Table 2 Estimated parameters for the best fitting sero-catalytic models to each of the 9 data sets

From: The utility of serology for elimination surveillance of trachoma

Study site

Model

λ T

γ

p

t c

λ UG

DIC

Nepal (Pgp3)

Scenario 2v1

0.143 (0.107–0.215)

0.053 (0.031–0.084)

0.026 (0.020–0.032)

16.06 (13.57–17.69)

—

1325.11

Nepal (CT694)

Scenario 2v1

0.142 (0.103–0.207)

0.062 (0.037–0.097)

0.017 (0.013–0.021)

16.84 (14.93–18.65)

—

1252.95

Gambia LRR

Scenario 3v4

0.021 (0.013–0.03)

0.677 (0.268–0.984)

—

—

0.067 (0.049–0.090)

866.64

Gambia URR

Scenario 3v4

0.023 (0.010–0.184)

0.591 (0.112–0.893)

—

—

0.063 (0.015–0.595)

678.64

Rombo (Pgp3)

Scenario 3v4

0.022 (0.009–0.041)

0.177 (0.019–0.881)

—

—

0.092 (0.061–0.127)

379.74

Rombo (CT694)

Scenario 3v4

0.008 (0.004–0.016)

0.172 (0.002–0.567)

—

—

0.048 (0.031–0.062)

326.74

Temotu

Scenario 3v4

0.045 (0.028–0.075)

0.585 (0.218–0.986)

—

—

0.021 (0.001–0.041)

1440.54

Rennell & Bellona

Scenario 3v4

0.092 (0.061–0.170)

0.746 (0.319–0.995)

—

—

0.255 (0.085–0.499)

247.34

Kiribati

Scenario 3v3

1.080 (0.345–1.737)

0.063 (0.034–0.204)

—

—

—

453.45

iTaukei

Scenario 1v2

0.053 (0.044–0.063)

—

—

—

—

554.99

Indo-Fijian

Scenario 1v2

0.006 (0.001–0.014)

—

—

—

—

23.30

  1. We present the median posterior estimates, the 2.5% and 97.5% credible intervals (CrI) for each parameter for each model and the Deviance information criteria (DIC) for each model (note that DIC values should not be compared between different model fits to different data sets). λT - rate of sero-conversion due to exposure to trachoma, λUG - rate of sero-conversion due to exposure to urogenital infection, ρ - rate of sero-reversion, tc - fixed time point at which transmission intensity changed, γ - proportional decline in transmission at tc or over time. Lower River Region (LRR), Upper River Region (URR)
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