Epidemiology of COVID-19 in Brazil: using a mathematical model to estimate the outbreak peak and temporal evolution

Internal Structure, Invariance, and Rasch Analyses: A Work-Life Integration-Blurring Scale

The aim of the study was to develop a role blurring (RB) tool to measure work-life integration in different contexts. A final number of 19 items was examined. Psychometric properties in both Spanish and Brazilian Portuguese versions were analysed, comparing the invariance of the measure between the two countries, and setting the difficulty parameter of the items. Thus, a total of two incidental samples volunteered to participate in the study: a Spanish sample of n = 498 and a Brazilian sample of n = 379 were recruited. Confirmatory factor analysis (CFA), as well as an invariance analysis, were performed between the two countries, in order to test the homogeneity structure of the measure. In addition, the item response theory (IRT) was used to establish the probability of response and difficulty of the items through a Rasch analysis. The confirmatory factor analysis pointed to a unifactorial structure. There were no statistically significant alterations in the model fit indices, and the Rasch rating scale model (RSM) showed adequate infit and outfit values, as well as successive response categories located in the expected order for all items.

Food promoted on an online food delivery platform in a Brazilian metropolis during the COVID-19 pandemic: a longitudinal analysis

OBJECTIVE

To analyse food advertised on an online food delivery (OFD) platform during 16 weeks of the COVID-19 pandemic in Brazil.

DESIGN

Longitudinal study. We randomly selected foods advertised on the OFD app's home page, classifying the food items into water; natural juices and smoothies; vegetables; fruits; traditional meals and pasta; ultra-processed beverages; ice cream and candies, and salty packaged snacks; sandwiches; savoury snacks; and pizza. We also registered the marketing strategies used to promote the food items, such as photos, discounts, 'combo deals', and messages on healthiness, value for the money, tastiness, and pleasure.

SETTING

Belo Horizonte, Minas Gerais, Brazil.

PARTICIPANTS

1,593 food items.

RESULTS

In general, the OFD platform most commonly promoted traditional meals and pasta, ultra-processed beverages, and sandwiches-these food groups were offered 20-25% of the time during the 16 weeks. There were no promotions for water during the whole period, and the least common promotions were those for natural juices and smoothies, vegetables, and fruits (<5%). The most common food promotion strategies were photos (>80%) and discounts (>95%), while approximately 30% of the offers featured combo deals. Messages on tastiness, pleasure, and value for the money varied from 15% to 40%.

CONCLUSION

Although the OFD platform offered traditional meals and pasta, most of the foods and beverages advertised were unhealthy and promoted with persuasive strategies. This finding highlights a growing public health concern: an increase in unhealthy eating patterns during the pandemic.

Poor prognosis indicators of type-2 diabetic COVID-19 patients

Diabetes is associated with a worse prognosis and a high risk of morbidity and mortality in COVID-19 patients. We aimed to evaluate the main factors involved in the poor prognosis in diabetic patients. A total of 984 patients diagnosed with COVID-19 admitted to the hospital were included in this study. Patients were first divided into type-2 diabetic (DM+) and non-diabetic (DM–) groups. The participants were analyzed based on the National Early Warning Score (NEWS) and on the Quick-Sequential Organ Failure Assessment (qSOFA) to find the best prognostic risk score for our study. The DM+ and DM– groups were divided into non-severe and severe groups. Comparative and correlative analyses were used to identify the physiological parameters that could be employed for creating a potential risk indicator for DM+ COVID-19 patients. We found a poorer prognosis for the DM+ COVID-19 patients with a higher ICU admission rate, mechanical ventilation rate, vasopressor use, dialysis, and longer treatment times compared with the DM– group. DM+ COVID-19 patients had increased plasma glucose, lactate, age, urea, NEWS, and D-dimer levels, herein referred to as the GLAUND set, and worse prognosis and outcomes when compared with infected DM– patients. The NEWS score was a better indicator for assessing COVID-19 severity in diabetic patients than the q-SOFA score. In conclusion, diabetic COVID-19 patients should be assessed with the NEWS score and GLAUND set for determining their prognosis COVID-19 prognosis.

Mental health, quality of life and optimism during the covid-19 pandemic: a comparison between Brazil and Portugal

PURPOSE

Although there have been numerous studies investigating the mental health of individuals during the pandemic, a comparison between countries is still scarce in the literature. To explore this gap, the present study aimed to compare the mental health (i.e., anxiety and depression), quality of life (QoL), and optimism/pessimism among individuals from Brazil and Portugal during the COVID-19 pandemic and the associated factors.

METHOD

A cross-sectional population-based study was conducted during the COVID-19 pandemic in Brazil and Portugal. Data collection was carried out between May and June 2020, using an online form which was sent through social networks. A total of 2069 participants (1156 from Brazil and 913 from Portugal) were included. Depressive symptoms (PHQ-9), Anxiety (GAD-7), optimism/pessimism (Revised Life Orientation Test - LOT), QoL (WHOQOL-Bref), and sociodemographic, health, and social distancing variables were assessed. Data was analyzed using univariate and multivariate models.

RESULTS

There were remarkable differences between Brazil and Portugal in all outcomes during the COVID-19 pandemic, including higher levels of depressive symptoms, anxiety, and optimism for the Brazilian individuals and higher levels of QoL and pessimism for the Portuguese individuals. The following factors were associated with the mental health and QoL in both Brazilian and Portuguese populations: gender, age, being a healthcare professional, and days in social distancing.

CONCLUSION

Despite the fact that Brazilians were more optimistic during the COVID-19 pandemic, they had lower levels of mental health and QoL as compared to the Portuguese individuals.

A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil

We propose herein a mathematical model to predict the COVID-19 evolution and evaluate the impact of governmental decisions on this evolution, attempting to explain the long duration of the pandemic in the 26 Brazilian states and their capitals well as in the Federative Unit. The prediction was performed based on the growth rate of new cases in a stable period, and the graphics plotted with the significant governmental decisions to evaluate the impact on the epidemic curve in each Brazilian state and city. Analysis of the predicted new cases was correlated with the total number of hospitalizations and deaths related to COVID-19. Because Brazil is a vast country, with high heterogeneity and complexity of the regional/local characteristics and governmental authorities among Brazilian states and cities, we individually predicted the epidemic curve based on a specific stable period with reduced or minimal interference on the growth rate of new cases. We found good accuracy, mainly in a short period (weeks). The most critical governmental decisions had a significant temporal impact on pandemic curve growth. A good relationship was found between the predicted number of new cases and the total number of inpatients and deaths related to COVID-19. In summary, we demonstrated that interventional and preventive measures directly and significantly impact the COVID-19 pandemic using a simple mathematical model. This model can easily be applied, helping, and directing health and governmental authorities to make further decisions to combat the pandemic.

Merits and Limitations of Mathematical Modeling and Computational Simulations in Mitigation of COVID-19 Pandemic: A Comprehensive Review

Mathematical models have assisted in describing the transmission and propagation dynamics of various viral diseases like MERS, measles, SARS, and Influenza; while the advanced computational technique is utilized in the epidemiology of viral diseases to examine and estimate the influences of interventions and vaccinations. In March 2020, the World Health Organization (WHO) has declared the COVID-19 as a global pandemic and the rate of morbidity and mortality triggers unprecedented public health crises throughout the world. The mathematical models can assist in improving the interventions, key transmission parameters, public health agencies, and countermeasures to mitigate this pandemic. Besides, the mathematical models were also used to examine the characteristics of epidemiological and the understanding of the complex transmission mechanism. Our literature study found that there were still some challenges in mathematical modeling for the case of ecology, genetics, microbiology, and pathology pose; also, some aspects like political and societal issues and cultural and ethical standards are hard to be characterized. Here, the recent mathematical models about COVID-19 and their prominent features, applications, limitations, and future perspective are discussed and reviewed. This review can assist in further improvement of mathematical models that will consider the current challenges of viral diseases.

Nine Months of COVID-19 Pandemic in Europe: A Comparative Time Series Analysis of Cases and Fatalities in 35 Countries

(1) Background: to describe the dynamic of the pandemic across 35 European countries over a period of 9 months. (2) Methods: a three-phase time series model was fitted for 35 European countries, predicting deaths based on SARS-CoV-2 incidences. Hierarchical clustering resulted in three clusters of countries. A multiple regression model was developed predicting thresholds for COVID-19 incidences, coupled to death numbers. (3) Results: The model showed strongly connected deaths and incidences during the waves in spring and fall. The corrected case-fatality rates ranged from 2% to 20.7% in the first wave, and from 0.5% to 4.2% in the second wave. If the incidences stay below a threshold, predicted by the regression model (R2=85.0%), COVID-19 related deaths and incidences were not necessarily coupled. The clusters represented different regions in Europe, and the corrected case-fatality rates in each cluster flipped from high to low or vice versa. Severely and less severely affected countries flipped between the first and second wave. (4) Conclusions: COVID-19 incidences and related deaths were uncoupled during the summer but coupled during two waves. Once a country-specific threshold of infections is reached, death numbers will start to rise, allowing health care systems and countries to prepare.

Study of the trend pattern of COVID-19 using spline-based time series model: a Bayesian paradigm

A vast majority of the countries are under economic and health crises due to the current epidemic of coronavirus disease 2019 (COVID-19). The present study analyzes the COVID-19 using time series, an essential gizmo for knowing the enlargement of infection and its changing behavior, especially the trending model. We consider an autoregressive model with a non-linear time trend component that approximately converts into the linear trend using the spline function. The spline function splits the series of COVID-19 into different piecewise segments between respective knots in the form of various growth stages and fits the linear time trend. First, we obtain the number of knots with their locations in the COVID-19 series to identify the transmission stages of COVID-19 infection. Then, the estimation of the model parameters is obtained under the Bayesian setup for the best-fitted model. The results advocate that the proposed model appropriately determines the location of knots based on different transmission stages and know the current transmission situation of the COVID-19 pandemic in a country.

Emergence of novel SARS-CoV-2 variants in the Netherlands

Coronavirus disease 2019 (COVID-19) has emerged in December 2019 when the first case was reported in Wuhan, China and turned into a pandemic with 27 million (September 9th) cases. Currently, there are over 95,000 complete genome sequences of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing COVID-19, in public databases, accompanying a growing number of studies. Nevertheless, there is still much to learn about the viral population variation when the virus is evolving as it continues to spread. We have analyzed SARS-CoV-2 genomes to identify the most variant sites, as well as the stable, conserved ones in samples collected in the Netherlands until June 2020. We identified the most frequent mutations in different geographies. We also performed a phylogenetic study focused on the Netherlands to detect novel variants emerging in the late stages of the pandemic and forming local clusters. We investigated the S and N proteins on SARS-CoV-2 genomes in the Netherlands and found the most variant and stable sites to guide development of diagnostics assays and vaccines. We observed that while the SARS-CoV-2 genome has accumulated mutations, diverging from reference sequence, the variation landscape is dominated by four mutations globally, suggesting the current reference does not represent the virus samples circulating currently. In addition, we detected novel variants of SARS-CoV-2 almost unique to the Netherlands that form localized clusters and region-specific sub-populations indicating community spread. We explored SARS-CoV-2 variants in the Netherlands until June 2020 within a global context; our results provide insight into the viral population diversity for localized efforts in tracking the transmission of COVID-19, as well as sequenced-based approaches in diagnostics and therapeutics. We emphasize that little diversity is observed globally in recent samples despite the increased number of mutations relative to the established reference sequence. We suggest sequence-based analyses should opt for a consensus representation to adequately cover the genomic variation observed to speed up diagnostics and vaccine design.

Prevalence and Associated Factors for Depressive Symptomatology in Chinese Adults During COVID-19 Epidemic

Background: The coronavirus disease 2019 (COVID-19) has been rapidly transmitted worldwide, which contributed to various psychological problems (such as fear, depression, and anxiety) among the general population in China. The purpose of this study is to investigate the prevalence and associated factors of depressive symptoms among Chinese adults.

Methods: A cross-sectional study of Chinese adults was conducted during 17–29 February 2020. Symptoms of depression were assessed using the Center for Epidemiologic Studies Depression scale (CES-D).

Results: A total of 3,399 respondents were included in the analysis. It was observed that 14.2% (481/3,399) of the participants were screened positive for depressive symptoms. In a multivariate logistic regression analysis, older age (OR = 0.98; 95% CI, 0.97–0.99), smoking (OR = 1.57; 95% CI, 1.10–2.26), self-rated health (good: OR = 0.49; 95% CI, 0.37–0.66; fairly: OR = 0.60; 95% CI, 0.45–0.80), having greater support scores (OR = 0.95; 95% CI, 0.94–0.96), knowledge about the main symptom of COVID-19 (very clearly: OR = 0.58; 95% CI, 0.42–0.79; relatively clearly: OR = 0.59; 95% CI, 0.44–0.79), and staying in Wuhan within 3 months before the outbreak of epidemic (OR = 1.78; 95% CI, 1.34–2.38) were associated with depressive symptoms.

Conclusion: A considerable proportion of the general population in China had depressive symptoms during the COVID-19 epidemic. Routine screening and targeted interventions for depression are needed among high-risk depressed individuals during the COVID-19 epidemic.

The values and limitations of mathematical modelling to COVID-19 in the world: a follow up report

We previously described a mathematical model to simulate the course of the COVID-19 pandemic and try to predict how this outbreak might evolve in the following two months when the pandemic cases will drop significantly. Our original paper prepared in March 2020 analyzed the outbreaks of COVID-19 in the US and its selected states to identify the rise, peak, and decrease of cases within a given geographic population, as well as a rough calculation of accumulated total cases in this population from the beginning to the end of June 2020. The current report will describe how well the later actual trend from March to June fit our model and prediction. Similar analyses are also conducted to include countries other than the US. From such a wide global data analysis, our results demonstrated that different US states and countries showed dramatically different patterns of pandemic trend. The values and limitations of our modelling are discussed.

Dynamic analysis of the mathematical model of COVID-19 with demographic effects

The coronavirus is currently extremely contagious for humankind, which is a zoonotic tropical disease. The pandemic is the largest in history, affecting almost the whole world. What makes the condition the worst of all is no specific effective treatment available. In this article, we present an extended and modified form of SIR and SEIR model, respectively. We begin by investigating a simple mathematical model that describes the pandemic. Then we apply different safety measures to control the pandemic situation. The mathematical model with and without control is solved by using homotopy perturbation method. Obtained solutions have been presented graphically. Finally, we develop another mathematical model, including quarantine and hospitalization.

Healthcare Workers in Brazil during the COVID-19 Pandemic: A Cross-Sectional Online Survey

Brazil is in a critical situation due to the COVID-19 pandemic. Healthcare workers that are in the front line face challenges with a shortage of personal protective equipment, high risk of contamination, low adherence to the social distancing measures by the population, low coronavirus testing with underestimation of cases, and also financial concerns due to the economic crisis in a developing country. This study compared the impact of COVID-19 pandemic among three categories of healthcare workers in Brazil: physicians, nurses, and dentists, about workload, income, protection, training, feelings, behavior, and level of concern and anxiety. The sample was randomly selected and a Google Forms questionnaire was sent by WhatsApp messenger. The survey comprised questions about jobs, income, workload, PPE, training for COVID-19 patient care, behavior and feelings during the pandemic. The number of jobs reduced for all healthcare workers in Brazil during the pandemic, but significantly more for dentists. The workload and income reduced to all healthcare workers. Most healthcare workers did not receive proper training for treating COVID-19 infected patients. Physicians and nurses were feeling more tired than usual. Most of the healthcare workers in all groups reported difficulties in sleeping during the pandemic. The healthcare workers reported a significant impact of COVID-19 pandemic in their income, workload and anxiety, with differences among physicians, nurses and dentists.