Mental Health and Social Contact During the COVID-19 Pandemic: An Ecological Momentary Assessment Study

For many students, the COVID-19 pandemic caused once-in-a-lifetime disruptions of daily life. In March 2020, during the beginning of the outbreak in the Netherlands, we used ecological momentary assessment to follow 80 undergraduate students four times per day for 14 days to assess mental health, social contact, and COVID-19-related variables. Despite rapidly increasing rates of infections and deaths, we observed decreases in anxiety, loneliness, and COVID-19-related concerns, especially in the first few days. Other mental health variables, such as stress levels, remained stable, whereas depressive symptoms increased. Despite social-distancing measures implemented by the Dutch government halfway through our study, students showed no changes in the frequency of in-person social activities. Dynamic network models identified potential vicious cycles between mental health variables and being alone, which predicted concerns about COVID-19 and was followed by further mental health problems. Findings and implications are discussed in detail.

Affect and Personality

The use of dynamic network models has grown in recent years. These models allow researchers to capture both lagged and contemporaneous effects in longitudinal data typically as variations, reformulations, or extensions of the standard vector autoregressive (VAR) models. To date, many of these dynamic networks have not been explicitly compared to one another. We compare three popular dynamic network approaches – GIMME, uSEM, and LASSO gVAR – in terms of their differences in modeling assumptions, estimation procedures, statistical properties based on a Monte Carlo simulation, and implications for affect and personality researchers. We found that all three dynamic network approaches provided yielded group-level empirical results in partial support of affect and personality theories. However, individual-level results revealed a great deal of heterogeneity across approaches and participants. Reasons for discrepancies are discussed alongside these approaches’ respective strengths and limitations.

Mental Health and Social Contact During the COVID-19 Pandemic: An Ecological Momentary Assessment Study

Students are at elevated risk for mental health problems. The COVID-19 pandemic and public health responses such as school and university closures caused once-in-a-lifetime disruptions of daily life for most students. In March 2020, during the beginning of the outbreak in the Netherlands, we used Ecological Momentary Assessment to follow 80 bachelor students 4 times a day for 2 weeks. Despite rapidly increasing rates of infections and deaths, short-term dynamics revealed slight decreases of mental health problems, COVID-19 related concerns, and loneliness, especially in the first few days of the study. Students showed no changes in the frequency of in-person social activities. Dynamic network models indicated that social activities were negatively related to being at home, and identified reinforcing vicious cycles among mental health problems and being alone, which in turn predicted concerns about COVID-19. Findings and implications are discussed in detail.

Model Adequacy Checking/Goodness-of-fit Testing for Behavior in Joint Dynamic Network/Behavior Models, with an Extension to Two-mode Networks

The recent popularity of models that capture the dynamic coevolution of both network structure and behavior has driven the need for summary indices to assess the adequacy of these models to reproduce dynamic properties of scientific or practical importance. Whereas there are several existing indices for assessing the ability of the model to reproduce network structure over time, to date there are few indices for assessing the ability of the model to reproduce individuals’ behavior patterns. Drawing on the widely used strategy of assessing model adequacy by comparing index values summarizing features of the observed data to the distribution of those index values on simulated data from the fitted model, we propose four goals that a researcher could reasonably expect of a joint structure/behavior model regarding how well it captures behavior and describe indices for assessing each of these. These reasonably simple and easily implemented indices can be used for assessing model adequacy with any dynamic network models jointly working with networks and behavior, including the stochastic actor-based models implemented within software packages such as RSien version 1.2-24. We demonstrate the use of our indices with an empirical example to show how they can be employed in practical settings, with an additional extension to modeling affiliation dynamics in two-mode networks. Key scripts are provided in the Supplemental Document (which can be found at http://smr.sagepub.com/supplemental/ ).

Comparison of Static and Dynamic Neural Network Models in Predicting Outlet Temperature of Shell and Tube Heat Exchanger

This paper presents the comparison of static and dynamic neural network (NN), model to predict the exit temperature of the heat exchangers. Feed forward NN was used as a static network while Time delay NN was used for a dynamic network. Experimental data was collected from a shell and tube heat exchanger to provide sufficient data processing, namely training, test and validation data to develop the models. The static and dynamic network models of the heat exchanger have been developed using Matlab. The performances of the models were evaluated by their statistical validity using the correlation co-efficient and the mean squared error. For time series predictions, the dynamic NN has shown better results than the static NN.