Corpus ID: 235436258

A stochastic metapopulation state-space approach to modeling and estimating Covid-19 spread

  title={A stochastic metapopulation state-space approach to modeling and estimating Covid-19 spread},
  author={Yu-Ting Tan and Durward Cator and M. Ndeffo-Mbah and U. Braga-Neto},
Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, based on a discrete-time spatiotemporal susceptible/exposed/infected/recovered/deceased (SEIRD) model. The proposed framework allows the hidden SEIRD… Expand

Figures and Tables from this paper


Parameterizing state–space models for infectious disease dynamics by generalized profiling: measles in Ontario
The utility of an alternative approach, generalized profiling, is demonstrated, which provides robustness to violations of a deterministic model without needing to specify a complete probabilistic model, and avoids many challenges that have limited Monte Carlo inference for state–space models. Expand
A Time-Dependent SEIRD Model for Forecasting the COVID-19 Transmission Dynamics
Using the SEIRD model, attempts are made to forecast Infected, Recovered and Death rates of COVID-19 up to a week using an incremental approach and a method of optimizing the parameters of the model is discussed thoroughly in this work. Expand
Predicting intervention effect for COVID-19 in Japan: state space modeling approach.
Even though the epidemic appears to be settling down during this intervention period, the prediction results under various scenarios reveal that the temporary reduction in the infection rate would still result in a delayed the epidemic peak unless the long-term reproduction number is controlled. Expand
Tracking Epidemics with State-space SEIR and Google Flu Trends
In this paper we use Google Flu Trends data together with a sequential surveillance model based on the state-space methodology, to track the evolution of an epidemic process over time. We embed aExpand
Monitoring Italian COVID-19 spread by an adaptive SEIRD model
Preliminary results on Lombardia and Emilia-Romagna regions confirm that SEIRD(rm) fits the data more accurately than the original SEIRD model with constant rate infection parameter, and the increased flexibility in the choice of the infection rate function makes it possible to better control the predictions. Expand
Monitoring Italian COVID-19 spread by a forced SEIRD model
This paper proposes a forced Susceptible-Exposed-Infected-Recovered-Dead (fSEIRD) differential model for the analysis and forecast of the COVID-19 spread in Italian regions, using the data from the Italian Protezione Civile from 24/02/2020. Expand
Simulation of the spread of infectious diseases in a geographical environment
The model is supposed to model dynamics of infectious diseases on complex networks, which is nearly impossible to be achieved with differential equations because of the complexity of the problem. Expand
SEIR Modeling of the Italian Epidemic of SARS-CoV-2 Using Computational Swarm Intelligence
We applied a generalized SEIR epidemiological model to the recent SARS-CoV-2 outbreak in the world, with a focus on Italy and its Lombardy, Piedmont, and Veneto regions. We focused on the applicationExpand
An agent-based approach for modeling dynamics of contagious disease spread
The GIS-agent based model designed for this study can be easily customized to study the disease spread dynamics of any other communicable disease by simply adjusting the modeled disease timeline and/or the infection model and modifying the transmission process. Expand
Forecasting seasonal influenza with a state-space SIR model.
This workfits a probabilistic state-space model motivated by a deterministic mathematical model [a susceptible-infectious-recovered (SIR) model] with a conditionally specified prior that allows it to exploit known relationships between latent SIR initial conditions and parameters and functions of surveillance data. Expand