Abstract
The purpose of this study was to evaluate the relationship between violent events targeted against aid workers and the incidence of Ebola virus disease (EVD) during the 2018-19 Democratic Republic of the Congo (DRC) outbreak in the North Kivu and Ituri provinces. Time series models using vector autoregression were constructed using violent event data confined to the outbreak region from the Armed Conflict Location and Event Dataset (ACLED), in combination with EVD incidence reporting from the World Health Organization and DRC Ministry of Health, to examine intervariable temporal relationships, paired with Granger causality testing to assess both uni- and multidirectional associations. Violent events against aid workers and Granger-causing EVD incidence were found to be significant across 2 principal lag-ranges, 8 to 14 days and 22 to 29 days, both suggesting plausible causal associations. The multivariate model for violent events and violence-related fatalities Granger-causing EVD incidence was also found to be significant at lags greater than 9, reinforcing the plausible causal association. Findings from the study suggested that the relationship between targeted violence and EVD incidence may be explained etiologically, as significant lag-ranges corresponded to plausible patient presentation timeframes, based on the incubation period for EVD. Additionally, the findings may also be explained through impact on operations, when events targeting facilities, supply lines, and personnel affect treatment capability.
This study evaluated the relationship between violent events targeted against aid workers and the incidence of Ebola virus disease (EVD) during the 2018-19 DRC outbreak. Time series models using vector autoregression were constructed using violent event data confined to the outbreak region from the Armed Conflict Location and Event Dataset (ACLED), in combination with EVD incidence reporting from the WHO and DRC Ministry of Health, to examine intervariable temporal relationships, paired with Granger causality testing to assess both uni- and multidirectional associations.
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