Forecasting of the COVID-19 pandemic situation of Korea

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020‒December 31, 2020 and January 20, 2020‒January 31, 2021) and testing data (January 1, 2021‒February 28, 2021 and February 1, 2021‒February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values’ comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

Forecasting of the COVID-19 pandemic situation of Korea

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020‒December 31, 2020 and January 20, 2020‒January 31, 2021) and testing data (January 1, 2021‒February 28, 2021 and February 1, 2021‒February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values’ comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

Suicidal Ideation of the Elderly According to Their Involvement in Grandchild Care

The purpose of the present study was to examine the severity of suicidal ideation of the older adults according to the amount of involvement in grandchild care. Data for this research were drawn from a cross-sectional study conducted on community-dwelling adults aged 65 years or older. The 922 participants were divided into three groups according to their involvement in grandchild care: 18.5% had provided daily care, 12.4% had provided occasional care, and 69.1% had never cared for their grandchildren. ANCOVA analysis showed that the scores for depression was significantly lower in the group which took care of their grandchildren occasionally compared to the other two groups. The scores for suicidal ideation was significantly higher in the group which had never taken care of their grandchildren compared to the other two groups. Current study suggests that grandparenting may have a positive effect on suicidal ideation of the older adults.

A Study on Variables Influencing Acceptability and Perception Towards Suicide among the Elderly in the City of Bucheon / 신경정신의학

OBJECTIVES: The aim of this study was to investigate the variables influencing acceptability and perception towards suicide among the elderly in Bucheon city, South Korea. METHODS: A total of 1099 elderly over 65 years old participated in this study. The subjects completed a self-questionnaire including their demographic characteristics, psychiatric characteristics, factor 1 and 4 of the Attitudes Towards Suicide-20, and Geriatric Depression Scale Short Form Korea Version. One-way analysis of variance was performed to identify the variables associated with the acceptability and perception towards suicide. RESULTS: As result of this study, the demographic characteristics (older age, lower education level, lower economic state, bereavement, divorce or separated marital status, and life without spouse) and psychiatric characteristics (psychiatric past history, treatment history, and suicidal attempt history) were found to be associated with a more acceptable attitude toward suicide. In addition, a lower education level, no psychiatric history, and no psychiatric treatment history influenced the lack of perception to suicide. CONCLUSION: For public services to prevent suicide of the elderly population who lack spontaneity and accessibility to suicidal evaluations, it would be important to focus on the variables identified in this study for enhancing the effectiveness of the services.