Short-term leprosy forecasting from an expert opinion survey.
Ontology highlight
ABSTRACT:
SUBMITTER: Deiner MS
PROVIDER: S-EPMC5558979 | biostudies-other | 2017
REPOSITORIES: biostudies-other
Similar Datasets
Project description:BackgroundTrachoma programs rely on guidelines made in large part using expert opinion of what will happen with and without intervention. Large community-randomized trials offer an opportunity to actually compare forecasting methods in a masked fashion.MethodsThe Program for the Rapid Elimination of Trachoma trials estimated longitudinal prevalence of ocular chlamydial infection from 24 communities treated annually with mass azithromycin. Given antibiotic coverage and biannual assessments from baseline through 30 months, forecasts of the prevalence of infection in each of the 24 communities at 36 months were made by three methods: the sum of 15 experts' opinion, statistical regression of the square-root-transformed prevalence, and a stochastic hidden Markov model of infection transmission (Susceptible-Infectious-Susceptible, or SIS model). All forecasters were masked to the 36-month results and to the other forecasts. Forecasts of the 24 communities were scored by the likelihood of the observed results and compared using Wilcoxon's signed-rank statistic.FindingsRegression and SIS hidden Markov models had significantly better likelihood than community expert opinion (p = 0.004 and p = 0.01, respectively). All forecasts scored better when perturbed to decrease Fisher's information. Each individual expert's forecast was poorer than the sum of experts.InterpretationRegression and SIS models performed significantly better than expert opinion, although all forecasts were overly confident. Further model refinements may score better, although would need to be tested and compared in new masked studies. Construction of guidelines that rely on forecasting future prevalence could consider use of mathematical and statistical models.Trial registrationClinicaltrials.gov NCT00792922.
| S-EPMC4547743 | biostudies-literature
Project description:The continuously growing number of COVID-19 cases pressures healthcare services worldwide. Accurate short-term forecasting is thus vital to support country-level policy making. The strategies adopted by countries to combat the pandemic vary, generating different uncertainty levels about the actual number of cases. Accounting for the hierarchical structure of the data and accommodating extra-variability is therefore fundamental. We introduce a new modelling framework to describe the pandemic's course with great accuracy and provide short-term daily forecasts for every country in the world. We show that our model generates highly accurate forecasts up to seven days ahead and use estimated model components to cluster countries based on recent events. We introduce statistical novelty in terms of modelling the autoregressive parameter as a function of time, increasing predictive power and flexibility to adapt to each country. Our model can also be used to forecast the number of deaths, study the effects of covariates (such as lockdown policies), and generate forecasts for smaller regions within countries. Consequently, it has substantial implications for global planning and decision making. We present forecasts and make all results freely available to any country in the world through an online Shiny dashboard.
| S-EPMC8024267 | biostudies-literature
Project description:Following Snyderman and Rothman (1987, 1988), we surveyed expert opinions on the current state of intelligence research. This report examines expert opinions on causes of international differences in student assessment and psychometric IQ test results. Experts were surveyed about the importance of culture, genes, education (quantity and quality), wealth, health, geography, climate, politics, modernization, sampling error, test knowledge, discrimination, test bias, and migration. The importance of these factors was evaluated for diverse countries, regions, and groups including Finland, East Asia, sub-Saharan Africa, Southern Europe, the Arabian-Muslim world, Latin America, Israel, Jews in the West, Roma (gypsies), and Muslim immigrants. Education was rated by N = 71 experts as the most important cause of international ability differences. Genes were rated as the second most relevant factor but also had the highest variability in ratings. Culture, health, wealth, modernization, and politics were the next most important factors, whereas other factors such as geography, climate, test bias, and sampling error were less important. The paper concludes with a discussion of limitations of the survey (e.g., response rates and validity of expert opinions).
| S-EPMC4804158 | biostudies-literature
Project description:BackgroundThe worldwide Coronavirus disease 2019 (COVID-19) public health pandemic has restructured clinical care of patients with cancer throughout the world. The specific changes in the management of genitourinary (GU) cancers in different cancer centers owing to COVID-19 are not known, and some clinical scenarios remain controversial. We conducted an opinion survey to determine what changes in cancer treatment strategies are occurring owing to the COVID-19 pandemic.Materials and methodsA 20-item online survey was sent on May 25, 2020 to 170 expert GU medical oncologists from Europe and North America. The survey solicited responses to changes in GU cancer management in the setting of the COVID-19 pandemic. Data was collected and managed via a secure REDCap Database.ResultsSurveys were completed by 78 (45.8%) of 170 GU oncologists between May 25, 2020 and June 25, 2020. Clinical practice changes owing to COVID-19 in at least one scenario were reported by 79.1% of responders, most pronounced in prostate cancer (71.8%) and least pronounced in urothelial cancer (23%). Preferences for change in management varied by country, with 78% (37/47) of United States oncologists indicating a change in their practice, 57% (4/7) of Canadian oncologists, and 79% (19/24) of European oncologists.ConclusionsThis study suggests international practice changes are occurring in GU cancer care during the COVID-19 pandemic. The variability in practice changes between countries may reflect differences in COVID-19 case load during the time point of data collection. These results, based on expert opinion during this rapidly changing crisis, may inform the oncologic community regarding the effects of COVID-19 on GU cancer care.
| S-EPMC7787909 | biostudies-literature
Project description:Short-term wind speed forecasting for Colonia Eulacio, Soriano Department, Uruguay, is performed by applying an artificial neural network (ANN) technique to the hourly time series representative of the site. To train the ANN and validate the technique, data for one year are collected by one tower, with anemometers installed at heights of 101.8, 81.8, 25.7, and 10.0 m. Different ANN configurations are applied for each site and height; then, a quantitative analysis is conducted, and the statistical results are evaluated to select the configuration that best predicts the real data. This method has lower computational costs than other techniques, such as numerical modelling. For integrating wind power into existing grid systems, accurate short-term wind speed forecasting is fundamental. Therefore, the proposed short-term wind speed forecasting method is an important scientific contribution for reliable large-scale wind power forecasting and integration in Uruguay. The results of the short-term wind speed forecasting showed good accuracy at all the anemometer heights tested, suggesting that the method is a powerful tool that can help the Administración Nacional de Usinas y Transmissiones Eléctricas manage the national energy supply.
| S-EPMC6517329 | biostudies-other
Project description:In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves' occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.
| S-EPMC8310337 | biostudies-literature
Project description:As the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states.
| S-EPMC8194983 | biostudies-literature
Project description:Short-term metro passenger flow forecasting is an essential component of intelligent transportation systems (ITS) and can be applied to optimize the passenger flow organization of a station and offer data support for metro passenger flow early warning and system management. LSTM neural networks have recently achieved remarkable recent in the field of natural language processing (NLP) because they are well suited for learning from experience to predict time series. For this purpose, we propose an empirical mode decomposition (EMD)-based long short-term memory (LSTM) neural network model for predicting short-term metro inbound passenger flow. The EMD algorithm decomposes the original sequential passenger flow into several intrinsic mode functions (IMFs) and a residual. Selected IMFs that are strongly correlated with the original data can be obtained via feature selection. The selected IMFs and the original data are integrated into inputs for LSTM neural networks, and a single LSTM prediction model and an EMD-LSTM hybrid forecasting model are developed. Finally, historical real automatic fare collection (AFC) data from metro passengers are collected from Chengdu Metro to verify the validity of the proposed EMD-LSTM prediction model. The results indicate that the proposed EMD-LSTM hybrid forecasting model outperforms the LSTM, ARIMA and BPN models.
| S-EPMC6738919 | biostudies-other
Project description:Background and purposeThe aim of this study was to survey the expert opinions on treatments for convulsive status epilepticus (CSE) and nonconvulsive status epilepticus (NCSE) in adults.MethodsForty-two South Korean epileptologists participated in this survey. They completed an online questionnaire regarding various patient scenarios and evaluated the appropriateness of medications used to treat CSE and NCSE.ResultsInitial treatment with a benzodiazepine (BZD) followed by either a second BZD or an antiepileptic drug (AED) monotherapy was the preferred treatment strategy. More than two-thirds of the experts used a second BZD when the first one failed, and consensus was reached for 84.8% of the survey items. The preferred BZD was intravenous (IV) lorazepam for the initial treatment of status epilepticus. IV fosphenytoin and IV levetiracetam were chosen for AED monotherapy after the failure of BZD. The treatments for NCSE were similar to those for CSE. Continuous IV midazolam infusion was the treatment of choice for iatrogenic coma in refractory CSE, but other AEDs were preferred over iatrogenic coma in refractory NCSE.ConclusionsThe results of this survey are consistent with previous guidelines, and can be cautiously applied in clinical practice when treating patients with CSE or NCSE.
| S-EPMC7840316 | biostudies-literature
Project description:Osteoarthritis (OA) is a leading cause of disability among older adults worldwide. Treatment aims are to alleviate inflammatory pain and improve physical function through non-pharmacological and pharmacological interventions. Non-steroidal anti-inflammatory drugs (NSAIDs) are recommended as first-line therapy. However, selection is challenged by patient age, comorbidities and polypharmacy, and by the drug's benefit/risk balance, all of which together influence the risk of cardiovascular (CV), gastrointestinal (GI) and renal adverse events (AEs). While the efficacy profile of the various NSAIDs is delineated, the differences in their safety profile are not straightforward. This narrative review provides practical indications by a multidisciplinary Italian expert panel for general practitioners and specialists managing OA patients with chronic inflammatory pain; the goal is to maximize therapy efficacy while reducing untoward effects caused by inappropriate NSAID use. The discussion on the best approach to NSAIDs spanned the following topics: (1) patient evaluation: investigate pain origin, duration and components together with possible risk factors for CV, GI and renal AEs; (2) non-pharmacological interventions: the physiatrist provides a person-centered, holistic approach accounting for all patient aspects; (3) pharmacological interventions: patient profile and drugs' pharmacological properties affect NSAID selection, which drugs to be used in combination or to be avoided, formulation and therapy duration; (4) the pharmacologist's, general practitioner's and pain therapist's points of view; (5) NSAID safety: the individual baseline risk and the drug's safety profile are major determinants of CV, GI and renal risk; consider possible drug-drug interactions; (6) periodical re-evaluation of treatment response and adherence, using scales to assess pain and function.
| S-EPMC8586433 | biostudies-literature