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Various phenomena in our daily world exhibit similarities to epidemic spreading. Fashion or technical trends are referred to as viral when they reach a large part of a society. In general, trends, opinions, diseases and failure in social or technical networks are characterized by the fact that they spread from an ``infected'' source to a ``non-infected'' state. We use the term epidemic process to refer to the described variety of different spreading phenomena. In addition to infection processes, external influences such as media in the case opinion spreading among people also play a major role. In contrast to a simple contagion, where one source is sufficient to sustain spreading, there also exist complex contagion phenomena where multiple sources are required. Social opinion formation is often described by the latter process. Until now, different spreading models have been developed and applied to account for different numbers of sources. We propose a general contagion model which accounts for both external influences and different numbers of ``infectious'' sources. We show that our contagion model exhibits a novel characteristic critical behavior which we describe using mathematical concepts that have their origin in catastrophe theory. Besides the stationary critical behavior, we also verify dynamical universal behavior by analyzing the corresponding correlation and response functions. The identification of universal critical behavior allows to demonstrate similarities between epidemic models that appear different at first sight. Based on those similarities, it is possible to classify spreading models. This way of classifying models is closely related to the study of phase transitions in statistical physics. Typically, one studies an order parameter (e.g. the number of infected) as a function of a control parameter (e.g. infection rate). In many situations, one finds a transition point or critical point of the control parameter at which the order parameter starts growing. At this point, there exists a characteristic behavior of the order parameter as a function of the control parameter. Considerations like these allow to apply methods from statistical physics which are important for describing phase transitions of percolation and magnetic systems. We consider different applications after defining and classifying our general modeling framework. We use the gained insights from the analysis of our general contagion model to propose a model for political campaigns. The suggested campaign model accounts for the complex interplay between activists, persuadables, political clout, budget and costs. Furthermore, we also study the opinion formation process in online petitions by applying different time series analysis methods. The last two examples have their origin in the field of disease spreading. We describe a very effective method of targeted recovery of infected individuals, which makes it possible to drastically reduce the number of infected cases. This protocol is also applicable to other spreading processes. Another application deals with the spatio-temporal correlations of the spreading of dengue.

Many democratic societies have become more politically polarized, with the U.S. being the main example. The origins of this phenomenon are still not well-understood and subject to debate. To provide insight into some of the mechanisms underlying political polarization, we develop a mathematical framework and employ Bayesian Markov chain Monte-Carlo (MCMC) and information-theoretic concepts to analyze empirical data on political polarization that has been collected by Pew Research Center from 1994 to 2017. Our framework can capture the evolution of polarization in the Democratic- and Republican-leaning segments of the U.S. public and allows us to identify its drivers. Our empirical and quantitative evidence suggests that political polarization in the U.S. is mainly driven by strong political/cultural initiatives in the Democratic party. ; ISSN:2193-1127

Many democratic societies have become more politically polarized, with the U.S. being the main example. The origins of this phenomenon are still not well-understood and subject to debate. To provide insight into some of the mechanisms underlying political polarization, we develop a mathematical framework and employ Bayesian Markov chain Monte-Carlo (MCMC) and information-theoretic concepts to analyze empirical data on political polarization that has been collected by Pew Research Center from 1994 to 2017. Our framework can capture the evolution of polarization in the Democratic- and Republican-leaning segments of the U.S. public and allows us to identify its drivers. Our empirical and quantitative evidence suggests that political polarization in the U.S. is mainly driven by strong political/cultural initiatives in the Democratic party. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1140/epjds/s13688-020-00249-4) contains supplementary material.

Antibiotic resistance has developed into a major public health concern due to the widespread prevalence of bacterial infections such as sepsis and urethritis and the frequent occurrence of opportunistic infections in immunocompromised patients. Unfortunately, the pipeline for new antibiotics has been almost stagnant for more than three decades. The main reason is that the antibiotics R&D market is dysfunctional since antibiotics R&D is a very risky business model that is not profitable and attractive for investors under current market and policy conditions. Our work analyzes the main economic and policy challenges in antibiotics R&D and highlights the need of rapid action in developing new push and pull incentives for antibiotics R&D. We suggest three core elements of a redesign of the R&D market: (1) levying a fee on the nonhuman use of antibiotics, (2) using these revenues to pay for market entry rewards, and (3) rewarding companies for the development of new antibiotics that are effective against resistant bacteria ('the resistance premium').

Political campaigns involve, in the simplest case, two competing campaign groups which try to obtain a majority of votes. We propose a novel mathematical framework to study political campaign dynamics on social networks whose constituents are either political activists or persuadable individuals. Activists are convinced and do not change their opinion and they are able to move around in the social network to motivate persuadable individuals to vote according to their opinion. We describe the influence of the complex interplay between the number of activists, political clout, budgets, and campaign costs on the campaign result. We also identify situations where the choice of one campaign group to send a certain number of activists already pre-determines their victory. Moreover, we show that a candidate's advantage in terms of political clout can overcome a substantial budget disadvantage or a lower number of activists, as illustrated by the US presidential election 2016. ; ISSN:1932-6203

Online petitions are an important avenue for direct political action, yet the dynamics that determine when a petition will be successful are not well understood. Here we analyze the temporal characteristics of online-petition signing behavior in order to identify systematic differences between popular petitions, which receive a high volume of signatures, and unpopular ones. We find that, in line with other temporal characterizations of human activity, the signing process is typically non-Poissonian and non-homogeneous in time. However, this process exhibits anomalously high memory for human activity, possibly indicating that synchronized external influence or contagion play and important role. More interestingly, we find clear differences in the characteristics of the inter-event time distributions depending on the total number of signatures that petitions receive, independently of the total duration of the petitions. Specifically, popular petitions that attract a large volume of signatures exhibit more variance in the distribution of inter-event times than unpopular petitions with only a few signatures, which could be considered an indication that the former are more bursty. However, petitions with large signature volume are less bursty according to measures that consider the time ordering of inter-event times. Our results, therefore, emphasize the importance of accounting for time ordering to characterize human activity. ; ISSN:1932-6203

Abstract
The drug-overdose crisis in the United States continues to intensify. Fatalities have increased 5-fold since 1999 reaching a record high of 108,000 deaths in 2021. The epidemic has unfolded through distinct waves of different drug types, uniquely impacting various age, gender, race, and ethnic groups in specific geographical areas. One major challenge in designing interventions and efficiently delivering treatment is forecasting age-specific overdose patterns at the local level. To address this need, we develop a forecasting method that assimilates observational data obtained from the CDC WONDER database with an age-structured model of addiction and overdose mortality. We apply our method nationwide and to three select areas: Los Angeles County, Cook County, and the five boroughs of New York City, providing forecasts of drug-overdose mortality and estimates of relevant epidemiological quantities, such as mortality and age-specific addiction rates.

AbstractThe growing interconnections among societies have facilitated the emergence of systemic crises, i.e., shocks that rapidly spread around the world and cause major disruptions. Advances in the interdisciplinary field of complexity can help understand the mechanisms underpinning systemic crises. This article reviews the most important concepts and findings from the pertinent literature. It demonstrates that an understanding of the nature of disruptions of globally interconnected systems and their implications is critical to prevent, react to, and recover from systemic crises. The resulting analytical framework is applied to two prominent examples of global systemic crises: the 2008 global financial crisis and the COVID‐19 pandemic. The article provides evidence that relying on reactive and recovery capacities to face systemic crises is not sustainable because of the extraordinary costs they impose on societies. Efforts are needed to develop a multipronged strategy to strengthen our capacities to face systemic crises and address fundamental mismatches between the nature of global challenges and the necessary collective action to address these challenges.