ArticlePloS one2021
Prediction of confirmed cases of and deaths caused by COVID-19 in Chile through time series techniques: A comparative study.
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 12 papers.
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Who cites it
12 citing papers in PubMed, 25 citations in OpenAlex.
- Improving outbreak forecasts through model augmentation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Exploring different modelling approaches to forecast the community acute respiratory infections burden in children: an Italian epidemiological time series study.BMC public health · 2025Article
- Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model.PLoS computational biology · 2024Article
- Article
- Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic.World journal of clinical cases · 2023Review
- Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks.PloS one · 2023Article
- Article
- Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic.Journal of epidemiology and global health · 2022Article
- The seasonal behaviour of COVID-19 and its galectin-like culprit of the viral spike.Methods in microbiology · 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
- Forecasting the spread of the third wave of COVID-19 pandemic using time series analysis in Bangladesh.Informatics in medicine unlocked · 2022Article
- Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data.Journal of healthcare engineering · 2022Article
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Authors and funding
4 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundChile has become one of the countries most affected by COVID-19, a pandemic that has generated a large number of cases worldwide. If not detected and treated in time, COVID-19 can cause multi-organ failure and even death. Therefore, it is necessary to understand the behavior of the spread of COVID-19 as well as the projection of infections and deaths. This information is very relevant so that public health organizations can distribute financial resources efficiently and take appropriate containment measures. In this research, we compare different time series methodologies to predict the number of confirmed cases of and deaths from COVID-19 in Chile.
methodsThe methodology used in this research consisted of modeling cases of both confirmed diagnoses and deaths from COVID-19 in Chile using Autoregressive Integrated Moving Average (ARIMA henceforth) models, Exponential Smoothing techniques, and Poisson models for time-dependent count data. Additionally, we evaluated the accuracy of the predictions using a training set and a test set.
resultsThe dataset used in this research indicated that the most appropriate model is the ARIMA time series model for predicting the number of confirmed COVID-19 cases, whereas for predicting the number of deaths from COVID-19 in Chile, the most suitable approach is the damped trend method.
conclusionThe ARIMA models are an alternative to modeling the behavior of the spread of COVID-19; however, depending on the characteristics of the dataset, other methodologies can better predict the behavior of these records, for example, the Holt-Winter method implemented with time-dependent count data.
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