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1.
Hypertension ; 76(5): 1526-1536, 2020 11.
Article in English | MEDLINE | ID: covidwho-2153220

ABSTRACT

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.


Subject(s)
Coronavirus Infections/epidemiology , Disease Outbreaks/statistics & numerical data , Disease Progression , Heart Failure/enzymology , Heart Failure/physiopathology , Peptidyl-Dipeptidase A/blood , Pneumonia, Viral/epidemiology , Academic Medical Centers , Analysis of Variance , Angiotensin-Converting Enzyme 2 , Biomarkers/metabolism , COVID-19 , Cohort Studies , Coronavirus Infections/prevention & control , Female , Humans , Linear Models , Male , Middle Aged , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Prognosis , Proportional Hazards Models , Proteomics/methods , Retrospective Studies , Sensitivity and Specificity , Severity of Illness Index , United States
2.
BMC Public Health ; 22(1): 2163, 2022 Nov 24.
Article in English | MEDLINE | ID: covidwho-2139225

ABSTRACT

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.


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Government , Population Density , Linear Models , Principal Component Analysis
4.
An Acad Bras Cienc ; 94(suppl 3): e20210921, 2022.
Article in English | MEDLINE | ID: covidwho-2079841

ABSTRACT

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.


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , SARS-CoV-2 , Linear Models , Brazil/epidemiology , Neural Networks, Computer
5.
Sci Rep ; 12(1): 15827, 2022 09 22.
Article in English | MEDLINE | ID: covidwho-2036878

ABSTRACT

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.


Subject(s)
COVID-19 , COVID-19/epidemiology , Causality , Humans , Linear Models , Machine Learning , Socioeconomic Factors , United States/epidemiology
6.
PLoS One ; 17(8): e0273840, 2022.
Article in English | MEDLINE | ID: covidwho-2021943

ABSTRACT

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.


Subject(s)
Body Height , Forensic Anthropology , Adolescent , Adult , Anthropometry/methods , Cross-Sectional Studies , Female , Forensic Anthropology/methods , Humans , Linear Models , Male , Multivariate Analysis , Young Adult
7.
Int J Environ Res Public Health ; 19(17)2022 Aug 27.
Article in English | MEDLINE | ID: covidwho-2006015

ABSTRACT

Life expectancy (LE) is a core measure of population health. Studies have confirmed the predictive importance of modifiable determinants on LE, but less is known about their association with LE change over time at the US county level. In addition, we explore the predictive association of LE change with COVID-19 mortality. We used a linear regression model to calculate county-level annual LE change from 2011 to 2016, and categorized LE change (≤-0.1 years change per year as decreasing, ≥0.1 years as increasing, otherwise no change). A multinomial regression model was used to determine the association between modifiable determinants of health indicators from the County Health Rankings and LE change. A Poisson regression model was used to evaluate the relationship between change in life expectancy and COVID-19 mortality through September 2021. Among 2943 counties, several modifiable determinants of health were significantly associated with odds of being in increasing LE or decreasing LE counties, including adult smoking, obesity, unemployment, and proportion of children in poverty. The presence of an increasing LE in 2011-2016, as compared to no change, was significantly associated with a 5% decrease in COVID-19 mortality between 2019 and 2021 (ß = 0.953, 95% CI: 0.943, 0.963). We demonstrated that change in LE at the county level is a useful metric for tracking public health progress, measuring the impact of public health initiatives, and gauging preparedness and vulnerability for future public health emergencies.


Subject(s)
COVID-19 , Public Health , Adult , COVID-19/epidemiology , Child , Humans , Life Expectancy , Linear Models , Poverty
8.
Brief Bioinform ; 23(5)2022 09 20.
Article in English | MEDLINE | ID: covidwho-2001207

ABSTRACT

Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in the alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method that is capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories and outcomes.


Subject(s)
COVID-19 , COVID-19/genetics , Humans , Linear Models
9.
Math Biosci ; 351: 108887, 2022 09.
Article in English | MEDLINE | ID: covidwho-1983639

ABSTRACT

We consider inverse problems governed by systems of ordinary differential equations (ODEs) that contain uncertain parameters in addition to the parameters being estimated. In such problems, which are common in applications, it is important to understand the sensitivity of the solution of the inverse problem to the uncertain model parameters. It is also of interest to understand the sensitivity of the inverse problem solution to different types of measurements or parameters describing the experimental setup. Hyper-differential sensitivity analysis (HDSA) is a sensitivity analysis approach that provides tools for such tasks. We extend existing HDSA methods by developing methods for quantifying the uncertainty in the estimated parameters. Specifically, we propose a linear approximation to the solution of the inverse problem that allows efficiently approximating the statistical properties of the estimated parameters. We also explore the use of this linear model for approximate global sensitivity analysis. As a driving application, we consider an inverse problem governed by a COVID-19 model. We present comprehensive computational studies that examine the sensitivity of this inverse problem to several uncertain model parameters and different types of measurement data. Our results also demonstrate the effectiveness of the linear approximation model for uncertainty quantification in inverse problems and for parameter screening.


Subject(s)
COVID-19 , Algorithms , Humans , Linear Models , Models, Biological , Uncertainty
10.
Comput Math Methods Med ; 2022: 9554057, 2022.
Article in English | MEDLINE | ID: covidwho-1962507

ABSTRACT

Objective: To analyze the relationship between effective distance and epidemic spread trajectory and between arrival time and scale based on the COVID-19 data outbreak in Wuhan and thus to improve the prediction ability of the spread of infectious disease. Methods: Up to January 28, 2020, the reporting date, the onset date, and the cumulative number of confirmed cases of COVID-19 in each province and city were collected. Baidu migration data was used to calculate the effective distance from Wuhan city to other regions. The reporting date and onset date of the first diagnosed patient were taken as the arrival time, respectively, to establish a linear regression model of effective distance and arrival time. In different provinces and cities, the logarithm of the cumulative number of confirmed cases with a base of 5 was taken as the criteria to determine the level of the cumulative confirmed cases. Based on this, the linear regression model of effective distance and the level of cumulative confirmed cases in the provincial and municipal units was established. Results: The linear correlation between the reporting date of the first confirmed patient and the effective distance was not strong. The coefficients of determination (R 2) for cities with and without the cities of Hubei Province were 0.36 and 0.44, respectively. And the linear correlation between the onset date of the first confirmed patient and the effective distance was strong. And the coefficients of determination (R 2) for cities with and without the cities of Hubei Province were 0.67 and 0.83, respectively. And the linear correlation between the level of cumulative confirmed cases in the provincial and municipal units and the effective distance was strong, with an R 2 of 0.87 and 0.84, respectively. The regression coefficients of each linear model were statistically significant (P < 0.001). Conclusion: The effective distance has a good fit with the model of the onset date of the first confirmed patient and the level of cumulative confirmed cases, which can predict the trajectory, time, and transmission range of the epidemic. It can be taken as the reference for the early warning, prevention, and control of sudden acute infectious diseases from a macro perspective.


Subject(s)
COVID-19 , Communicable Diseases , Epidemics , COVID-19/epidemiology , China/epidemiology , Cities/epidemiology , Communicable Diseases/epidemiology , Humans , Linear Models , SARS-CoV-2
11.
Comput Intell Neurosci ; 2022: 3687598, 2022.
Article in English | MEDLINE | ID: covidwho-1962471

ABSTRACT

A divorce is a legal step taken by married people to end their marriage. It occurs after a couple decides to no longer live together as husband and wife. Globally, the divorce rate has more than doubled from 1970 until 2008, with divorces per 1,000 married people rising from 2.6 to 5.5. Divorce occurs at a rate of 16.9 per 1,000 married women. According to the experts, over half of all marriages ends in divorce or separation in the United States. A novel ensemble learning technique based on advanced machine learning algorithms is proposed in this study. The support vector machine (SVM), passive aggressive classifier, and neural network (MLP) are applied in the context of divorce prediction. A question-based dataset is created by the field specialist. The responses to the questions provide important information about whether a marriage is likely to turn into divorce in the future. The cross-validation is applied in 5 folds, and the performance results of the evaluation metrics are examined. The accuracy score is 100%, and Receiver Operating Characteristic (ROC) curve accuracy score, recall score, the precision score, and the F1 accuracy score are close to 97% confidently. Our findings examined the key indicators for divorce and the factors that are most significant when predicting the divorce.


Subject(s)
Divorce , Support Vector Machine , Developed Countries , Female , Humans , Linear Models , Neural Networks, Computer , United States
12.
Int J Environ Res Public Health ; 19(15)2022 07 23.
Article in English | MEDLINE | ID: covidwho-1957307

ABSTRACT

The aim of this research was to analyze how the need for psychological support of health workers (HCWs) influenced the beliefs, perceptions and attitudes towards their work during the COVID-19 pandemic and to predict the need of psychological assistance. A descriptive transversal study was conducted based on a self-administered questionnaire distributed to health professionals working in the Canary Islands, Spain. The data were analyzed using Pearson's chi-squared test and the linear trend test. The correlation test between ordinal and frequency variables was applied using Kendall's Tau B. Multiple logistic regression was used to predict dichotomous variables. The sample included 783 health professionals: 17.8% (n = 139) of them needed psychological or psychiatric support. Being redeployed to other services influenced the predisposition to request psychological help, and HCWs who required psychological support had more negative attitudes and perceptions towards their work. After five waves of COVID-19, these HCWs reported to be physically, psychologically and emotionally exhausted or even "burned out"; they did not feel supported by their institutions. The commitment of health personnel to fight against the COVID-19 pandemic decreased after the five waves, especially among professionals who required psychological support.


Subject(s)
COVID-19/epidemiology , COVID-19/psychology , Health Personnel/psychology , Pandemics , SARS-CoV-2 , Attitude of Health Personnel , Burnout, Professional , Chi-Square Distribution , Humans , Linear Models , Psychosocial Support Systems , Spain/epidemiology , Surveys and Questionnaires
13.
BMC Med Res Methodol ; 22(1): 202, 2022 Jul 25.
Article in English | MEDLINE | ID: covidwho-1957045

ABSTRACT

BACKGROUND: Interrupted time series (ITS) analysis has become a popular design to evaluate the effects of health interventions. However, the most common formulation for ITS, the linear segmented regression, is not always adequate, especially when the timing of the intervention is unclear. In this study, we propose a new model to overcome this limitation. METHODS: We propose a new ITS model, ARIMAITS-DL, that combines (1) the Autoregressive Integrated Moving Average (ARIMA) model and (2) distributed lag functional terms. The ARIMA technique allows us to model autocorrelation, which is frequently observed in time series data, and the decaying cumulative effect of the intervention. By contrast, the distributed lag functional terms represent the idea that the intervention effect does not start at a fixed time point but is distributed over a certain interval (thus, the intervention timing seems unclear). We discuss how to select the distribution of the effect, the model construction process, diagnosing the model fitting, and interpreting the results. Further, our model is implemented as an example of a statement of emergency (SoE) during the coronavirus disease 2019 pandemic in Japan. RESULTS: We illustrate the ARIMAITS-DL model with some practical distributed lag terms to examine the effect of the SoE on human mobility in Japan. We confirm that the SoE was successful in reducing the movement of people (15.0-16.0% reduction in Tokyo), at least between February 20 and May 19, 2020. We also provide the R code for other researchers to easily replicate our method. CONCLUSIONS: Our model, ARIMAITS-DL, is a useful tool as it can account for the unclear intervention timing and distributed lag effect with autocorrelation and allows for flexible modeling of different types of impacts such as uniformly or normally distributed impact over time.


Subject(s)
COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , Humans , Interrupted Time Series Analysis , Linear Models , Pandemics/prevention & control , Time Factors
14.
J Sci Med Sport ; 25(10): 850-854, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1926705

ABSTRACT

OBJECTIVES: Describe the highest frequency and variability for tackle events in rugby league. Investigate seasonal differences in total tackle events per match over a seven-year period. DESIGN: Retrospective observational. METHODS: Tackle events (i.e., ball carrier events [attacker] and tackler involvements [defender]) from 864 male professional rugby league players competing in 1176 Super League matches from 2014 to 2020 were included. A series of linear mixed effect models were used to determine the frequency and variability during peak 1-, 3-, 5-, 10-, 20-, 40-min and whole-match tackle events per player per match at a positional group level. Differences between seasons for the total number of tackle events per match were compared using a one-way analysis of variance and with Tukey's honestly significant difference test. RESULTS: Tackle events were greatest for Props (51.5 [47.7-55.4] per match). Within-players, between-matches, and between-seasons variability was <10 % for tackle events. There were significantly less tackle events and tackler involvements per match in 2014 and a significantly more tackle events per match in season 2020b when compared with all other seasons. CONCLUSIONS: Large between-position variability in peak tackle events, ball carrier events, and tackler involvements would suggest that coaches should separate players into positional groups and prescribe training accordingly. Total number of tackle events, ball carrier events, and tackler involvements were significantly greater in season 2020b when compared to season 2014 to 2019 (inclusive) which may be a consequence of rule changes introduced to the sport.


Subject(s)
Athletic Performance , Football , Humans , Linear Models , Male , Retrospective Studies , Rugby
15.
PLoS One ; 17(2): e0263767, 2022.
Article in English | MEDLINE | ID: covidwho-1910527

ABSTRACT

BACKGROUND: The perception of the transmission risks of SARS-CoV-2 in social and educational settings by US healthcare providers have not been previously quantified. METHODS: Respondents completed an online survey between September and October 2020 to estimate the risk of SARS-CoV-2 transmission on a scale of 0-10 for different social and educational activities prior to the availability of the SARS-CoV-2 vaccines. Demographic information and experiences during the pandemic were also collected. The risk assessment was emailed to three listservs of healthcare providers, including national listservs of pediatric (PID) and adult infectious diseases (AID) providers, and a listserv of general pediatric practitioners in the St Louis, USA metropolitan area. RESULTS: Respondents identified the highest risk of SARS-CoV-2 transmission in spending time in a bar, eating at a restaurant, and attending an indoor sporting event. In the school setting, lower risk was identified in elementary and daycare students compared to high school or university-level students. Comparatively, the risk of transmission to students and teachers was lower than the identified high-risk social activities. Factors increasing risk perception in social activities included the absence of children in the respondent's household and female gender. For the school setting, AID providers perceived greater risk compared to PID providers or pediatric practitioners. CONCLUSIONS: Respondents identified high risk activities that were associated with a high density of participants in an indoor space where masks are removed for eating and drinking. Differences were apparent in the school setting where pediatric providers perceived lower risks when compared to adult providers.


Subject(s)
COVID-19 Vaccines/immunology , COVID-19/immunology , COVID-19/virology , Health Personnel , Risk Management , SARS-CoV-2/physiology , Adolescent , Child , Cross-Sectional Studies , Female , Humans , Linear Models , Male , Odds Ratio , Pandemics/prevention & control , Young Adult
16.
Epidemiol Health ; 44: e2022016, 2022.
Article in English | MEDLINE | ID: covidwho-1884561

ABSTRACT

OBJECTIVES: The purpose of this study was to enhance the understanding of the local-level spatiotemporal dynamics of COVID-19 transmission in the Greater Seoul Area (GSA), Korea, after its initial outbreak in January 2020. METHODS: Using the weekly aggregates of coronavirus disease 2019 (COVID-19) cases of 77 municipalities in the GSA, we examined the relative risks of COVID-19 infection across local districts over 50 consecutive weeks in 2020. To this end, we employed a spatiotemporal generalized linear mixed model under the hierarchical Bayesian framework. This allowed us to empirically examine the random effects of spatial alignments, temporal autocorrelation, and spatiotemporal interaction, along with fixed effects. Specifically, we utilized the conditional autoregressive and the weakly informative penalized complexity priors for hyperparameters of the random effects. RESULTS: Spatiotemporal interaction dominated the overall variability of random influences, followed by spatial correlation, whereas the temporal correlation appeared to be small. Considering these findings, we present dynamic changes in the spread of COVID-19 across local municipalities in the GSA as well as regions at elevated risk for further policy intervention. CONCLUSIONS: The outcomes of this study can contribute to advancing our understanding of the local-level COVID-19 spread dynamics within densely populated regions in Korea throughout 2020 from a different perspective, and will contribute to the development of regional safety planning against infectious diseases.


Subject(s)
COVID-19 , Bayes Theorem , COVID-19/epidemiology , Disease Outbreaks , Humans , Linear Models , Seoul/epidemiology
17.
Sci Rep ; 11(1): 24449, 2021 12 27.
Article in English | MEDLINE | ID: covidwho-1852475

ABSTRACT

Syndromic surveillance systems monitor disease indicators to detect emergence of diseases and track their progression. Here, we report on a rapidly deployed active syndromic surveillance system for tracking COVID-19 in Israel. The system was a novel combination of active and passive components: Ads were shown to people searching for COVID-19 symptoms on the Google search engine. Those who clicked on the ads were referred to a chat bot which helped them decide whether they needed urgent medical care. Through its conversion optimization mechanism, the ad system was guided to focus on those people who required such care. Over 6 months, the ads were shown approximately 214,000 times and clicked on 12,000 times, and 722 people were informed they needed urgent care. Click rates on ads and the fraction of people deemed to require urgent care were correlated with the hospitalization rate ([Formula: see text] and [Formula: see text], respectively) with a lead time of 9 days. Males and younger people were more likely to use the system, and younger people were more likely to be determined to require urgent care (slope: [Formula: see text], [Formula: see text]). Thus, the system can assist in predicting case numbers and hospital load at a significant lead time and, simultaneously, help people determine if they need medical care.


Subject(s)
COVID-19/epidemiology , Sentinel Surveillance , Ambulatory Care/statistics & numerical data , COVID-19/pathology , COVID-19/virology , Hospitalization/statistics & numerical data , Humans , Israel/epidemiology , Linear Models , SARS-CoV-2/isolation & purification , Search Engine
18.
Clin Imaging ; 88: 4-8, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1819456

ABSTRACT

BACKGROUND: COVID-19 is a disease with high mortality worldwide, and which parameters that affect mortality in intensive care are still being investigated. This study aimed to show the factors affecting mortality in COVID-19 intensive care patients and write a model that can predict mortality. METHODS: The data of 229 patients in the COVID-19 intensive care unit were scanned. Laboratory tests, APACHE, SOFA, and GCS values were recorded. CT scores were calculated with chest CTs. The effects of these data on mortality were examined. The effects of the variables were modeled using the stepwise regression method. RESULTS: While the mean age of female (30.14%) patients was 69.1 ± 12.2, the mean age of male (69.86%) patients was 66.9 ± 11.5. The mortality rate was 69.86%. Age, CRP, D-dimer, creatinine, procalcitonin, APACHE, SOFA, GCS, and CT score were significantly different in the deceased patients than the survival group. When we attempted to create a model using stepwise linear regression analysis, the appropriate model was achieved at the fourth step. Age, CRP, APACHE, and CT score were included in the model, which has the power to predict mortality with 89.9% accuracy. CONCLUSION: Although, when viewed individually, there is a significant difference in parameters such as creatinine, procalcitonin, D-dimer, GCS, and SOFA score, the probability of mortality can be estimated by knowing only the age, CRP, APACHE, and CT scores. These four simple parameters will help clinicians effectively use resources in treatment.


Subject(s)
COVID-19 , Sepsis , APACHE , Creatinine , Female , Humans , Intensive Care Units , Linear Models , Male , Organ Dysfunction Scores , Procalcitonin , Prognosis , ROC Curve , Regression Analysis , Retrospective Studies , Sepsis/therapy , Tomography, X-Ray Computed
19.
Sci Rep ; 12(1): 5083, 2022 03 24.
Article in English | MEDLINE | ID: covidwho-1815586

ABSTRACT

The challenge of accurately short-term forecasting demand is due to model selection and the nature of data trends. In this study, the prediction model was determined based on data patterns (trend data without seasonality) and the accuracy of prediction measurement. The cumulative number of COVID-19 affected people in some ASEAN countries had been collected from the Worldometers database. Three models [Holt's method, Wright's modified Holt's method, and unreplicated linear functional relationship model (ULFR)] had been utilized to identify an efficient model for short-time prediction. Moreover, different smoothing parameters had been tested to find the best combination of the smoothing parameter. Nevertheless, using the day-to-day reported cumulative case data and 3-days and 7-days in advance forecasts of cumulative data. As there was no missing data, Holt's method and Wright's modified Holt's method showed the same result. The text-only result corresponds to the consequences of the models discussed here, where the smoothing parameters (SP) were roughly estimated as a function of forecasting the number of affected people due to COVID-19. Additionally, the different combinations of SP showed diverse, accurate prediction results depending on data volume. Only 1-day forecasting illustrated the most efficient prediction days (1 day, 3 days, 7 days), which was validated by the Nash-Sutcliffe efficiency (NSE) model. The study also validated that ULFR was an efficient forecasting model for the efficient model identifying. Moreover, as a substitute for the traditional R-squared, the study applied NSE and R-squared (ULFR) for model selection. Finally, the result depicted that the prediction ability of ULFR was superior to Holt's when it is compared to the actual data.


Subject(s)
COVID-19 , COVID-19/epidemiology , Forecasting , Humans , Linear Models
20.
PLoS One ; 17(2): e0263230, 2022.
Article in English | MEDLINE | ID: covidwho-1793532

ABSTRACT

Misophonia is a newly described condition characterized by sensory and emotional reactivity (e.g., anxiety, anger, disgust) to repetitive, pattern-based sounds (e.g., throat clearing, chewing, slurping). Individuals with misophonia report significant functional impairment and interpersonal distress. Growing research indicates ineffective coping and emotional functioning broadly (e.g., affective lability, difficulties with emotion regulation) are central to the clinical presentation and severity of misophonia. Preliminary evidence suggests an association between negative emotionality and deficits in emotion regulation in misophonia. Still, little is known about (a) the relationships among specific components of emotional functioning (e.g., emotion regulation, affective lability) with misophonia, and (b) which component(s) of misophonia (e.g., noise frequency, emotional and behavioral responses, impairment) are associated with emotional functioning. Further, despite evidence that mood and anxiety disorders co-occur with misophonia, investigation thus far has not controlled for depression and anxiety symptoms. Examination of these relationships will help inform treatment development for misophonia. The present study begins to disambiguate the relationships among affective lability, difficulties with emotion regulation, and components of misophonia. A sample of 297 participants completed questionnaires assessing misophonia, emotional functioning, depression, anxiety, and COVID-19 impact. Findings indicated that misophonia severity was positively associated with each of these constructs with small to medium effect sizes. When controlling for depression, anxiety, and COVID-19 impact, results from this preliminary study suggest that (a) difficulties with emotion regulation may be correlated with misophonia severity, and (b) misophonic responses, not number of triggers or perceived severity, are associated with difficulties with emotion regulation. Overall, these findings begin to suggest that emotion regulation is important to our understanding the risk factors and treatment targets for misophonia.


Subject(s)
Emotional Regulation/physiology , Phobic Disorders/psychology , Adult , Anxiety/psychology , Depression/psychology , Female , Humans , Linear Models , Male , Mental Health , Surveys and Questionnaires
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