Responding to the threat of urban yellow fever outbreaks
Authors: Annelies Wilder-Smith, Thomas P Monath
Journal: The Lancet Infectious Diseases (2016)

When in April, 2016, WHO declared the yellow fever epidemic in Angola a global threat, it was because yellow fever appeared in Luanda, the capital city of Angola, causing a rapidly spreading urban outbreak due to the massive movement of people to and from the city and easy access to international airports, with daily connections to Asia, Europe, and the Americas. Nearly 45 years had elapsed since a similar urban yellow fever epidemic occurred in Angola in 1971 (a smaller one occurred in 1988); in that interval, urbanisation has increased at record rates, with more than 62% of the population now living in urban areas.1Agencia Angola PressAngola records high levels of urbanization.http://www.angop.ao/angola/en_us/noticias/reconstrucao-nacional/2016/4/21/Angola-records-high-levels-urbanization,0ea95920-f3cf-4607-be07-1f8e72a23236.htmlGoogle Scholar For reasons that are still poorly understood, the yellow fever virus, which is maintained in a transmission cycle involving non-human primates and arboreal mosquitoes, crosses into a far more threatening human-to-human transmission cycle involving urban and domestic Aedes aegypti mosquitoes. The distribution of A aegypti is now the widest ever recorded and it is extensive in all continents, including parts of North America and Europe, with more than 3 billion people at risk.2Kraemer MUG Sinka ME Duda KA et al.The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus.Elife. 2015; 4: e08347Crossref PubMed Scopus (1125) Google Scholar Urbanisation with resulting increased population densities, further enhanced by man-made larval habitats, amplifies A aegypti-transmitted diseases, including those caused by the dengue, chikungunya, and Zika viruses.3Wilder-Smith A Gubler DJ Weaver SC Monath TP Heymann DL Scott TW Epidemic arboviral diseases: priorities for research and public health.Lancet Infect Dis. 2016; (published online Dec 20.)http://dx.doi.org/10.1016/S1473-3099(16)30518-7PubMed Google Scholar Among these viral infections, yellow fever is of major concern because the lethality of this haemorrhagic fever is 20–50%, rivalling that of Ebola virus disease. Of particular concern is that urban yellow fever has the potential for rapid spread via international travellers to vulnerable countries where A aegypti mosquitoes are present. Indeed, for the first time, such spread happened during the Angola outbreak when travellers infected with yellow fever in Angola entered China between March and April, 2016, thereby putting 1·8 billion largely unvaccinated people in Asia at risk. Fortunately, no autochthonous cases occurred. In late 2015 and the first months of 2016, the yellow fever outbreak in central Africa had a high reproductive rate (4·8 new people infected for every case). Although an effective vaccine, which protects against infection within 7–10 days after vaccination, has been available since the 1930s, implementation of an emergency vaccination campaign to contain the rapidly expanding outbreak was hampered by limited vaccine supplies and problems in the delivery of vaccinations. In such a setting it is essential to identify the areas at greatest risk of infection, to inform vaccine prioritisation decisions. Consequently, the analyses by Moritz Kraemer and colleagues4Kraemer MUG Faria NR Reiner Jr, RC et al.Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling study.Lancet Infect Dis. 2016; (published online Dec 22.)http://dx.doi.org/10.1016/S1473-3099(16)30513-8PubMed Google Scholar reported in The Lancet Infectious Diseases provide an important contribution with many practical implications. Although a WHO working group had previously identified a set of mainly ecological criteria relevant to the transmission of yellow fever virus and systematically applied these criteria to classify areas with risk for transmission of yellow fever virus,5Jentes ES Poumerol G Gershman MD et al.The revised global yellow fever risk map and recommendations for vaccination, 2010: consensus of the Informal WHO Working Group on Geographic Risk for Yellow Fever.Lancet Infect Dis. 2011; 11: 622-632Summary Full Text Full Text PDF PubMed Scopus (185) Google Scholar the model developed by Kraemer and colleagues adds mobility patterns and demographic determinants that govern transmission and spread of the virus in the region. The investigators used standard logistic models that discriminated districts with high risk of invasion from others. Notably, population density was a dominant predictive factor for onward transmission, corroborating previous findings.6Struchiner CJ Rocklov J Wilder-Smith A Massad E Increasing dengue incidence in Singapore over the past 40 years: population growth, climate and mobility.PLoS One. 2015; 10: e0136286Crossref PubMed Scopus (89) Google Scholar The spread of yellow fever in Angola was driven by high population density, including in locations that were distant from the origin of the outbreak in Luanda. Furthermore, the team captured different aspects of connectivity and were able to infer regular daily commuting patterns, longer-term movements, and general human diffusion processes. Their gravity model assumed that relative flow between districts is a log-linear function of the population sizes of the districts and the distance between them,7Wesolowski A O'Meara WP Eagle N Tatem AJ Buckee CO Evaluating spatial interaction models for regional mobility in sub-Saharan Africa.PLoS Comput Biol. 2015; 11: e1004267Crossref PubMed Scopus (56) Google Scholar thereby emphasising large population centres such as the capital cities Luanda (Angola) and Kinshasa (DR Congo), which were the epicentres of the epidemic. A radiation model additionally took account of the draw from other populations within the same radius of the districts considered, as well as the population sizes and distance of the origin and destination locations. Models based on travel volumes as shown by Kraemer and colleagues are increasingly used to predict international spread and identify the most vulnerable receiving areas or countries for infectious diseases such as influenza H1N1,8Khan K Arino J Hu W et al.Spread of a novel influenza A (H1N1) virus via global airline transportation.N Engl J Med. 2009; 361: 212-214Crossref PubMed Scopus (343) Google Scholar polio,9Wilder-Smith A Leong WY Lopez LF et al.Potential for international spread of wild poliovirus via travelers.BMC Med. 2015; 13: 133Crossref PubMed Scopus (33) Google Scholar dengue,10Massad E Wilder-Smith A Risk estimates of dengue in travelers to dengue endemic areas using mathematical models.J Travel Med. 2009; 16: 191-193Crossref PubMed Scopus (32) Google Scholar, 11Wilder-Smith A Quam M Sessions O et al.The 2012 dengue outbreak in Madeira: exploring the origins.Euro Surveill. 2014; 19: 20718Crossref PubMed Scopus (79) Google Scholar Zika virus,12Massad E Tan SH Khan K Wilder-Smith A Estimated Zika virus importations to Europe by travellers from Brazil.Global Health Action. 2016; 9: 31669Crossref PubMed Scopus (47) Google Scholar, 13Rocklöv J Quam MB Sudre B et al.Assessing seasonal risks for the introduction and mosquito-borne spread of Zika virus in Europe.EBioMedicine. 2016; 9: 250-256Summary Full Text Full Text PDF PubMed Scopus (75) Google Scholar, 14Quam MB Wilder-Smith A Estimated global exportations of Zika virus infections via travellers from Brazil from 2014 to 2015.J Travel Med. 2016; 23: taw059Crossref PubMed Scopus (29) Google Scholar and yellow fever,15Johansson MA Arana-Vizcarrondo N Biggerstaff BJ Gallagher N Marano N Staples JE Assessing the risk of international spread of yellow fever virus: a mathematical analysis of an urban outbreak in Asuncion, 2008.Am J Trop Med Hyg. 2012; 86: 349-358Crossref PubMed Scopus (52) Google Scholar and should become the basis for similar studies aiming to predict regional and international spread. The modelling techniques used by Kraemer and colleagues allowed the analysis of near real-time data to inform the control of an ongoing outbreak. If, in mid-February, the 50 districts with the highest calculated probability of infection had been targeted, their model would have correctly identified 27 (84%) of the 32 districts that were eventually affected. This approach, if applied in the future, could speed up prioritisation of areas to be targeted for rapid deployment of vaccines in the context of finite resources. However, the Article does not address how generalisable to other settings the pattern of spread was in this particular outbreak in which urban transmission initiated in a major city and radiated outward. Moreover the model does not account for the fact that yellow fever virus was introduced from Angola into densely populated parts of the southern DR Congo without the extent of spread seen in Angola. Yellow fever virus has caused substantial outbreaks in Africa (eg, in The Gambia in 1978–79, Nigeria in 1969, and Ethiopia in 1962) where sylvatic mosquito vectors have been mainly responsible for inter-human virus transmission, and where human population movements, population density, and A aegypti distribution have not clearly determined the pattern of the epidemic at the time. These occurrences emphasise the complex ecology of yellow fever and the importance of determining the role of specific mosquito vectors in each yellow fever epidemic. Nevertheless, in an increasingly urbanised Africa, application of Kraemer and colleagues' model will allow rapid assessment of the predicted pattern of spread and logistical approach to containment. Notably, the investigators found that the epidemic in Angola slowed around the time that vaccination was initiated in February, 2016, or even before it was implemented, suggesting that other factors, such as a possible change in human behaviour, slowed transmission as word spread of the dangers of yellow fever. In a 2013 epidemic of dengue in Luanda, mosquito avoidance strategies (such as application of mosquito repellent or sleeping under a bed net) were associated with reduced infections.16Sharp TM Moreira R Soares MJ et al.Underrecognition of dengue during 2013 epidemic in Luanda, Angola.Emerg Infect Dis. 2015; 21: 1311-1316Crossref PubMed Scopus (26) Google Scholar Urban yellow fever poses a substantial threat and large A aegypti-borne outbreaks have occurred despite the constrained transmission dynamics resulting from the relatively low competence of this vector for yellow fever,17Nasidi A Monath TP DeCock K et al.Urban yellow fever epidemic in western Nigeria, 1987.Trans R Soc Trop Med Hyg. 1989; 83: 401-406Summary Full Text PDF PubMed Scopus (74) Google Scholar the early recognition of the striking clinical presentation, and the availability of a safe and efficient vaccine. The main lesson learned from the recent Angolan yellow fever outbreak is that control efforts should not rely on reactive vaccination campaigns, which are always associated with delays that result in preventable deaths. The mainstay for yellow fever control remains adequate vaccine coverage. To achieve high vaccination coverage on a long-term basis, the best strategy is to incorporate yellow fever vaccination into routine infant immunisations and to implement catch up campaigns in the older population. To that end, a new effort led by WHO (Eliminating Yellow fever Epidemics [EYE]) is planned to roll out during the next 5 years. Until it is implemented, outbreaks will continue to arise from the enzootic cycles, with potential spread by urban transmission. We declare no competing interests. Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling studyOur findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy. Full-Text PDF Open Access