ArticleApplied soft computing2021
Forecasting COVID-19 daily cases using phone call data.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Impact of the COVID-19 pandemic on number of deaths in Ghana: an application of ARIMA with intervention analysis.Frontiers in public health · 2025Article
- Using meta-learning to recommend an appropriate time-series forecasting model.BMC public health · 2024Article
- Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data?Einstein (Sao Paulo, Brazil) · 2024Article
- Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK.Epidemiology and infection · 2023Article
- A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China.PloS one · 2023Article
- Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.Artificial intelligence in medicine · 2022Review
- COVID-19 Symptoms app analysis to foresee healthcare impacts: Evidence from Northern Ireland.Applied soft computing · 2022Article
- Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia.International journal of environmental research and public health · 2022Article
- Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach.International journal of environmental research and public health · 2022Article
- The comparative analysis of SARIMA, Facebook Prophet, and LSTM for road traffic injury prediction in Northeast China.Frontiers in public health · 2022Article
- Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data.Neurocomputing · 2021Article
- Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review.Heliyon · 2021Article
- Initial prehospital Rapid Emergency Medicine Score (REMS) to predict outcomes for COVID-19 patients.Journal of the American College of Emergency Physicians open · 2021Article
- Real-time measurement of the uncertain epidemiological appearances of COVID-19 infections.Applied soft computing · 2021Article
- Article
- Predicting COVID-19 Outbreaks in Correctional Facilities Using Machine Learning.MDM policy & practiceArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The need to forecast COVID-19 related variables continues to be pressing as the epidemic unfolds. Different efforts have been made, with compartmental models in epidemiology and statistical models such as AutoRegressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS) or computing intelligence models. These efforts have proved useful in some instances by allowing decision makers to distinguish different scenarios during the emergency, but their accuracy has been disappointing, forecasts ignore uncertainties and less attention is given to local areas. In this study, we propose a simple Multiple Linear Regression model, optimised to use phone call data to forecast the number of daily confirmed cases. Moreover, we produce a probabilistic forecast that allows decision makers to better deal with risk. Our proposed approach outperforms ARIMA, ETS, Seasonal Naive, Prophet and a regression model without call data, evaluated by three point forecast error metrics, one prediction interval and two probabilistic forecast accuracy measures. The simplicity, interpretability and reliability of the model, obtained in a careful forecasting exercise, is a meaningful contribution to decision makers at local level who acutely need to organise resources in already strained health services. We hope that this model would serve as a building block of other forecasting efforts that on the one hand would help front-line personal and decision makers at local level, and on the other would facilitate the communication with other modelling efforts being made at the national level to improve the way we tackle this pandemic and other similar future challenges.
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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.