scholarly journals A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19

2020 ◽  
Author(s):  
Zhijian Li ◽  
Yunling Zheng ◽  
Jack Xin ◽  
Guofa Zhou
Keyword(s):  
Spatial Information ◽  
Disease Model ◽  
Training Data ◽  
Data Driven ◽  
Disease Spread ◽  
Spatiotemporal Model ◽  
Infectious Disease Model ◽  
Temporal Models ◽  
Fully Connected ◽  
And Control

The outbreaks of Coronavirus Disease 2019 (COVID-19) have impacted the world significantly. Modeling the trend of infection and real-time forecasting of cases can help decision making and control of the disease spread. However, data-driven methods such as recurrent neural networks (RNN) can perform poorly due to limited daily samples in time. In this work, we develop an integrated spatiotemporal model based on the epidemic differential equations (SIR) and RNN. The former after simplification and discretization is a compact model of temporal infection trend of a region while the latter models the effect of nearest neighboring regions. The latter captures latent spatial information. We trained and tested our model on COVID-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 especially in the regime of limited training data.

Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Nurali Virani ◽  
Devesh K. Jha ◽  
Zhenyuan Yuan ◽  
Ishana Shekhawat ◽  
Asok Ray
Keyword(s):  
Particle Filtering ◽  
Dynamic Models ◽  
Training Data ◽  
Data Driven ◽  
Conditional Density ◽  
Bayesian Filtering ◽  
State Dependent ◽  
State Evolution ◽  
And Control ◽  
Nonparametric Kernel

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

scholarly journals A General Framework for Spatio-Temporal Modeling of Epidemics With Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen

2021 ◽  
Vol 7 ◽  
Author(s):  
Awino M. E. Ojwang' ◽  
Trevor Ruiz ◽  
Sharmodeep Bhattacharyya ◽  
Shirshendu Chatterjee ◽  
Peter S. Ojiambo ◽  
...  
Keyword(s):  
Downy Mildew ◽  
Data Driven ◽  
Disease Spread ◽  
Data Sets ◽  
Multiple Sources ◽  
Modeling Framework ◽  
Long Distance ◽  
Cucurbit Downy Mildew ◽  
Temporal Models ◽  
Spatio Temporal

The spread dynamics of long-distance-dispersed pathogens are influenced by the dispersal characteristics of a pathogen, anisotropy due to multiple factors, and the presence of multiple sources of inoculum. In this research, we developed a flexible class of phenomenological spatio-temporal models that extend a modeling framework used in plant pathology applications to account for the presence of multiple sources and anisotropy of biological species that can govern disease gradients and spatial spread in time. We use the cucurbit downy mildew pathosystem (caused by Pseudoperonospora cubensis) to formulate a data-driven procedure based on the 2008 to 2010 historical occurrence of the disease in the U.S. available from standardized sentinel plots deployed as part of the Cucurbit Downy Mildew ipmPIPE program. This pathosystem is characterized by annual recolonization and extinction cycles, generating annual disease invasions at the continental scale. This data-driven procedure is amenable to fitting models of disease spread from one or multiple sources of primary inoculum and can be specified to provide estimates of the parameters by regression methods conditional on a function that can accommodate anisotropy in disease occurrence data. Applying this modeling framework to the cucurbit downy mildew data sets, we found a small but consistent reduction in temporal prediction errors by incorporating anisotropy in disease spread. Further, we did not find evidence of an annually occurring, alternative source of P. cubensis in northern latitudes. However, we found a signal indicating an alternative inoculum source on the western edge of the Gulf of Mexico. This modeling framework is tractable for estimating the generalized location and velocity of a disease front from sparsely sampled data with minimal data acquisition costs. These attributes make this framework applicable and useful for a broad range of ecological data sets where multiple sources of disease may exist and whose subsequent spread is directional.

scholarly journals Evaluation and Prediction of COVID-19 Prevention and Control Strategy Based on the SEIR-AQ Infectious Disease Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yue Yu ◽  
Yuxing Zhou ◽  
Xiangzhong Meng ◽  
Wenfei Li ◽  
Yang Xu ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Prevention And Control ◽  
Control Strategies ◽  
Disease Model ◽  
Inhibitory Effect ◽  
Euler Method ◽  
Control Measures ◽  
Infectious Disease Model ◽  
Prevention And Control Measures ◽  
And Control

Based on the SEIR model, which takes into account prevention and control measures, prevention and control awareness, and economic level and medical level indicators, this paper proposes an infectious disease model of “susceptible-exposed-infected-removed-asymptomatic-isolated” (short for SEIR-AQ) to assess and predict the development of the COVID-19 pandemic with different prevention and control strategies. The kinetic parameters of the SEIR-AQ model were obtained by fitting, and the parameters of the SEIR-AQ model were solved through the Euler method. Furthermore, the effects of different countries’ prevention and control strategies on the number of infections, the proportion of isolation, the number of deaths, and the number of recoveries were also simulated. The theoretical analysis showed that measures such as isolation for prevention and control and medical tracking isolation had a significant inhibitory effect on the development of the COVID-19 pandemic, among which stratified treatment and enhanced awareness played a key role in the rapid regression of the peak of COVID-19-infected patients. Conclusion of the Simulation. The SEIR-AQ model can be used to evaluate the development status of the COVID-19 epidemic and has some theoretical value for the prediction of COVID-19.

Injury Prevention and Control

2010 ◽  
pp. 181-192
Author(s):  
Kodjo M. Bossou ◽  
Philip T. Hagen
Keyword(s):  
Prevention And Control ◽  
Disease Model ◽  
Causal Factors ◽  
Infectious Disease Model ◽  
Sufficient Magnitude ◽  
Definition Of ◽  
The Cost ◽  
Systematic Identification ◽  
And Control ◽  
Human Injury

In its most basic definition, human injury is the result of a transfer of energy of sufficient magnitude to damage tissues of the human recipient. Adoption of the infectious disease model of an agent (energy), a carrier (living or inanimate; a vector), and the affected person (host) has proved helpful in analyzing the chain of causation that leads to injury. For the inclusion of important injuries, the definition of causation is often modified to include exposures that prevent needed energy from reaching a host-for example, a lack of thermal energy (heat) that results in frostbite. For persons younger than 45, injury is the most frequent cause of death. Years of potential life lost is an important measure of the cost and health burden of injuries on society. With systematic identification of the causal factors of injury and the events leading up to and following injury, a comprehensive intervention can be carried out to reduce the occurrence of injury in various settings.

scholarly journals COVID-19 In Shang Hai: It is Worth Learning from the Successful Experience in Preventing and Controlling the Overseas Epidemic Situation

2020 ◽  
Author(s):  
Qi Dang ◽  
Miao Rui ◽  
Liang Yong
Keyword(s):  
Infectious Disease ◽  
Disease Model ◽  
Graph Model ◽  
Control Measures ◽  
Growth Data ◽  
Infectious Disease Model ◽  
Multiple Regression Models ◽  
Imported Cases ◽  
New Type ◽  
And Control

COVID-19 first appeared in Wuhan, Hubei Province,China in late December 2019 and spread rapidly in China. Currently, the spread of local epidemics has been basically blocked. The import of overseas epidemics has become the main form of growth in China's new epidemic. As an important international transportation hub in China, Shanghai is one of the regions with the highest risk of imported cases abroad. Due to imported of overseas cases are affected by the international epidemic trend. The traditional infectious disease model is difficult to accurately predict the cumulative trend of cumulative cases in the Shanghai areas. It is also difficult to accurately evaluate the effectiveness of the international traffic blockade. In this situation, this study takes Shanghai as an example to propose a new type of infectious disease prediction model. The model first uses the sparse graph model to analyze the international epidemic spread network to find countries and regions related to Shanghai. Next, multiple regression models were used to fit the existing COV-19 growth data in Shanghai. Finally, the model can predict the growth curve of Shanghai's epidemic without blocking traffic. The results show that the control measures taken by Shanghai are very effective. At present, more and more countries and regions will face the current situation in Shanghai. We recommend that other countries and regions learn from Shanghai's successful experience in preventing overseas imports in order to fully prepare for epidemic prevention and control.

scholarly journals Epidemic spread simulation in an area with a high-density crowd using a SEIR-based model

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253220
Author(s):  
Jibiao Zhou ◽  
Sheng Dong ◽  
Changxi Ma ◽  
Yao Wu ◽  
Xiao Qiu
Keyword(s):  
Infectious Diseases ◽  
Disease Model ◽  
Field Investigation ◽  
Primary Objective ◽  
Urban Rail Transit ◽  
Validity And Reliability ◽  
Infectious Disease Model ◽  
Urban Rail ◽  
Control Of Epidemics ◽  
And Control

Understanding the spread of infectious diseases is an extremely essential step to preventing them. Thus, correct modeling and simulation approaches are critical for elucidating the transmission of infectious diseases and improving the control of epidemics. The primary objective of this study is to simulate the spread of communicable diseases in an urban rail transit station. Data were collected by a field investigation in the city of Ningbo, China. A SEIR-based model was developed to simulate the spread of infectious diseases in Tianyi station, considering four groups of passengers (susceptible, exposed, infected, and recovered) and a 14-day incubation period. Based on the historical data of infectious diseases, the parameters of the SEIR infectious disease model were clarified, and a sensitivity analysis of the parameters was also performed. The results showed that the contact rate (CR), infectivity (I), and average illness duration (AID) were positively correlated with the number of infections. It was also found that the length of the average incubation time (AIT) was positively correlated with the number of exposed individuals and negatively correlated with the number of infectors. These simulation results provide support for the validity and reliability of using the SEIR model in studies of the spread of epidemics and facilitate the development of effective measures to prevent and control an epidemic.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 107
Author(s):  
Elahe Jamalinia ◽  
Faraz S. Tehrani ◽  
Susan C. Steele-Dunne ◽  
Philip J. Vardon
Keyword(s):  
Numerical Simulation ◽  
Water Flux ◽  
Temporal Stability ◽  
Synthetic Data ◽  
Climatic Conditions ◽  
Training Data ◽  
Data Driven ◽  
Data Set ◽  
Surface Cracking ◽  
Real Time Analysis

Climatic conditions and vegetation cover influence water flux in a dike, and potentially the dike stability. A comprehensive numerical simulation is computationally too expensive to be used for the near real-time analysis of a dike network. Therefore, this study investigates a random forest (RF) regressor to build a data-driven surrogate for a numerical model to forecast the temporal macro-stability of dikes. To that end, daily inputs and outputs of a ten-year coupled numerical simulation of an idealised dike (2009–2019) are used to create a synthetic data set, comprising features that can be observed from a dike surface, with the calculated factor of safety (FoS) as the target variable. The data set before 2018 is split into training and testing sets to build and train the RF. The predicted FoS is strongly correlated with the numerical FoS for data that belong to the test set (before 2018). However, the trained model shows lower performance for data in the evaluation set (after 2018) if further surface cracking occurs. This proof-of-concept shows that a data-driven surrogate can be used to determine dike stability for conditions similar to the training data, which could be used to identify vulnerable locations in a dike network for further examination.

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