Assessing the impact of small firm dynamics on public mental health amid the pandemic in Latin America.

OBJECTIVE: The aim of our study is to examine the relationship between the economic activity of small firms and the mental well-being of the population in five Latin American countries in the early stages of the pandemic. METHODS: We utilize the search volume of certain keywords on Google Trends (GT), such as "boredom," "frustration," "loneliness," "sleep", "anxiety", and "depression", as an indicator of the well-being of the population. By examining the data from Facebook Business Activity Trends, we investigate how social attention reacts to the activity levels of different economic sectors. RESULTS: Increased business activity is generally associated with reduced levels of boredom, loneliness, sleep problems and anxiety. The effect on depression varies by sector, with positive associations concentrated in onsite jobs. In addition, we observe that strict Non-Pharmaceutical Interventions (NPIs) tend to exacerbate feelings of boredom and loneliness, sleep issues, and anxiety. CONCLUSIONS: Our findings suggest a strong association between different indicators of psychological well-being and the level of activity in different sectors of the economy. Given the essential role of small and medium-sized enterprises (SMEs) in generating employment, especially during crises like the pandemic, it is imperative that they remain resilient and adaptable to support economic recovery and job preservation. To accomplish this, policymakers need to focus on providing financial stability and support for SMEs, fostering social support networks within companies, and incorporating mental health services into workplace environments. This comprehensive strategy can alleviate mental health challenges and enhance public health resilience.

Facial biotype classification for orthodontic treatment planning using an alternative learning algorithm for tree augmented Naive Bayes.

BACKGROUND: When designing a treatment in orthodontics, especially for children and teenagers, it is crucial to be aware of the changes that occur throughout facial growth because the rate and direction of growth can greatly affect the necessity of using different treatment mechanics. This paper presents a Bayesian network approach for facial biotype classification to classify patients' biotypes into Dolichofacial (long and narrow face), Brachyfacial (short and wide face), and an intermediate kind called Mesofacial, we develop a novel learning technique for tree augmented Naive Bayes (TAN) for this purpose. RESULTS: The proposed method, on average, outperformed all the other models based on accuracy, precision, recall, [Formula: see text], and kappa, for the particular dataset analyzed. Moreover, the proposed method presented the lowest dispersion, making this model more stable and robust against different runs. CONCLUSIONS: The proposed method obtained high accuracy values compared to other competitive classifiers. When analyzing a resulting Bayesian network, many of the interactions shown in the network had an orthodontic interpretation. For orthodontists, the Bayesian network classifier can be a helpful decision-making tool.

Heterogeneous responses in Google Trends measures of well-being to the COVID-19 dynamic quarantines in Chile.

We study how the Chilean population's well-being responded to the strategy implemented by their health authorities, known as Dynamic Quarantine, to contain the spread of coronavirus in which municipalities periodically entered and exited lockdowns. This unique scheme, together with the population's socioeconomic heterogeneity, facilitates the estimation of changes in this well-being as differentiated by socioeconomic status. Using Google Trends to compute measures of well-being, we find strong evidence that socioeconomic status induces heterogeneity in these changes; thus, neglecting this heterogeneity may lead to misleading prescriptions for the public policy that addresses the psychological effects of lockdowns.

Stock market volatility and the COVID-19 reproductive number.

The media has prominently featured the totemic reproductive number R in its COVID-19 coverage despite being an imperfect measure of the degree of infectivity of the virus. As such, it conveys information to the public regarding the state of the pandemic that affects market sentiment. We analyze how news about R affects the volatility in stock markets worldwide and find that when R is greater than one, which means the spread of the disease should soar, it has a positive and significant effect on volatility. Our results hold after controlling for government interventions and several robustness checks.

Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy.

The Chilean health authorities have implemented a sanitary strategy known as dynamic quarantine or strategic quarantine to cope with the COVID-19 pandemic. Under this system, lockdowns were established, lifted, or prolonged according to the weekly health authorities' assessment of municipalities' epidemiological situation. The public announcements about the confinement situation of municipalities country-wide are made typically on Tuesdays or Wednesdays before noon, have received extensive media coverage, and generated sharp stock market fluctuations. Municipalities are the smallest administrative division in Chile, with each city broken down typically into several municipalities. We analyze social media behavior in response to the confinement situation of the population at the municipal level. The dynamic quarantine scheme offers a unique opportunity for our analysis, given that municipalities display a high degree of heterogeneity, both in size and in the socioeconomic status of their population. We exploit the variability over time in municipalities' confinement situations, resulting from the dynamic quarantine strategy, and the cross-sectional variability in their socioeconomic characteristics to evaluate the impact of these characteristics on social sentiment. Using event study and panel data methods, we find that proxies for social sentiment based on Twitter queries are negatively related (more pessimistic) to increases in the number of confined people, but with a statistically significant effect concentrated on people from the wealthiest cohorts of the population. For indicators of social sentiment based on Google Trends, we found that search intensity during the periods surrounding government announcements is positively related to increases in the total number of confined people. Still, this effect does not seem to be dependent on the segments of the population affected by the quarantine. Furthermore, we show that the observed heterogeneity in sentiment mirrors heterogeneity in stock market reactions to government announcements. We provide evidence that the observed stock market behavior around quarantine announcements can be explained by the number of people from the wealthiest segments of the population entering or exiting lockdown.