ArticleInternational journal of environmental research and public health2021
Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A semi-mechanistic modeling strategy for infectious diseases forecasting: Error correction and probabilistic prediction.Biosafety and health · 2026Article
- Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer.PLoS computational biology · 2024Article
- An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM.Scientific reports · 2023Article
- Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks.Scientific reports · 2023Article
- Impact of the coronavirus disease 2019 (COVID-19) pandemic on the diagnosis of neck and low back pain in outpatient practices in Germany.Preventive medicine reports · 2023Article
- The Impact of Preventive Strategies Adopted during Large Events on the COVID-19 Pandemic: A Case Study of the Tokyo Olympics to Provide Guidance for Future Large Events.International journal of environmental research and public health · 2023Article
- Infant Low Birth Weight Prediction Using Graph Embedding Features.International journal of environmental research and public health · 2023Article
- Article
- The Chaotic Behavior of the Spread of Infection during the COVID-19 Pandemic in Japan.International journal of environmental research and public health · 2022Article
- The impact of weather condition and social activity on COVID-19 transmission in the United States.Journal of environmental management · 2022Article
- Article
- Developing an artificial neural network for detecting COVID-19 disease.Journal of education and health promotion · 2022Article
- Assessment of knowledge of COVID-19 among health care workers-a questionnaire-based cross-sectional study in a tertiary care hospital of India.AIMS public health · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
The COVID-19 pandemic has had unprecedented social and economic consequences in the United States. Therefore, accurately predicting the dynamics of the pandemic can be very beneficial. Two main elements required for developing reliable predictions include: (1) a predictive model and (2) an indicator of the current condition and status of the pandemic. As a pandemic indicator, we used the effective reproduction number (Rt), which is defined as the number of new infections transmitted by a single contagious individual in a population that may no longer be fully susceptible. To bring the pandemic under control, Rt must be less than one. To eliminate the pandemic, Rt should be close to zero. Therefore, this value may serve as a strong indicator of the current status of the pandemic. For a predictive model, we used graph neural networks (GNNs), a method that combines graphical analysis with the structure of neural networks. We developed two types of GNN models, including: (1) graph-theory-based neural networks (GTNN) and (2) neighborhood-based neural networks (NGNN). The nodes in both graphs indicated individual states in the United States. While the GTNN model's edges document functional connectivity between states, those in the NGNN model link neighboring states to one another. We trained both models with R
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