ArticleIEEE access : practical innovations, open solutions2020
DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
- Spatio-temporal epidemic forecasting using mobility data with LSTM networks and attention mechanism.Scientific reports · 2025Article
- Analyzing the impact of social security systems on video-based public health surveillance.Frontiers in public health · 2025Article
- Identifying First-Trimester Risk Factors for SGA-LGA Using Weighted Inheritance Voting Ensemble Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review.JMIR public health and surveillance · 2024Article
- Applying an interpretable machine learning framework to study mobility inequity in the recovery phase of COVID-19 pandemic.Travel behaviour & society · 2023Article
- Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task.IEEE transactions on artificial intelligence · 2023Article
- Forecasting COVID-19 spreading through an ensemble of classical and machine learning models: Spain's case study.Scientific reports · 2023Article
- Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach.EBioMedicine · 2023Article
- Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review.Progress in planning · 2023Article
- Human behavior in the time of COVID-19: Learning from big data.Frontiers in big data · 2023Review
- GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics.bioRxiv : the preprint server for biology · 2022Article
- Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey.Sustainable cities and society · 2022Review
- Data-driven contact network models of COVID-19 reveal trade-offs between costs and infections for optimal local containment policies.Cities (London, England) · 2022Article
- Body Language Analysis in Healthcare: An Overview.Healthcare (Basel, Switzerland) · 2022Article
- Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.Artificial intelligence in medicine · 2022Review
- A comparison of Covid-19 cases and deaths in Turkey and in other countries.Network modeling and analysis in health informatics and bioinformatics · 2022Article
- Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19.Chaos, solitons, and fractals · 2021Article
- Unraveling the dynamic importance of county-level features in trajectory of COVID-19.Scientific reports · 2021Article
- Benchmarking Attention-Based Interpretability of Deep Learning in Multivariate Time Series Predictions.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days and we present a novel method to compute equidimensional representations of multivariate time series and multivariate spatial time series data. Using this novel method, the proposed model can both take in a large number of heterogeneous features, such as census data, intra-county mobility, inter-county mobility, social distancing data, past growth of infection, among others, and learn complex interactions between these features. Using data collected from various sources, we estimate the range of increase in infected cases seven days into the future for all U.S. counties. In addition, we use the model to identify the most influential features for prediction of the growth of infection. We also analyze pairs of features and estimate the amount of observed second-order interaction between them. Experiments show that the proposed model obtains satisfactory predictive performance and fairly interpretable feature analysis results; hence, the proposed model could complement the standard epidemiological models for national-level surveillance of pandemics, such as COVID-19. The results and findings obtained from the deep learning model could potentially inform policymakers and researchers in devising effective mitigation and response strategies. To fast-track further development and experimentation, the code used to implement the proposed model has been made fully open source.
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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.