Pratyush K Kollepara, M.Sc, Alexander F Siegenfeld, S.B, Nassim Nicholas Taleb, Ph.D, Yaneer Bar-Yam, Ph.D

Journal of Travel Medicine, taab144

Background: Pre-pandemic empirical studies have produced mixed statistical results on the effectiveness of masks against respiratory viruses, leading to confusion that may have contributed to organizations such as the WHO and CDC initially not recommending that the general public wear masks during the COVID-19 pandemic.

Methods: A threshold-based dose–response curve framework is used to analyse the effects of interventions on infection probabilities for both single and repeated exposure events. Empirical studies on mask effectiveness are evaluated with a statistical power analysis that includes the effect of adherence to mask usage protocols.

Results: When the adherence to mask-usage guidelines is taken into account, the empirical evidence indicates that masks prevent disease transmission: all studies we analysed that did not find surgical masks to be effective were under-powered to such an extent that even if masks were 100% effective, the studies in question would still have been unlikely to find a statistically significant effect. We also provide a framework for understanding the effect of masks on the probability of infection for single and repeated exposures. The framework demonstrates that masks can have a disproportionately large protective effect and that more frequently wearing a mask provides super-linearly compounding protection.

Conclusions: This work shows (1) that both theoretical and empirical evidence is consistent with masks protecting against respiratory infections and (2) that nonlinear effects and statistical considerations regarding the percentage of exposures for which masks are worn must be taken into account when designing empirical studies and interpreting their results.

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City planners and urban policy makers require simulation models to understand, predict, design and manage urban areas so that cities can become more sustainable, equitable and efficient. Recently, the idea that one might build ‘digital twins’ of cities has captured the imagination of many scientists, engineers and policy makers. To unleash the full potential of data, science, and technology, such an approach requires a clear idea of how similar a digital twin would have to be to the system of interest and in what way. We thus argue that we urgently need theories and methods from complexity science to guide the development and use of digital twins. Different applications-such as the avoidance of traffic congestion or the simulation of emergent social segregation-may actually require different kinds of data and different kinds of twins. Hence, the complexity science approach considers different perspectives on cities which-to some extent-evolve and self-organize themselves like living systems.

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The speed and uncertainty of environmental change in the Anthropocene challenge the capacity of coevolving social–ecological–technological systems (SETs) to adapt or transform to these changes. Formal government and legal structures further constrain the adaptive capacity of our SETs. However, new, self-organized forms of adaptive governance are emerging at multiple scales in natural resource-based SETs. Adaptive governance involves the private and public sectors as well as formal and informal institutions, self-organized to fill governance gaps in the traditional roles of states. While new governance forms are emerging, they are not yet doing so rapidly enough to match the pace of environmental change. Furthermore, they do not yet possess the legitimacy or capacity needed to address disparities between the winners and losers from change. These emergent forms of adaptive governance appear to be particularly effective in managing complexity. We explore governance and SETs as coevolving complex systems, focusing on legal systems to understand the potential pathways and obstacles to equitable adaptation. We explore how governments may facilitate the emergence of adaptive governance and promote legitimacy in both the process of governance despite the involvement of nonstate actors, and its adherence to democratic values of equity and justice. To manage the contextual nature of the results of change in complex systems, we propose the establishment of long-term study initiatives for the coproduction of knowledge, to accelerate learning and synergize interactions between science and governance and to foster public science and epistemic communities dedicated to navigating transitions to more just, sustainable, and resilient futures.

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Vicky Chuqiao Yang, Mirta Galesic, Harvey McGuinness, and Ani Harutyunyan
PNAS August 31, 2021 118 (35) e2106292118;

In collective decision-making systems, such as committees and governments, many individuals follow others instead of evaluating the options on their own. Can a group settle on the option with higher merit when social learners prevail? Previous research has reached mixed conclusions because collective decisions emerge from a complex interaction of cognitive and social factors, which are rarely studied together. This paper develops a simple yet general mathematical framework to study this interaction and predicts a critical threshold for the proportion of social learners, above which an option may prevail regardless of its merit. The results suggest predictable limits to the proportion of social learners in collective situations from teamwork to democratic elections, beyond which the collective performance is affected negatively.

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Surendra Hazarie, David Soriano-Paños, Alex Arenas, Jesús Gómez-Gardeñes & Gourab Ghoshal 
Communications Physics volume 4, Article number: 191 (2021)

The increasing agglomeration of people in dense urban areas coupled with the existence of efficient modes of transportation connecting such centers, make cities particularly vulnerable to the spread of epidemics. Here we develop a data-driven approach combines with a meta-population modeling to capture the interplay between population density, mobility and epidemic spreading. We study 163 cities, chosen from four different continents, and report a global trend where the epidemic risk induced by human mobility increases consistently in those cities where mobility flows are predominantly between high population density centers. We apply our framework to the spread of SARS-CoV-2 in the United States, providing a plausible explanation for the observed heterogeneity in the spreading process across cities. Based on this insight, we propose realistic mitigation strategies (less severe than lockdowns), based on modifying the mobility in cities. Our results suggest that an optimal control strategy involves an asymmetric policy that restricts flows entering the most vulnerable areas but allowing residents to continue their usual mobility patterns.

Read the full article at: www.nature.com