A distributed dynamics for virus-spread control

A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic

“…0; 1 ½ denoting the ratio of people from region i interacting with those in region j at time t, such that P j ϕ ij t ð Þ ¼ 1: Note that, as a result of the identification procedure illustrated in Supplementary Notes, in Eqs. (10) and (11) the mortality rate ζ is expressed as a function of the saturation of the regional health systems whose expression is given in Supplementary Notes.…”

“…The regional presidents and their councils can independently take their own actions, strengthening or, at times, weakening national containment rules. Previous studies have modelled the spread of the epidemics and its evolution in the country at the national level [1][2][3][4][5] , and some have looked at the effects of different types of containment and mitigation strategies [6][7][8][9][10][11] . Limited work [12][13][14][15][16][17][18][19][20][21] has taken into account the spatial dynamics of the epidemic but, to the best of our knowledge, no previous paper in the literature has explicitly taken into consideration the pseudo-federalist nature of the Italian Republic and its strong regional heterogeneity when it comes to health matters, hospital capacity, economic costs of a lockdown and the presence of inter-regional people's flows.…”

A survey on the analysis and control of evolutionary matrix games

“…Although beyond the scope of this article, the shifting of equilibrium points has been considered in the context of stochastically stable equilibria of evolutionary snowdrift games [73]. Also in a systems and control setting, RD has been invoked to study task assignment among team members [74], virus spread control [75], extremumseeking controllers [76] and direct reciprocity [77].…”

Control‐channel interactions in diffusive dynamical networks: A graph‐theoretic perspective

“…[27][28][29] Likewise, actors in social networks (eg, different advertisers) may have competing influences on decision-making, and control mechanisms for infectious diseases may interact. 5,30,31 In many application domains, these interactions among control systems are becoming increasingly sophisticated and frequent, as new cyber technologies enable application of remote controls by a wider range of stakeholders. Given these concerns, there is a motivation for developing assessment techniques for control-systems interdependencies in dynamical networks, ideally with the goal of providing simple graph-theoretic insights into the interdependencies.…”

“…For example, in the bulk power transmission network, the tuning of some wide‐area controls may dictate whether fault resolution by other control systems is successful or causes poorly‐damped oscillations 27‐29 . Likewise, actors in social networks (eg, different advertisers) may have competing influences on decision‐making, and control mechanisms for infectious diseases may interact 5,30,31 . In many application domains, these interactions among control systems are becoming increasingly sophisticated and frequent, as new cyber technologies enable application of remote controls by a wider range of stakeholders.…”