ArticleJournal of mathematical analysis and applications2022
Correcting notification delay and forecasting of COVID-19 data.
Article in Journal of mathematical analysis and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Can past variants of SARS-CoV-2 predict the impact of future variants? Machine learning for early warning of US counties at risk.Health care management science · 2025Article
- 20-Month monitoring of SARS-CoV-2 in wastewater of Curitiba, in Southern Brazil.Environmental science and pollution research international · 2023Article
- Mathematical Modeling of SARS-CoV-2 Omicron Wave under Vaccination Effects.Computation (Basel, Switzerland) · 2023Article
- Nowcasting COVID-19 deaths in England by age and region.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022Article
- Real-time mortality statistics during the COVID-19 pandemic: A proposal based on Spanish data, January-March, 2021.Frontiers in public health · 2022Article
- Nonlinear Dynamics of the Introduction of a New SARS-CoV-2 Variant with Different Infectiousness.Mathematics (Basel, Switzerland) · 2021Article
- A simple mathematical tool to forecast COVID-19 cumulative case numbers.Clinical epidemiology and global healthArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the first official case of COVID-19 was reported, many researchers around the world have spent their time trying to understand the dynamics of the virus by modeling and predicting the number of infected and deaths. The rapid spread and highly contagiousness motivate the necessity of monitoring cases in real-time, aiming to keep control of the epidemic. As pointed out by [3], some pitfalls like limited infrastructure, laboratory confirmation and logistical problems may cause reporting delay, leading to distortions of the real dynamics of the confirmed cases and deaths. The aim of this study is to propose a suitable statistical methodology for modeling and forecasting daily deaths and reported cases of COVID-19, considering key features as overdispersion of data and correction of notification delay. Both, reporting delays and forecasting consider a Bayesian approach in which the daily deaths and the confirmed cases are modelled using the negative binomial (NB) distribution in order to accommodate the population heterogeneity. For the correction of notification delay, the mean number of occurrences regarding time
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