ArticleJournal of medical Internet research2020
Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- The Impact and Applications of Social Media Platforms for Public Health Responses Before and During the COVID-19 Pandemic: Systematic Literature Review.Journal of medical Internet research · 2022Pooled it
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- How politics affect pandemic forecasting: spatio-temporal early warning capabilities of different geo-social media topics in the context of state-level political leaning.Frontiers in public health · 2025Article
- Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study.Journal of medical Internet research · 2024Article
- Cluster analysis and forecasting of viruses incidence growth curves: Application to SARS-CoV-2.Expert systems with applications · 2023Article
- Multi-source dynamic ensemble prediction of infectious disease and application in COVID-19 case.Journal of thoracic disease · 2023Article
- Editorial: Infectious Disease Surveillance Using Artificial Intelligence (AI) and its Role in Epidemic and Pandemic Preparedness.Medical science monitor : international medical journal of experimental and clinical research · 2023Article
- Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States.Science advances · 2023Article
- Impact of public health interventions to curb SARS-CoV-2 spread assessed by an evidence-educated Delphi panel and tailored SEIR model.Zeitschrift fur Gesundheitswissenschaften = Journal of public health · 2023Article
- Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela.Spatial and spatio-temporal epidemiology · 2022Article
- Deep learning for Covid-19 forecasting: State-of-the-art review.Neurocomputing · 2022Article
- Drivers and forecasts of multiple waves of the coronavirus disease 2019 pandemic: A systematic analysis based on an interpretable machine learning framework.Transboundary and emerging diseases · 2022Article
- COVID-19 forecasts using Internet search information in the United States.Scientific reports · 2022Article
- Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data, Online News Articles, and Search Queries.JMIR public health and surveillance · 2022Article
- The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19.Social science & medicine (1982) · 2022Review
- Early COVID-19 respiratory risk stratification using machine learning.Trauma surgery & acute care open · 2022Article
- Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic.IEEE internet of things journal · 2021Article
- Predicting seasonal influenza using supermarket retail records.PLoS computational biology · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
backgroundThe inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus disease (COVID-19) outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events.
objectiveWe present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real time.
methodsOur method uses the following as inputs: (a) official health reports, (b) COVID-19-related internet search activity, (c) news media activity, and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine learning methodology uses a clustering technique that enables the exploitation of geospatial synchronicities of COVID-19 activity across Chinese provinces and a data augmentation technique to deal with the small number of historical disease observations characteristic of emerging outbreaks.
resultsOur model is able to produce stable and accurate forecasts 2 days ahead of the current time and outperforms a collection of baseline models in 27 out of 32 Chinese provinces.
conclusionsOur methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention.
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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.