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Abstrait
Regression Modelling and Analysis of Cell phone-Based Syndromic Surveillance Data for Ebola in Sierra Leone
Jia Bainga Kangbai
Aim: To determine the out degree centrality for multivariate analysis, examine the demographic differences and characteristics of callers who make more calls, and determine 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 cell phone technology. UCINET and Net Draw software were used to determine the out degree centrality used for multivariate analysis. Poisson and logistic regression analyses were used to do multivariable analysis.
Result: Women (AOR=0.33, 95% CI [0.14, 0.81]) was 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]) was also associated with making fewer Ebola surveillance calls compared to men.
Conclusion: This study shows that the combination of cell phone technology with user-friendly and open-source social network software such as UCINET and Net draw will provide an important adjunct to the traditional measures of epidemiologic surveillance.