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Table 1 Results of Bayesian geostatistical models to predict prevalence of P. vivax and P. falciparum for Tanna Island, 2008.

From: Baseline spatial distribution of malaria prior to an elimination programme in Vanuatu

 

Coefficient, posterior mean

Odds ratio, posterior mean (95% Bayes credible intervals#)

DIC

Model of P. vivax with distance to coastline fixed effect

   

α (intercept)

-4.680 (-5.317- -4.137)

  

Distance from coastline (OR per 1 km)

-0.690 (-1.151- -0.242)

0.730 (0.591-0.895)

 

Φ (rate of decay of spatial correlation)*

251.5(51.26-569.4)

  

σ2 (variance of geostatistical random effect)**

0.214 (0.056 - 0.624)

  

DIC

  

306.9

Model of P. vivax with elevation fixed effect

   

α (intercept)

-4.611 (-5.327 - -3.931)

  

Elevation (OR per 100 m)

-0.547 (-0.992- 0.115)

0.654 (0.468, 0.917)

 

Φ (rate of decay of spatial correlation)*

167.8 (33.71, 461.2)

  

σ2 (variance of geostatistical random effect)**

2.471 (1.271, 4.304)

  

DIC

  

308.9

Model of P. falciparum with distance to coastline fixed effect

   

α

-5.238 (-6.027 - -4.625)

  

Distance from coastline (OR per 1 km)

-0.101 (-0.534 - 0.334)

0.955 (0.783, 1.165)

 

Φ

289.5 (51.61 - 575.4)

  

σ 2

0.584 (0.057 - 4.713)

  

DIC

  

219.5

Model of P. falciparum with elevation fixed effect

   

α

-5.129 (-5.976 - -4.416)

  

Elevation (OR per 100 m)

-0.207 (-0.673 - 0.146)

0.864 (0.603, 1.149)

 

Φ

238.2 (53.28 - 478.9)

  

σ 2

1.753 (0.4521 - 4.079)

  

DIC

  

218.9

  1. * The unit is change in spatial autocorrelation per decimal degree. A lower Φ indicates that spatial correlation occurs over longer distances (i.e. spatial clusters are larger).
  2. ** A higher variance indicates a greater tendency toward spatial clustering.
  3. # Bayes credible intervals can be interpreted as having a similar meaning to confidence intervals used in frequentist statistics.