A Space-Time Point Process Model for Analyzing and Predicting Case Patterns of Diarrheal Disease in Northwestern Ecuador

By Ahn Jaeil, Johnson Timothy, Bhavnani Darlene, and Eisenberg Joseph
Spatial and Spatio-temporal Epidemiology (2014)

  • Jaeil Ahn

    Georgetown University

    USA

Created

February 5, 2014

Last update

February 5, 2014

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C

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1609

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/****************************************************************************************** Simulation : Multi step procedure Recent updated : 05-11-2011 Auhtor : Jaeil Ahn, Timothy Johnson Step 1: LGCP for small box with finer grid Step 2: Spatial Misalignment : Map_converge Ft added Step 3: Prediction Based on Conditional Gaussian Field To run this program, put all data files into newcov folder and make a 'res' folder to save output To run nohup ./gp 4 1 > PPopRemo.out & Output will be saved in res folder strcpy(filename, "./res/PopNoRemo_GF.out"); // Gaussian Field strcpy(filename, "./res/PopNoRemo_Bigint.out"); // Intensity field on the Gaussian Field strcpy(filename, "./res/PopNoRemo_PR.out"); // Association at stage II strcpy(filename, "./res/PopNoRemo_Beta.out"); // Association parameter at stage I strcpy(filename, "./res/PopNoRemo_int.out"); // Intensity of second strcpy(filename, "./res/PopNoRemo_CNT_OBS.out"); // Observed cnt strcpy(filename, "./res/PopNoRemo_CNT_UNOBS.out"); // Prediction Out of 21 communities, 4 communities 4, 8, 14, 17 are chosen based on the remoteness metric. The prediction will be made on these four communities. *******************************************************************************************/

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