# Difference between revisions of "Computational Epidemiology Task"

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A [[Computational Epidemiology Task]] is an [[epidemiological task]] that is a [[computational task]]. | A [[Computational Epidemiology Task]] is an [[epidemiological task]] that is a [[computational task]]. | ||

− | * <B>See:</B> [[Epidemiology | + | * <B>See:</B> [[Epidemiology]], [[Geographic Information Science]], [[Public Health]]. |

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## Revision as of 16:51, 26 March 2020

A Computational Epidemiology Task is an epidemiological task that is a computational task.

## References

### 2020

- (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/computational_epidemiology Retrieved:2020-3-26.
**Computational epidemiology**is a multidisciplinary field that uses techniques from computer science, mathematics, geographic information science and public health to better understand issues central to epidemiology such as the spread of diseases or the effectiveness of a public health intervention.

### 2014

- (Beckman et al., 2014) ⇒ [[::Richard Beckman]], [[::Keith R. Bisset]], [[::Jiangzhuo Chen]], [[::Bryan Lewis]], [[::Madhav Marathe]], and [[::Paula Stretz]]. ([[::2014]]). “ISIS: A Networked-epidemiology based Pervasive Web App for Infectious Disease Pandemic Planning and Response.” In: [[::Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining]] ([[::KDD-2014]]) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623375
- QUOTE: We describe ISIS, a high-performance-computing-based application to support computational epidemiology of infectious diseases. ... Using ISIS, one can carry out detailed computational experiments as they pertain to planning and response in the event of a pandemic. ISIS is designed to support networked epidemiology -- study of epidemic processes over social contact networks. ...

### 2009

- (McKinley et al., 2009) ⇒ [[::Trevelyan McKinley]], [[::Alex R Cook]], and [[::Robert Deardon]]. ([[::2009]]). “Inference in Epidemic Models Without Likelihoods.” In: The International Journal of Biostatistics, 5(1).
- QUOTE: ... Likelihood-based inference for epidemic models can be challenging, in part due to difficulties in evaluating the likelihood. The problem is particularly acute in models of large-scale outbreaks, and unobserved or partially observed data further complicates this process. Here we investigate the performance of Markov Chain Monte Carlo and Sequential Monte Carlo algorithms for parameter inference, where the routines are based on approximate likelihoods generated from model simulations. ...