Relationships between mastery, forgiveness, optimism, and resilience, and PTSD and anxiety during the COVID-19 pandemic.

The COVID-19 pandemic has had devastating emotional consequences. The present study aimed to examine the relationships between personal resources and emotional distress while accounting for COVID-19-related variables. Seven hundred and seventy-seven (N = 777) participants completed demographic, mastery, forgiveness, optimism, resilience, PTSD, and anxiety questionnaires. A stepped hierarchical multiple regression revealed that mastery, forgiveness, cultural group, age, acquaintance with a person who died of COVID-19, and having been infected with COVID-19 contributed significantly to the explained variance in PTSD symptoms. Mastery, forgiveness, optimism, age, and acquaintance with a person who died of COVID-19 contributed significantly to the explained variance in anxiety. However, resilience was not found to significantly contribute to the explained variance in PTSD symptoms or anxiety. This study demonstrates the importance of being aware of both PTSD symptoms and anxiety associated with COVID-19. Thus, it is suggested that therapy programs should pay special attention to mastery and forgiveness as coping resources. In addition, among medical and mental-health personnel awareness should be given to individuals who have been in close acquaintance with those who died of COVID-19, those with COVID-19 risk factors, and those who have been infected. Special attention should also be paid to minority groups as they might tend to experience more emotional distress and trauma symptoms.

Learning from Others' Disasters? A Comparative Study of SARS/MERS and COVID-19 Responses in Five Polities.

The ability to successfully manage disasters is a function of the extent to which lessons are learned from prior experience. We focus on the extent to which lessons from SARS/MERS have been learned and implemented during the first wave of COVID-19, and the extent to which the source affects governance learning: from a polity's own experience in previous episodes of the same disaster type; from the experience of other polities with regard to the same disaster type; or by cross-hazard learning - transferring lessons learned from experience with other types of disasters. To assess which types of governance learning occurred we analyze the experience of four East Asian polities that were previously affected by SARS/MERS: South Korea, Taiwan, Singapore and Hong-Kong. Their experience is compared with that of Israel. Having faced other emergencies but not a pandemic, Israel could have potentially learned from its experience with other emergencies, or from the experience of others with regard to pandemics before the onset of COVID-19. We find that governance learning occurred in the polities that experienced either SARS or MERS, but not cross-hazard or cross-polity learning. The consequences in the 5 polities at the end of the first six months of Covid-19, reflected by the numbers of infected and deaths, on one hand, and by the level of disruption to normal life, on the other, verifies these findings. Research insights point to the importance of modifying governance structures to establish effective emergency institutions and necessary legislation as critical preparation for future unknown emergencies.

Analysis of complex neural circuits with nonlinear multidimensional hidden state models.

A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain.