Linear Regression Analysis of Sleep Quality in People with Insomnia in Wuhan City during the COVID-19 Pandemic.

Objective: COVID-19 has evolved into a major global public health event. The number of people reporting insomnia is growing exponentially during the pandemic. This study aimed to explore the relationship between aggravated insomnia and COVID-19-induced psychological impact on the public, lifestyle changes, and anxiety about the future. Methods: In this cross-sectional study, we used the questionnaires from 400 subjects who were obtained from the Department of Encephalopathy of the Wuhan Hospital of Traditional Chinese Medicine between July 2020 and July 2021. The data collected for the study included demographic characteristics of the participants and psychological scales consisting of the Spiegel Sleep Questionnaire, the Fear of COVID-19 Scale (FCV-19S), the Zung Self-Rating Anxiety Scale (SAS), and the Zung Self-Rating Depression Scale (SDS). The independent sample t-test and one-way ANOVA were used to compare the results. Correlation analysis of variables affecting insomnia was performed using Pearson correlation analysis. The degree of influence of the variables on insomnia was determined using linear regression, and a regression equation was derived. Results: A total of 400 insomnia patients participated in the survey. The median age was 45.75 ± 15.04 years. The average score of the Spiegel Sleep Questionnaire was 17.29 ± 6.36, that of SAS was 52.47 ± 10.39, that of SDS was 65.89 ± 8.72, and that of FCV-19S was 16.09 ± 6.81. The scores of FCV-19S, SAS, and SDS were closely related to insomnia, and the influencing degree was in the following order: fear, depression, and anxiety (OR = 1.30, 0.709, and 0.63, respectively). Conclusion: Fear of COVID-19 can be one of the primary contributors to worsening insomnia.

Promoting COVID-19 vaccine confidence through public responses to misinformation: The joint influence of message source and message content.

OBJECTIVE: During the pandemic healthcare professionals and political leaders routinely used traditional and new media outlets to publicly respond to COVID-19 myths and inaccuracies. We examine how variations in the sources and messaging strategies of these public statements affect respondents' beliefs about the safety of COVID-19 vaccines. METHODS: We analyzed the results of an experiment embedded within a multi-wave survey deployed to US and UK respondents in January-February 2022 to examine these effects. We employ a test-retest between-subjects experimental protocol with a control group. Respondents were randomly assigned to one of four experimental conditions reflecting discrete pairings of message source (political authorities vs. healthcare professionals) and messaging strategy (debunking misinformation vs. discrediting mis-informants) or a control condition. We use linear regression to compare the effects of exposure to treatment conditions on changes in respondent beliefs about the potential risks associated with COVID-19 vaccination. RESULTS: In the UK sample, we observe a statistically significant decrease in beliefs about the risks of COVID-19 vaccines among respondents exposed to debunking messages by healthcare professionals. We observe a similar relationship in the US sample, but the effect was weaker and not significant. Identical messages from political authorities had no effect on respondents' beliefs about vaccine risks in either sample. Discrediting messages critical of mis-informants likewise had no influence on respondent beliefs, regardless of the actor to which they were attributed. Political ideology moderated the influence of debunking statements by healthcare professionals on respondent vaccine attitudes in the US sample, such that the treatment was more effective among liberals and moderates than among conservatives. CONCLUSIONS: Brief exposure to public statements refuting anti-vaccine misinformation can help promote vaccine confidence among some populations. The results underscore the joint importance of message source and messaging strategy in determining the effectiveness of responses to misinformation.

Mechanisms influencing the factors of urban built environments and coronavirus disease 2019 at macroscopic and microscopic scales: The role of cities.

In late 2019, the coronavirus disease 2019 (COVID-19) pandemic soundlessly slinked in and swept the world, exerting a tremendous impact on lifestyles. This study investigated changes in the infection rates of COVID-19 and the urban built environment in 45 areas in Manhattan, New York, and the relationship between the factors of the urban built environment and COVID-19. COVID-19 was used as the outcome variable, which represents the situation under normal conditions vs. non-pharmacological intervention (NPI), to analyze the macroscopic (macro) and microscopic (micro) factors of the urban built environment. Computer vision was introduced to quantify the material space of urban places from street-level panoramic images of the urban streetscape. The study then extracted the microscopic factors of the urban built environment. The micro factors were composed of two parts. The first was the urban level, which was composed of urban buildings, Panoramic View Green View Index, roads, the sky, and buildings (walls). The second was the streets' green structure, which consisted of macrophanerophyte, bush, and grass. The macro factors comprised population density, traffic, and points of interest. This study analyzed correlations from multiple levels using linear regression models. It also effectively explored the relationship between the urban built environment and COVID-19 transmission and the mechanism of its influence from multiple perspectives.

Minimizing population health loss due to scarcity in OR capacity: validation of quality of life input.

OBJECTIVES: A previously developed decision model to prioritize surgical procedures in times of scarce surgical capacity used quality of life (QoL) primarily derived from experts in one center. These estimates are key input of the model, and might be more context-dependent than the other input parameters (age, survival). The aim of this study was to validate our model by replicating these QoL estimates. METHODS: The original study estimated QoL of patients in need of commonly performed procedures in live expert-panel meetings. This study replicated this procedure using a web-based Delphi approach in a different hospital. The new QoL scores were compared with the original scores using mixed effects linear regression. The ranking of surgical procedures based on combined QoL values from the validation and original study was compared to the ranking based solely on the original QoL values. RESULTS: The overall mean difference in QoL estimates between the validation study and the original study was - 0.11 (95% CI: -0.12 - -0.10). The model output (DALY/month delay) based on QoL data from both studies was similar to the model output based on the original data only: The Spearman's correlation coefficient between the ranking of all procedures before and after including the new QoL estimates was 0.988. DISCUSSION: Even though the new QoL estimates were systematically lower than the values from the original study, the ranking for urgency based on health loss per unit of time delay of procedures was consistent. This underscores the robustness and generalizability of the decision model for prioritization of surgical procedures.

Association between proximity to COVID-19 and the quality of life of healthcare workers.

BACKGROUND: The coronavirus disease 2019 (COVID-19) affects almost all countries in the world and it impacts every aspect of people's life-physically, mentally, and socio-economically. There are several research studies examining the impact of this pandemic on health, however, very few studies examining the impact of this pandemic on quality of life. This study aimed to investigate the association between proximity to the COVID-19 and quality of life of healthcare workers and identify factors influencing quality of life. METHODS: A cross-sectional study was conducted among hospital staff in a tertiary hospital in Singapore. Data on demographic, medical history, lifestyle factors, psychosocial factors, and quality of life were collected using online self-administered questionnaire. Quality of life (QoL) was measured by the WHOQOL-BREF questionnaire. Robust linear regression was used to determine factors associated with quality of life. RESULTS: A total of 1911 participants were included in the analysis. The average age of participants was 38.25 (SD = 11.28) years old. 26.90% of participants had been quarantined, hospitalised, being suspected or diagnosed of having COVID-19 infection and they were found to have the lowest levels of QoL across all four domains (physical, psychological, social, and environmental domains). Participants who were singles or nurses, worked in shifts or worked longer hours, had chronic diseases were likely to have lower QoL scores compared to participants in other categories. Healthy lifestyle, social connectivity, resilience, social and workplace support were associated with higher QoL scores. CONCLUSIONS: In planning of measures which aim to improve QoL of healthcare workers, priority should be given to individuals who have been quarantined, hospitalised, being suspected, or diagnosed of having COVID-19 infection. In addition to the proximity of the COVID, lifestyle and psychosocial factors contribute to QoL of healthcare workers. Hence, multifaceted interventions are needed to improve QoL of healthcare workers.

The association between the proportion of Brexiters and COVID-19 death rates in England.

RATIONALE: A cultural divide may exist between a set of people who accept and a set of people who reject the advice of experts. This cultural divide may have important consequences and policy implications, especially in times of severe crisis. OBJECTIVE: Ecological study of whether there exists a significant conditional correlation between two variables that appear unrelated except for attitude towards experts: (1) Proportion of people voting in favour of remaining in the European Union in 2016 and (2) COVID-19 outcomes measured by death rates and vaccination rates. A significant conditional correlation would indicate that polarized beliefs have important consequences across a broad spectrum of societal challenges. METHODS: This study uses simple descriptive statistics and multiple linear regression, considering confounders suggested in the related literature, with data at the District level in England. RESULTS: Districts where people voted most heavily in favour of remaining in the EU (top quintile) had nearly half the death rate of districts in the bottom quintile. This relationship was stronger after the first wave, which was a time when protective measures were communicated to the public by experts. A similar relationship was observed with the decision to get vaccinated, and results were strongest for the booster dose, which was the dose that was not mandatory, but highly advised by experts. The Brexit vote is the variable most correlated with COVID-19 outcomes among many variables including common proxies for trust and civic capital or differences in industry composition across Districts. CONCLUSIONS: Our results suggest a need for designing incentive schemes that take into consideration different belief systems. Scientific prowess - such as finding effective vaccines - may not be sufficient to solve crises.

Intensity and lag-time of non-pharmaceutical interventions on COVID-19 dynamics in German hospitals.

Introduction: Evaluating the potential effects of non-pharmaceutical interventions on COVID-19 dynamics is challenging and controversially discussed in the literature. The reasons are manifold, and some of them are as follows. First, interventions are strongly correlated, making a specific contribution difficult to disentangle; second, time trends (including SARS-CoV-2 variants, vaccination coverage and seasonality) influence the potential effects; third, interventions influence the different populations and dynamics with a time delay. Methods: In this article, we apply a distributed lag linear model on COVID-19 data from Germany from January 2020 to June 2022 to study intensity and lag time effects on the number of hospital patients and the number of prevalent intensive care patients diagnosed with polymerase chain reaction tests. We further discuss how the findings depend on the complexity of accounting for the seasonal trends. Results and discussion: Our findings show that the first reducing effect of non-pharmaceutical interventions on the number of prevalent intensive care patients before vaccination can be expected not before a time lag of 5 days; the main effect is after a time lag of 10-15 days. In general, we denote that the number of hospital and prevalent intensive care patients decrease with an increase in the overall non-pharmaceutical interventions intensity with a time lag of 9 and 10 days. Finally, we emphasize a clear interpretation of the findings noting that a causal conclusion is challenging due to the lack of a suitable experimental study design.

Social Isolation Affects the Mimicry Response in the Use of Smartphones : An Ethological Experiment during the COVID-19 Pandemic.

Humans are social animals that rely on different ways to interact with each other. The COVID-19 pandemic strongly changed our communication strategies. Because of the importance of direct contact for our species, we predict that immediately after the forced social isolation, people were more prone to engage in direct rather than in virtual interactions, thus showing a lower mimicry response in the use of smartphones. In a non-longitudinal study, we collected behavioral data under naturalistic contexts and directly compared the data of the mimicry response gathered immediately following the Italian lockdown (May-September 2020) with those gathered one year later (May-October 2021). Contrary to our expectations, the mimicry response in the use of smartphones was higher immediately after the lockdown than a year later. Probably the large use of these devices during the lockdown translated into a greater sensitivity to be affected by others' smartphone manipulation. Indeed, social isolation modified, at least in the short term, the ways we interact with others by making us more prone to engage in "virtual" social interactions. The bright side of the coin unveiled by our findings is that the effect seems to diminish over time. The large behavioral dataset analyzed here (1,608 events; 248 people) also revealed that the mimicry response in the use of smartphones was higher between familiar subjects than between strangers. In this view, mimicry in manipulating smartphones can be considered an example of joint action that fosters behavioral synchrony between individuals that, in the long-term, can translate into the formation of social bonding.

COVID-19 Containment Measures-a Step Back for Walking Mobility? A 2-Year, 60-Country Analysis of the Apple Mobility Data.

BACKGROUND: COVID-19 containment measures curb viral spread but may hamper walking mobility. As a low daily step count is associated with increased noncommunicable diseases and mortality, assessing the relationship between pandemic responses and walking mobility can help trade-off public health measures. We investigated the association between containment stringency and walking mobility across 60 countries in the period between January 21, 2020 and January 21, 2022 and modeled how this could impact mortality hazard. METHODS: Walking mobility was measured through the Apple Mobility Trends, containment measures stringency index through the Oxford COVID-19 response tracker (which considers local policies on closures, healthcare, and economy), and meteorological data by National Oceanic and Atmospheric Administration weather stations. Walking mobility was regressed over stringency in a mixed-effect model with weather variables as covariates. The impact of stringency on all-cause mortality due to reduced mobility was modeled based on regression results, prepandemic walking mobility, and the association between step count and all-cause mortality hazard. RESULTS: Across the 60 countries, the average stringency was 55 (9) (mean [SD]) out of 100. Stringency was negatively associated with walking mobility; a log-linear model fitted data better than a linear one, with a regression coefficient for stringency on ln (walking mobility) (95% confidence interval) of  -1.201 × 10-2 (-1.221 × 10-2 to -1.183 × 10-2). Increasing stringency, thus decreasing walking mobility, nonlinearly incremented the modeled all-cause mortality hazard by up to ∼40%. CONCLUSIONS: In this study, walking mobility was negatively associated with containment measures stringency; the relationship between stringency, mobility, and the subsequent impact on health outcomes may be nonlinear. These findings can help in balancing pandemic containment policies.

Meaning Salience and Meaning in Life Prospectively Predict Lower Distress During the COVID-19 Pandemic.

BACKGROUND: The COVID-19 pandemic is a widespread source of stress with adverse mental health impacts. Meaning in life, both as a trait and as momentary awareness of what is personally meaningful (meaning salience), is associated with positive health outcomes and may buffer against the deleterious effects of stress. PURPOSE: This project examines prospective associations between baseline meaning salience (daily, post-laboratory stressor) and meaning in life with perceived stress during COVID-19. METHODS: A community sample of healthy adults (n = 147) completed a laboratory stress protocol in 2018-2019, where perceived stress, meaning in life, and meaning salience (daily, post-stressor) were assessed. During April and July 2020 (n = 95, and 97, respectively), participants were re-contacted and reported perceived stress. General linear mixed-effects models accounting for repeated measures of stress during COVID-19 were conducted. RESULTS: Partial correlations holding constant baseline perceived stress showed that COVID-19 perceived stress was correlated with daily meaning salience (r = -.28), post-stressor meaning salience (r = -.20), and meaning in life (r = -.22). In mixed-effects models, daily and post-stressor meaning salience and higher meaning in life, respectively, predicted lower perceived stress during COVID-19, controlling for age, gender, and baseline perceived stress. CONCLUSIONS: Individuals more capable of accessing meaning when exposed to laboratory stress reported lower perceived stress during a global health crisis. Despite study limitations concerning generalizability, results support meaning in life and meaning salience as important aspects of psychological functioning that may promote well-being by affecting stress appraisals and available resources for coping.

The COVID-19 pandemic is a widespread source of stress. Having a sense of meaning in life, or that you have goals in life and a sense that the things you do are worthwhile and significant, is an important part of psychological well-being and might help reduce stress. We collected data on 147 healthy adults in 2018­2019 regarding their stress levels, sense of meaning in life, and how often they were aware of their life's meaning on daily basis and after a stress task in the laboratory. We re-contacted these adults in both April and July 2020 to ask about their stress, and 95 adults responded. Adults who had higher meaning in life in 2018­2019 experienced less stress during the early months of the COVID-19 pandemic. Adults who were more aware of their life's meaning each day and immediately after a stress task in the laboratory also experienced less stress during the COVID-19 pandemic. Results from this study provide evidence that having a strong sense of meaning in life overall and being aware of your life's meaning each day and during times of stress, may promote psychological well-being and reduce stress during times when stress is widespread and abundant.

Frontline work and racial disparities in social and economic pandemic stressors during the first COVID-19 surge.

OBJECTIVE: To assess the magnitude of racial-ethnic disparities in pandemic-related social stressors and examine frontline work's moderating relationship on these stressors. DATA SOURCES: Employed Californians' responses to the Institute for Governmental Studies (IGS) poll from April 16-20, 2020, were analyzed. The Pandemic Stressor Scale (PSS) assessed the extent to which respondents experienced or anticipated problems resulting from the inability to pay for basic necessities, job instability, lacking paid sick leave, unavailability of childcare, and reduced wages or work hours due to COVID-19. STUDY DESIGN: Mixed-effects generalized linear models estimated (1) racial-ethnic disparities in pandemic stressors among workers during the first COVID-19 surge, adjusting for covariates, and (2) tested the interaction between race-ethnicity and frontline worker status, which includes a subset of essential workers who must perform their job on-site, to assess differential associations of frontline work by race-ethnicity. DATA COLLECTION: The IGS poll data from employed workers (n = 4795) were linked to the 2018 Centers for Disease Control and Prevention Social Vulnerability Index at the zip code level (N = 1068). PRINCIPAL FINDINGS: The average PSS score was 37.34 (SD = 30.49). Whites had the lowest PSS score (29.88, SD = 26.52), and Latinxs had the highest (50.74, SD = 32.61). In adjusted analyses, Black frontline workers reported more pandemic-related stressors than White frontline workers (PSS = 47.73 vs. 36.96, p < 0.001). Latinxs reported more pandemic stressors irrespective of frontline worker status. However, the 5.09-point difference between Latinx frontline and non-frontline workers was not statistically different from the 4.6-point disparity between White frontline and non-frontline workers. CONCLUSION: Latinx workers and Black frontline workers disproportionately reported pandemic-related stressors. To reduce stress on frontline workers during crises, worker protections like paid sick leave, universal access to childcare, and improved job security are needed, particularly for those disproportionately affected by structural inequities, such as racially minoritized populations.

Short-term forecasting of confirmed daily COVID-19 cases in the Southern African Development Community region.

Background: The coronavirus pandemic has resulted in complex challenges worldwide, and the Southern African Development Community (SADC) region has not been spared. The region has become the epicentre for coronavirus in the African continent. Combining forecasting techniques can help capture other attributes of the series, thus providing crucial information to address the problem. Objective: To formulate an effective model that timely predicts the spread of COVID-19 in the SADC region. Methods: Using the Quantile regression approaches; linear quantile regression averaging (LQRA), monotone composite quantile regression neural network (MCQRNN), partial additive quantile regression averaging (PAQRA), among others, we combine point forecasts from four candidate models namely, the ARIMA (p, d, q) model, TBATS, Generalized additive model (GAM) and a Gradient Boosting machine (GBM). Results: Among the single forecast models, the GAM provides the best model for predicting the spread of COVID-19 in the SADC region. However, it did not perform well in some periods. Combined forecasts models performed significantly better with the MCQRNN being the best (Theil's U statistic=0.000000278). Conclusion: The findings present an insightful approach in monitoring the spread of COVID-19 in the SADC region. The spread of COVID-19 can best be predicted using combined forecasts models, particularly the MCQRNN approach.

Influence of COVID-19 vaccination on the dynamics of new infected cases in the world.

The initial COVID-19 vaccinations were created and distributed to the general population in 2020 thanks to emergency authorization and conditional approval. Consequently, numerous countries followed the process that is currently a global campaign. Taking into account the fact that people are being vaccinated, there are concerns about the effectiveness of that medical solution. Actually, this study is the first one focusing on how the number of vaccinated people might influence the spread of the pandemic in the world. From the Global Change Data Lab "Our World in Data", we were able to get data sets about the number of new cases and vaccinated people. This study is a longitudinal one from 14/12/2020 to 21/03/2021. In addition, we computed Generalized log-Linear Model on count time series (Negative Binomial distribution due to over dispersion in data) and implemented validation tests to confirm the robustness of our results. The findings revealed that when the number of vaccinated people increases by one new vaccination on a given day, the number of new cases decreases significantly two days after by one. The influence is not notable on the same day of vaccination. Authorities should increase the vaccination campaign to control well the pandemic. That solution has effectively started to reduce the spread of COVID-19 in the world.

Clinical and Proteomic Correlates of Plasma ACE2 (Angiotensin-Converting Enzyme 2) in Human Heart Failure.

ACE2 (angiotensin-converting enzyme 2) is a key component of the renin-angiotensin-aldosterone system. Yet, little is known about the clinical and biologic correlates of circulating ACE2 levels in humans. We assessed the clinical and proteomic correlates of plasma (soluble) ACE2 protein levels in human heart failure. We measured plasma ACE2 using a modified aptamer assay among PHFS (Penn Heart Failure Study) participants (n=2248). We performed an association study of ACE2 against ≈5000 other plasma proteins measured with the SomaScan platform. Plasma ACE2 was not associated with ACE inhibitor and angiotensin-receptor blocker use. Plasma ACE2 was associated with older age, male sex, diabetes mellitus, a lower estimated glomerular filtration rate, worse New York Heart Association class, a history of coronary artery bypass surgery, and higher pro-BNP (pro-B-type natriuretic peptide) levels. Plasma ACE2 exhibited associations with 1011 other plasma proteins. In pathway overrepresentation analyses, top canonical pathways associated with plasma ACE2 included clathrin-mediated endocytosis signaling, actin cytoskeleton signaling, mechanisms of viral exit from host cells, EIF2 (eukaryotic initiation factor 2) signaling, and the protein ubiquitination pathway. In conclusion, in humans with heart failure, plasma ACE2 is associated with various clinical factors known to be associated with severe coronavirus disease 2019 (COVID-19), including older age, male sex, and diabetes mellitus, but is not associated with ACE inhibitor and angiotensin-receptor blocker use. Plasma ACE2 protein levels are prominently associated with multiple cellular pathways involved in cellular endocytosis, exocytosis, and intracellular protein trafficking. Whether these have a causal relationship with ACE2 or are relevant to novel coronavirus-2 infection remains to be assessed in future studies.

Long-term persistence of IgG antibodies in recovered COVID-19 individuals at 18 months post-infection and the impact of two-dose BNT162b2 (Pfizer-BioNTech) mRNA vaccination on the antibody response: Analysis using fixed-effects linear regression model.

This era of emerging variants needs a thorough evaluation of data on the long-term efficacy of immune responses in vaccinated as well as recovered individuals, to understand the overall evolution of the pandemic. In this study, we aimed to assess the dynamics of IgG response over 18 months in n = 36 patients from the Umbria region in Italy, who had a documented history of COVID-19 infection in March 2020, and then compared the impact of two-dose BNT162b2 (Pfizer-BioNTech) vaccination on the antibody responses of these patients with the ones who did not receive any dose of vaccine. This is the longest observation (March 2020-September 2021) for the presence of antibodies against SARS-CoV-2 in recovered individuals along with the impact of 2 dose-BNT162b2 vaccination on these responses. Fixed-effect regression models were used for statistical analysis which could be also used to predict future titer trends. At 18 months, 97% participants tested positive for anti-NCP hinting towards the persistence of infection-induced immunity even for the vaccinated individuals. Our study findings demonstrate that while double dose vaccination boosted the IgG levels in recovered individuals 161 times, this "boost" was relatively short-lived. The unvaccinated recovered individuals, in contrast, continued to show a steady decline but detectable antibody levels. Further studies are required to re-evaluate the timing and dose regimen of vaccines for an adequate immune response in recovered individuals.

Exploration of the impact of political ideology disparity on COVID-19 transmission in the United States.

BACKGROUND: Based on individual-level studies, previous literature suggested that conservatives and liberals in the United States had different perceptions and behaviors when facing the COVID-19 threat. From a state-level perspective, this study further explored the impact of personal political ideology disparity on COVID-19 transmission before and after the emergence of Omicron. METHODS: A new index was established, which depended on the daily cumulative number of confirmed cases in each state and the corresponding population size. Then, by using the 2020 United States presidential election results, the values of the built index were further divided into two groups concerning the political party affiliation of the winner in each state. In addition, each group was further separated into two parts, corresponding to the time before and after Omicron predominated. Three methods, i.e., functional principal component analysis, functional analysis of variance, and function-on-scalar linear regression, were implemented to statistically analyze and quantify the impact. RESULTS: Findings reveal that the disparity of personal political ideology has caused a significant discrepancy in the COVID-19 crisis in the United States. Specifically, the findings show that at the very early stage before the emergence of Omicron, Democratic-leaning states suffered from a much greater severity of the COVID-19 threat but, after July 2020, the severity of COVID-19 transmission in Republican-leaning states was much higher than that in Democratic-leaning states. Situations were reversed when the Omicron predominated. Most of the time, states with Democrat preferences were more vulnerable to the threat of COVID-19 than those with Republican preferences, even though the differences decreased over time. CONCLUSIONS: The individual-level disparity of political ideology has impacted the nationwide COVID-19 transmission and such findings are meaningful for the government and policymakers when taking action against the COVID-19 crisis in the United States.

Predicting COVID-19 cases in various scenarios using RNN-LSTM models aided by adaptive linear regression to identify data anomalies.

The evolution of the Sars-CoV-2 (COVID-19) virus pandemic has revealed that the problems of social inequality, poverty, public and private health systems guided by controversial public policies are much more complex than was conceived before the pandemic. Therefore, understanding how COVID-19 evolves in society and looking at the infection spread is a critical task to support efficient epidemiological actions capable of suppressing the rates of infections and deaths. In this article, we analyze daily COVID-19 infection data with two objectives: (i) to test the predictive power of a Recurrent Neural Network - Long Short Term Memory (RNN-LSTM) on the daily stochastic fluctuation in different scenarios, and (ii) analyze, through adaptive linear regression, possible anomalies in the reported data to provide a more realistic and reliable scenario to support epidemic control actions. Our results show that the approach is even more suitable for countries, states or cities where the rate of testing, diagnosis and prevention were low during the virus dissemination. In this sense, we focused on investigating countries and regions where the disease evolved in a severe and poorly controlled way, as in Brazil, highlighting the favelas in Rio de Janeiro as a regional scenario.

Causal connections between socioeconomic disparities and COVID-19 in the USA.

With the increasing use of machine learning models in computational socioeconomics, the development of methods for explaining these models and understanding the causal connections is gradually gaining importance. In this work, we advocate the use of an explanatory framework from cooperative game theory augmented with do calculus, namely causal Shapley values. Using causal Shapley values, we analyze socioeconomic disparities that have a causal link to the spread of COVID-19 in the USA. We study several phases of the disease spread to show how the causal connections change over time. We perform a causal analysis using random effects models and discuss the correspondence between the two methods to verify our results. We show the distinct advantages a non-linear machine learning models have over linear models when performing a multivariate analysis, especially since the machine learning models can map out non-linear correlations in the data. In addition, the causal Shapley values allow for including the causal structure in the variable importance computed for the machine learning model.

A multivariate analysis to propose linear models for the stature estimation in the Sabahan young adult population.

BACKGROUND: Stature is one of the significant parameters to confirm a biological profile besides sex, age, and ancestry. Sabah is in the Eastern part of Malaysia and is populated by multi-ethnic groups. To date, limited studies on stature estimation have been conducted in Sabah. Hence, this study aims to construct population-specific stature estimation equations for the large ethnic groups in Sabah, Malaysia. OBJECTIVE: The aim is to propose linear models using different hand dimensions (hand span, handbreadth, hand length, middle finger length, and the second inter-crease in the middle finger) for the young adult male and females of the major ethnic groups in Sabah. MATERIALS & METHODS: This cross-sectional study framework used stratified random sampling on 184 male and 184 female young adults. An unpaired t-test and a one-way ANOVA were used to assess the differences in the mean between sex and ethnicities, respectively. The link between the response variable and explanatory variables was initially investigated using simple linear regression, followed by multiple linear regression. RESULT: The present study demonstrated the highest association for the quantitative explanatory variables among hand length and stature (right side: r = 0.833; left side: r = 0.842). Simple equations were specifically developed without sex indicators, and ethnic and multiple linear regression was developed with sex and ethnic indicators. Multiple linear regression provided good estimation r2 = 0.7886 and adjusted r2 = 0.7853. The stature of 18 to 25 year old large ethnic groups in Sabah can be estimated using the developed models 90.218 + 3.845 LHL -5.950 Sex-2.308 Bajau -1.673 KadazanDusun + 2.676 L2ICL. While, formula for each ethnic and sex KadazanDusun Male: Stature = 88.545 + 3.845 LHL+ 2.676 L2ICL, KadazanDusun Female: Stature = 82.595 + 3.845 LHL+ 2.676 L2ICL, Bajau Male: Stature = 87.910 + 3.845 LHL+ 2.676 L2ICL, Bajau Female: Stature = 81.960 + 3.845 LHL+ 2.676 L2ICL, Malay Male: Stature = 90.218 + 3.845 LHL+ 2.676 L2ICL, Malay Female: Stature = 84.268 + 3.845 LHL+ 2.676 L2ICL, Chinese Male: Stature = 90.218 + 3.845 LHL+ 2.676 L2ICL, and Chinese Female: Stature = 84.268 + 3.845 LHL+ 2.676 L2ICL. CONCLUSION: The study reports anthropometric data and formulas for measuring the stature of major ethnic groups in Sabah, which can be used to compare future work.