Physics

Eco-evolutionary game dynamics which characterizes the mutual interactions and the coupled evolutions of strategies and environments has been of growing interests in very recent years. Since such feedback loops widely exist in a range of coevolutionary systems, such as microbial systems, social-ecological system and psychological-economic system, recent modeling frameworks that unveil the oscillating dynamics of social dilemmas have great potential for practical applications. In this perspective article, we overview the latest progress of evolutionary game theory in this direction. We describe both mathematical methods and interdisciplinary applications across different fields. The ideas worthy of further consideration are discussed in prospects, with the central role of promoting cooperations in a changing world.

We study mechanisms for reopening economic activities that explore the trade off between containing the spread of COVID-19 and maximizing economic impact. This is of current importance as many organizations, cities, and states are formulating reopening strategies. Our mechanisms, referred to as group scheduling, are based on partitioning the population into groups and scheduling each group on appropriate days with possible gaps (when all are quarantined). Each group interacts with no other group and, importantly, any person who is symptomatic in a group is quarantined. Specifically, our mechanisms are characterized by three parameters (g,d,t) , where g is the number of groups, d is the number of days a group is continuously scheduled, and t is the gap between cycles. We show that our mechanisms effectively trade off economic activity for more effective control of the COVID-19 virus. In particular, we show that the (2,5,0) mechanism, which partitions the population into two groups that alternatively work for five days each, flat lines the number of COVID-19 cases quite effectively, while still maintaining economic activity at 70% of pre-COVID-19 level. We also study mechanisms such as (2,3,2) and (3,3,0) that achieve a somewhat lower economic output (about 50%) at the cost of more aggressive control of the virus; these could be applicable in situations when the disease spread is more rampant in the population. We demonstrate the efficacy of our mechanisms by theoretical analysis and extensive experimental simulations on various epidemiological models. Our mechanisms prove beneficial just by regulating human interactions. Moreover, our results show that if the disease transmission (reproductive) rate is made lower by following social distancing, mask wearing, and other public health guidelines, it can further increase the efficacy of our mechanisms.

The pandemic caused by the novel Coronavirus SARS-CoV2 has been responsible for life threatening health complications, and extreme pressure on healthcare systems. While preventive and definite curative medical interventions are yet to arrive, Non-Pharmaceutical Interventions (NPIs) like physical isolation, quarantine and drastic social measures imposed by governing agencies are effective in arresting the spread of infections in a population. In densely populated countries like India, lockdown interventions are partially effective due to social and administrative complexities. Using detailed demographic data, we present an agent based model to imitate the behavior of the population and its mobility features, even under intervention. We demonstrate the effectiveness of contact tracing policies and how our model efficiently relates to empirical findings on testing efficiency. We also present various lockdown intervention strategies for mitigation - using the bare number of infections, the effective reproduction rate, as well as using reinforcement learning. Our analysis can help assess the socio-economic consequences of such interventions, and provide useful ideas and insights to policy makers for better decision making.

The bicycle is a low-cost means of transport linked to low risk of COVID-19 transmission. Governments have incentivized cycling by redistributing street space as part of their post-lockdown strategies. Here, we evaluate the impact of provisional bicycle infrastructure on cycling traffic in European cities. We scrape daily bicycle counts spanning over a decade from 736 bicycle counters in 106 European cities. We combine this with data on announced and completed pop-up bike lane road work projects. On average 11.5 kilometers of provisional pop-up bike lanes have been built per city. Each kilometer has increased cycling in a city by 0.6%. We calculate that the new infrastructure will generate $2.3 billion in health benefits per year, if cycling habits are sticky.

The effect of undecided agents is studied within populations in an opinion-forming dynamic, varying the number of undecided agents for different proportions of populations in a complete opinion-exchange network. The result is that the dynamic depends on the number of undecided agents, with 10\% of the undecided population potentially affecting the change in consensus and then becoming linear with a negative slope.

The SIR infection theory initiated by Kermack-Mckendrick in 1927 discusses the infection in an isolated population with uniform properties such as the uniform population distribution. In the infection, there exist two aspects: (1) The quantitative aspect and (2) the temporal aspect. Since the SIR theory is a mean-field theory, it can't match both aspects simultaneously. If the quantitative aspect is matched, the temporal aspect can't be matched, versa. The infection starts from a cluster, and it spreads to different places increasing the size of the infection. In general, even in the case of the infection in a big city, the infection grows within a limited population. Namiki found and named this kind of population as an effective population. He proposes that if the hypothesis is adopted, the quantitative and temporal aspects can be matched simultaneously.

With increasing threats by large attacks or disasters, the time has come to reconstruct network infrastructures such as communication or transportation systems rather than to recover them as before in case of accidents, because many real networks are extremely vulnerable. Thus, we consider self-healing mechanisms by rewirings (reuse or addition of links) to be sustainable and resilient networks even against malicious attacks. In distributed local process for healing, the key strategies are the extension of candidates of linked nodes and enhancing loops by applying a message-passing algorithm inspired from statistical physics. Simulation results show that our proposed combination of ring formation and enhancing loops is particularly effective in comparison with the conventional methods, when more than half damaged links alive or are compensated from reserved ones.

Air pollution is a threat to human health, in particular since it aggravates respiratory diseases. Early COVID-19 outbreaks in Wuhan, China and Lombardy, Italy coincided with high levels of air pollution drawing attention to a potential role of particulate matter and other pollutants in infections and more severe outcomes of the new lung disease. Both air pollution and COVID-19 outcomes are driven by human mobility and economic activity leading to spurious correlations in regression estimates. We use district-level panel data from Belgium, Brazil, Germany, Italy, the UK, and the US to estimate the impact of daily variation in air pollution levels on COVID-19 infections and deaths. Using random variation in air pollution generated by thermal inversions, we rule out that changes in mobility and economic activity are driving the results. We find that a 1%-increase in air pollution levels over the three preceding weeks leads to a 1.5% increase in weekly cases. A 1%-increase in air pollution over four weeks leads to 5.1% more COVID-19 deaths. These results indicate that short-term measures to reduce air pollution can help mitigate the health damages of the virus.

Community detection or clustering is a crucial task for understanding the structure of complex systems. In some networks, nodes are permitted to be linked by either "positive" or "negative" edges; such networks are called signed networks. Discovering communities in signed networks is more challenging than that in unsigned networks. In this study, we innovatively develop a non-backtracking matrix of signed networks, theoretically derive a detectability threshold for this matrix, and demonstrate the feasibility of using the matrix for community detection. We further improve the developed matrix by considering the balanced paths in the network (referred to as a balanced non-backtracking matrix). Simulation results demonstrate that the algorithm based on the balanced nonbacktracking matrix significantly outperforms those based on the adjacency matrix and the signed non-backtracking matrix. The proposed (improved) matrix shows great potential for detecting communities with or without overlap.

Homicide is without doubt one of Mexico's most important security problems, with data showing that this dismal kind of violence sky-rocketed shortly after the war on drugs was declared in 2007. Since then, violent war-like zones have appeared and disappeared throughout Mexico, causing unfathomable human, social and economic losses. One of the most emblematic of these zones is the city of Monterrey, a central scenario in the narco-war. To better understand the underlying mechanisms by which violence has evolved and spread through the city, here we propose a network-based approach. For this purpose, we define a homicide network where nodes are geographical entities that are connected through spatial proximity and crime similarity. Data is taken from a crime database spanning 86 months in the Monterrey metropolitan area, containing manually curated geo-located and dated homicides, as well as from Open Street Map for urban environment. Under this approach, we first identify independent crime sectors corresponding to different connected components. Each of these clusters of crime presents crime evolution similar to the one at state and national levels. We then show how crime spread from neighborhood to adjacent neighborhoods when violence was mainly cartel-related and how it was chiefly static at a different time. Finally, we show a relation between homicidal crime and urban landscape by studying the distance of safe and violent neighborhoods to the closest highway and by studying the evolution of highway and crime distance over the cartel-related years and the following period. With this approach, we are able to describe more accurately the evolution of homicidal crime in a metropolitan area.