Reduced mobility of infected agents suppresses but lengthens disease in biased random walk

Abstract

Various theoretical models have been proposed to understand the basic nature of epidemics. Recent studies focus on the effects of mobility to epidemic process. However, uncorrelated random walk is typically assumed as the type of movement. In our daily life, the movement of people sometimes tends to be limited to a certain direction, which can be described by biased random walk. Here, we developed an agent-based model of susceptible-infected-recovered (SIR) epidemic process in a 2D continuous space where agents tend to move in a certain direction in addition to random movement. Moreover, we mainly focus on the effect of the reduced mobility of infected agents. Our model assumes that, when people are infected, their movement activity is greatly reduced because they are physically weakened by the disease. By conducting extensive simulations, we found that when the movement of infected people is limited, the final epidemic size becomes small. However, that crucially depended on the movement type of agents. Furthermore, the reduced mobility of infected agents lengthened the duration of the epidemic because the infection progressed slowly.

Dynamic Hidden-Variable Network Models

“…An example of real-world links influencing nodeproperties is social influence, whereby acquainted pairs can become more similar over time [177,178] -or geographically move to closer-by coordinate locations. The inclusion of interdependencies relating to dynamical processes [179,180] can allow for more interesting dynamics and realism, but at the cost of increased model complexity.…”

Epidemic spreading in populations of mobile agents with adaptive behavioral response

“…Despite the increasing availability of data from human mobility [1,2], there are several situations in which such data is not available. Therefore, the use of synthetic mobility models to feed epidemic modeling [3][4][5][6][7][8][9][10][11][12][13][14] is a promising, yet understudied topic. Note that in this context, mobility refers to short-range displacement of individuals -such as walking -and not to the transfer of individuals from one place to another.…”

“…For this reason, despite some insights from reaction-diffusion processes [16], the mid-term between these regimes essentially relies on computational Monte Carlo simulations. More recently, other works have considered spatial heterogeneity and separate communities [7][8][9], heterogeneous interaction radii [11][12][13] and different agent's velocities [6,10].…”

Improving epidemic testing and containment strategies using machine learning

“…where the population consists of N = 10 5 individuals distributed uniformly on a square lattice with 320 × 320 cells, resulting in an average density of 0.98. The individuals move as random walkers on the lattice [24,25] being each confined to a region with an average radius of r = 10 cells [26]. All their positions are updated simultaneously at each time step.…”