Assessment of the Risk of Ebola Importation to Australia

Robert C. Cope; Phillip Cassey; Graeme J. Hugo; Joshua V. Ross

doi:10.1371/currents.outbreaks.aa0375fd48a92c7c9422aa543a88711f

Objectives: To assess the risk of Ebola importation to Australia during the first six months of 2015, based upon the current outbreak in West Africa.

Methodology: We assessed the risk under two distinct scenarios: (i) assuming that significant numbers of cases of Ebola remain confined to Guinea, Liberia and Sierra Leone, and using historic passenger arrival data into Australia; and, (ii) assuming potential secondary spread based upon international flight data. A model appropriate to each scenario is developed, and parameterised using passenger arrival card or international flight data, and World Health Organisation case data from West Africa. These models were constructed based on WHO Ebola outbreak data as at 17 October 2014 and 3 December 2014. An assessment of the risk under each scenario is reported. On 27 October 2014 the Australian Government announced a policy change, that visas from affected countries would be refused/cancelled, and the predicted effect of this policy change is reported.

Results: The current probability of at least one case entering Australia by 1 July 2015, having travelled directly from West Africa with historic passenger arrival rates into Australia, is 0.34. Under the new Australian Government policy of restricting visas from affected countries (as of 27 October 2014), the probability of at least one case entering Australia by 1 July 2015 is reduced to 0.16. The probability of at least one case entering Australia by 1 July 2015 via an outbreak from a secondary source country is approximately 0.12.

Conclusions: Our models suggest that if the transmission of Ebola remains unchanged, it is possible that a case will enter Australia within the first six months of 2015, either directly from West Africa (even when current visa restrictions are considered), or via secondary outbreaks elsewhere. Government and medical authorities should be prepared to respond to this eventuality. Control measures within West Africa over recent months have contributed to a reduction in projected risk of a case entering Australia. A significant further reduction of the rate at which Ebola is proliferating in West Africa, and control of the disease if and when it proliferates elsewhere, will continue to result in substantially lower risk of the disease entering Australia.

Background: In mid-October 2014, the number of cases of the West Africa Ebola virus epidemic in Guinea, Sierra Leone and Liberia exceeded 9,000 cases. The early growth dynamics of the epidemic has been qualitatively different for each of the three countries. However, it is important to understand these disparate dynamics as trends of a single epidemic spread over regions with similar geographic and cultural aspects, with likely common parameters for transmission rates and reproduction number R0.

Methods: We combine a discrete, stochastic SEIR model with a three-scale community network model to demonstrate that the different regional trends may be explained by different community mixing rates. Heuristically, the effect of different community mixing rates may be understood as the observation that two individuals infected by the same chain of transmission are more likely to share the same contacts in a less-mixed community. Local saturation effects occur as the contacts of an infected individual are more likely to already be exposed by the same chain of transmission.

Results: The effects of community mixing, together with stochastic effects, can explain the qualitative difference in the growth of Ebola virus cases in each country, and why the probability of large outbreaks may have recently increased. An increase in the rate of Ebola cases in Guinea in late August, and a local fitting of the transient dynamics of the Ebola cases in Liberia, suggests that the epidemic in Liberia has been more severe, and the epidemic in Guinea is worsening, due to discrete seeding events as the epidemic spreads into new communities.

Conclusions: A relatively simple network model provides insight on the role of local effects such as saturation that would be difficult to otherwise quantify. Our results predict that exponential growth of an epidemic is driven by the exposure of new communities, underscoring the importance of limiting this spread.