The Impacts of SARS-CoV-2 Pandemic on Suicide: A Lexical Analysis.

Background: Although COVID-19 is a public health emergency, its consequences for the mental health of the population are still scarce. Likewise, its impact on critical situations such as suicide is still poorly explored in the literature. Therefore, this study aimed to analyze in a pioneering way, through lexical and content analysis techniques, the possible impacts of the new COVID-19 pandemic on suicide behavior. Methods: A lexical analysis, whose sample (not probabilistic, i.e., for convenience) was made up of full-length papers (abstracts) and short communications, about suicide behavior in COVID-19 pandemic, in PubMed and Virtual Health Library (VHL) was carried out following a lexical and content analysis using the software IRaMuTeQ, version 0.7 alpha 2. Results: The most frequent active words were suicide behavior (n = 649), covid (n = 439), health (n = 358), mental (n = 268), and social (n = 220). Four lexical classes were found and organized into two large groups: the first group, formed by the classes 2 ("methods for psychological treatment") and 3 ("strategies to minimize the COVID-19 impacts"), was the most representative, totaling 50.6% of the text segments and second group formed by classes 1 ("signs of clinical depression") and 4 ("COVID-19 pandemic as a public health problem") with 49.4% of the text segments. Conclusion: Facing suicide behavior, the direct effects of the COVID-19 pandemic, and the negative feelings and trigger of previous psychiatric illnesses; the measures to deal with the pandemic such as social isolation, decrease in the number of professionals, the opening hours of health establishments, and decrease in the demand for medications; and competing phenomena such as the spread of fake news and lack of empathy are aggressive and potentiating factors of suicidal ideation.

Analysis of the prison population's mental health in Sars-Cov-2 pandemic: Qualitative analysis.

This study aimed to analyze the possible impacts on the prison population's mental health in the context of the new COVID-19 pandemic. Qualitative study was carried out following a lexical and content analysis using the software IRaMuTeQ, version 0.7 alpha 2, in the speech of the short communication and headlines from newspapers. Three groups emerged from the analysis: "spatial conditions for infection" (39.2% of the text segments); "disease outbreaks in prisons" (30,4%) and "public responsibility" (30,4%). Precarious conditions of prisons, high rate of infections and psychiatric illnesses, and lack of government assistance are issues that should be given special attention in order to formulate health promotion and prevention policies focusing on mental health in prison population.

A non-central beta model to forecast and evaluate pandemics time series.

Government, researchers, and health professionals have been challenged to model, forecast, and evaluate pandemics time series (e.g. new coronavirus SARS-CoV-2, COVID-19). The main difficulty is the level of novelty imposed by these phenomena. Information from previous epidemics is only partially relevant. Further, the spread is local-dependent, reflecting a number of social, political, economic, and environmental dynamic factors. The present paper aims to provide a relatively simple way to model, forecast, and evaluate the time incidence of a pandemic. The proposed framework makes use of the non-central beta (NCB) probability density function. Specifically, a probabilistic optimisation algorithm searches for the best NCB model of the pandemic, according to the mean square error metric. The resulting model allows one to infer, among others, the general peak date, the ending date, and the total number of cases as well as to compare the level of difficult imposed by the pandemic among territories. Case studies involving COVID-19 incidence time series from countries around the world suggest the usefulness of the proposed framework in comparison with some of the main epidemic models from the literature (e.g. SIR, SIS, SEIR) and established time series formalisms (e.g. exponential smoothing - ETS, autoregressive integrated moving average - ARIMA).

A crisis within the crisis: The mental health situation of refugees in the world during the 2019 coronavirus (2019-nCoV) outbreak.

BACKGROUND: 68.5 million people around the world have been forced to leave their houses. Refugees have mainly to face their adaption in a host country, which involves bureaucracy, different culture, poverty, and racism. The already fragile situation of refugees becomes worrying and challenged in the face of the new coronavirus (COVID-19) epidemic. Therefore, we aimed to describe the factors that can worsen the mental health of refugees. METHOD: The studies were identified in well-known international journals found in three electronic databases: PubMed, Scopus, and Embase. The data were cross-checked with information from the main international newspapers. RESULTS: According to the literature, the difficulties faced by refugees with the COVID-19 pandemic are potentiated by the pandemic state. There are several risk factors common to coronavirus and psychiatric illnesses as overcrowding, disruption of sewage disposal, poor standards of hygiene, poor nutrition, negligible sanitation, lack of access to shelter, health care, public services, and safety. These associated with fear and uncertainty create a closed ground for psychological sickness and COVID-19 infection. CONCLUSIONS: There should be not only a social mobilization to contain the virus, but also a collective effort on behalf of the most vulnerable populations.