Predictions of 2019-nCoV Transmission Ending via Comprehensive Methods

• URL: http://arxiv.org/abs/2002.04945v2
• Date: Thu, 20 Feb 2020 06:08:07 GMT
• Title: Predictions of 2019-nCoV Transmission Ending via Comprehensive Methods
• Authors: Tianyu Zeng, Yunong Zhang, Zhenyu Li, Xiao Liu, and Binbin Qiu
• Abstract summary: We propose a multi-model ordinary differential equation set neural network (MMODEs-NN) and model-free methods to predict the interprovincial transmissions in mainland China. According to the numerical experiments and the realities, the special policies for controlling the disease are successful in some provinces. The proposed mathematical and artificial intelligence methods can give consistent and reasonable predictions of the 2019-nCoV ending.
• Score: 11.496215213608988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since the SARS outbreak in 2003, a lot of predictive epidemiological models have been proposed. At the end of 2019, a novel coronavirus, termed as 2019-nCoV, has broken out and is propagating in China and the world. Here we propose a multi-model ordinary differential equation set neural network (MMODEs-NN) and model-free methods to predict the interprovincial transmissions in mainland China, especially those from Hubei Province. Compared with the previously proposed epidemiological models, the proposed network can simulate the transportations with the ODEs activation method, while the model-free methods based on the sigmoid function, Gaussian function, and Poisson distribution are linear and fast to generate reasonable predictions. According to the numerical experiments and the realities, the special policies for controlling the disease are successful in some provinces, and the transmission of the epidemic, whose outbreak time is close to the beginning of China Spring Festival travel rush, is more likely to decelerate before February 18 and to end before April 2020. The proposed mathematical and artificial intelligence methods can give consistent and reasonable predictions of the 2019-nCoV ending. We anticipate our work to be a starting point for comprehensive prediction researches of the 2019-nCoV.

Related papers

• A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the United States [2.9822184411723645]
  We present COVID-LSTM, a data-driven model based on a Long Short-term memory architecture for forecasting COVID-19 incidence at the county-level in the US. We use the weekly number of new cases as temporal input, and hand-engineered spatial features from Facebook to capture the spread of the disease in time and space. Over the 4-week forecast horizon, our model is on average 50 cases per county more accurate than the COVIDhub-ensemble.
  arXiv  Detail & Related papers  (2021-09-24T17:40:08Z)
• When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [70.54920804222031]
  Most existing forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations. We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP.
  arXiv  Detail & Related papers  (2021-06-07T18:31:47Z)
• Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks [48.7576911714538]
  Portugal was the country in the world with the largest incidence rate, with 14-days incidence rates per 100,000 inhabitants in excess of 1000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge.
  arXiv  Detail & Related papers  (2021-05-06T15:24:00Z)
• STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization [76.57716281104938]
  We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
  arXiv  Detail & Related papers  (2020-12-08T21:21:47Z)
• Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread [61.9008166652035]
  We study the spread of COVID-19 using a semi-supervised neural network. We assume a passive part of the population remains isolated from the virus dynamics.
  arXiv  Detail & Related papers  (2020-10-10T19:33:53Z)
• Backtesting the predictability of COVID-19 [0.0]
  We use historical data of COVID-19 infections from 253 regions from the period of 22nd January 2020 until 22nd June 2020. Prediction errors are substantially higher in early stages of the pandemic, resulting from limited data. The more confirmed cases a country exhibits at any point in time, the lower the error in forecasting future confirmed cases.
  arXiv  Detail & Related papers  (2020-07-22T13:18:00Z)
• A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19 [3.464871689508835]
  We develop an integrated disease model based on epidemic differential equations (SIR) and recurrent neural networks (RNN) We trained and tested our model on CO-19 data in Italy, and show that it out-performs existing temporal models (fully connected NN, SIR, ARIMA) in 1-day, 3-day, and 1-week ahead forecasting.
  arXiv  Detail & Related papers  (2020-07-14T07:04:57Z)
• Adaptive County Level COVID-19 Forecast Models: Analysis and Improvement [1.8899300124593645]
  We adapt the state and county level model, TDEFSI-LONLY, to national and county level COVID-19 data. We show that this model poorly forecasts the current pandemic. We propose an alternate forecast model, it County Level Epidemiological Inference Recurrent Network (alg) that trains an LSTM backbone on national confirmed cases to learn a low dimensional time pattern.
  arXiv  Detail & Related papers  (2020-06-16T17:20:54Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)
• Forecasting the Spread of Covid-19 Under Control Scenarios Using LSTM and Dynamic Behavioral Models [2.11622808613962]
  This study proposes a novel hybrid model which combines a Long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries and Australia.
  arXiv  Detail & Related papers  (2020-05-24T10:43:55Z)
• A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models [53.900779250589814]
  Our method is able to produce stable and accurate forecasts 2 days ahead of current time. Our model's predictive power outperforms a collection of baseline models in 27 out of the 32 Chinese provinces.
  arXiv  Detail & Related papers  (2020-04-08T14:39:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.