Altruism in a Time of Crisis: Dissociable Social Valuation and Perception during COVID-19 in the United States (preprint)

Human prosocial behaviors are constantly shaped by the push-and-pull between societal need for cooperation and one’s natural tendency to self-prioritize. Nevertheless, it remains elusive how our valuation and perceptual systems might contribute to altruistic acts under the influence of a real-world crisis. Here, using computational modeling and a game-theoretic approach, we investigated how the coronavirus pandemic perturbed altruistic choices in the United States, April-May, 2020. Overall, people made more altruistic choices as the pandemic worsened, an effect primarily driven by increased preference for social welfare. Paradoxically, participants also processed self-relevant information (i.e., “self-prioritization”) more efficiently at the perceptual level, as the pandemic became worse. These effects were not observed one year later (May-June, 2021) when the variability of the pandemic diminished. Furthermore, individuals’ prosocial choices and preferences did not correlate with their self-prioritization efficiency. Collectively, these results revealed a more nuanced view of human altruism — that as a dynamic and context-dependent construct, altruism can co-exist with increased attention to the self.

Altruism in a Time of Crisis: Dissociable Social Valuation and Perception during COVID-19 in the United States (preprint)

Human prosocial behaviors are constantly shaped by the push-and-pull between societal need for cooperation and one’s natural tendency to self-prioritize. Nevertheless, it remains elusive how our valuation and perceptual systems might contribute to altruistic acts under the influence of a real-world crisis. Here, using computational modeling and a game-theoretic approach, we investigated how the coronavirus pandemic perturbed altruistic choices in the United States between April and May 2020. Overall, people made more altruistic choices as the pandemic became worse, an effect primarily driven by increased preference for social welfare. Paradoxically, participants also processed self-relevant information (i.e., “self-prioritization”) more efficiently at the perceptual level, as the pandemic became worse. These effects were not observed one year later (May-June, 2021) when the variability of the pandemic diminished. Furthermore, individuals’ prosocial choices and preferences did not correlate with their self-prioritization efficiency. Collectively, these results revealed a more nuanced view of human altruism — that as a dynamic and context-dependent construct, altruism can co-exist with increased attention to the self.

Emotional Adaptation During A Crisis: Decline in Anxiety and Depression After the Initial Weeks of COVID-19 in the United States (preprint)

Objective: Crises such as the COVID-19 pandemic are known to exacerbate depression and anxiety, though their temporal trajectories remain unclear. The present study aims to investigate fluctuations in depression and anxiety using COVID-19 as a model crisis. Methods: 1,512 adults living in the U.S. enrolled in this online study on April 2nd, 2020 and were assessed weekly for 10 weeks (until June 4th, 2020; final n=537). Depression and anxiety were measured using the Zung Self-Rating Depression scale and State-Trait Anxiety Inventory (state subscale), respectively, along with demographic and COVID-related questions. Mixed-effects linear regression models were used to examine factors contributing to longitudinal changes in depression and anxiety. Results: Depression and anxiety levels were high in early April, but declined over time (F(9,4824)=17.53, p

Emotional Adaptation During a Crisis: Decline in Anxiety and Depression after the Initial Weeks of COVID-19 in the United States (preprint)

Background: Crises such as the COVID-19 pandemic are known to exacerbate mental health issues such as depression and anxiety. The temporal trajectories of depression and anxiety under crises, however, remain unclear.Methods: 1,512 adults living in the U.S. enrolled in this online study on April 2nd, 2020 and were assessed weekly for 10 weeks (until June 4th, 2020; final n=537). We measured depression and anxiety using the Zung Self-Rating Depression scale and State-Trait Anxiety Inventory (state subscale), respectively, along with demographic and COVID-related questions. Mixed-effects linear regression models were used to examine factors contributing to longitudinal changes in depression and anxiety.Outcomes: Depression and anxiety levels were high in early April, but declined over time ( F (9,4824)=17·53, p Interpretation: Alongside evidence for overall emotional adaptation, these findings highlight overlapping (economic), yet distinct (change in COVID-19 cases, social media use, and projected COVID-19 duration) factors contributing to fluctuations in depression and anxiety throughout the first wave of COVID-19. These results provide insight into socioeconomic policies and behavioral changes that can be made to increase emotional adaptation in times of crisis.Funding Statement: Internal institutional funding.Declaration of Interests: The authors declare no conflicts of interest.Ethics Approval Statement: Participants provided informed consent via an online form. The Institutional Review Board of the Icahn School of Medicine at Mount Sinai determined this research to be exempt following review.

Containment of future waves of COVID-19: simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation (preprint)

We used multi-agents simulations to estimate the testing capacity required to find and isolate a number of infections sufficient to break the chain of transmission of SARS-CoV-2. Depending on the mitigation policies in place, a daily capacity between 0.7 to 3.6 tests per thousand was required to contain the disease. However, if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of infections kept growing exponentially, irrespective of any testing capacity. Under these conditions, the population's geographical distribution and travel behaviour could inform sampling policies to aid a successful containment.