ArticlePloS one2021
COVID-19: Short term prediction model using daily incidence data.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Google search interests and new cases of COVID-19 in Bangladesh: a vector autoregression analysis for disease surveillance.BMC research notes · 2025Article
- Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques.Scientific reports · 2024Article
- Compartmental modeling for pandemic data analysis: The gap between statistics and models.Heliyon · 2024Article
- Country-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz model.Scientific reports · 2024Article
- Forecasting the spread of COVID-19 based on policy, vaccination, and Omicron data.Scientific reports · 2024Article
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
- Learning from the past: A short term forecast method for the COVID-19 incidence curve.PLoS computational biology · 2023Article
- Improved healthcare disaster decision-making utilizing information extraction from complementary social media data during the COVID-19 pandemic.Decision support systems · 2023Article
- Article
- Analysis of COVID-19 mathematical model for predicting the impact of control measures in Rwanda.Informatics in medicine unlocked · 2023Article
- Time-dependent force of infection and effective reproduction ratio in an age-structure dengue transmission model in Bandung City, Indonesia.Infectious Disease Modelling · 2022Article
- A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning.Computers in biology and medicine · 2022Article
- Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic.Journal of epidemiology and global health · 2022Article
- Predicting the mutational drivers of future SARS-CoV-2 variants of concern.Science translational medicine · 2022Article
- Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data.Journal of healthcare engineering · 2022Article
- Artificial intelligence: Potential tool to subside SARS-CoV-2 pandemic.Process biochemistry (Barking, London, England) · 2021Review
- Forecasting the number of confirmed new cases of COVID-19 in Italy for the period from 19 May to 2 June 2020.Infectious Disease Modelling · 2021Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundPrediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions.
methodsOur approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree.
resultsWe apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time.
conclusionWe presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.
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Registered trials
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.