The preparedness of health systems to detect, treat, and prevent onward transmission of Ebola virus disease (EVD) is central to mitigating future outbreaks. Early detection of outbreaks is critical to timely response, but estimating detection rates is difficult because unreported spillover events and outbreaks do not generate data. Using three independent datasets available on the distributions of secondary infections during EVD outbreaks across West Africa, in a single district (Western Area) of Sierra Leone, and in the city of Conakry, Guinea, we simulated realistic outbreak size distributions and compared them to reported outbreak sizes. These three empirical distributions lead to estimates for the proportion of detected spillover events and small outbreaks of 26% (range 8–40%, based on the full outbreak data), 48% (range 39–62%, based on the Sierra Leone data), and 17% (range 11–24%, based on the Guinea data). We conclude that at least half of all spillover events have failed to be reported since EVD was first recognized. We also estimate the probability of detecting outbreaks of different sizes, which is likely less than 10% for single-case spillover events. Comparing models of the observation process also suggests the probability of detecting an outbreak is not simply the cumulative probability of independently detecting any one individual. Rather, we find that any individual’s probability of detection is highly dependent upon the size of the cluster of cases. These findings highlight the importance of primary health care and local case management to detect and contain undetected early stage outbreaks at source.
Emerging infectious diseases are often not investigated in rural Africa unless outbreaks involve a sizeable number of cases. A number of different Ebola virus disease (EVD) outbreaks have been reported in the literature and in surveillance reports since its discovery in 1976. The majority of the reports are of large outbreaks. Given the low reported rate of transmission of Ebola, and the high frequency with which cases infect no one else, one might expect most outbreaks to be very small (<5 people). This is the first study to the authors’ knowledge that quantitatively estimates the number of undetected EVD outbreaks or probabilities of EVD outbreak detection by outbreak size. Although the total amount of evidence in this area is still limited, this study’s main result—that at least half of EVD outbreaks go undetected—is consistent under many different sets of assumptions. This is the most thorough estimation of EVD outbreak detection to date and corroborates the majority of more qualitative work on EVD surveillance, suggesting greater investment in primary health care and local surveillance will be important to detect EVD outbreaks early and consistently.