Latitude impact on Pandemic Sars-Cov-2 2020 outbreaks and possible utility of UV indexes in predictions of regional daily infections and deaths.
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ABSTRACT:
SUBMITTER: de Carvalho HN
PROVIDER: S-EPMC8755417 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundGiven the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19's disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction.ObjectiveThe aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods.MethodsWe used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics.ResultsGLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for "thermometer" and "mask strap," showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models.ConclusionsNAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions.
| S-EPMC8698803 | biostudies-literature
Project description:Structured protocols offer a transparent and systematic way to elicit and combine/aggregate, probabilistic predictions from multiple experts. These judgements can be aggregated behaviourally or mathematically to derive a final group prediction. Mathematical rules (e.g., weighted linear combinations of judgments) provide an objective approach to aggregation. The quality of this aggregation can be defined in terms of accuracy, calibration and informativeness. These measures can be used to compare different aggregation approaches and help decide on which aggregation produces the "best" final prediction. When experts' performance can be scored on similar questions ahead of time, these scores can be translated into performance-based weights, and a performance-based weighted aggregation can then be used. When this is not possible though, several other aggregation methods, informed by measurable proxies for good performance, can be formulated and compared. Here, we develop a suite of aggregation methods, informed by previous experience and the available literature. We differentially weight our experts' estimates by measures of reasoning, engagement, openness to changing their mind, informativeness, prior knowledge, and extremity, asymmetry or granularity of estimates. Next, we investigate the relative performance of these aggregation methods using three datasets. The main goal of this research is to explore how measures of knowledge and behaviour of individuals can be leveraged to produce a better performing combined group judgment. Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.
| S-EPMC8412308 | biostudies-literature
Project description:Motor prediction, i.e., the ability to predict the sensory consequences of motor commands, is critical for adapted motor behavior. Like speed or force, the accuracy of motor prediction varies in a 24-hour basis. Although the prevailing view is that basic biological markers regulate this circadian modulation, behavioral factors such as physical activity, itself modulated by the alternation of night and day, can also regulate motor prediction. Here, we propose that physical activity updates motor prediction on a daily basis. We tested our hypothesis by up- and down-regulating physical activity via arm-immobilization and high-intensity training, respectively. Motor prediction was assessed by measuring the timing differences between actual and mental arm movements. Results show that although mental movement time was modulated during the day when the arm was unconstrained, it remained constant when the arm was immobilized. Additionally, increase of physical activity, via release from immobilization or intense bout of training, significantly reduced mental movement time. Finally, mental and actual times were similar in the afternoon in the unconstrained condition, indicating that predicted and actual movements match after sufficient amount of physical activity. Our study supports the view that physical activity calibrates motor predictions on a daily basis.
| S-EPMC4668580 | biostudies-literature
Project description:ObjectivesAcute gastroenteritis (AGE) outbreaks commonly occur in congregate settings, including schools and childcare facilities. These outbreaks disrupt institutions, causing absences and temporary facility closures. This study analyzed the epidemiology of school and childcare AGE outbreaks in the United States.MethodsWe analyzed AGE outbreaks occurring in kindergarten to grade 12 schools and childcare facilities reported via the National Outbreak Reporting System in the United States from 2009 to 2019 and compared this information to 2020 data. Outbreak and case characteristics were compared using the Kruskal-Wallis rank sum test, χ2 goodness-of-fit test, and Fisher exact test.ResultsFrom 2009 to 2019, there were 2623 school, 1972 childcare, and 38 school and childcare outbreaks. School outbreaks were larger (median, 29 cases) than childcare outbreaks (median, 10 cases). Childcare outbreaks were longer (median, 15 days) than school outbreaks (median, 9 days). Norovirus (2383 outbreaks; 110 190 illnesses) and Shigella spp. (756 outbreaks; 9123 illnesses) were the most reported etiologies. Norovirus was the leading etiology in schools; norovirus and Shigella spp. were dominant etiologies in childcare centers. Most (85.7%) outbreaks were spread via person-to-person contact. In 2020, 123 outbreaks were reported, 85% in the first quarter.ConclusionsSchools and childcare centers are common AGE outbreak settings in the United States. Most outbreaks were caused by norovirus and Shigella spp. and spread via person-to-person transmission. Fewer outbreaks were reported in 2020 from the COVID-19 pandemic. Prevention and control efforts should focus on interrupting transmission, including environmental disinfection, proper handwashing, safe diapering, and exclusion of ill persons.
| S-EPMC10061552 | biostudies-literature
Project description: Not available
| S-EPMC10125210 | biostudies-literature
Project description:BackgroundMany civil liberties organizations have raised concerns that substandard medical care in United States Immigration and Customs Enforcement (ICE) detention facilities have led to preventable deaths. The 2018 Department of Homeland Security Appropriations Bill required ICE to make public all reports regarding in-custody deaths within 90 days beginning in Fiscal Year (FY) 2018. Accordingly, ICE has released death reports following each in-custody death since April of 2018. This study describes characteristics of deaths among individuals in ICE detention following the FY2018 mandate.MethodsData was extracted from death reports published by ICE following the FY2018 mandate. Causes of death were categorized as suicide or medical, and medical deaths as COVID-19-related or not. Characteristics were compared between medical and suicide deaths, and among medical deaths between COVID-19-related and non-COVID-19-related deaths. Additionally, death rates per person-year and per 100,000 admissions were calculated for FY2018, 2019, and 2020 using methods from prior work evaluating deaths among detained immigrants in the United States.ResultsSince April 2018, 35 individuals have died in ICE detention. The death rate per 100,000 admissions in ICE detention was 2.303 in FY2018, 1.499 in FY2019, and 10.833 in FY2020. Suicide by hanging was identified as the cause of death in 9 (25.7%), and medical causes in the remaining 26 (74.3%). Among 26 deaths attributable to medical causes, 8 (30.8%) were attributed to COVID-19, representing 72.7% of 11 deaths occurring since April 2020.ConclusionsThe death rate among individuals in ICE detention is increasing amidst the COVID-19 pandemic. Potentially preventable causes of death including COVID-19 and suicide contribute to at least half of recent deaths. Findings suggest that individuals detained by ICE may benefit from improved psychiatric care and prevention measures to combat suicide, as well as increased infection control efforts to reduce mortality associated with COVID-19.
| S-EPMC7870381 | biostudies-literature
Project description:COVID-19 is caused by SARS-CoV-2 infection and has reached pandemic proportions. Since then, several clinical characteristics have been associated with poor outcomes. This study aimed to describe the morbidity profile of COVID-19 deaths in Portugal. A study was performed including deaths certificated in Portugal with "COVID-19" (ICD-10: U07.1 or U07.2) coded as the underlying cause of death from the National e-Death Certificates Information System between 16 March and 31 December 2020. Comorbidities were derived from ICD-10 codes using the Charlson and Elixhauser indexes. The resident Portuguese population estimates for 2020 were used. The study included 6701 deaths (death rate: 65.1 deaths/100,000 inhabitants), predominantly males (72.1). The male-to-female mortality ratio was 1.1. The male-to-female mortality rate ratio was 1.2; however, within age groups, it varied 5.0-11.4-fold. COVID-19 deaths in Portugal during 2020 occurred mainly in individuals aged 80 years or older, predominantly in public healthcare institutions. Uncomplicated hypertension, uncomplicated diabetes mellitus, congestive heart failure, renal failure, cardiac arrhythmias, dementia, and cerebrovascular disease were observed among COVID-19 deceased patients, with prevalences higher than 10%. A high prevalence of zero morbidities was registered using both the Elixhauser and Charlson comorbidities lists (above 40.2%). Nevertheless, high multimorbidity was also identified at the time of COVID-19 death (about 36.5%). Higher multimorbidity levels were observed in men, increasing with age up to 80 years old. Zero-morbidity prevalence and high multimorbidity prevalences varied throughout the year 2020, seemingly more elevated in the mortality waves' peaks, suggesting variation according to the degree of disease incidence at a given period. This study provides detailed sociodemographic and clinical information on all certificated deaths from COVID-19 in Portugal during 2020, showing complex and extreme levels of morbidity (zero-morbidity vs. high multimorbidity) dynamics during the first year of the pandemic in Portugal.
| S-EPMC8999817 | biostudies-literature
Project description:Fecal contamination of water sources and open defecation have been linked to cholera outbreaks in India. However, a systematic review on the drivers responsible for these outbreaks has yet to be published. Here, we systematically review the published literature on cholera outbreaks in India between 2011 and 2020. We searched studies in English in three databases (MEDLINE, EMBASE, and Web of Science) and the Integrated Disease Surveillance Program that tracks cholera outbreaks throughout India. Two authors independently extracted data and assessed the quality of the included studies. Quantitative data on the modes of transmission reviewed in this study were assessed for any change over time between 2011-2015 and 2016-2020. Our search retrieved 10823 records initially, out of which 81 full-text studies were assessed for eligibility. Among these 81 studies, 20 were eligible for inclusion in this review. There were 565 reported outbreaks between 2011 and 2020 that led to 45,759 cases and 263 deaths. Outbreaks occurred throughout the year; however, they exploded with monsoons (June through September). In Tamil Nadu, a typical peak of cholera outbreaks was observed from December to January. Seventy-two percent (33,089/45,759) of outbreak-related cases were reported in five states, namely Maharashtra, West Bengal, Punjab, Karnataka, and Madhya Pradesh. Analysis of these outbreaks highlighted the main drivers of cholera including contaminated drinking water and food, inadequate sanitation and hygiene (including open defecation), and direct contact between households. The comparison between 2011-2015 and 2016-2020 showed a decreasing trend in the outbreaks that arose due to damaged water pipelines. Many Indians still struggle with open defecation, sanitation, and clean water access. These issues should be addressed critically. In addition, it is essential to interrupt cholera short-cycle transmission (mediated by households, stored drinking water and foodstuffs) during an outbreak. As cholera is associated with deprivation, socio-economic development is the only long-term solution.
| S-EPMC9099871 | biostudies-literature
Project description:ObjectivesThe purpose of this study was to determine predictors of the height of coronavirus disease 2019 (COVID-19) daily deaths' peak and time to the peak, to explain their variability across European countries.Study designFor 34 European countries, publicly available data were collected on daily numbers of COVID-19 deaths, population size, healthcare capacity, government restrictions and their timing, tourism and change in mobility during the pandemic.MethodsUnivariate and multivariate generalised linear models using different selection algorithms (forward, backward, stepwise and genetic algorithm) were analysed with height of COVID-19 daily deaths' peak and time to the peak as dependent variables.ResultsThe proportion of the population living in urban areas, mobility at the day of first reported death and number of infections when borders were closed were assessed as significant predictors of the height of COVID-19 daily deaths' peak. Testing the model with a variety of selection algorithms provided consistent results. Total hospital bed capacity, population size, the number of foreign travellers and the day of border closure were found to be significant predictors of time to COVID-19 daily deaths' peak.ConclusionsOur analysis demonstrated that countries with higher proportions of the population living in urban areas, countries with lower reduction in mobility at the beginning of the pandemic and countries having more infected people when closing borders experienced a higher peak of COVID-19 deaths. Greater bed capacity, bigger population size and later border closure could result in delaying time to reach the deaths' peak, whereas a high number of foreign travellers could accelerate it.
| S-EPMC7980183 | biostudies-literature
Project description:BackgroundExposure to COVID-19 is more likely among certain occupations compared with others. This descriptive study seeks to explore occupational differences in mortality due to COVID-19 among workers in Massachusetts.MethodsDeath certificates of those who died from COVID-19 in Massachusetts between March 1 and July 31, 2020 were collected. Occupational information was coded and age-adjusted mortality rates were calculated according to occupation.ResultsThere were 555 deaths among MA residents of age 16-64, with usable occupation information, resulting in an age-adjusted mortality rate of 16.4 per 100,000 workers. Workers in 11 occupational groups including healthcare support and transportation and material moving had mortality rates higher than that for workers overall. Hispanic and Black workers had age-adjusted mortality rates more than four times higher than that for White workers overall and also had higher rates than Whites within high-risk occupation groups.ConclusionEfforts should be made to protect workers in high-risk occupations identified in this report from COVID-19 exposure.
| S-EPMC8013546 | biostudies-literature