https://github.com/bansallab/optimize-flu-surveillance

mapping and modeling drivers of influenza disease burden in the United States

https://github.com/bansallab/optimize-flu-surveillance

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mapping and modeling drivers of influenza disease burden in the United States

https://github.com/bansallab/optimize-flu-surveillance/blob/master/

# Mapping and modeling drivers of influenza disease burden using hierarchical models
## Approximate Bayesian inference performed in R-INLA

This repository provides the source code and model outputs for a spatial Bayesian hierarchical model that maps county-level disease burden for influenza-like illness in the United States.

The model and mapping outputs are described in the following paper:
[Lee, Elizabeth C., Ali Arab, Sandra M. Goldlust, Cécile Viboud, Bryan T. Grenfell, and Shweta Bansal. (2018). "Deploying digital health data to optimize influenza surveillance at national and local scales." PLOS Computational Biology. doi:10.1371/journal.pcbi.1006020](https://doi.org/10.1371/journal.pcbi.1006020).

### model codes and inputs
The source code for the primary models presented in the manuscript may be found in `model_codes/`. The codes here demonstrate the specific settings and parameters in our INLA models and provide additional examples for how to utilize the [R-INLA software](http://www.r-inla.org/). Please note that the input data to run the source code are not posted in this repository. 

Descriptions of the files are as follows:
  * `model_epidemicIntensity.R`: INLA code for county-level multi-season epidemic intensity models for the total, adult, and child population models
  * `model_epidemicDuration.R`: INLA code for county-level multi-season epidemic duration model
  * `model_epidemicIntensity_pandemic.R`: INLA model code for county-level 2009 H1N1 pandemic epidemic intensity models for the total population
  * `model_epidemicIntensity_state.R`: INLA code for the state-level multi-season epidemic intensity models used to examine aggregation bias
  * `custom_functions.R`: functions to prepare covariate data for INLA
  * `US_county_adjacency.graph`: county neighborhood structure, an input file for the county-level multi-season models

### model outputs
Summary statistics for estimated model parameters and fitted model values are provided as follows:
  * `summaryStats_epidemicDuration.csv`
  * `summaryStatsFitted_epidemicDuration.csv`

  * `summaryStats_epidemicIntensity.csv`
  * `summaryStatsFitted_epidemicIntensity.csv`

  * `summaryStats_epidemicIntensity_adult.csv`
  * `summaryStatsFitted_epidemicIntensity_adult.csv`

  * `summaryStats_epidemicIntensity_child.csv`
  * `summaryStatsFitted_epidemicIntensity_child.csv`

  * `summaryStats_epidemicIntensity_pandemic.csv`
  * `summaryStatsFitted_epidemicIntensity_pandemic.csv`
  
  * `summaryStats_epidemicIntensity_state.csv`
  * `summaryStatsFitted_epidemicIntensity_state.csv`


email: ecl48@georgetown.edu, shweta.bansal@georgetown.edu

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