Early warning signals for bifurcations embedded in high dimensions.

Recent work has highlighted the utility of methods for early warning signal detection in dynamic systems approaching critical tipping thresholds. Often these tipping points resemble local bifurcations, whose low dimensional dynamics can play out on a manifold embedded in a much higher dimensional state space. In many cases of practical relevance, the form of this embedding is poorly understood or entirely unknown. This paper explores how measurement of the critical phenomena that generically precede such bifurcations can be used to make inferences about some properties of their embeddings, and, conversely, how prior knowledge about the mechanism of bifurcation can robustify predictions of an oncoming tipping event. These modes of analysis are first demonstrated on a simple fluid flow system undergoing a Hopf bifurcation. The same approach is then applied to data associated with the West African monsoon shift, with results corroborated by existing models of the same system. This example highlights the effectiveness of the methodology even when applied to complex climate data, and demonstrates how a well-resolved spatial structure associated with the onset of atmospheric instability can be inferred purely from time series measurements.

Effect of homophily on coupled behavior-disease dynamics near a tipping point.

Understanding the interplay between social activities and disease dynamics is crucial for effective public health interventions. Recent studies using coupled behavior-disease models assumed homogeneous populations. However, heterogeneity in population, such as different social groups, cannot be ignored. In this study, we divided the population into social media users and non-users, and investigated the impact of homophily (the tendency for individuals to associate with others similar to themselves) and online events on disease dynamics. Our results reveal that homophily hinders the adoption of vaccinating strategies, hastening the approach to a tipping point after which the population converges to an endemic equilibrium with no vaccine uptake. Furthermore, we find that online events can significantly influence disease dynamics, with early discussions on social media platforms serving as an early warning signal of potential disease outbreaks. Our model provides insights into the mechanisms underlying these phenomena and underscores the importance of considering homophily in disease modeling and public health strategies.

Predicting Covid-19 pandemic waves with biologically and behaviorally informed universal differential equations.

During the COVID-19 pandemic, it became clear that pandemic waves and population responses were locked in a mutual feedback loop in a classic example of a coupled behavior-disease system. We demonstrate for the first time that universal differential equation (UDE) models are able to extract this interplay from data. We develop a UDE model for COVID-19 and test its ability to make predictions of second pandemic waves. We find that UDEs are capable of learning coupled behavior-disease dynamics and predicting second waves in a variety of populations, provided they are supplied with learning biases describing simple assumptions about disease transmission and population response. Though not yet suitable for deployment as a policy-guiding tool, our results demonstrate potential benefits, drawbacks, and useful techniques when applying universal differential equations to coupled systems.

Health and equity implications of individual adaptation to air pollution in a changing climate.

Future climate change can cause more days with poor air quality. This could trigger more alerts telling people to stay inside to protect themselves, with potential consequences for health and health equity. Here, we study the change in US air quality alerts over this century due to fine particulate matter (PM2.5), who they may affect, and how they may respond. We find air quality alerts increase by over 1 mo per year in the eastern United States by 2100 and quadruple on average. They predominantly affect areas with high Black populations and leakier homes, exacerbating existing inequalities and impacting those less able to adapt. Reducing emissions can offer significant annual health benefits ($5,400 per person) by mitigating the effect of climate change on air pollution and its associated risks of early death. Relying on people to adapt, instead, would require them to stay inside, with doors and windows closed, for an extra 142 d per year, at an average cost of $11,000 per person. It appears likelier, however, that people will achieve minimal protection without policy to increase adaptation rates. Boosting adaptation can offer net benefits, even alongside deep emission cuts. New adaptation policies could, for example: reduce adaptation costs; reduce infiltration and improve indoor air quality; increase awareness of alerts and adaptation; and provide measures for those working or living outdoors. Reducing emissions, conversely, lowers everyone's need to adapt, and protects those who cannot adapt. Equitably protecting human health from air pollution under climate change requires both mitigation and adaptation.

Predicting discrete-time bifurcations with deep learning.

Many natural and man-made systems are prone to critical transitions-abrupt and potentially devastating changes in dynamics. Deep learning classifiers can provide an early warning signal for critical transitions by learning generic features of bifurcations from large simulated training data sets. So far, classifiers have only been trained to predict continuous-time bifurcations, ignoring rich dynamics unique to discrete-time bifurcations. Here, we train a deep learning classifier to provide an early warning signal for the five local discrete-time bifurcations of codimension-one. We test the classifier on simulation data from discrete-time models used in physiology, economics and ecology, as well as experimental data of spontaneously beating chick-heart aggregates that undergo a period-doubling bifurcation. The classifier shows higher sensitivity and specificity than commonly used early warning signals under a wide range of noise intensities and rates of approach to the bifurcation. It also predicts the correct bifurcation in most cases, with particularly high accuracy for the period-doubling, Neimark-Sacker and fold bifurcations. Deep learning as a tool for bifurcation prediction is still in its nascence and has the potential to transform the way we monitor systems for critical transitions.

Universal early warning signals of phase transitions in climate systems.

The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and often irreversible shift is well established, but prediction of these events using standard forecast modelling techniques is quite difficult. This has led to the development of an alternative suite of methods that seek to identify signatures of critical phenomena in data, which are expected to occur in advance of many classes of dynamical bifurcation. Crucially, the manifestations of these critical phenomena are generic across a variety of systems, meaning that data-intensive deep learning methods can be trained on (abundant) synthetic data and plausibly prove effective when transferred to (more limited) empirical datasets. This paper provides a proof of concept for this approach as applied to lattice phase transitions: a deep neural network trained exclusively on two-dimensional Ising model phase transitions is tested on a number of real and simulated climate systems with considerable success. Its accuracy frequently surpasses that of conventional statistical indicators, with performance shown to be consistently improved by the inclusion of spatial indicators. Tools such as this may offer valuable insight into climate tipping events, as remote sensing measurements provide increasingly abundant data on complex geospatially resolved Earth systems.

Modelling coupled human-environment complexity for the future of the biosphere: strengths, gaps and promising directions.

Humans and the environment form a single complex system where humans not only influence ecosystems but also react to them. Despite this, there are far fewer coupled human-environment system (CHES) mathematical models than models of uncoupled ecosystems. We argue that these coupled models are essential to understand the impacts of social interventions and their potential to avoid catastrophic environmental events and support sustainable trajectories on multi-decadal timescales. A brief history of CHES modelling is presented, followed by a review spanning recent CHES models of systems including forests and land use, coral reefs and fishing and climate change mitigation. The ability of CHES modelling to capture dynamic two-way feedback confers advantages, such as the ability to represent ecosystem dynamics more realistically at longer timescales, and allowing insights that cannot be generated using ecological models. We discuss examples of such key insights from recent research. However, this strength brings with it challenges of model complexity and tractability, and the need for appropriate data to parameterize and validate CHES models. Finally, we suggest opportunities for CHES models to improve human-environment sustainability in future research spanning topics such as natural disturbances, social structure, social media data, model discovery and early warning signals. This article is part of the theme issue 'Ecological complexity and the biosphere: the next 30 years'.

Interventions to mitigate COVID-19 misinformation: protocol for a scoping review.

BACKGROUND: The duration and impact of the COVID-19 pandemic depends in a large part on individual and societal actions which is influenced by the quality and salience of the information to which they are exposed. Unfortunately, COVID-19 misinformation has proliferated. To date, no systematic efforts have been made to evaluate interventions that mitigate COVID-19-related misinformation. We plan to conduct a scoping review that seeks to fill several of the gaps in the current knowledge of interventions that mitigate COVID-19-related misinformation. METHODS: A scoping review focusing on interventions that mitigate COVID-19 misinformation will be conducted. We will search (from January 2020 onwards) MEDLINE, EMBASE, CINAHL, PsycINFO, Web of Science Core Collection, Africa-Wide Information, Global Health, WHO Global Literature on Coronavirus Disease Database, WHO Global Index Medicus, and Sociological Abstracts. Gray literature will be identified using Disaster Lit, Google Scholar, Open Science Framework, governmental websites, and preprint servers (e.g., EuropePMC, PsyArXiv, MedRxiv, JMIR Preprints). Study selection will conform to Joanna Briggs Institute Reviewers' Manual 2020 Methodology for JBI Scoping Reviews. Only English language, original studies will be considered for inclusion. Two reviewers will independently screen all citations, full-text articles, and abstract data. A narrative summary of findings will be conducted. Data analysis will involve quantitative (e.g., frequencies) and qualitative (e.g., content and thematic analysis) methods. DISCUSSION: Original research is urgently needed to design interventions to mitigate COVID-19 misinformation. The planned scoping review will help to address this gap. SYSTEMATIC REVIEW REGISTRATIONS: Systematic Review Registration: Open Science Framework (osf/io/etw9d).

Estimating COVID-19 cases and deaths prevented by non-pharmaceutical interventions, and the impact of individual actions: A retrospective model-based analysis.

Simulation models from the early COVID-19 pandemic highlighted the urgency of applying non-pharmaceutical interventions (NPIs), but had limited empirical data. Here we use data from 2020-2021 to retrospectively model the impact of NPIs in Ontario, Canada. Our model represents age groups and census divisions in Ontario, and is parameterized with epidemiological, testing, demographic, travel, and mobility data. The model captures how individuals adopt NPIs in response to reported cases. We compare a scenario representing NPIs introduced within Ontario (closures of workplaces/schools, reopening of schools/workplaces with NPIs in place, individual-level NPI adherence) to counterfactual scenarios wherein alternative strategies (e.g. no closures, reliance on individual NPI adherence) are adopted to ascertain the extent to which NPIs reduced cases and deaths. Combined school/workplace closure and individual NPI adoption reduced the number of deaths in the best-case scenario for the case fatality rate (CFR) from 178548 [CI: 171845, 185298] to 3190 [CI: 3095, 3290] in the Spring 2020 wave. In the Fall 2020/Winter 2021 wave, the introduction of NPIs in workplaces/schools reduced the number of deaths from 20183 [CI: 19296, 21057] to 4102 [CI: 4075, 4131]. Deaths were several times higher in the worst-case CFR scenario. Each additional 9-16 (resp. 285-578) individuals who adopted NPIs in the first wave prevented one additional infection (resp., death). Our results show that the adoption of NPIs prevented a public health catastrophe. A less comprehensive approach, employing only closures or individual-level NPI adherence, would have resulted in a large number of cases and deaths.

COVID-19 vaccine perceptions in the initial phases of US vaccine roll-out: an observational study on reddit.

BACKGROUND: Open online forums like Reddit provide an opportunity to quantitatively examine COVID-19 vaccine perceptions early in the vaccine timeline. We examine COVID-19 misinformation on Reddit following vaccine scientific announcements, in the initial phases of the vaccine timeline. METHODS: We collected all posts on Reddit (reddit.com) from January 1 2020 - December 14 2020 (n=266,840) that contained both COVID-19 and vaccine-related keywords. We used topic modeling to understand changes in word prevalence within topics after the release of vaccine trial data. Social network analysis was also conducted to determine the relationship between Reddit communities (subreddits) that shared COVID-19 vaccine posts, and the movement of posts between subreddits. RESULTS: There was an association between a Pfizer press release reporting 90% efficacy and increased discussion on vaccine misinformation. We observed an association between Johnson and Johnson temporarily halting its vaccine trials and reduced misinformation. We found that information skeptical of vaccination was first posted in a subreddit (r/Coronavirus) which favored accurate information and then reposted in subreddits associated with antivaccine beliefs and conspiracy theories (e.g. conspiracy, NoNewNormal). CONCLUSIONS: Our findings can inform the development of interventions where individuals determine the accuracy of vaccine information, and communications campaigns to improve COVID-19 vaccine perceptions, early in the vaccine timeline. Such efforts can increase individual- and population-level awareness of accurate and scientifically sound information regarding vaccines and thereby improve attitudes about vaccines, especially in the early phases of vaccine roll-out. Further research is needed to understand how social media can contribute to COVID-19 vaccination services.

Stochasticity-induced persistence in coupled social-ecological systems.

Stochasticity is often associated with negative consequences for population dynamics since a population may die out due to random chance during periods when population size is very low (stochastic fade-out). Here we develop a coupled social-ecological model based on stochastic differential equations that includes natural expansion and harvesting of a forest ecosystem, and dynamics of conservation opinions, social norms and social learning in a human population. Our objective was to identify mechanisms that influence long-term persistence of the forest ecosystem in the presence of noise. We found that most of the model parameters had a significant influence on the time to extinction of the forest ecosystem. Increasing the social learning rate and the net benefits of conservation significantly increased the time to extinction, for instance. Most interestingly, we found a parameter regime where an increase in the amount of system stochasticity caused an increase in the mean time to extinction, instead of causing stochastic fade-out. This effect occurs for a subset of realizations, but the effect is large enough to increase the mean time to extinction across all realizations. Such "stochasticity-induced persistence" occurs when stochastic dynamics in the social system generates benefits in the forest system at crucial points in its temporal dynamics. We conclude that studying relatively simple social-ecological models has the benefit of facilitating characterization of dynamical states and thereby enabling us to formulate new hypothesis about mechanisms that could be operating in empirical social-ecological systems.

Local Overfishing Patterns Have Regional Effects on Health of Coral, and Economic Transitions Can Promote Its Recovery.

Overfishing has the potential to severely disrupt coral reef ecosystems worldwide, while harvesting at more sustainable levels instead can boost fish yield without damaging reefs. The dispersal abilities of reef species mean that coral reefs form highly connected environments, and the viability of reef fish populations depends on spatially explicit processes such as the spillover effect and unauthorized harvesting inside marine protected areas. However, much of the literature on coral conservation and management has only examined overfishing on a local scale, without considering how different spatial patterns of fishing levels can affect reef health both locally and regionally. Here, we simulate a coupled human-environment model to determine how coral and herbivorous reef fish respond to overfishing across multiple spatial scales. We find that coral and reef fish react in opposite ways to habitat fragmentation driven by overfishing, and that a potential spillover effect from marine protected areas into overfished patches helps coral populations far less than it does reef fish. We also show that ongoing economic transitions from fishing to tourism have the potential to revive fish and coral populations over a relatively short timescale, and that large-scale reef recovery is possible even if these transitions only occur locally. Our results show the importance of considering spatial dynamics in marine conservation efforts and demonstrate the ability of economic factors to cause regime shifts in human-environment systems.

Correction: Go big or go home: A model-based assessment of general strategies to slow the spread of forest pests via infested firewood.

[This corrects the article DOI: 10.1371/journal.pone.0238979.].

Deep learning for early warning signals of tipping points.

Many natural systems exhibit tipping points where slowly changing environmental conditions spark a sudden shift to a new and sometimes very different state. As the tipping point is approached, the dynamics of complex and varied systems simplify down to a limited number of possible "normal forms" that determine qualitative aspects of the new state that lies beyond the tipping point, such as whether it will oscillate or be stable. In several of those forms, indicators like increasing lag-1 autocorrelation and variance provide generic early warning signals (EWS) of the tipping point by detecting how dynamics slow down near the transition. But they do not predict the nature of the new state. Here we develop a deep learning algorithm that provides EWS in systems it was not explicitly trained on, by exploiting information about normal forms and scaling behavior of dynamics near tipping points that are common to many dynamical systems. The algorithm provides EWS in 268 empirical and model time series from ecology, thermoacoustics, climatology, and epidemiology with much greater sensitivity and specificity than generic EWS. It can also predict the normal form that characterizes the oncoming tipping point, thus providing qualitative information on certain aspects of the new state. Such approaches can help humans better prepare for, or avoid, undesirable state transitions. The algorithm also illustrates how a universe of possible models can be mined to recognize naturally occurring tipping points.

When conflicts get heated, so does the planet: coupled social-climate dynamics under inequality.

Climate dynamics are inextricably linked to processes in social systems that are highly unequal. This suggests a need for coupled social-climate models that capture pervasive real-world asymmetries in the population distribution of the consequences of anthropogenic climate change and climate (in)action. Here, we use evolutionary game theory to develop a social-climate model with group structure to investigate how anthropogenic climate change and population heterogeneity coevolve. We find that greater homophily and resource inequality cause an increase in the global peak temperature anomaly by as much as 0.7°C. Also, climate change can structure human populations by driving opinion polarization. Finally, climate mitigation achieved by reducing the cost of mitigation measures paid by individuals tends to be contingent upon socio-economic conditions, whereas policies that achieve communication between different strata of society show climate mitigation benefits across a broad socio-economic regime. We conclude that advancing climate change mitigation efforts can benefit from a social-climate systems perspective.

"Hot-spotting" to improve vaccine allocation by harnessing digital contact tracing technology: An application of percolation theory.

Vaccinating individuals with more exposure to others can be disproportionately effective, in theory, but identifying these individuals is difficult and has long prevented implementation of such strategies. Here, we propose how the technology underlying digital contact tracing could be harnessed to boost vaccine coverage among these individuals. In order to assess the impact of this "hot-spotting" proposal we model the spread of disease using percolation theory, a collection of analytical techniques from statistical physics. Furthermore, we introduce a novel measure which we call the efficiency, defined as the percentage decrease in the reproduction number per percentage of the population vaccinated. We find that optimal implementations of the proposal can achieve herd immunity with as little as half as many vaccine doses as a non-targeted strategy, and is attractive even for relatively low rates of app usage.

Targeted pandemic containment through identifying local contact network bottlenecks.

Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts-between individuals or between population centres-are increasingly used for these purposes. Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another. In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods.

Network structural metrics as early warning signals of widespread vaccine refusal in social-epidemiological networks.

Sudden shifts in vaccine uptake, vaccine opinion, and infection incidence can occur in coupled behaviour-disease systems going through a bifurcation as the perceived risk of the vaccine increases. Literature shows that such regime shifts are sometimes foreshadowed by early warning signals (EWS). We propose and compare the performance of various measures of network structure as potential EWS indicators of epidemics and changes in population vaccine opinion. We construct a multiplex model coupling transmission of a vaccine-preventable childhood infectious disease and social dynamics concerning vaccine opinion. We find that the modularity of pro- and anti-vaccine network communities perform well as EWS, as do several measures of the number and size of opinion-based communities, and the size of pro-vaccine echo chambers. The number of opinion changes also gives early warnings, although the clustering coefficient and metrics concerning anti-vaccine echo chambers provide little warning. Stronger social norms are found to compromise the ability of all EWS metrics to provide advance warning. These exploratory results suggest that EWS indicators based on the network structure of online social media communities might assist public health preparedness by providing early warning of potential regime shifts.

Projected impact of a plant-derived vaccine on the burden of seasonal influenza in Canada.

OBJECTIVE: The analysis estimates projected population outcomes resulting from the introduction of a plant-derived influenza vaccine formulated as quadrivalent virus-like particles (QVLP) in Canada. METHODS: Using Monte Carlo simulations, the number of influenza cases, general practitioner visits, inpatient admissions, intensive care unit (ICU) admissions, and deaths due to influenza-associated illness were estimated under no vaccination, plant-derived QVLP vaccines only, or egg-derived vaccines only. The base case analysis examined the adult Canadian population in two subgroups: 18-64 years of age during the 2017/18 season and 65+ years of age during the 2018/19 season. Efficacy data were obtained from QVLP clinical trials. Vaccine effectiveness data for egg-derived vaccines were calculated from observational studies from the corresponding influenza seasons. Scenario analyses examined the impact of varying absolute vaccine effectiveness or vaccination coverage from base case inputs. RESULTS: In the base case analysis, plant-derived QVLP vaccines led to an additional reduction in the burden of influenza over egg-derived vaccines for both population subgroups. In the 18-64 subgroup, QVLP vaccines were associated with 2.63% (48,029; 95% credible interval [Crl]: 42,723-53,336) fewer influenza cases than egg-derived vaccines. In the 65+ subgroup, QVLP vaccines led to 4.82% (27,918; 95% Crl: 25,440-30,397) fewer influenza cases, and reductions in the number of inpatient admissions by 4.77% (1167; 95% CrI: 851-1483) and deaths by 4.75% (326; 95% CrI: 107-546) compared to egg-derived vaccines. Further reductions were observed in scenario analyses considering the potential increase in vaccine coverage. CONCLUSION: Use of plant-derived QVLP influenza vaccines may contribute to greater reductions in influenza cases and influenza-related outcomes, including inpatient admissions and deaths, compared to egg-derived vaccines currently available in Canada.