Inferring spatial source of disease outbreaks using maximum entropy.

@article{Ansari2021InferringSS,
  title={Inferring spatial source of disease outbreaks using maximum entropy.},
  author={Mehrad Ansari and David Soriano-Pa{\~n}os and Gourab Ghoshal and Andrew D White},
  journal={Physical review. E},
  year={2021},
  volume={106 1-1},
  pages={
          014306
        }
}
Mathematical modeling of disease outbreaks can infer the future trajectory of an epidemic, allowing for making more informed policy decisions. Another task is inferring the origin of a disease, which is relatively difficult with current mathematical models. Such frameworks, across varying levels of complexity, are typically sensitive to input data on epidemic parameters, case counts, and mortality rates, which are generally noisy and incomplete. To alleviate these limitations, we propose a… 

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References

SHOWING 1-10 OF 140 REFERENCES

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.

Bayesian inference for nonlinear stochastic SIR epidemic model

This work develops a powerful method for Bayesian paradigm for susceptible–infected–removed stochastic epidemic models via data-augmented Markov Chain Monte Carlo, based on the approximation of the discrete-time epidemic by diffusion process.

Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London

A new statistical approach to analyse epidemic time-series data and introduces a diffusion process that mimicks the susceptible–infectious–removed (SIR) epidemic process, but that is more tractable analytically.

Assessing parameter identifiability in compartmental dynamic models using a computational approach: application to infectious disease transmission models

A parametric bootstrap approach to generate simulated data from dynamical systems to quantify parameter uncertainty and identifiability is described and enhances the essential toolkit for conducting model-based inferences using compartmental dynamic models.

The turning point and end of an expanding epidemic cannot be precisely forecast

This study warns against precise forecasts of the evolution of epidemics based on mean-field, effective, or phenomenological models and supports that only probabilities of different outcomes can be confidently given.

Multiscale, resurgent epidemics in a hierarchical metapopulation model.

This work introduces a class of metapopulation models in which homogeneous mixing holds within local contexts, and that these contexts are embedded in a nested hierarchy of successively larger domains and allow diseases to spread stochastically.

Bayesian inference for epidemics with two levels of mixing

Abstract.  Methodology for Bayesian inference is considered for a stochastic epidemic model which permits mixing on both local and global scales. Interest focuses on estimation of the within‐ and

Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions

Modeling and Bayesian inference reveal the time dependence of SARS-CoV-2 interventions on the number of new infections using the example of Germany and the impact of these measures on the disease spread using change point analysis.
...