scholarly journals Models of Identity Signaling

2022 ◽  
Author(s):  
Paul E. Smaldino
Keyword(s):  
Public Health ◽  
Social Learning ◽  
Human Behavior ◽  
Disease Transmission ◽  
Computational Models ◽  
Social Systems ◽  
Formal Models ◽  
Social Environments ◽  
Identity Signaling ◽  
Encrypted Communication

Identity signals inform receivers of a signaler’s membership in a subset of individuals, and in doing so shape cooperation, conflict, and social learning. Understanding the use and consequences of identity signaling is therefore critical for a complete science of collective human behavior. And, as with all complex social systems, this understanding is aided by the use of formal mathematical and computational models. Here I review some formal models of identity signaling. I divide these models into two categories. The first concerns models that assert how identity functions as a signal and test the consequences of those assertions, with a focus on public health behavior and disease transmission. The second concerns models used to understand how identity signals operate strategically in different social environments, with a focus on covert or encrypted communication.

scholarly journals A functional paradigm for evaluating culture: An example with cetaceans

2012 ◽  
Vol 58 (2) ◽  
pp. 271-286 ◽  
Author(s):  
Alison Linda Greggor
Keyword(s):  
Social Learning ◽  
Social Systems ◽  
Social Environments ◽  
Species Analysis ◽  
Learning Capabilities ◽  
Physical Attributes ◽  
Fitness Benefits ◽  
The Individual ◽  
Taxonomic Groups ◽  
Learning Ecology

Abstract Nonhuman culture was first considered in nonhuman primates because they are genetically similar to humans. However, evolution is not progressive and therefore many species may occupy niches that favor socially transmitted, group specific behavior. Not surprisingly, evidence for culture has accrued in several taxonomic groups, including cetaceans. If culture is an adaptation, it is imperative we understand the factors that favor its formation. Understanding the evolutionary origin of culture will allow for a wider range of species to be studied, including those that are difficult to test in the laboratory. I propose a broad-based functional paradigm for evaluating nonhuman culture; based on the idea that while not all cultural behaviors may garner fitness benefits to the individual, the ecological and social environments in which cultural behaviors evolved must have favored the physical attributes and social learning capabilities that allow for cultural formation. Specifically this framework emphasizes the relationships between social learning, ecology, social systems, and biology in relation to culture. I illustrate the utility of the functional paradigm with evidence from the ceteacean group, while setting the stage for a stringent species by species analysis. By means of contextualizing culture, the Functional Paradigm can evaluate a species’ potential to exhibit culture and can investigate potentially cultural behaviors [Current Zoology 58 (2): 271–286, 2012].

scholarly journals Beyond collective intelligence: Collective adaptation

2022 ◽  
Author(s):  
Mirta Galesic ◽  
Daniel Barkoczi ◽  
Andrew Berdahl ◽  
Dora Biro ◽  
Giuseppe Carbone ◽  
...  
Keyword(s):  
Conceptual Framework ◽  
Statistical Physics ◽  
Computational Models ◽  
Collective Intelligence ◽  
Social Systems ◽  
Cognitive Strategies ◽  
Social Environments ◽  
Collective Phenomena ◽  
Integrate Research

We develop a conceptual framework for studying collective adaptation: the process of iterative co-adaptation of cognitive strategies, social environments, and problem structures. Going beyond searching for “intelligent” collectives, we integrate research from different disciplines to show how collective adaptation perspective can help explain why similar collectives can follow very different and sometimes counter-intuitive trajectories. We further discuss how this perspective explains why successful collectives appear to have a general collective intelligence factor, why collectives rarely optimize their behaviour for a single problem, why their behaviours can appear myopic, and why playful exploration of alternative social systems can be useful. We describe different approaches for the study of collective adaptation, including computational models inspired by evolution and statistical physics. The framework of collective adaptation enables the integration and formalization of knowledge about human collective phenomena and opens doors to a rigorous transdisciplinary pursuit of important outstanding questions about human sociality.

Communicable and Other Infectious Diseases

2020 ◽  
Author(s):  
Markus Frischhut
Keyword(s):  
Public Health ◽  
European Union ◽  
Infectious Diseases ◽  
Disease Transmission ◽  
Eu Law ◽  
The European Union ◽  
Ethical Perspective ◽  
Health Measures ◽  
Environment Agency ◽  
And Control

This chapter discusses the most important features of EU law on infectious diseases. Communicable diseases not only cross borders, they also often require measures that cross different areas of policy because of different vectors for disease transmission. The relevant EU law cannot be attributed to one sectoral policy only, and thus various EU agencies participate in protecting public health. The key agency is the European Centre for Disease Prevention and Control. Other important agencies include the European Environment Agency; European Food Safety Authority; and the Consumers, Health, Agriculture and Food Executive Agency. However, while integration at the EU level has facilitated protection of the public's health, it also has created potential conflicts among the different objectives of the European Union. The internal market promotes the free movement of products, but public health measures can require restrictions of trade. Other conflicts can arise if protective public health measures conflict with individual human rights. The chapter then considers risk assessment and the different tools of risk management used in dealing with the challenges of infectious diseases. It also turns to the external and ethical perspective and the role the European Union takes in global health.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

scholarly journals Mask-Ematics: Modeling the Effects of Masks in COVID-19 Transmission in High-Risk Environments

Epidemiologia ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 207-226
Author(s):  
Anthony Morciglio ◽  
Bin Zhang ◽  
Gerardo Chowell ◽  
James M. Hyman ◽  
Yi Jiang
Keyword(s):  
Public Health ◽  
High Risk ◽  
Disease Transmission ◽  
Large Fraction ◽  
Critical Mass ◽  
Transmission Model ◽  
Reproductive Number ◽  
High Quality ◽  
Moderate Quality ◽  
Epidemic Size

The COVID-19 pandemic has placed an unprecedented burden on public health and strained the worldwide economy. The rapid spread of COVID-19 has been predominantly driven by aerosol transmission, and scientific research supports the use of face masks to reduce transmission. However, a systematic and quantitative understanding of how face masks reduce disease transmission is still lacking. We used epidemic data from the Diamond Princess cruise ship to calibrate a transmission model in a high-risk setting and derive the reproductive number for the model. We explain how the terms in the reproductive number reflect the contributions of the different infectious states to the spread of the infection. We used that model to compare the infection spread within a homogeneously mixed population for different types of masks, the timing of mask policy, and compliance of wearing masks. Our results suggest substantial reductions in epidemic size and mortality rate provided by at least 75% of people wearing masks (robust for different mask types). We also evaluated the timing of the mask implementation. We illustrate how ample compliance with moderate-quality masks at the start of an epidemic attained similar mortality reductions to less compliance and the use of high-quality masks after the epidemic took off. We observed that a critical mass of 84% of the population wearing masks can completely stop the spread of the disease. These results highlight the significance of a large fraction of the population needing to wear face masks to effectively reduce the spread of the epidemic. The simulations show that early implementation of mask policy using moderate-quality masks is more effective than a later implementation with high-quality masks. These findings may inform public health mask-use policies for an infectious respiratory disease outbreak (such as one of COVID-19) in high-risk settings.

scholarly journals A computational reward learning account of social media engagement

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Björn Lindström ◽  
Martin Bellander ◽  
David T. Schultner ◽  
Allen Chang ◽  
Philippe N. Tobler ◽  
...  
Keyword(s):  
Social Media ◽  
Human Behavior ◽  
Computational Models ◽  
Average Rate ◽  
Human Life ◽  
Modern Human ◽  
Reward Learning ◽  
Learning Mechanisms ◽  
Social Media Engagement ◽  
Social Rewards

AbstractSocial media has become a modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (likes), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account. Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior.

scholarly journals Genomic analysis of group B Streptococcus from milk demonstrates the need for improved biosecurity: a cross-sectional study of pastoralist camels in Kenya

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dinah Seligsohn ◽  
Chiara Crestani ◽  
Taya L. Forde ◽  
Erika Chenais ◽  
Ruth N. Zadoks
Keyword(s):  
Public Health ◽  
Milk Production ◽  
Disease Transmission ◽  
Tetracycline Resistance ◽  
Genomic Analysis ◽  
Sub Saharan Africa ◽  
Camel Milk ◽  
Interspecies Transmission ◽  
Cross Sectional ◽  
Group B

Abstract Background Streptococcus agalactiae (Group B Streptococcus, (GBS)) is the leading cause of mastitis (inflammation of the mammary gland) among dairy camels in Sub-Saharan Africa, with negative implications for milk production and quality and animal welfare. Camel milk is often consumed raw and presence of GBS in milk may pose a public health threat. Little is known about the population structure or virulence factors of camel GBS. We investigated the molecular epidemiology of camel GBS and its implications for mastitis control and public health. Results Using whole genome sequencing, we analysed 65 camel milk GBS isolates from 19 herds in Isiolo, Kenya. Six sequence types (STs) were identified, mostly belonging to previously described camel-specific STs. One isolate belonged to ST1, a predominantly human-associated lineage, possibly as a result of interspecies transmission. Most (54/65) isolates belonged to ST616, indicative of contagious transmission. Phylogenetic analysis of GBS core genomes showed similar levels of heterogeneity within- and between herds, suggesting ongoing between-herd transmission. The lactose operon, a marker of GBS adaptation to the mammary niche, was found in 75 % of the isolates, and tetracycline resistance gene tet(M) in all but two isolates. Only the ST1 isolate harboured virulence genes scpB and lmb, which are associated with human host adaptation. Conclusions GBS in milk from Kenyan camel herds largely belongs to ST616 and shows signatures of adaptation to the udder. The finding of similar levels of within- and between herd heterogeneity of GBS in camel herds, as well as potential human-camel transmission highlights the need for improved internal as well as external biosecurity to curb disease transmission and increase milk production.

scholarly journals The effect of social distancing on the reach of an epidemic in social networks

2021 ◽  
Author(s):  
Gregory Gutin ◽  
Tomohiro Hirano ◽  
Sung-Ha Hwang ◽  
Philip R. Neary ◽  
Alexis Akira Toda
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Social Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Disease Transmission ◽  
Global Level ◽  
Social Distancing ◽  
The Public ◽  
Simulation Results

AbstractHow does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a susceptible–infected–removed with social distancing model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the ‘global’ level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.

scholarly journals What is Communicable? Unaccounted Injuries and “Catching” Diabetes in an Illegible Epidemic

2019 ◽  
Vol 34 (4) ◽  
Author(s):  
Amy Moran-Thomas
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Disease Transmission ◽  
Communicable Disease ◽  
Treatment Access ◽  
Infectious Disease Transmission ◽  
The World ◽  
Historically Situated ◽  
Over Time ◽  
Non Communicable Disease

Long-accepted models of causality cast diseases into the binary of either “contagious” or “non-communicable,” typically with institutional resources focused primarily on interrupting infectious disease transmission. But in southern Belize, as in much of the world today, epidemic diabetes has become a leading cause of death and a notorious contributor to organ failure and amputated limbs. This ethnographic essay follows caregivers’ and families’ work to survive in-between public health categories, and asks what responses a bifurcated model of infectious versus non-communicable disease structures or incapacitates in practice. It proposes an alternative focus on diabetes as a “para-communicable” condition—materially transmitted as bodies and ecologies intimately shape each other over time, with unequal and compounding effects for historically situated groups of people. The article closes by querying how communicability relates to community, and why it matters to reframe narratives about contributing causalities in relation to struggles for treatment access.

scholarly journals Estimating the burden of United States workers exposed to infection or disease: a key factor in containing risk of COVID-19 infection

2020 ◽  
Author(s):  
Marissa G. Baker ◽  
Trevor K. Peckham ◽  
Noah S. Seixas
Keyword(s):  
Public Health ◽  
United States ◽  
Social Services ◽  
Police Officers ◽  
Disease Transmission ◽  
Health Workers ◽  
Septic Tank ◽  
Risk Response ◽  
Increased Risk ◽  
Exposed Workers

AbstractIntroductionWith the global spread of COVID-19, there is a compelling public health interest in quantifying who is at increased risk of disease. Occupational characteristics, such as interfacing with the public and being in close quarters with other workers, not only put workers at high risk for disease, but also make them a nexus of disease transmission to the community. This can further be exacerbated through presenteeism, the term used to describe the act of coming to work despite being symptomatic for disease. Understanding which occupational groups are exposed to infection and disease in the workplace can help to inform public health risk response and management for COVID-19, and subsequent infectious disease outbreaks.MethodsTo estimate the burden of United States workers exposed to infection and disease in the workplace, national employment data (by Standard Occupational Classification) maintained by the Bureau of Labor Statistics (BLS) was merged with BLS O*NET survey data, which ranks occupations with particular physical, ergonomic, and structural exposures. For this analysis, occupations reporting exposure to infection or disease more than once a month was the focus.ResultsBased on our analyses, approximately 10% (14.4 M) of United States workers are employed in occupations where exposure to disease or infection occurs at least once per week. Approximately 18.4% (26.7 M) of all United States workers are employed in occupations where exposure to disease or infection occurs at least once per month. While the majority of exposed workers are employed in healthcare sectors, other occupational sectors also have high proportions of exposed workers. These include protective service occupations (e.g. police officers, correctional officers, firefighters), office and administrative support occupations (e.g. couriers and messengers, patient service representatives), education occupations (e.g. preschool and daycare teachers), community and social services occupations (community health workers, social workers, counselors), and even construction and extraction occupations (e.g. plumbers, septic tank installers, elevator repair).ConclusionsThe large number of persons employed in a wide variety of occupations with frequent exposure to infection and disease underscore the importance of all workplaces developing risk response plans for COVID-19. This work also serves as an important reminder that the workplace is a key locus for public health interventions, which could protect both workers and the communities they serve.

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