Coupled disease-behavior dynamics on complex networks: A review

Abstract

It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

Keywords: Decision-making; Disease–behavior dynamics; Networks; Social distancing; Vaccination.

Fig. 1

Schematic illustration of disease natural histories within the compartmental models: (a) SIS natural history and (b) SIR natural history.

Fig. 2

Schematic illustration of disease–behavior interactions as a negative feedback loop. In this example, the loop from disease dynamics to behavioral dynamics is positive (+) since an increase in disease prevalence will cause an increase in perceived risk and thus an increase in protective behaviors. The loop from behavioral dynamics back to disease dynamics is negative (−) since an increase in protective behaviors such as contact precautions and social distancing will generally suppress disease prevalence.

Fig. 3

The faction of vaccinated individuals f_V (a) and infected individuals f_I (b) as a function of vaccine cost in the ER random graph. The symbols and lines correspond, respectively, to the simulation results and mean-field predictions (whose analytical framework is shown in Appendix A). The parameter α determines just how seriously the peer pressure is considered in the decision making process of the individuals to taking vaccine.

Fig. 4

Typical snapshots of the state configuration of the square lattice population after an epidemic season. The fraction of committed individuals (denoted by black), who always take vaccine, is 1% in (a) and 5% in (b). Other parameters are the same, and blue (dark gray) is for vaccinated individuals, gray successful free-riders, and yellow (light gray) infected individuals, respectively.

Fig. 5

Schematic illustration of multilayer architecture composed of two networks. Though the social network (namely, Network A) and infection contact network (namely, Network B) possess the same nodes marked by numbers, they support different dynamic processes, which are separately studied in most previous literature. Now, if both networks are encapsulated into a multilayer framework (namely, Network C), the interaction between them may create completely different outcomes that go beyond what isolated networks can capture.

Fig. 6

Transition probability trees of the combined states for coupled awareness–disease dynamics each time step in the multilayer networks. Here aware (A) state can become unaware (U) with transition probability δ and of course re-obtains awareness with other probability. For disease, μ represents the transition probability from infected (I) to susceptible (S). There are thus four state combinations: aware-infected, (AI) aware-susceptible, (AS) unaware-infected, (UI) and unaware-susceptible (US), and the transition of these combinations is controlled by probability r_i, $q_i^A$ and $q_i^U$. They respectively denote the transition probability from unaware to aware given by neighbors; transition probability from susceptible to infected, if node is aware, given by neighbors; and transition probability from susceptible to infected, if node is unaware, given by neighbors. We refer to , from where this figure has been adapted, for further details.

Fig. 7

Panel (a) denotes the time course for the number of infected nodes when the network growth, the link-removal process, and isolation avoidance are simultaneously involved into the adaptive framework. It is clear that this mechanism creates the reemergence of epidemic, which dies out after several such repetitions. While for this interesting phenomenon, it is closely related with the formation of giant component of susceptible nodes. Panel (b) shows the snapshot of the network topology of 5000th time step (before the next abrupt outbreak), when there is a giant component of susceptible nodes (yellow). However, the invasion of the infection individuals (red) makes the whole network split into many fragments, as shown by the snapshot of 5400th time step (after the explosion) in panel (c). We refer to , from where this figure has been adapted, for further details. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 8

Vaccination coverage as a function of the relative cost of vaccination and the fraction of imitators Θ in different networks. It is obvious that for small cost of vaccination, imitation behavior increases vaccination coverage but impedes vaccination at high cost, irrespective of potential interaction topology.

Fig. 9

Age-specific contact matrices for each of eight examined European countries. High contact rates are represented by white color, intermediate contact rates are green and low contact rates are blue. We refer to , from where this figure has been adapted, for further details. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 10

Distribution of the number of distinct persons each individual encounters each day. Experiments are conducted in different conference settings (HT09 is ACM Hypertext 2009, Torino, IT; SG means Science Gallery, Dublin, IE). We refer to , from where this figure has been adapted, for further details.

Fig. 11

(a) Distributions of contact durations measured in different social circumstances. (b) Distributions of time intervals between two successive contacts of a given individual, measured in the same social circumstances as in Fig. 11(a). Similar to Fig. 10, each symbol denotes one venue (SFHH denotes SFHH, Nice, FR; ESWC09 (ESWC10) is ESWC 2009 (2010), Crete, GR; and PS corresponds to primary school, Lyon, FR). We refer to , from where this figure has been adapted, for further details.

Fig. 12

Schematic illustration of questionnaire used in the voluntary vaccination survey. The survey items can be divided into self-interest ones (i.e., outcomes-for-self) and altruism ones (i.e., outcomes-for-others), which have corresponding scores. Based on both, it becomes possible to indirectly estimate the degree of altruism, which plays a significant role in vaccination uptake and epidemic elimination. We refer to , from where this figure has been adapted, for further details.