Using Non-traditional Data Sources for Near Real-Time Estimation of Transmission Dynamics in the Hepatitis-E Outbreak in Namibia, 2017-2018

Abstract

Background: Google Trends (GT) is an emerging source of data that can be used to predict, detect, and track infectious disease outbreaks. GT cumulative search volume data has been shown to correlate with cumulative case counts and to produce basic and observed reproduction number estimates analogous to those derived from more traditional epidemiological data sources. An outbreak of Hepatitis-E (Hep-E) occurred in Namibia in the fall and winter of 2017-2018. We used GT data to estimate transmission dynamics of the outbreak and compared these results with those estimated via data from HealthMap, a relatively new digital data source, and with surveillance reports from the government of Namibia published in the World Health Organization Bulletin, which is a traditional data source. Objective: Aim 1: To determine the correlation between GT relative search volume data (RSV) and cumulative case counts from the HealthMap (HM) and World Health Organization (WHO) data sources. Aim 2: To estimate and compare transmission dynamics including basic reproduction numbers (R0), observed reproduction numbers (Robs), and final outbreak size (Imax) for each of the three sources of data. Methods: GT relative search volume data regarding the term “hepatitis” in Namibia was acquired from October 13, 2017-March 2, 2018. Cumulative reported case counts were obtained from the HealthMap and WHO data sources. The Incidence Decay and Exponential Adjustment (IDEA) model was used to calculate R0, Robs, and final outbreak size for the three data sources. Results: The correlation coefficient between GT cumulative relative search volume and both HM and WHO cumulative case counts measured R = 0.93. The mean R0 and Robs estimates for the hepatitis-E outbreak in Namibia were similar between the GT, HM, and WHO data sources and are similar to previously published Hep-E R0 estimates from Uganda. Final outbreak size was similar between HM and WHO data sources; however, estimates using GT-derived data sources were smaller. Conclusions: GT cumulative search volume correlated with cumulative case counts from the HM and WHO data sources. Mean R0 and Robs values were similar among the data sources considered. GT-derived final outbreak size was smaller than both HM and WHO estimates due to diminishing search volume later in the epidemic possibly due to search fatigue; nevertheless, this data source was useful in describing the transmission dynamics of the outbreak including correlation with case counts and reproduction numbers.