ArticleStatistics in medicine2021
Monitoring COVID-19 contagion growth.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A Systematic Review of INGARCH Models for Integer-Valued Time Series.Entropy (Basel, Switzerland) · 2023Review
- Prediction of the COVID-19 transmission: a case study of Pakistan.Epidemiology and infection · 2023Article
- Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.eLife · 2023Article
- A New Interactive Tool to Visualize and Analyze COVID-19 Data: The PERISCOPE Atlas.International journal of environmental research and public health · 2022Article
- Endemic-epidemic models to understand COVID-19 spatio-temporal evolution.Spatial statistics · 2022Article
- Estimating COVID-19-induced excess mortality in Lombardy, Italy.Aging clinical and experimental research · 2022Article
- When to end a lock down? How fast must vaccination campaigns proceed in order to keep health costs in check?Royal Society open science · 2022Article
- Unreliable predictions about COVID-19 infections and hospitalizations make people worry: The case of Italy.Journal of medical virology · 2022Article
- A COVINDEX based on a GAM beta regression model with an application to the COVID-19 pandemic in Italy.Statistical methods & applications · 2022Article
- A log linear Poisson autoregressive model to understand COVID-19 dynamics in Saudi Arabia.Beni-Suef University journal of basic and applied sciences · 2022Article
- A data-driven approach to measuring epidemiological susceptibility risk around the world.Scientific reports · 2021Article
- A multivariate statistical approach to predict COVID-19 count data with epidemiological interpretation and uncertainty quantification.Statistics in medicine · 2021Article
- Monitoring COVID-19 contagion growth.Statistics in medicine · 2021Article
- Article
- Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.International journal of forecastingArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We present a statistical model that can be employed to monitor the time evolution of the COVID-19 contagion curve and the associated reproduction rate. The model is a Poisson autoregression of the daily new observed cases and dynamically adapt its estimates to explain the evolution of contagion in terms of a short-term and long-term dependence of case counts, allowing for a comparative evaluation of health policy measures. We have applied the model to 2020 data from the countries most hit by the virus. Our empirical findings show that the proposed model describes the evolution of contagion dynamics and determines whether contagion growth can be affected by health policies. Based on our findings, we can draw two health policy conclusions that can be useful for all countries in the world. First, policy measures aimed at reducing contagion are very useful when contagion is at its peak to reduce the reproduction rate. Second, the contagion curve should be accurately monitored over time to apply policy measures that are cost-effective.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.