Social network analysis and modeling of cellphone-based syndromic surveillance data for Ebola in Sierra Leone.

OBJECTIVE To explore and visualize the connectivity of suspected Ebola cases and surveillance callers who used cellphone technology in Moyamba District in Sierra Leone for Ebola surveillance, and to examine the demographic differences and characteristics of Ebola surveillance callers who make more calls as well as those callers who are more likely to make at least one positive Ebola call. METHODS Surveillance data for 393 suspected Ebola cases (192 males, 201 females) were collected from October 23, 2014 to June 28, 2015 using cellphone technology. UCINET and NetDraw software were used to explore and visualize the social connectivity between callers and suspected Ebola cases. Poisson and logistic regression analyses were used to do multivariable analysis. RESULTS The entire social network was comprised of 393 ties and 745 nodes. Women (AOR = 0.33, 95% CI [0.14, 0.81]) were associated with decreased odds of making at least one positive Ebola surveillance call compared to men. Women (IR = 0.63, 95% CI [0.49, 0.82]) were also associated with making fewer Ebola surveillance calls compared to men. CONCLUSION Social network visualization can analyze syndromic surveillance data for Ebola collected by cellphone technology with unique insights.