A self-supervised neural-analytic method to predict the evolution of COVID-19 in Romania

• URL: http://arxiv.org/abs/2006.12926v2
• Date: Sat, 5 Sep 2020 12:10:27 GMT
• Title: A self-supervised neural-analytic method to predict the evolution of COVID-19 in Romania
• Authors: Radu D. Stochi\c{t}oiu, Marian Petrica, Traian Rebedea, Ionel Popescu, Marius Leordeanu
• Abstract summary: We use a recently published improved version of SEIR, which is the classic, established model for infectious diseases. We propose a self-supervised approach to train a deep convolutional network to guess the correct set of ModifiedSEIR model parameters. We find an optimistic result in the case fatality rate for Romania which may be around 0.3% and we also demonstrate that our model is able to correctly predict the number of daily fatalities for up to three weeks in the future.
• Score: 10.760851506126105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Analysing and understanding the transmission and evolution of the COVID-19 pandemic is mandatory to be able to design the best social and medical policies, foresee their outcomes and deal with all the subsequent socio-economic effects. We address this important problem from a computational and machine learning perspective. More specifically, we want to statistically estimate all the relevant parameters for the new coronavirus COVID-19, such as the reproduction number, fatality rate or length of infectiousness period, based on Romanian patients, as well as be able to predict future outcomes. This endeavor is important, since it is well known that these factors vary across the globe, and might be dependent on many causes, including social, medical, age and genetic factors. We use a recently published improved version of SEIR, which is the classic, established model for infectious diseases. We want to infer all the parameters of the model, which govern the evolution of the pandemic in Romania, based on the only reliable, true measurement, which is the number of deaths. Once the model parameters are estimated, we are able to predict all the other relevant measures, such as the number of exposed and infectious people. To this end, we propose a self-supervised approach to train a deep convolutional network to guess the correct set of Modified-SEIR model parameters, given the observed number of daily fatalities. Then, we refine the solution with a stochastic coordinate descent approach. We compare our deep learning optimization scheme with the classic grid search approach and show great improvement in both computational time and prediction accuracy. We find an optimistic result in the case fatality rate for Romania which may be around 0.3% and we also demonstrate that our model is able to correctly predict the number of daily fatalities for up to three weeks in the future.

Related papers

• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Coronavirus disease situation analysis and prediction using machine learning: a study on Bangladeshi population [1.7188280334580195]
  In recent months, the number of death and infected rates has increased more distinguished than before in Bangladesh. This study distinguishes machine learning models and creates a prediction system to anticipate the infected and death rate for the coming days.
  arXiv  Detail & Related papers  (2022-07-12T09:48:41Z)
• Inverse problem for parameters identification in a modified SIRD epidemic model using ensemble neural networks [0.0]
  The main goal was to apply this approach on the analysis of COVID-19 evolution in Romania. We propose a parameter identification methodology of the SIRD model, that considers the deceased as a separate category. We illustrate the predictions for different periods of time, from 10 up to 45 days, for the number of deaths.
  arXiv  Detail & Related papers  (2022-02-28T10:54:29Z)
• 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)
• Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak [10.307715136465056]
  We present a comparative analysis of various machine learning (ML) approaches in predicting the COVID-19 outbreak in the epidemiological domain. The results reveal the advantages of ML algorithms for supporting decision making of evolving short term policies.
  arXiv  Detail & Related papers  (2021-02-11T11:57:33Z)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• 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)
• PECAIQR: A Model for Infectious Disease Applied to the Covid-19 Epidemic [0.0]
  Current state of the art predictions of future daily deaths have confidence intervals that are unacceptably wide. We used US county-level data on daily deaths and population statistics to forecast future deaths. We generate longer-time horizon predictions over various 1-month windows in the past, forecast how many medical resources will be needed in counties, and evaluate the efficacy of our model in other countries.
  arXiv  Detail & Related papers  (2020-06-17T17:59:55Z)
• A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
  We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models. We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
  arXiv  Detail & Related papers  (2020-06-08T19:24:54Z)

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.