Interactive tool for clustering and forecasting patterns of Taiwan COVID-19 spread.
Ontology highlight
ABSTRACT:
SUBMITTER: Ashouri M
PROVIDER: S-EPMC9246234 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe novel coronavirus (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, and within a few months, it has become a global pandemic. This forced many affected countries to take stringent measures such as complete lockdown, shutting down businesses and trade, as well as travel restrictions, which has had a tremendous economic impact. Therefore, having knowledge and foresight about how a country might be able to contain the spread of COVID-19 will be of paramount importance to the government, policy makers, business partners and entrepreneurs. To help social and administrative decision making, a model that will be able to forecast when a country might be able to contain the spread of COVID-19 is needed.ResultsThe results obtained using our long short-term memory (LSTM) network-based model are promising as we validate our prediction model using New Zealand's data since they have been able to contain the spread of COVID-19 and bring the daily new cases tally to zero. Our proposed forecasting model was able to correctly predict the dates within which New Zealand was able to contain the spread of COVID-19. Similarly, the proposed model has been used to forecast the dates when other countries would be able to contain the spread of COVID-19.ConclusionThe forecasted dates are only a prediction based on the existing situation. However, these forecasted dates can be used to guide actions and make informed decisions that will be practically beneficial in influencing the real future. The current forecasting trend shows that more stringent actions/restrictions need to be implemented for most of the countries as the forecasting model shows they will take over three months before they can possibly contain the spread of COVID-19.
| S-EPMC8190741 | biostudies-literature
Project description:We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID-19 cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19.
| S-EPMC7683602 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2022-02-28 | GSE197259 | GEO
Project description:BackgroundUnequal access to healthcare is a global medical problem. Telemedicine, recently made possible by technological advances, may mitigate this inequity. However, the usefulness of telemedicine for procedure-driven disciplines, such as otolaryngology, under infectious conditions (e.g., the COVID-19 pandemic) is unknown.MethodsTelemedicine was made legal in Taiwan by an amendment to the Physician Act in 2018. Kaohsiung Chang Gung Memorial Hospital was the first hospital in Taiwan to provide the telemedicine service by connecting to the Chenggong Branch of Taitung Hospital (CGBTH) in November 2018. This retrospective cohort study included all new and established otolaryngology outpatient consultations between November 2018 and May 2020 at CGBTH. The Current Procedural Terminology and International Classification of Disease, 10th Revision codes, patient demographic data, and questionnaire data were obtained.ResultsThe study included 123 patients with 218 encounters over 19 months. The majority of complaints were ear-related (52.6%). Overall, 49% of the encounters required a specialized procedure for diagnosis and treatment; of these, cerumen removal was the most common procedure. The patient subjective improvement rate increased over the study period (from 62.0% to 78.9%). The rates of return and case closure were both around 90% in 2018 and 2019. The number of otolaryngology consultations and rate of return declined after the start of the COVID-19 pandemic; however, the subjective improvement and case closure rates remained stable. The telemedicine service saved at least 2 h driving time per visit.ConclusionTelemedicine for otolaryngology is a promising approach for remote and underserved regions, as well as during an infectious disease pandemic.
| S-EPMC8556875 | biostudies-literature
Project description:Human mobility data are indispensable in modeling large-scale epidemics, especially in predicting the spatial spread of diseases and in evaluating spatial heterogeneity intervention strategies. However, statistical data that can accurately describe large-scale population migration are often difficult to obtain. We propose an algorithm model based on the network science approach, which estimates the travel flow data in mainland China by transforming location big data and airline operation data into network structure information. In addition, we established a simplified deterministic SEIR (Susceptible-Exposed-Infectious-Recovered)-metapopulation model to verify the effectiveness of the estimated travel flow data in the study of predicting epidemic spread. The results show that individual travel distance in mainland China is mainly within 100 km. There is far more travel between prefectures within the same province than across provinces. The epidemic spatial spread model incorporating estimated travel data accurately predicts the spread of COVID-19 in mainland China. The results suggest that there are far more travelers than usual during the Spring Festival in mainland China, and the number of travelers from Wuhan mainly determines the number of confirmed cases of COVID-19 in each prefecture.
| S-EPMC8468459 | biostudies-literature
Project description:ObjectivesForecasting epidemics like COVID-19 is of crucial importance, it will not only help the governments but also, the medical practitioners to know the future trajectory of the spread, which might help them with the best possible treatments, precautionary measures and protections. In this study, the popular autoregressive integrated moving average (ARIMA) will be used to forecast the cumulative number of confirmed, recovered cases, and the number of deaths in Pakistan from COVID-19 spanning June 25, 2020 to July 04, 2020 (10 days ahead forecast).MethodsTo meet the desire objectives, data for this study have been taken from the Ministry of National Health Service of Pakistan's website from February 27, 2020 to June 24, 2020. Two different ARIMA models will be used to obtain the next 10 days ahead point and 95% interval forecast of the cumulative confirmed cases, recovered cases, and deaths. Statistical software, RStudio, with "forecast", "ggplot2", "tseries", and "seasonal" packages have been used for data analysis.ResultsThe forecasted cumulative confirmed cases, recovered, and the number of deaths up to July 04, 2020 are 231239 with a 95% prediction interval of (219648, 242832), 111616 with a prediction interval of (101063, 122168), and 5043 with a 95% prediction interval of (4791, 5295) respectively. Statistical measures i.e. root mean square error (RMSE) and mean absolute error (MAE) are used for model accuracy. It is evident from the analysis results that the ARIMA and seasonal ARIMA model is better than the other time series models in terms of forecasting accuracy and hence recommended to be used for forecasting epidemics like COVID-19.ConclusionIt is concluded from this study that the forecasting accuracy of ARIMA models in terms of RMSE, and MAE are better than the other time series models, and therefore could be considered a good forecasting tool in forecasting the spread, recoveries, and deaths from the current outbreak of COVID-19. Besides, this study can also help the decision-makers in developing short-term strategies with regards to the current number of disease occurrences until an appropriate medication is developed.
| S-EPMC7703963 | biostudies-literature
Project description:SummaryAmidst the continuing spread of coronavirus disease-19 (COVID-19), real-time data analysis and visualization remain critical the general public to track the pandemic's impact and to inform policy making by officials. Multiple metrics permit the evaluation of the spread, infection and mortality of infectious diseases. For example, numbers of new cases and deaths provide easily interpretable measures of absolute impact within a given population and time frame, while the effective reproduction rate provides an epidemiological measure of the rate of spread. By evaluating multiple metrics concurrently, users can leverage complementary insights into the impact and current state of the pandemic when formulating prevention and safety plans for oneself and others. We describe COVID-19 Spread Mapper, a unified framework for estimating and quantifying the uncertainty in the smoothed daily effective reproduction number, case rate and death rate in a region using log-linear models. We apply this framework to characterize COVID-19 impact at multiple geographic resolutions, including by US county and state as well as by country, demonstrating the variation across resolutions and the need for harmonized efforts to control the pandemic. We provide an open-source online dashboard for real-time analysis and visualization of multiple key metrics, which are critical to evaluate the impact of COVID-19 and make informed policy decisions.Availability and implementationOur model and tool are publicly available as implemented in R and hosted at https://metrics.covid19-analysis.org/. The source code is freely available from https://github.com/lin-lab/COVID19-Rt and https://github.com/lin-lab/COVID19-Viz.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9890301 | biostudies-literature
Project description:Infections caused by SARS-CoV-2 may cause a severe disease, termed COVID-19, with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms, and their modulation is the only therapeutic strategy that has shown a mortality benefit. Herein, we used peripheral blood transcriptomes of critically-ill COVID-19 patients obtained at admission in an Intensive Care Unit, to identify two clusters that, in spite of no major clinical differences, have different gene expression profiles that reveal different underlying pathogenetic mechanisms and ultimately have different ICU outcome. A transcriptomic signature was used to identify these clusters in an external validation cohort, yielding a similar result. These results illustrate the potential of transcriptomic profiles to identify patient endotypes and point to relevant pathogenetic mechanisms in COVID-19.
2023-04-29 | GSE206264 | GEO
Project description:BackgroundInternational travel has been recognized as a risk factor contributing to the spread of antimicrobial resistance (AMR). However, tools focused on AMR in the context of international travel and designed to guide decision-making are limited. We aimed at developing an evidence-based educational tool targeting both healthcare professionals (HCPs) and international travellers to help prevent the spread of AMR.MethodsA literature review on 12 antimicrobial-resistant bacteria (ARB) listed as critical and high tiers in the WHO Pathogen Priority List covering four key areas was carried out: AMR surveillance data; epidemiological studies reporting ARB prevalence data on carriage in returning travellers; guidance documents reporting indications on screening for ARB in returning travellers and recommendations for ARB prevention for the public. The evidence, catalogued at country-level, provided the content for a series of visualizations that allow assessment of the risk of AMR acquisition through travel.ResultsUp to January 2021, the database includes data on: (i) AMR surveillance for 2.018.241 isolates from 86 countries; (ii) ARB prevalence of carriage from 11.679 international travellers and (iii) 15 guidance documents published by major public health agencies. The evidence allowed the development of a consultation scheme for the evaluation of risk factors, prevalence of carriage, proportion and recommendations for screening of AMR. For the public, pre-travel practical measures to minimize the risk of transmission were framed.ConclusionsThis easy-to-use, annually updated, freely accessible AMR travel tool (https://epi-net.eu/travel-tool/overview/), is the first of its kind to be developed. For HCPs, it can provide a valuable resource for teaching and a repository that facilitates a stepwise assessment of the risk of AMR spread and strengthen implementation of optimized infection control measures. Similarly, for travellers, the tool has the potential to raise awareness of AMR and outlines preventive measures that reduce the risk of AMR acquisition and spread.
| S-EPMC9282094 | biostudies-literature
Project description:BackgroundMathematical and statistical models are used to predict trends in epidemic spread and determine the effectiveness of control measures. Automatic regressive integrated moving average (ARIMA) models are used for time-series forecasting, but only few models of the 2019 coronavirus disease (COVID-19) pandemic have incorporated protective behaviors or vaccination, known to be effective for pandemic control.MethodsTo improve the accuracy of prediction, we applied newly developed ARIMA models with predictors (mask wearing, avoiding going out, and vaccination) to forecast weekly COVID-19 case growth rates in Canada, France, Italy, and Israel between January 2021 and March 2022. The open-source data was sourced from the YouGov survey and Our World in Data. Prediction performance was evaluated using the root mean square error (RMSE) and the corrected Akaike information criterion (AICc).ResultsA model with mask wearing and vaccination variables performed best for the pandemic period in which the Alpha and Delta viral variants were predominant (before November 2021). A model using only past case growth rates as autoregressive predictors performed best for the Omicron period (after December 2021). The models suggested that protective behaviors and vaccination are associated with the reduction of COVID-19 case growth rates, with booster vaccine coverage playing a particularly vital role during the Omicron period. For example, each unit increase in mask wearing and avoiding going out significantly reduced the case growth rate during the Alpha/Delta period in Canada (-0.81 and -0.54, respectively; both p < 0.05). In the Omicron period, each unit increase in the number of booster doses resulted in a significant reduction of the case growth rate in Canada (-0.03), Israel (-0.12), Italy (-0.02), and France (-0.03); all p < 0.05.ConclusionsThe key findings of this study are incorporating behavior and vaccination as predictors led to accurate predictions and highlighted their significant role in controlling the pandemic. These models are easily interpretable and can be embedded in a "real-time" schedule with weekly data updates. They can support timely decision making about policies to control dynamically changing epidemics.
| S-EPMC10408098 | biostudies-literature