such data, yet this source of highly detailed information often remains under-used (i.e., either partially read manually or ignored) because of a lack of suitable methodologies to extract interpretable content.

Complex functions of the human brain rely on dynamic coordination of functionally different neural systems. To understand such brain activity at a network level, here we applied the so-called energy landscape analysis (Fig. 1; [1, 2]) to resting-state fMRI data obtained from healthy younger and older adults. This analysis characterizes large-scale brain dynamics within an energy landscape (Fig. 1(e)) by inferring the maximum entropy model from empirical data (Fig. 1(d)). We used this method to measure the correlation between the efficiency of the neural dynamics and a behavioral index [2].