Data driven high resolution modeling and spatial analyses of the COVID-19 pandemic in Germany.
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ABSTRACT:
SUBMITTER: Schuler L
PROVIDER: S-EPMC8372931 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The coronavirus (COVID-19) started in China in 2019, has spread rapidly in every single country and has spread in millions of cases worldwide. This paper presents a proposed approach that involves identifying the relative impact of COVID-19 on a specific gender, the mortality rate in specific age, investigating different safety measures adopted by each country and their impact on the virus growth rate. Our study proposes data-driven analysis and prediction modeling by investigating three aspects of the pandemic (gender of patients, global growth rate, and social distancing). Several machine learning and ensemble models have been used and compared to obtain the best accuracy. Experiments have been demonstrated on three large public datasets. The motivation of this study is to propose an analytical machine learning based model to explore three significant aspects of COVID-19 pandemic as gender, global growth rate, and social distancing. The proposed analytical model includes classic classifiers, distinctive ensemble methods such as bagging, feature based ensemble, voting and stacking. The results show a superior prediction performance comparing with the related approaches.
| S-EPMC9877906 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation.MethodsWe developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021.ResultsThe model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible.ConclusionsWe successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units.
| S-EPMC9019290 | biostudies-literature
Project description:Pakistan is currently facing the fourth wave of the deadly coronavirus, which was first reported in Wuhan, China, in December 2019. This work utilizes the epidemiological models to analyze Pakistan's COVID-19 data. The basic susceptible, infected, and recovered (SIR) model is studied assuming Bayesian and time-series SIR (tSIR) approaches. Many studies have been conducted from different perspectives, but to the best of our knowledge, no study is available using the SIR models for Pakistan. The coronavirus incubation period has been set to 14 days across the globe; however, this study noticed that the assumption of 14 days is not suitable for Pakistan's data. Furthermore, on the basis of R 0, we infer that COVID-19 is not a pandemic in Pakistan, as it was in other nations, such as the United States, India, Brazil, and Italy, among others. We attribute this to the best strategy adopted by the Government of Pakistan to minimize the burden of COVID-19 cases in Pakistani hospitals. It is also noticed that the posterior-based SIR (pSIR) model with uniform prior toR 0 and Poisson distribution (of log-likelihood) provides better results as compared to other distributions. From time-series SIR (tSIR), we observed that the value of the reporting rate (ρ) is less than 1 which means that cases are underreported.
| S-EPMC9256309 | biostudies-literature
Project description:During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions became a widely adopted strategy to limit physical movements and interactions to mitigate virus transmissions. For situational awareness and decision-support, quickly available yet accurate big-data analytics about human mobility and social distancing is invaluable to agencies and decision-makers. This paper presents a big-data-driven analytical framework that ingests terabytes of data on a daily basis and quantitatively assesses the human mobility trend during COVID-19. Using mobile device location data of over 150 million monthly active samples in the United States (U.S.), the study successfully measures human mobility with three main metrics at the county level: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. A set of generalized additive mixed models is employed to disentangle the policy effect on human mobility from other confounding effects including virus effect, socio-demographic effect, weather effect, industry effect, and spatiotemporal autocorrelation. Results reveal the policy plays a limited, time-decreasing, and region-specific effect on human movement. The stay-at-home orders only contribute to a 3.5%-7.9% decrease in human mobility, while the reopening guidelines lead to a 1.6%-5.2% mobility increase. Results also indicate a reasonable spatial heterogeneity among the U.S. counties, wherein the number of confirmed COVID-19 cases, income levels, industry structure, age and racial distribution play important roles. The data informatics generated by the framework are made available to the public for a timely understanding of mobility trends and policy effects, as well as for time-sensitive decision support to further contain the spread of the virus.
| S-EPMC7796660 | biostudies-literature
Project description:In the paper, we propose a semiparametric framework for modeling the COVID-19 pandemic. The stochastic part of the framework is based on Bayesian inference. The model is informed by the actual COVID-19 data and the current epidemiological findings about the disease. The framework combines many available data sources (number of positive cases, number of patients in hospitals and in intensive care, etc.) to make outputs as accurate as possible and incorporates the times of non-pharmaceutical governmental interventions which were adopted worldwide to slow-down the pandemic. The model estimates the reproduction number of SARS-CoV-2, the number of infected individuals and the number of patients in different disease progression states in time. It can be used for estimating current infection fatality rate, proportion of individuals not detected and short term forecasting of important indicators for monitoring the state of the healthcare system. With the prediction of the number of patients in hospitals and intensive care units, policy makers could make data driven decisions to potentially avoid overloading the capacities of the healthcare system. The model is applied to Slovene COVID-19 data showing the effectiveness of the adopted interventions for controlling the epidemic by reducing the reproduction number of SARS-CoV-2. It is estimated that the proportion of infected people in Slovenia was among the lowest in Europe (0.350%, 90% CI [0.245-0.573]%), that infection fatality rate in Slovenia until the end of first wave was 1.56% (90% CI [0.94-2.21]%) and the proportion of unidentified cases was 88% (90% CI [83-93]%). The proposed framework can be extended to more countries/regions, thus allowing for comparison between them. One such modification is exhibited on data for Slovene hospitals.
| S-EPMC7482592 | biostudies-literature
Project description: Not available
| S-BSST1055 | biostudies-other
Project description:The coronavirus disease 2019 (COVID-19) pandemic has had a catastrophic impact on human health and the world economy. The response of the scientific community was unparalleled, and a combined global effort has resulted in the creation of vaccines in a shorter time frame than previously unimaginable. Reflecting this concerted effort, the structural analysis of the etiological agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has progressed with an unprecedented pace. Since the onset of the pandemic, over 1000 high-resolution structures of a broad range of SARS-CoV-2 proteins have been solved and made publicly available. These structures have aided in the identification of numerous potential druggable targets and have contributed to the design of different vaccine candidates. This opinion article will discuss the impact of high-resolution structures in understanding SARS-CoV-2 biology and explore their role in the development of vaccines and antivirals.
| S-EPMC8173484 | biostudies-literature
Project description:BackgroundThe "coronavirus disease 2019" (COVID-19) pandemic, caused by the "severe-acute-respiratory-syndrome-coronavirus 2" (SARS-CoV-2), challenges healthcare systems worldwide and impacts not only COVID-19 patients but also other emergencies. To date, data are scarce on the extent to which the COVID-19 pandemic impacted status epilepticus (SE) and its treatment.ObjectiveTo assess the influence of the COVID-19 pandemic on the incidence, management and outcome of SE patients.Study designThis is a retrospective, multicentre trial, approved by the University of Cologne (21-1443-retro).MethodsAll SE patients from the urban area of Cologne transmitted to all acute neurological departments in Cologne between 03/2019 and 02/2021 were retrospectively analysed and assessed for patient characteristics, SE characteristics, management, and outcome in the first pandemic year compared to the last pre-pandemic year.Results157 pre-pandemic (03/2019-02/2020) and 171 pandemic (from 03/2020 to 02/2021) SE patients were included in the analyses. Acute SARS-CoV-2 infections were rarely detected. Patient characteristics, management, and outcome did not reveal significant groupwise differences. In contrast, regarding prehospital management, a prolonged patient transfer to the hospital and variations in SE aetiologies compared to the last pre-pandemic year were observed with less chronic vascular and more cryptogenic and anoxic SE cases. No infections with SARS-CoV-2 occurred during inpatient stays.ConclusionsSARS-CoV-2 infections did not directly affect SE patients, but the transfer of SE patients to emergency departments was delayed. Interestingly, SE aetiology rates shifted, which warrants further exploration. Fears of contracting an in-hospital SARS-CoV-2-infection were unfounded due to consequent containment measures.
| S-EPMC9266085 | biostudies-literature
Project description:In the context of the energy transition, municipalities are increasingly attempting to exploit renewable energies. Socio-energetic data are required as input for municipal energy system analyses. This Data Descriptor provides a compilation of 40 indicators for all 11,131 German municipalities. In addition to census data such as population density, mobility data such as the number of vehicles and data on the potential of renewables such as wind energy are included. Most of the data set also contains public data, the allocation of which to municipalities was an extensive task. The data set can support in addressing a wide range of energy-related research challenges. A municipality typology has already been developed with the data, and the resulting municipality grouping is also included in the data set.
| S-EPMC6821823 | biostudies-literature
Project description:BackgroundIn order to contain the coronavirus disease 2019 (COVID-19) pandemic, Germany has implemented drastic restrictions on public or social life, while health institutions are invoked to postpone elective procedures. Although urologists are less involved in the direct treatment of COVID-19 patients, the current situation strongly affects the urological work routine.ObjectiveTo analyze the impact of the COVID-19 pandemic on various aspects of work and personal life among urologists in Germany.Design, setting, and participantsA total of 589 urologists in Germany participated in an online survey between March 27 and April 11, 2020.Outcome measurements and statistical analysisParticipants were stratified into subgroups according to professional characteristics.Results and limitationsMost urologists rated Germany as "well prepared" and the increasing restrictions of social life as "very positive." Routine operation was more restricted in hospitals than in the outpatient sector (p = 0.046). Moreover, urologists from the outpatient sector felt significantly less prepared for the COVID-19 pandemic (p = 0.001), reported a higher shortage of protective medical equipment (p < 0.001), and described a tendency toward a higher level of threat (p = 0.054). Although restrictions regarding telemedicine approaches were reported by 60% of participants, the outpatient sector used telehealth more frequently than hospitals (25.5% vs 17.0%, p < 0.001). Limitations include the national design and the restricted survey period.ConclusionsThis survey systematically evaluates the impact of the COVID-19 pandemic on personal and professional aspects of German urologists. We identified several issues, such as a higher shortage of medical protective equipment in the outpatient sector that could trigger specific measures to further improve the quality of urological care in Germany.Patient summaryWe evaluated a potential impact of the coronavirus disease 2019 (COVID-19) pandemic on professional and personal aspects of the urologists in Germany. Our results suggest that the outpatient sector should receive specific attention as, for example, shortage of protective equipment was more common.
| S-EPMC7834409 | biostudies-literature