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Table 2 Number of reported contact persons per participant per day by different characteristics and relative number of contacts from the Poisson Inverse-Gaussian Regression model

From: Tracking social contact networks with online respondent-driven detection: who recruits whom?

Category Covariate Number of participants Mean (standard deviation) of number of reported contacts Relative number of reported contacts (95 % CI)a
Age of participant 0–39 268 20.98 (24.88) 1.00
  40–49 256 25.35 (37.24) 0.97 (0.80–1.17)
  50–64 656 19.94 (35.16) 0.93 (0.79–1.09)
  65+ 379 14.19 (39.63) 0.69 (0.58–0.83)
Sex of participant Female 1010 18.94 (30.78) 1.00
  Male 549 20.83 (42.41) 1.05 (0.94–1.18)
Household size 1 389 17.85 (29.49) 1.00
  2 648 15.73 (23.91) 1.02 (0.89–1.17)
  3 192 26.54 (58.17) 1.44 (1.20–1.73)
  4 218 24.93 (43.10) 1.55 (1.29–1.87)
  ≥5 112 25.92 (37.37) 1.81 (1.43–2.29)
ILI No 1519 19.93 (35.68) 1.00
  Yes 40 7.25 (9.70) 0.37 (0.25–0.53)
Days of the week Sunday 224 16.68 (51.25) 1.00
  Monday 414 17.94 (32.15) 1.33 (1.12–1.59)
  Tuesday 249 24.27 (36.80) 1.84 (1.52–2.23)
  Wednesday 192 22.41 (31.73) 1.60 (1.30–1.96)
  Thursday 182 21.16 (28.29) 1.61 (1.31–1.99)
  Friday 117 18.76 (28.11) 1.42 (1.12–1.81)
  Saturday 181 16.65 (29.16) 1.27 (1.03–1.57)
  1. aDispersion parameter λ = 1.7 (95 % CI 1.4–2.1). The Poisson Inverse-Gaussian model is appropriate for modelling correlated counts with long sparse extended tails. The over-dispersion parameter in the model was significantly different from zero, indicating the necessity to use this model instead of a generalised Poisson model. Comparing AIC statistics, the Poisson Inverse-Gaussian model gave a better fit as opposed to a negative binomial model and a generalised Poisson model [22]