More Like this

1. Onset of Daily Activity in a Female Songbird Is Related to Peak-Induced Estradiol Levels

   https://doi.org/10.1093/icb/icz112  

   Graham, Jessica L.; Needham, Katie B.; Bertucci, Emily M.; Pearson, Alexis A.; Bauer, Carolyn M.; Greives, Timothy J. (June 2019, Integrative and Comparative Biology)

   Abstract Research in captive birds and mammals has demonstrated that circadian (i.e., daily) behavioral rhythms are altered in response to increases in sex-steroid hormones. Recently, we and others have demonstrated a high degree of individual repeatability in peak (gonadotropin-releasing hormone [GnRH]-induced sex) steroid levels, and we have found that these GnRH-induced levels are highly correlated with their daily (night-time) endogenous peak. Whether or not individual variation in organization and activity of the reproductive endocrine axis is related to daily timing in wild animals is not well known. To begin to explore these possible links, we tested the hypothesis that maximal levels of the sex steroid hormone estradiol (E2) and onset of daily activity are related in a female songbird, the dark-eyed junco (Junco hyemalis). We found that females with higher levels of GnRH-induced E2 departed from their nest in the morning significantly earlier than females with lower stimulated levels. We did not observe a relationship between testosterone and this measure of onset of activity. Our findings suggest an interaction between an individual’s reproductive endocrine axis and the circadian system and variation observed in an individuals’ daily activity onset. We suggest future studies examine the relationship between maximal sex-steroid hormones and timing of daily activity onset. 

   more » « less
2. Exo-SIR: an epidemiological model to analyze the impact of exogenous spread of infection

   https://doi.org/10.1007/s41060-022-00334-z  

   Sivaraman, Nirmal Kumar; Gaur, Manas; Baijal, Shivansh; Muthiah, Sakthi Balan; Sheth, Amit (June 2022, International Journal of Data Science and Analytics)

   Epidemics like Covid-19 and Ebola have impacted people’s lives signifcantly. The impact of mobility of people across the countries or states in the spread of epidemics has been signifcant. The spread of disease due to factors local to the population under consideration is termed the endogenous spread. The spread due to external factors like migration, mobility, etc., is called the exogenous spread. In this paper, we introduce the Exo-SIR model, an extension of the popular SIR model and a few variants of the model. The novelty in our model is that it captures both the exogenous and endogenous spread of the virus. First, we present an analytical study. Second, we simulate the Exo-SIR model with and without assuming contact network for the population. Third, we implement the Exo-SIR model on real datasets regarding Covid-19 and Ebola. We found that endogenous infection is infuenced by exogenous infection. Furthermore, we found that the Exo-SIR model predicts the peak time better than the SIR model. Hence, the Exo-SIR model would be helpful for governments to plan policy interventions at the time of a pandemic. Keywords Covid-19, Ebola, Epidemic modeling, Compartment model, Exogenous infection, Endogenous infection, SIR, Exo-SIR 

   more » « less
3. Variation in primate decision-making under uncertainty and the roots of human economic behaviour

   https://doi.org/10.1098/rstb.2019.0671  

   De Petrillo, Francesca; Rosati, Alexandra G. (March 2021, Philosophical Transactions of the Royal Society B: Biological Sciences)

   null (Ed.)

   Uncertainty is a ubiquitous component of human economic behaviour, yet people can vary in their preferences for risk across populations, individuals and different points in time. As uncertainty also characterizes many aspects of animal decision-making, comparative research can help evaluate different potential mechanisms that generate this variation, including the role of biological differences or maturational change versus cultural learning, as well as identify human-unique components of economic decision-making. Here, we examine decision-making under risk across non-human primates, our closest relatives. We first review theoretical approaches and current methods for understanding decision-making in animals. We then assess the current evidence for variation in animal preferences between species and populations, between individuals based on personality, sex and age, and finally, between different contexts and individual states. We then use these primate data to evaluate the processes that can shape human decision-making strategies and identify the primate foundations of human economic behaviour. This article is part of the theme issue ‘Existence and prevalence of economic behaviours among non-human primates’. 

   more » « less
4. When pandemic threat does not stoke xenophobia: evidence from a panel survey around COVID-19

   https://doi.org/10.1080/21565503.2024.2392240  

   Zhou, Yang-Yang; Rojas, Daniel; Peters, Margaret E (May 2025, Politics, Groups, and Identities)

   Studies have found that pandemics can heighten xenophobia among host citizens, often explained by the behavioral immune system theory or elite-driven scapegoating. However, most research has overlooked the role of pandemic-related economic restrictions and job loss on sentiment toward immigrants. To isolate this economic mechanism, we examine the case of Venezuelan migrants in Colombia before and during COVID-19. Despite the Colombian government's severe economic lockdown, few politicians blamed Venezuelans for the pandemic. Thus, any economic impact on xenophobia should be evident. Using a panel experimental survey of 374 Colombians, supplemented with 550 new respondents at endline, we find no evidence that exposure to COVID-19 changed attitudes towards Venezuelans, even for those directly affected by the pandemic. Yet, those who did not lose their jobs viewed Venezuelan migration more positively at endline, providing support for the economic effects of pandemics. 

   more » « less
5. Getting Local and Personal: Toward Building a Predictive Model for COVID in Three United States Cities.

   Edwards, April; Metcalf, Leigh; Casey, William A; Chatterjee, Shirshendu; Janwa, Heeralal; Battifarano, Ernest (May 2023, Springer)

   Kacprzyk, Janusz; Pal, Nikhil R; Perez, Rafael B; Corchado, Emilio S; Hagras, Hani; Kóczy, László T; Kreinovich, Vladik; Lin, Chin-Teng; Lu, Jie; Melin, Patricia (Ed.)

   The COVID-19 pandemic was lived in real-time on social media. In the current project, we use machine learning to explore the relationship between COVID-19 cases and social media activity on Twitter. We were particularly interested in determining if Twitter activity can be used to predict COVID-19 surges. We also were interested in exploring features of social media, such as replies, to determine their promise for understanding the views of individual users. With the prevalence of mis/disinformation on social media, it is critical to develop a deeper and richer understanding of the relationship between social media and real-world events in order to detect and prevent future influence operations. In the current work, we explore the relationship between COVID-19 cases and social media activity (on Twitter) in three major United States cities with different geographical and political landscapes. We find that Twitter activity resulted in statistically significant correlations using the Granger causality test, with a lag of one week in all three cities. Similarly, the use of replies, which appear more likely to be generated by individual users, not bots or public relations operations, was also strongly correlated with the number of COVID-19 cases using the Granger causality test. Furthermore, we were able to build promising predictive models for the number of future COVID-19 cases using correlation data to select features for input to our models. In contrast, significant correlations were not identified when comparing the number of COVID-19 cases with mainstream media sources or with a sample of all US COVID-related tweets. We conclude that, even for an international event such as COVID-19, social media tracks closely with local conditions. We also suggest that replies can be a valuable feature within a machine learning task that is attempting to gauge the reactions of individual users. 

   more » « less