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During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO; N = 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread. Author summary Societies have a rich inter-individual variability, different cultures, wealth distribution, and ways of functioning, but most epidemic models describe the dynamics of a homogeneous population in different disease stages. The COVID-19 pandemic is exacerbating existing global and national inequalities, which typically hit the most vulnerable groups hardest. The focus of policymakers has been on containing the spread of COVID-19 and mitigating the socioeconomic effects of the pandemic. With the introduction of psychosocial coupling in epidemic disease models, the consideration of effects linked to societal diversity becomes possible. The model parameters absorb all properties characterizing the diversity of society including psychosocial factors. We estimated the strength and direction of psychosocial causes within a causal inference framework and demonstrated their effect upon the infectious spread to be smaller, but on a similar scale of magnitude as current political interventions and seasonality. Our cause-effect analysis considered minor parametric changes due to psychosocial variation, but has the capacity to integrate a large range of multi-dimensional data including societal, national and cultural differences, political strategies, and trust in the government and media. Given the here estimated size of psychosocial causes, the synergy of these interactions may likely reach scales that cannot be ignored in epidemic spread.

Low-dimensional attractive manifolds with flows prescribing the evolution of state variables are commonly used to capture the lawful behavior of behavioral and cognitive variables. Neural network dynamics underlie many of the mechanistic explanations of function and demonstrate the existence of such low-dimensional attractive manifolds. In this study, we focus on exploring the network mechanisms due to asymmetric couplings giving rise to the emergence of arbitrary flows in low dimensional spaces. Here we use a spiking neural network model, specifically the theta neuron model and simple synaptic dynamics, to show how a qualitatively identical set of basic behaviors arises from different combinations of couplings with broken symmetry, in fluctuations of both firing rate and spike timing. We further demonstrate how such network dynamics can be combined to create more complex processes. These results suggest that 1) asymmetric coupling is not always a variance to be averaged over, 2) different networks may produce the same dynamics by different dynamical routes and 3) complex dynamics may be formed by simpler dynamics through a combination of couplings.