ArticleCommunications in nonlinear science & numerical simulation2020
On the uncertainty of real-time predictions of epidemic growths: A COVID-19 case study for China and Italy.
Article in Communications in nonlinear science & numerical simulation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 69 citations in OpenAlex.
- A real-time forecasting framework for emerging infectious diseases affecting animal populations.PLoS computational biology · 2026Article
- COVID-19 epidemic peaks distribution in the United-States of America, from epidemiological modeling to public health policies.Scientific reports · 2023Article
- Analysis and prediction of improved SEIR transmission dynamics model: taking the second outbreak of COVID-19 in Italy as an example.Frontiers in public health · 2023Article
- COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach.The European journal of health economics : HEPAC : health economics in prevention and care · 2022Article
- Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela.Spatial and spatio-temporal epidemiology · 2022Article
- Is Covid-19 a dread risk? The death toll of the pandemic year 2020 in long-term and transnational perspective.Futures · 2022Article
- Study on the virulence evolution of SARS-CoV-2 and the trend of the epidemics of COVID-19.Mathematical methods in the applied sciences · 2022Article
- Article
- Global prediction model for COVID-19 pandemic with the characteristics of the multiple peaks and local fluctuations.BMC medical research methodology · 2022Article
- Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic.Journal of epidemiology and global health · 2022Article
- Nonlinear growth and mathematical modelling of COVID-19 in some African countries with the Atangana-Baleanu fractional derivative.Communications in nonlinear science & numerical simulation · 2022Article
- Objective evaluation of deep uncertainty predictions for COVID-19 detection.Scientific reports · 2022Article
- Framework for enhancing the estimation of model parameters for data with a high level of uncertainty.Nonlinear dynamics · 2022Article
- Merits and Limitations of Mathematical Modeling and Computational Simulations in Mitigation of COVID-19 Pandemic: A Comprehensive Review.Archives of computational methods in engineering : state of the art reviews · 2022Review
- A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective.International journal of environmental research and public health · 2021Article
- Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview.Healthcare (Basel, Switzerland) · 2021Review
- Prediction and Analysis of Tourist Management Strategy Based on the SEIR Model during the COVID-19 Period.International journal of environmental research and public health · 2021Article
- Dynamical analysis of novel COVID-19 epidemic model with non-monotonic incidence function.Journal of public affairs · 2021Article
- Time-variant reliability-based prediction of COVID-19 spread using extended SEIVR model and Monte Carlo sampling.Results in physics · 2021Article
- A new grey quadratic polynomial model and its application in the COVID-19 in China.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While COVID-19 is rapidly propagating around the globe, the need for providing real-time forecasts of the epidemics pushes fits of dynamical and statistical models to available data beyond their capabilities. Here we focus on statistical predictions of COVID-19 infections performed by fitting asymptotic distributions to actual data. By taking as a case-study the epidemic evolution of total COVID-19 infections in Chinese provinces and Italian regions, we find that predictions are characterized by large uncertainties at the early stages of the epidemic growth. Those uncertainties significantly reduce after the epidemics peak is reached. Differences in the uncertainty of the forecasts at a regional level can be used to highlight the delay in the spread of the virus. Our results warn that long term extrapolation of epidemics counts must be handled with extreme care as they crucially depend not only on the quality of data, but also on the stage of the epidemics, due to the intrinsically non-linear nature of the underlying dynamics. These results suggest that real-time epidemiological projections should include wide uncertainty ranges and urge for the needs of compiling high-quality datasets of infections counts, including asymptomatic patients.
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