Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk

• URL: http://arxiv.org/abs/2012.00082v1
• Date: Mon, 30 Nov 2020 20:17:19 GMT
• Title: Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk
• Authors: Lingxiao Wang, Tian Xu, Till Hannes Stoecker, Horst Stoecker, Yin Jiang and Kai Zhou
• Abstract summary: We develop a framework with machine assisted to extract epidemic dynamics from infection data. New toolbox is first utilized to the projection of the multi-level CO-19 prevalence over 412 Landkreis (counties) in Germany. As a practical, we predict the situation at Christmas where the worst fatalities are 34.5 thousand, effective policies could contain it to below 21 thousand.
• Score: 26.228330223358952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level. To implement it and evaluate the public health policies, we develop a framework with machine learning assisted to extract epidemic dynamics from the infection data, in which contains a county-level spatiotemporal epidemiological model that combines a spatial Cellular Automaton (CA) with a temporal Susceptible-Undiagnosed-Infected-Removed (SUIR) model. Compared with the existing time risk prediction models, the proposed CA-SUIR model shows the multi-level risk of the county to the government and coronavirus transmission patterns under different policies. This new toolbox is first utilized to the projection of the multi-level COVID-19 prevalence over 412 Landkreis (counties) in Germany, including t-day-ahead risk forecast and the risk assessment to the travel restriction policy. As a practical illustration, we predict the situation at Christmas where the worst fatalities are 34.5 thousand, effective policies could contain it to below 21 thousand. Such intervenable evaluation system could help decide on economic restarting and public health policies making in pandemic.

Related papers

• Digital Epidemiology after COVID-19: impact and prospects [0.5953513005270838]
  We review how Digital Epidemiology (DE) was at the beginning of 2020 and how it was changed by the COVID-19 pandemic. We argue that DE will become a progressively useful tool as long as its potential is recognized and its risks are minimized.
  arXiv  Detail & Related papers  (2023-12-08T05:18:30Z)
• Modelling the Spread of COVID-19 in Indoor Spaces using Automated Probabilistic Planning [0.0]
  coronavirus disease 2019 (COVID-19) pandemic has been ongoing for around 3 years. Several strategies for controlling the spread of the disease have been debated by healthcare professionals. To anticipate the potential impact of the disease, and to simulate the effectiveness of different mitigation strategies, a robust model of disease spread is needed.
  arXiv  Detail & Related papers  (2023-08-16T07:41:53Z)
• 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)
• COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction [29.919578191688274]
  This paper proposes a method named COURAGE to generate a short-term prediction of 2-week-ahead COVID-19 related deaths for each county in the United States. Our model fully utilizes publicly available information of COVID-19 related confirmed cases, deaths, community mobility trends and demographic information, and can produce state-level prediction as an aggregation of the corresponding county-level predictions.
  arXiv  Detail & Related papers  (2021-05-03T04:00:59Z)
• Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study [0.0]
  The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases.
  arXiv  Detail & Related papers  (2021-04-19T11:51:20Z)
• 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)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• Data-driven Simulation and Optimization for Covid-19 Exit Strategies [16.31545249131776]
  The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. We have built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease.
  arXiv  Detail & Related papers  (2020-06-12T11:18:25Z)
• 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)
• A Deep Q-learning/genetic Algorithms Based Novel Methodology For Optimizing Covid-19 Pandemic Government Actions [63.669642197519934]
  We use the SEIR epidemiological model to represent the evolution of the virus COVID-19 over time in the population. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system. We prove that our methodology is a valid tool to discover actions governments can take to reduce the negative effects of a pandemic in both senses.
  arXiv  Detail & Related papers  (2020-05-15T17:17:45Z)
• When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes [111.69190108272133]
  coronavirus disease 2019 (COVID-19) global pandemic has led many countries to impose unprecedented lockdown measures. Data-driven models that predict COVID-19 fatalities under different lockdown policy scenarios are essential. This paper develops a Bayesian model for predicting the effects of COVID-19 lockdown policies in a global context.
  arXiv  Detail & Related papers  (2020-05-13T18:21:50Z)

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.