ArticleMathematical biosciences2020
Modeling COVID-19 pandemic using Bayesian analysis with application to Slovene data.
Article in Mathematical biosciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The methodologies to assess the effectiveness of non-pharmaceutical interventions during COVID-19: a systematic review.European journal of epidemiology · 2022Pooled it
- A vaccination-based COVID-19 model: Analysis and prediction using Hamiltonian Monte Carlo.Heliyon · 2024Article
- Article
- Flow and access: Driving forces of COVID-19 spreading in the first stage around Hubei, China.PloS one · 2023Article
- Extended compartmental model for modeling COVID-19 epidemic in Slovenia.Scientific reports · 2022Article
- Assessing the impact of non-pharmaceutical interventions (NPIs) and BCG vaccine cross-protection in the transmission dynamics of SARS-CoV-2 in eastern Africa.BMC research notes · 2022Article
- Estimation of SARS-CoV-2 antibody prevalence through serological uncertainty and daily incidence.The Canadian journal of statistics = Revue canadienne de statistique · 2022Article
- Modelling the effect of non-pharmaceutical interventions on COVID-19 transmission from mobility maps.Infectious Disease Modelling · 2022Article
- Comparing the dynamics of COVID-19 infection and mortality in the United States, India, and Brazil.Physica D. Nonlinear phenomena · 2022Article
- A review on COVID-19 transmission, epidemiological features, prevention and vaccination.Medical review (2021) · 2022Review
- COVID-19 hospitalizations and patients' age at admission: The neglected importance of data variability for containment policies.Frontiers in public health · 2022Article
- Estimating a continuously varying offset between multivariate time series with application to COVID-19 in the United States.The European physical journal. Special topics · 2022Article
- Trends in COVID-19 prevalence and mortality: A year in review.Physica D. Nonlinear phenomena · 2021Article
- COVID-19 in Slovenia, from a Success Story to Disaster: What Lessons Can Be Learned?Life (Basel, Switzerland) · 2021Article
- Non-pharmaceutical interventions during the COVID-19 pandemic: A review.Physics reports · 2021Review
- Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2021Article
- Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19.Biology · 2020Article
- Modeling COVID-19 pandemic using Bayesian analysis with application to Slovene data.Mathematical biosciences · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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