Background: Similar to outbreaks of many other infectious diseases, success in controlling the novel 2019 coronavirus infection requires a timely and accurate monitoring of the epidemic, particularly during its early period with rather limited data while the need for information increases explosively. Methods: In this study, we used a second derivative model to characterize the coronavirus epidemic in China with cumulatively diagnosed cases during the first 2 months. The analysis was further enhanced by an exponential model with a close-population assumption. This model was built with the data and used to assess the detection rate during the study period, considering the differences between the true infections, detectable and detected cases. Results: Results from the second derivative modeling suggest the coronavirus epidemic as nonlinear and chaotic in nature. Although it emerged gradually, the epidemic was highly responsive to massive interventions initiated on January 21, 2020, as indicated by results from both second derivative and exponential modeling analyses. The epidemic started to decelerate immediately after the massive actions. The results derived from our analysis signaled the decline of the epidemic 14 days before it eventually occurred on February 4, 2020. Study findings further signaled an accelerated decline in the epidemic starting in 14 days on February 18, 2020. Conclusions: The coronavirus epidemic appeared to be nonlinear and chaotic, and was responsive to effective interventions. The methods used in this study can be applied in surveillance to inform and encourage the general public, public health professionals, clinicians and decision-makers to take coordinative and collaborative efforts to control the epidemic.

Valid measures are essential for evidence-based planning and decision-making in suicide prevention to control the increasing trends in suicide among youths in the United States.^1,2 The suicide mortality rate, or number of suicide deaths per 100 000 individuals, has been used in describing the epidemic of suicide.^1,3 Based on the unadjusted rate in the past 2 decades, suicide among US youths showed a higher and V-shaped trend for male youths and a lower and relatively smooth trend of increase for female youths, with a declining sex difference.^3-5

Background: Childhood adversities pose deleterious consequences on health and well-being, but limited studies explore whether unique patterns of adverse childhood experiences (ACEs) impact the mental health of emerging adults and the mediating role of current stressful events (CSEs). This study examined classes of ACEs and how they relate to CSEs, psychological distress, and subjective well-being among Eritrean College undergraduates. Methods: Cross-sectional data on ACEs, CSEs, symptoms of psychological distress, and subjective well-being were collected from a national sample of college students (N = 507). We identified ACE patterns using latent class analysis and further examined whether CSEs mediated the effects of ACE classes on psychological distress and subjective well-being. Results: 86.4% of the sample experienced at least one ACE. Collective violence, domestic violence, and physical abuse were the most common ACEs. Three subgroups, low ACEs (66.3%), household violence (19.1%), and multiple ACEs (14.6%) were identified. We found that relative to low ACEs, household violence (β = 0.142, 95% CI 0.046, 0.248) and multiple ACEs (β = 0.501, 95% CI 0.357, 0.666) indirectly influenced psychological distress through CSEs, and CSEs mediated the relationships between household violence (β = −0.096, 95% CI −0.176, −0.033), multiple ACEs (β = −0.338, 95% CI −0.498, −0.210), and subjective well-being. However, there were nonsignificant relative direct effects of ACE patterns on both psychological distress and subjective well-being. Conclusions: Experiencing multiple ACEs and household violence in conjunction with CSEs significantly predict psychological distress and subjective well-being. Contextual interventions for the early identification of ACEs and the management of CSEs may play a crucial role in the prevention of mental health problems.