Community lockdowns and travel restrictions are commonly employed to decelerate epidemic spreading. We here use a stochastic susceptible-infectious-recovered model on different social networks to determine when and to what degree such lockdowns are likely to be effective. Our research shows that community lockdowns are effective only if the links outside of the communities are virtually completely sealed off. The benefits of targeting specifically these links, as opposed to links uniformly at random across the whole network, are inferable only beyond 90% lockdown effectiveness. And even then the peak of the infected curve decreases by only 20% and its onset is delayed by a factor of 1.5. This holds for static and temporal social networks, regardless of their size and structural particularities. Networks derived from cell phone location data and online location-based social platforms yield the same results as a large family of hyperbolic geometric network models where characteristic path lengths, clustering, and community structure can be arbitrarily adjusted. The complex connectedness of modern human societies, which enables the ease of global communication and the lightning speeds at which news and information spread, thus makes it very difficult to halt epidemic spreading with top-down measures. We therefore emphasize the outstanding importance of endogenous self-isolation and social distancing for successfully arresting epidemic spreading.
Evolutionary game theory in the realm of network science appeals to a lot of research communities, as it constitutes a popular theoretical framework for studying the evolution of cooperation in social dilemmas. Recent research has shown that cooperation is markedly more resistant in interdependent networks, where traditional network reciprocity can be further enhanced due to various forms of interdependence between different network layers. However, the role of mobility in interdependent networks is yet to gain its well-deserved attention. Here we consider an interdependent network model, where individuals in each layer follow different evolutionary games, and where each player is considered as a mobile agent that can move locally inside its own layer to improve its fitness. Probabilistically, we also consider an imitation possibility from a neighbor on the other layer. We show that, by considering migration and stochastic imitation, further fascinating gateways to cooperation on interdependent networks can be observed. Notably, cooperation can be promoted on both layers, even if cooperation without interdependence would be improbable on one of the layers due to adverse conditions. Our results provide a rationale for engineering better social systems at the interface of networks and human decision making under testing dilemmas.
Thermoregulation plays a vital role in homeostasis. Many species of animals as well as humans have evolved various physiological mechanisms for body temperature control, which are characteristically flexible and enable a fine‐tuned spatial and temporal regulation of body temperature in different environmental conditions and circumstances. Human beings normally maintain a core body temperature at around 37°C, and maintenance of this relatively high temperature is critical for survival. Therefore, principles of thermoregulatory control have also important clinical implications. Infections can cause the body temperature to rise internally and several diseases can cause a dysfunction of thermoregulatory mechanisms. Moreover, the utilization of thermotherapies in treating various diseases has been known for thousands of years with a recent resurgence of interest. An increasing amount of research suggests that targeted temperature management is of paramount importance to patient outcomes in certain clinical scenarios. We provide a concise summary of the basic concepts of thermoregulation. Emphasis is given to the principles of thermoregulation in humans in basic pathological states and to targeted temperature management strategies in the clinical environment, with special attention on therapeutic hypothermia in postcardiac arrest patients. Finally, the discussion is focused on the potential offered by computational thermophysiological models for predicting thermal responses of patients in various clinical circumstances, for proposing new perspectives in the design of novel thermal therapies, and to optimize targeted temperature management strategies.This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Environmental Factors Cardiovascular Diseases > Biomedical Engineering
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