ArticleInternational journal of forecasting
COVID-19: Forecasting confirmed cases and deaths with a simple time series model.
Article in International journal of forecasting. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Is Excess Mortality Returning to Pre-Pandemic Levels? A Multi-Model Stochastic Approach for COVID-19: The Spanish Case.Epidemiologia (Basel, Switzerland) · 2026Article
- Feasibility of short-term hospital mask demand forecasting using a backpropagation neural network under data scarcity.Scientific reports · 2026Article
- Emerging and re-emerging vector-borne and other zoonotic RNA viruses: pathogenesis, climate-driven dynamics, and strategies for global control.Frontiers in microbiology · 2026Review
- Comparative estimation of the spread of acute diarrhea and dengue in India using statistical mathematical and deep learning models.Scientific reports · 2025Article
- An ensemble approach improves the prediction of the COVID-19 pandemic in South Korea.Journal of global health · 2025Article
- Bayesian modeling of the effect of vaccination and the delta and omicron variants on the COVID-19 epidemic in Burkina Faso using poisson log-linear autoregressive model.Frontiers in public health · 2025Article
- A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics.International journal of environmental research and public health · 2024Article
- COVID-19 latent age-specific mortality in US states: a county-level spatio-temporal analysis with counterfactuals.Frontiers in epidemiology · 2024Article
- Article
- A new time-varying coefficient regression approach for analyzing infectious disease data.Scientific reports · 2023Article
- An innovative ensemble model based on multiple neural networks and a novel heuristic optimization algorithm for COVID-19 forecasting.Expert systems with applications · 2023Article
- COVID-19 and beyond: leveraging artificial intelligence for enhanced outbreak control.Frontiers in artificial intelligence · 2023Review
- Predicting and preventing COVID-19 outbreaks in indoor environments: an agent-based modeling study.Scientific reports · 2022Article
- Spatio-temporal predictions of COVID-19 test positivity in Uppsala County, Sweden: a comparative approach.Scientific reports · 2022Article
- Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid-19 Pandemic.Regional science policy & practice · 2022Article
- Modeling the effects of the governmental responses to COVID-19 on transit demand: The case of Athens, Greece.Case studies on transport policy · 2022Article
- Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement.Chaos, solitons, and fractals · 2022Article
- Article
- Comprehensive Analysis of the COVID-19: Based on the Social-Related Indexes From NUMBEO.Frontiers in public health · 2022Article
- Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission.Knowledge-based systems · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision-making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics, making the prediction of their severity difficult. At the same time, the decisions made to enforce lockdowns and other mitigating interventions versus their socioeconomic consequences are not only hard to make, but also highly uncertain. The majority of modeling approaches to outbreaks, epidemics, and pandemics take an epidemiological approach that considers biological and disease processes. In this paper, we accept the limitations of forecasting to predict the long-term trajectory of an outbreak, and instead, we propose a statistical, time series approach to modelling and predicting the short-term behavior of COVID-19. Our model assumes a multiplicative trend, aiming to capture the continuation of the two variables we predict (global confirmed cases and deaths) as well as their uncertainty. We present the timeline of producing and evaluating 10-day-ahead forecasts over a period of four months. Our simple model offers competitive forecast accuracy and estimates of uncertainty that are useful and practically relevant.
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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.