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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.

The duration and impact of the COVID-19 pandemic depends largely on individual and societal actions which are influenced by the quality and salience of the information to which they are exposed. Unfortunately, COVID-19 misinformation has proliferated. Despite growing attempts to mitigate COVID-19 misinformation, there is still uncertainty regarding the best way to ameliorate the impact of COVID-19 misinformation. To address this gap, the current study uses a meta-analysis to evaluate the relative impact of interventions designed to mitigate COVID-19-related misinformation. We searched multiple databases and gray literature from January 2020 to September 2021. The primary outcome was COVID-19 misinformation belief. We examined study quality and meta-analysis was used to pool data with similar interventions and outcomes. 16 studies were analyzed in the meta-analysis, including data from 33378 individuals. The mean effect size of interventions to mitigate COVID-19 misinformation was positive, but not statistically significant [d = 2.018, 95% CI (-0.14, 4.18)

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.

The continued growth of the world’s population and increased interconnectivity heighten the risk that infectious diseases pose for human health worldwide. Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. Throughout, we discuss the importance of designing a model that is appropriate to the research question and the available data. We highlight pitfalls that can arise in model development, validation and interpretation. Close collaboration between empiricists and modellers continues to improve the accuracy of predictions and the optimization of models for public health decision-making.

Natural Selection is frequently modelled via proportional selection where survival is proportional to the average payoff differential. There has been little attention devoted to modelling truncation selection where replicators below a threshold are culled and survivors reproduce. Here, we systematically explore truncation selection for two strategy games in a spatial setting. We employ two variations of truncation selection: independent, where the threshold is fixed; and dependent, where the proportion culled is fixed. Further, we explore the effects of diffusion with the algorithms: contest-diffusion-offspring (CDO), and diffusion-contest-offspring (DCO). CDO and DCO frequently facilitate and diminish cooperation, respectively. For independent truncation, there are three qualitative regimes determined by the payoff threshold: cooperation decreases as the threshold rises; polymorphisms are stable; and extinction is frequent. Further, an intermediate payoff to cooperators playing defectors can maximize cooperation for the DCO algorithm with a high payoff threshold. Dependent truncation affects games differently; lower levels reduce cooperation for the Hawk Dove game and increase it for the Stag Hunt, and higher levels produce the opposite effects. Comparing these truncation methods to proportional selection, we show how they impact the prevalence of cooperation.

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.

Tuberculosis (TB) is among the 10 top causes of deaths worldwide, and one-quarter of the world population hosts latent TB pathogens. Therefore, avoiding the emergence of drug-resistant strains has become a central issue in TB control. In this work, we propose a nested model for TB transmission and control, wherein both within-host and between-host dynamics are modeled. We use the model to compare the effects of three types of antibiotic treatment protocols and combinations thereof in an in silico population. For a fixed value of antibiotics clearance rate and relative efficacy against resistant strains, the oscillating intermittent protocol, pure or combined, is the most effective against the sensitive strains. However, this protocol also creates a selective advantage for the resistant strains, returning the worst result in comparison to the other protocols. We suggest that nested models should be further developed, since they might be able to inform decision-makers regarding the optimal TB control protocols to be applied under the specific parameters and other epidemiological factors in different populations.

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.

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., Comment: 38 pages, 21 figures

Given trade’s importance to maintaining food security, it is crucial to understand the relationship between human population growth, land use, food supply, and trade. We develop a metapopulation model coupling human population dynamics to agricultural land use and food production in “patches” (regions and countries) connected via trade networks. Patches that import sparingly or fail to adjust their demand sharply in response to changes in food per capita experience food insecurity. They fall into a feedback loop between increasing population growth and decreasing food per capita, particularly if they are peripheral to the network. A displacement effect is also evident; patches that are more central and/or import more heavily preserve their natural land states. Their reliance on imports means other patches must expand their agricultural land. These results emphasize that strategies for improving food security and equality must account for the combined effects of network topology and patch-level characteristics.

The critical community size (CCS) is the minimum closed population size in which a pathogen can persist indefinitely. Below this threshold, stochastic extinction eventually causes pathogen extinction. Here we use a simulation model to explore behaviour-mediated persistence: a novel mechanism by which the population response to the pathogen determines the CCS. We model severe coronavirus 2 (SARS-CoV-2) transmission and non-pharmaceutical interventions (NPIs) in a population where both individuals and government authorities restrict transmission more strongly when SARS-CoV-2 case numbers are higher. This results in a coupled human-environment feedback between disease dynamics and population behaviour. In a parameter regime corresponding to a moderate population response, this feedback allows SARS-CoV-2 to avoid extinction in the trough of pandemic waves. The result is a very low CCS that allows long term pathogen persistence. Hence, an incomplete pandemic response represents a “sour spot” that not only ensures relatively high case incidence and unnecessarily long lockdown, but also promotes long-term persistence of the pathogen by reducing the CCS. Given the worldwide prevalence of small, isolated populations in which a pathogen with low CCS can persist, these results emphasize the need for a global approach to coronavirus disease 2019 (COVID-19) vaccination.

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. Grey 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).

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).

ObjectivesAs COVID-19 vaccinations accelerate in many countries, narratives skeptical of vaccination have also spread through social media. Open online forums like Reddit provide an opportunity to quantitatively examine COVID-19 vaccine perceptions over time. We examine COVID-19 misinformation on Reddit following vaccine scientific announcements.MethodsWe collected all posts on Reddit 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.ResultsThere 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, LockdownSkepticism).ConclusionsOur findings can inform the development of interventions where individuals determine the accuracy of vaccine information, and communications campaigns to improve COVID-19 vaccine perceptions. Such efforts can increase individual- and population-level awareness of accurate and scientifically sound information regarding vaccines and thereby improve attitudes about vaccines. Further research is needed to understand how social media can contribute to COVID-19 vaccination services.FundingStudy was funded by the Yale Institute for Global Health and the Whitney and Betty MacMillan Center for International and Area Studies at Yale University. The funding bodies had no role in the design, analysis or interpretation of the data in the study.

Many natural systems exhibit regime shifts where slowly changing environmental conditions suddenly shift the system to a new and sometimes very different state. As the tipping point is approached, the dynamics of complex and varied systems all simplify down to a small number of possible ‘normal forms’ that determine how the new regime will look. Indicators such as increasing lag-1 autocorrelation and variance provide generic early warning signals (EWS) by detecting how dynamics slow down near the tipping point. But they do not indicate what type of new regime will emerge. Here we develop a deep learning algorithm that can detect EWS in systems it was not explicitly trained on, by exploiting information about normal forms and scaling behaviour of dynamics near tipping points that are common to many dynamical systems. The algorithm detects 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 will characterize the oncoming regime shift. Such approaches can help humans better manage regime shifts. The algorithm also illustrates how a universe of possible models can be mined to recognize naturally-occurring tipping points.

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 parameterised 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.

Summary Background During the COVID-19 pandemic, authorities must decide which groups to prioritise for vaccination in a shifting social–epidemiological landscape in which the success of large-scale non-pharmaceutical interventions requires broad social acceptance. We aimed to compare projected COVID-19 mortality under four different strategies for the prioritisation of SARS-CoV-2 vaccines. Methods We developed a coupled social–epidemiological model of SARS-CoV-2 transmission in which social and epidemiological dynamics interact with one another. We modelled how population adherence to non-pharmaceutical interventions responds to case incidence. In the model, schools and workplaces are also closed and reopened on the basis of reported cases. The model was parameterised with data on COVID-19 cases and mortality, SARS-CoV-2 seroprevalence, population mobility, and demography from Ontario, Canada (population 14·5 million). Disease progression parameters came from the SARS-CoV-2 epidemiological literature. We assumed a vaccine with 75% efficacy against disease and transmissibility. We compared vaccinating those aged 60 years and older first (oldest-first strategy), vaccinating those younger than 20 years first (youngest-first strategy), vaccinating uniformly by age (uniform strategy), and a novel contact-based strategy. The latter three strategies interrupt transmission, whereas the first targets a vulnerable group to reduce disease. Vaccination rates ranged from 0·5% to 5% of the population per week, beginning on either Jan 1 or Sept 1, 2021. Findings Case notifications, non-pharmaceutical intervention adherence, and lockdown undergo successive waves that interact with the timing of the vaccine programme to determine the relative effectiveness of the four strategies. Transmission-interrupting strategies become relatively more effective with time as herd immunity builds. The model predicts that, in the absence of vaccination, 72 000 deaths (95% credible interval 40 000–122 000) would occur in Ontario from Jan 1, 2021, to March 14, 2025, and at a vaccination rate of 1·5% of the population per week, the oldest-first strategy would reduce COVID-19 mortality by 90·8% on average (followed by 89·5% in the uniform, 88·9% in the contact-based, and 88·2% in the youngest-first strategies). 60 000 deaths (31 000–108 000) would occur from Sept 1, 2021, to March 14, 2025, in the absence of vaccination, and the contact-based strategy would reduce COVID-19 mortality by 92·6% on average (followed by 92·1% in the uniform, 91·0% in the oldest-first, and 88·3% in the youngest-first strategies) at a vaccination rate of 1·5% of the population per week. Interpretation The most effective vaccination strategy for reducing mortality due to COVID-19 depends on the time course of the pandemic in the population. For later vaccination start dates, use of SARS-CoV-2 vaccines to interrupt transmission might prevent more deaths than prioritising vulnerable age groups. Funding Ontario Ministry of Colleges and Universities.

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.

The results of COVID-19 vaccine clinical trials suggest that an end to the pandemic is within reach. However, public health authorities world-wide are faced with the difficult task of prioritizing their allocation [17, 10, 2]. Theory indicates that prioritizing vaccination of individuals with more contacts can be disproportionately effective [25, 8, 5, 3]. However, implementation of such strategies has been hampered by inability to determine population contact structure. One of the novel tools introduced during the pandemic has been the use of Bluetooth technology to assist in contact tracing and exposure notification [6, 13, 12]. Here we show that the technology underlying these Bluetooth exposure notification applications can be leveraged to efficiently prioritise vaccine allocation. Our approach is based on the insight that these apps also act as local sensing devices measuring each user’s total exposure time to other app users, thereby enabling the implementation of a previously impossible vaccine strategy that prioritises potential super-spreaders based on total exposure time. To compare vaccination strategies we introduce a novel and widely generalizable measure of vaccination efficiency. By extending percolation theory we furthermore demonstrate that our proposed “hot-spotting” strategy can achieve herd immunity with less than half as many vaccines as distributing the vaccines uniformly in the population.

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.

After vaccinating health care workers and vulnerable groups against COVID-19, authorities will need to decide how to vaccinate everyone else. Prioritising individuals with more contacts can be disproportionately effective, in theory, but identifying these individuals is difficult. Here we show that the technology underlying Bluetooth exposure notification applications, such as used for digital contact tracing, can be leveraged to prioritise vaccination based on individual contact data. Our approach is based on the insight that these apps also act as local sensing devices measuring each user’s total exposure time to other users, thereby enabling the implementation of a previously impossible strategy that prioritises potential super-spreaders. Furthermore, by generalising percolation theory and introducing a novel measure of vaccination efficiency, we demonstrate that this “hot-spotting” strategy can achieve herd immunity with up to half as many vaccines as a non-targeted strategy, and is attractive even for relatively low rates of app usage.

Vaccines and climate change have much in common. In both cases, a scientific consensus contrasts with a divided public opinion. They also exemplify coupled human-environment systems involving common pool resources. Here we used machine learning algorithms to analyze the sentiment of 87 million tweets on climate change and vaccines in order to characterize Twitter user sentiment and the structure of user and community networks. We found that the vaccine conversation was characterized by much less interaction between individuals with differing sentiment toward vaccines. Community-level interactions followed this pattern, showing less interaction between communities of opposite sentiment toward vaccines. Additionally, vaccine community networks were more fragmented and exhibited numerous isolated communities of neutral sentiment. Finally, pro-vaccine individuals overwhelmingly believed in anthropogenic climate change, but the converse was not true. We propose mechanisms that might explain these results, pertaining to how the spatial scale of an environment system can structure human populations.

Biogeochemistry has an important role to play in many environmental issues of current concern related to global change and air, water, and soil quality. However, reliable predictions and tangible take-up of solutions offered by biogeochemistry will need further integration of disciplines. Here, we emphasize how further developing ties between biology, geology, and chemistry and social sciences will advance biogeochemistry through: 1) better integration of mechanisms including contemporary evolutionary adaptation to predict changing biogeochemical cycles; 2) better integration of data from long-term monitoring sites in terrestrial, aquatic, and human systems across temporal and spatial scales, including the continental and global scale, for use in modeling efforts; and 3) implementing insights from social sciences to better understand how sustainable and equitable responses by society are achieved. The challenges of 21st century biogeochemists are formidable, and will require both the capacity to respond fast to pressing issues and intense collaboration with government officials, the public, and internationally-funded programs. Keys to its success will be the degree to which biogeochemistry succeeds in making biogeochemical knowledge more available to policy makers and educators, in predicting future changes in the biosphere in response to climate change and other anthropogenic impacts on time scales from seasons to centuries, and in facilitating sustainable and equitable responses by society.

Sudden shifts in population health and vaccination rates occur as the dynamics of some epidemiological models go through a critical point; literature shows that this is sometimes foreshadowed by early warning signals (EWS). We investigate different structural measures of a network as candidate EWS of infectious disease outbreaks and changes in popular vaccine sentiment. We construct a multiplex disease model coupling infectious disease spread and social contact dynamics. We find that the number and mean size of echo chambers predict transitions in the infection dynamics, as do opinion-based communities. Graph modularity also gives early warnings, though the clustering coefficient shows no significant pre-outbreak changes. Change point tests applied to the EWS show decreasing efficacy as social norms strengthen. Therefore, many measures of social network connectivity can predict approaching critical changes in vaccine uptake and aggregate health, thereby providing valuable tools for improving public health.

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.

SummaryBackgroundDuring the COVID-19 pandemic, authorities must decide which groups to prioritise for vaccination. These decision will occur in a constantly shifting social-epidemiological landscape where the success of large-scale non-pharmaceutical interventions (NPIs) like physical distancing requires broad population acceptance.MethodsWe developed a coupled social-epidemiological model of SARS-CoV-2 transmission. Schools and workplaces are closed and re-opened based on reported cases. We used evolutionary game theory and mobility data to model individual adherence to NPIs. We explored the impact of vaccinating 60+ year-olds first; FindingsCase notifications, NPI adherence, and lockdown periods undergo successive waves during the simulated pandemic. Vaccination reduces median deaths by 32% – 77% (22% – 63%) for January (July) availability, depending on the scenario. Vaccinating 60+ year-olds first prevents more deaths (up to 8% more) than transmission-interrupting strategies for January vaccine availability across most parameter regimes. In contrast, transmission-interrupting strategies prevent up to 33% more deaths than vaccinating 60+ year-olds first for July availability, due to higher levels of natural immunity by that time. Sensitivity analysis supports the findings.InterpretationFurther research is urgently needed to determine which populations can benefit from using SARS-CoV-2 vaccines to interrupt transmission.FundingOntario Ministry of Colleges and Universities.Research in contextEvidence before this studyWhether to vaccinate individuals who cause the most transmission or those who are at highest risk of death is relevant to prioritizing COVID-19 vaccination. We searched PubMed and medRxiv for the terms COVID19, vaccin*, model, and priorit* up to September 24, 2020, with no date or language restrictions. We identified 4 papers on mathematical models of COVID-19 vaccine prioritization that explored the conditions under which different age groups should be vaccinated first. We did not find any coupled social-epidemiological models that capture feedback between social dynamics and epidemic trajectories.Added value of this studyThe dynamic interaction between SARS-CoV-2 epidemics and the population response through scalable non-pharmaceutical interventions will continue to play a large role in the course of the pandemic, both before and after vaccines become available. Hence, social-epidemiological models may be useful. Our social-epidemiological model identifies the conditions under which COVID-19 deaths can be reduced most effectively by prioritizing older individuals first, versus other strategies designed to interrupt transmission. We explore how the best vaccination strategy varies depending on a wide range of socio-epidemiological and vaccine program parameters. We identify clear and interpretable conditions under which using COVID-19 vaccines to interrupt transmission can reduce mortality most effectively.Implications of all the available evidenceSeroprevalence surveys before the onset of vaccination could measure population-level SARS-CoV-2 immunity. In populations where seropositivity is high due to previous waves, vaccinating to interrupt transmission may reduce deaths more effectively than targeting older individuals. More research is urgently required to evaluate how to prioritise vaccination in populations that have experienced one or more waves of COVID-19.

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 develop a simple social-climate model with group structure to investigate how anthropogenic climate change and population heterogeneity co-evolve. 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.

Significance During the COVID-19 pandemic, decision makers are grappling with how to reopen (and possibly reclose) their jurisdictions as the number of cases ebbs and flows. Establishing a criterion for each county/municipality to open and close based on their case count has appeal, given the wide disparity in COVID-19 rates in urban versus rural settings. Our simulation model is based on the geography, epidemiology, and travel patterns of Ontario, Canada. It shows that the county-by-county approach causes fewer days of closure and impacts fewer people than a strategy that opens or closes the entire province together. This is true even if individuals begin traveling to reopened counties with higher frequency. The county-by-county strategy is most effective when the criteria are coordinated., In the late stages of an epidemic, infections are often sporadic and geographically distributed. Spatially structured stochastic models can capture these important features of disease dynamics, thereby allowing a broader exploration of interventions. Here we develop a stochastic model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission among an interconnected group of population centers representing counties, municipalities, and districts (collectively, “counties”). The model is parameterized with demographic, epidemiological, testing, and travel data from Ontario, Canada. We explore the effects of different control strategies after the epidemic curve has been flattened. We compare a local strategy of reopening (and reclosing, as needed) schools and workplaces county by county, according to triggers for county-specific infection prevalence, to a global strategy of province-wide reopening and reclosing, according to triggers for province-wide infection prevalence. For trigger levels that result in the same number of COVID-19 cases between the two strategies, the local strategy causes significantly fewer person-days of closure, even under high intercounty travel scenarios. However, both cases and person-days lost to closure rise when county triggers are not coordinated and when testing rates vary among counties. Finally, we show that local strategies can also do better in the early epidemic stage, but only if testing rates are high and the trigger prevalence is low. Our results suggest that pandemic planning for the far side of the COVID-19 epidemic curve should consider local strategies for reopening and reclosing.

The disruption of professional childcare has emerged as a substantial collateral consequence of the public health precautions related to COVID-19. Increasingly, it is becoming clear that childcare centers must be (at least partially) operational in order to further mitigate the socially debilitating challenges related to pandemic induced closures. However, proposals to safely reopen childcare while limiting COVID-19 outbreaks remain understudied, and there is a pressing need for evidence-based scrutiny of the plans that are being proposed. Thus, in order to support safe childcare reopening procedures, the present study employed an agent-based modeling approach to generate predictions surrounding risk of COVID-19 infection and student-days lost within a hypothetical childcare center hosting 50 children and educators. Based on existing proposals for childcare and school reopening in Ontario, Canada, six distinct room configurations were evaluated that varied in terms of child-to-educator ratio (15:2, 8:2, 7:3), and family clustering (siblings together vs. random assignment). The results for the 15:2 random assignment configuration are relevant to early childhood education in Ontario primary schools, which require two educators per classroom. High versus low transmission rates were also contrasted, keeping with the putative benefit of infection control measures within centers, yielding a total of 12 distinct scenarios. Simulations revealed that the 7:3 siblings together configuration demonstrated the lowest risk, whereas centres hosting classrooms with more children (15:2) experienced 3 to 5 times as many COVID-19 cases. Across scenarios, having less students per class and grouping siblings together almost always results in significantly lower peaks for number of active infected and infectious cases in the institution. Importantly, the total student-days lost to classroom closure were between 5 and 8 times higher in the 15:2 ratios than for 8:2 or 7:3. These results suggest that current proposals for childcare reopening could be enhanced for safety by considering lower ratios and sibling groupings.

Recent attempts at cooperating on climate change mitigation highlight the limited efficacy of large-scale negotiations, when commitment to mitigation is costly and initially rare. Deepening existing voluntary mitigation pledges could require more stringent, legally-binding agreements that currently remain untenable at the global scale. Building-blocks approaches promise greater success by localizing agreements to regions or few-nation summits, but risk slowing mitigation adoption globally. Here, we show that a well-timed policy shift from local to global legally-binding agreements can dramatically accelerate mitigation compared to using only local, only global, or both agreement types simultaneously. This highlights the scale-specific roles of mitigation incentives: local agreements promote and sustain mitigation commitments in early-adopting groups, after which global agreements rapidly draw in late-adopting groups. We conclude that focusing negotiations on local legally-binding agreements and, as these become common, a renewed pursuit of stringent, legally-binding world-wide agreements could best overcome many current challenges facing climate mitigation., Do we mitigate climate change in a Kyoto style global agreement or via multiple agreements among smaller groups of states? Here the authors show that the best strategy may begin with regional legally binding, aggressive agreements and, as these become common, renew pursuit of a global legally-binding treaty.

Significance Collective risks trigger social dilemmas that require balancing selfish interests and common good. One important example is mitigating climate change, wherein without sufficient investments, worldwide negative consequences become increasingly likely. To study the social aspects of this problem, we organized a game experiment that reveals how group size, communication, and behavioral type drive prosocial action. We find that communicating sentiment and outlook leads to more positive outcomes, even among culturally heterogeneous groups. Although genuine free riders remain unfazed by communication, prosocial players better endure accumulated investment deficits, and thus fight off inaction as the failure looms. This suggests that climate negotiations may achieve more by leveraging existing goodwill than persuading skeptics to act., Collective risks permeate society, triggering social dilemmas in which working toward a common goal is impeded by selfish interests. One such dilemma is mitigating runaway climate change. To study the social aspects of climate-change mitigation, we organized an experimental game and asked volunteer groups of three different sizes to invest toward a common mitigation goal. If investments reached a preset target, volunteers would avoid all consequences and convert their remaining capital into monetary payouts. In the opposite case, however, volunteers would lose all their capital with 50% probability. The dilemma was, therefore, whether to invest one’s own capital or wait for others to step in. We find that communicating sentiment and outlook helps to resolve the dilemma by a fundamental shift in investment patterns. Groups in which communication is allowed invest persistently and hardly ever give up, even when their current investment deficits are substantial. The improved investment patterns are robust to group size, although larger groups are harder to coordinate, as evidenced by their overall lower success frequencies. A clustering algorithm reveals three behavioral types and shows that communication reduces the abundance of the free-riding type. Climate-change mitigation, however, is achieved mainly by cooperator and altruist types stepping up and increasing contributions as the failure looms. Meanwhile, contributions from free riders remain flat throughout the game. This reveals that the mechanisms behind avoiding collective risks depend on an interaction between behavioral type, communication, and timing.

Pre-exposure prophylaxis (PrEP) has been shown to be highly effective in reducing the risk of HIV infection in gay and bisexual men who have sex with men (GbMSM). However, PrEP does not protect against other sexually transmitted infections (STIs). In some populations, PrEP has also led to riskier behaviour such as reduced condom usage, with the result that the prevalence of bacterial STIs like gonorrhea has increased. Here we develop a compartmental model of the transmission of HIV and gonorrhea, and the impacts of PrEP, condom usage, STI testing frequency and potential changes in sexual risk behaviour stemming from the introduction of PrEP in a population of GbMSM. We find that introducing PrEP causes an increase in gonorrhea prevalence for a wide range of parameter values, including at the current recommended frequency of STI testing once every 3 months for individuals on PrEP. Moreover, the model predicts that a higher STI testing frequency alone is not enough to prevent a rise in gonorrhea prevalence, unless the testing frequency is increased to impractical levels. However, testing every 2 months in combination with sufficiently high condom usage by individuals on PrEP would be successful in maintaining gonorrhea prevalence at pre-PrEP levels. The results emphasize that programs making PrEP more available should be accompanied by efforts to support condom usage and frequent STI testing, in order to avoid an increase in the prevalence of gonorrhea and other bacterial STIs.

In May 2020, many jurisdictions around the world began lifting physical distancing restrictions against the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), giving rise to concerns about a possible second wave of coronavirus disease 2019 (COVID-19). These restrictions were imposed as a collective population response to the presence of COVID-19 in communities. However, lifting restrictions is also a population response to their socio-economic impacts, and is expected to increase COVID-19 cases, in turn. This suggests that the COVID-19 pandemic exemplifies a coupled behaviour-disease system. Here we develop a minimal mathematical model of the interaction between social support for school and workplace closure and the transmission dynamics of SARS-CoV-2. We find that a second wave of COVID-19 occurs across a broad range of plausible model input parameters, on account of instabilities generated by behaviour-disease interactions. We conclude that second waves of COVID-19–should they materialize–can be interpreted as the outcomes of nonlinear interactions between disease dynamics and population behaviour.

In the late stages of an epidemic, infections are often sporadic and geographically distributed. Spatially structured stochastic models can capture these important features of disease dynamics, thereby allowing a broader exploration of interventions. Here we develop a stochastic model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission amongst an interconnected group of population centres representing counties, municipalities and districts (collectively, “counties”). The model is parameterized with demographic, epidemiological, testing, and travel data from Ontario, Canada. We explore the effects of different control strategies after the epidemic curve has been flattened. We compare a local strategy of re-opening (and re-closing, as needed) schools and workplaces county-by-county according to triggers for county-specific infection prevalence, to a global strategy of province-wide re-opening and re-closing according to triggers for province-wide infection prevalence. We find that the local strategy results in a similar number coronavirus disease (COVID-19) cases but significantly fewer person-days of closure, even under high inter-county travel scenarios. However, both cases and person-days lost to closure rise when county triggers are not coordinated and when testing rates vary among counties. Finally, we show that local strategies can also do better in the early epidemic stage but only if testing rates are high and the trigger prevalence is low. Our results suggest that pandemic planning for the far side of the COVID-19 epidemic curve should consider local strategies for re-opening and re-closing.

Dynamic transmission models of influenza are sometimes used in decision-making to identify which vaccination strategies might best reduce influenza-associated health burdens. Our goal was to use laboratory confirmed influenza cases to fit model parameters in an age-structured, two-type (influenza A/B) dynamic model of influenza. We compared the fitted model under two fitting methodologies: using longitudinal weekly case notification data versus using cross-sectional age-stratified cumulative case notification data. The longitudinal data came from a Canadian province (Ontario)whereasthecross-sectionaldatacamefromthenationallevel(allofCanada). Wefindthatthe longitudinal fitting method provides best fitting parameter sets that have a higher variance between the respective parameters in each set than the cross-sectional cumulative case method. Model predictions-particularly for influenza A-are very different for the two fitting methodologies under hypothetical vaccination scenarios that expand coverage in either younger age classes or older age classes: the cross-sectional method predicts much larger decreases in total cases under expanded vaccine coverage than the longitudinal method. Also, the longitudinal method predicts that vaccinating younger age groups yields greater declines in total cases than vaccinating older age groups, whereas the cross- sectional method predicts the opposite. We conclude that model predictions of vaccination impacts under different strategies may differ at national versus provincial levels. Finally, we discuss whether usinglongitudinalversuscross-sectionaldatainmodelfittingmaygeneratefurtherdifferencesinmodel predictions (above and beyond population-specific differences) and how such a hypothesis could be tested in future studies.

It is widely recognized that coupled socio-ecological dynamics can be qualitatively different from the dynamics of social or ecological systems in isolation from one another. The influence of the type of ecological dynamics on the dynamics of the larger socio-ecological system is less well studied, however. Here, we carry out such a comparison using a mathematical model of a common pool resource problem. A population must make decisions about harvesting a renewable resource. Individuals may either be cooperators, who harvest at a sustainable level, or defectors, who overharvest. Cooperators punish defectors through social ostracism. Individuals can switch strategies according to the costs and benefits of harvesting and the strength of social ostracism. These mechanisms are represented by a differential equation for social dynamics, which is coupled to three different types of resource dynamics: logistic growth, constant inflow, and threshold growth. We find that when human influence is sufficiently weak, the form of resource dynamics leaves a strong imprint on the socio-ecological dynamics, and human social dynamics are qualitatively very different from resource dynamics. However, stronger human influence introduces a broad intermediate parameter regime where dynamical patterns converge to a common type: the three types of ecological systems exhibit similar dynamics, but also, social and ecological dynamics mirror one another. This regime of strong human influence includes generation of stable limit cycles at high rates of social learning. Such oscillations are a consequence of stronger coupling and are reminiscent of synchrony in other fields, such as the classic problem of coupled oscillators. Socio-ecological convergence has implications for how we understand and manage complex socio-ecological systems. In an era of growing human influence on ecological systems, further empirical and theoretical work is required to determine whether socio-ecological convergence is present in real systems.

The Nilgiri Hills in the Western Ghats of India constitute a region of high biological and cultural diversity, and include an endangered shola forest-grassland mosaic ecosystem. A mosaic ecosystem is one consisting of adjacent, coexisting patches of highly distinct naturally occurring land states (in this case, shola forest and natural grassland). Changes in the landscape since the nineteenth century have severely impacted the shola-grassland mosaic and challenged the traditional lifestyles of the indigenous Toda people. However, the responses of traditional Toda socio-ecological perspectives and landscape management to these changes have not been explored through population surveys. Here, using a survey method, the article explores traditional Toda perspectives of ecosystem value and landmanagement practices. The survey consists of interviews of 50 respondents belonging to 24 munds (villages), covering ten clans, neighbouring mosaic lands, plantations and agricultural areas. The findings show that traditional socio-ecological landscape management is robust and has persisted despite marked ecological and socio-economic changes during the nineteenth and twenty-first centuries, and despite frequent gathering of land management advice from non-Toda. Elements of traditional socio-ecological knowledge that have persisted include prevalent collective traditional decision-making and long-held preferences for a landscape composition with a strong mosaic component. The highly robust nature of Toda socio-ecological culture and land management suggests that the Todas have a valuable role to play in supporting the long-term persistence of the shola-grassland mosaic. Increasing their stewardship role would help conserve this endangered and highly biodiverse ecosystem, while at the same time preserving a unique indigenous culture.