The global burden of yellow fever

Yellow fever is a flavivirus endemic in tropical regions of Africa and South America. In Africa, it is the third most commonly reported type of disease outbreak. In the Americas, yellow fever produces extensive epizootics in non-human primates (NHPs) and outbreaks of human cases (Mboussou et al., 2019). It is vaccine preventable, with a safe and effective vaccine available since the 1930s that has been introduced into the Expanded Programme on Immunisation (EPI) of many countries (Region V, 2003). Yellow fever is transmitted by numerous vectors including Aedes spp. and Haemogogus spp. in Africa and the Americas, respectively. A component of the sylvatic reservoir system is in NHPs, and as a result of this, yellow fever cannot be eradicated. The clinical course of yellow fever infection leads to a variety of non-specific symptoms with severe infections potentially exhibiting fever, nausea, vomiting, jaundice, and haemorrhaging, which can result in death (Monath and Vasconcelos, 2015).

The transmission dynamics of yellow fever have numerous components. There are two main ‘cycles’ of transmission: urban and sylvatic. The sylvatic cycle is said to be the driver of most reported transmission with infection occurring mainly between NHPs, mediated by tree-hole breeding mosquitos. These vectors are diurnal and feed mostly on NHP; however, people can be infected if they encroach on this cycle through occupational or recreational activities (Monath and Vasconcelos, 2015). In South America, this accounts for the majority of cases and can potentially lead to large outbreaks; a recent yellow fever season saw over 1000 cases in Brazil alone (Couto-Lima et al., 2017). The urban cycle of yellow fever is less common, but the outbreaks have the potential to be devastating. Whilst urban outbreaks have largely been eradicated in South America (Câmara et al., 2011), they still occur in Africa with a recent urban outbreak, in Angola and the Democratic Republic of the Congo, causing 962 reported cases. Although this is thought to be only a fraction of the actual transmission (Organization, WH, 2017). Large outbreaks as a result of urban transmission are due to the combination of densely populated urban areas, and large populations of Aedes aegypti, which bites humans preferentially and breeds rapidly in urban environments (Harrington et al., 2014). The World Health Organisation (WHO) developed the Eliminate Yellow fever Epidemics (EYE) strategy in order to eliminate urban yellow fever outbreaks by 2026 (World Health Organization, 2017). The intermediate cycle currently only occurs in Africa when tree-hole breeding anthropophilic Aedes reach particularly high densities (Monath and Vasconcelos, 2015).

Control of yellow fever is primarily through vaccination, and there is no specific anti-viral treatment available. The 17D vaccine is live attenuated and was developed in 1936 (Monath, 2005). The vaccine is considered safe; estimates of adverse event occurrence are at most 0.6 per 100,000 doses, and reactions are generally mild (de Menezes Martins et al., 2015). Immunity due to yellow fever vaccination is suggested to be lifelong with WHO recommendations recently updated to reflect this (Staples et al., 2015). Efficacy is also thought to be high with recent estimates suggesting that serological response was 97.9% (95% credible interval [CrI] [82.9–99.7]) (Jean et al., 2016). An issue with the vaccine is production and corresponding stockpiles. As the vaccine is live, production is slow, which has led to vaccine shortages for large outbreaks and for travellers (Gershman et al., 2017). As a result of this, fractional dosing has become a recommendation in outbreak settings (Barrett, 2020).

Due to limited vaccine supply, efficient planning of interventions is vital to avoid large outbreaks. To facilitate this, robust estimates of disease burden and projections of future dynamics are key. Previous studies have focused on evaluating historical vaccine impact and projecting future potential impact. Garske et al., 2014 produced vaccine impact estimates for the African endemic region focusing on mass vaccination campaigns until 2013. They found that mass vaccination activities have averted burden by 57% (95% CI [54–59]) in countries where they took place, accounting for 27% (95% CI [22–31]) of the burden across the region. More recently, Shearer et al., 2018 examined the impact of vaccination globally, across Africa and South America, and found that (all) vaccination activities averted between approximately 94,0000 and 119,000 cases each year.

In this study, we refine and extend the model of Garske et al. to encompass new geographic regions (South America) and new data (on occurrence, serology, vaccination, and NHPs), and produce updated estimates of burden and vaccine impact for yellow fever. In the following sections, we describe the new data and extension to the modelling approach, particularly focusing on the updated model of yellow fever occurrence. Then we present results of our projected transmission intensity considering uncertainty from estimation and the structural uncertainty of the models. Finally, we present burden estimates and reassess the impact of mass vaccination activities in Africa.

We expand the existing framework of Garske et al., 2014 for Africa to account for transmission in South America as well. Countries are included in the analysis if they have been listed as at risk, endemic, or potentially at risk for yellow fever (World Health Organization, 2017). We fit a generalised linear model (GLM) of yellow fever reports to occurrence data available from 1984 to 2019, shown in Figure 1. This occurrence data denotes whether yellow fever has been reported at all over the observation period, irrespective of number of cases. The GLM then provides a probability of a reported yellow fever outbreak for the entire region. In order to estimate the force of infection that would result in these outbreaks, we use serological survey data. We use this to independently estimate the seroprevalence in the survey locations and thus the force of infection. These individual estimates allow us to calculate a probability of detection over the observation period which we may then use to provide force of infection estimates for the entire region. Finally, using demographic and vaccination data, we can calculate the burden in all provinces.

Figure 1

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Data

We combine multiple data sets within a Bayesian framework to account for areas with sparse data and under-reporting. The model is estimated at province level, to match the available occurrence data. All data was from secondary sources, and ethics approval was thereby not required. Figure 2 summarises the included data.

Figure 2

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In this study, we further developed models of yellow fever transmission in Africa and South America. We calculated disease burden in terms of severe infections (or cases) and deaths from an ensemble of best-fitting GLMs of yellow fever reports between 1984 and 2019 coupled to catalytic models of seroprevalence. We used this approach to evaluate the impact of mass vaccination campaigns in Africa as well as produce updated burden estimates of yellow fever in endemic regions.

We estimate that there are between 63,000 and 158,000 severe infections of yellow fever in Africa, resulting in 29,000–75,000 deaths. In South America, we estimate there are 4000–15,000 severe infections, resulting in 2000–7000 deaths. These estimates are contained within the bounds of Garske et al. who estimated between 51,000 and 380,000 severe infections and between 19,000 and 180,000 deaths occur each year in Africa. We also compare to Shearer et al., 2018 who found approximately 256,000 cases on the African continent and 28,000 within Latin America, which are at the higher end of our predicted ranges. All the above estimates fall within a similar range despite different scopes and modelling approaches.

In order to produce our burden estimates, we first estimate transmission intensity through a force of infection for each province. These estimates differ from those of Garske et al. in West Africa and the DRC. In order to account for the extended model scope, we revisited the covariates used in the GLM leading to the changes in West African forces of infection shown. This can partly be explained by the exclusion of certain covariates, such as longitude, and the inclusion of others, such as NHP species richness. The latter highlights the DRC as an area of high transmission potential, increasing the local estimates of force of infection. The former reduces the force of infection estimates in West Africa. This decrease in West Africa has led to our range of burden lying within the lower range of Garske et al., and whilst the same proportional impact of vaccination is found, the number of deaths averted is also in the lower range of previous estimates. These differences may also indicate a general sensitivity of the approach as, whilst burden is generally agreed to be higher in West Africa, it may be difficult to determine the exact magnitude from occurrence data alone. In South America, the force of infections are estimated to be highest in the Amazon region of Brazil, in part due to the high NHP species richness found there. This is consistent with vaccination efforts which have focused on this area leading to relatively low burden despite the high intensity of transmission.

In refining the GLMs of yellow fever occurrence, we expanded the pool of possible covariates. This was partly facilitated by new data becoming available, such as NHP species occurrence, and partly motivated by the need to capture more of the inherent variability of yellow fever occurrence, through a temperature suitability index. We find that certain features such as human population size and certain land cover types were consistently featured in the top models of occurrence. Similarly, primate families, Cercopithecidae, Cebidae, and Aotidae—were all found to be important to yellow fever occurrence. The collection of covariates and model choice lead to high AUCs for each model ranging from 0.935 to 0.949, higher than Gaythorpe et al., 2019. One element that is omitted explicitly is vector abundance although we do include occurrence of Ae. aegypti and Ae. albopictus. In recent years, there have been a number of excellent efforts to map and predict vector distributions (Kraemer et al., 2015; Brady et al., 2014). We utilise many of the same model covariates and as such have chosen to omit modelled vector distributions in this analysis. Additionally, yellow fever is transmitted by many vectors whose distributions have yet to be characterised.

There are a number of additional limitations with the range of covariates used. We utilise NHP species presence/absence data which we aggregate to province level through counts. As such, we do not have information of the population sizes of NHP, only diversity. This could be reassessed as further data becomes available. Additionally, as a component of the covariate selection process, we cluster our covariates based on their correlation with each other. As such, NHP families who coexist in the same geographic locations are put in the same cluster, for example Atelidae, leading the model selection process to potentially include a NHP that is present in areas with yellow fever, but not necessarily causing or carrying the virus. As with the other covariates, the selection process implies correlation not causation between the covariate and the occurrence of yellow fever.

Apart from the NHP families, we utilise the same environmental covariates for each continent. Whilst this improves consistency, we use elements such as temperature suitability for Ae. aegypti as a proxy for the vectors of yellow fever and there are different species in each continent, which may differ in their own ways to Ae. aegypti. An additional issue is that we aggregate our environmental covariates to province level, reducing resolution and potentially biasing the results. In this study, we feel the sparsity of the data, long time window of interest, and general uncertainty in other features will eclipse biases introduced by the aggregation mechanism, but if this model were to be refined spatially, the bias from aggregation could be readdressed. Finally, whilst we include structural uncertainties from the GLM, we do not included uncertainty in the covariates themselves.

We estimate our models from two main sources of data that we have updated where possible. The occurrence data was expanded to account for additional years and locations from Garske et al., 2014; Gaythorpe et al., 2019; Jean et al., 2020; Hamlet et al., 2019. Yet there are assumptions and uncertainty that are inherent in this data. Firstly, the case definition relies on a vague symptom set, which is prone to mis-attribution and may vary by location, this will affect under-reporting. We accommodate variation between countries though country-specific reporting factors in the GLMs; however, there are number of elements that contribute to surveillance which we essentially aggregate into one component. The most stark difference may be that surveillance in South America uses that the fact that some NHP species experience disease-related mortality as sentinels for yellow fever. In Africa, due to the co-evolution of NHPs and virus, NHPs are not known to be significantly affected in the same way. As such, the surveillance systems are substantially different.

One of the important components to assess inherent under-reporting of yellow fever is serology data. We have significantly expanded the data sources for this aspect of the model, with an addition of 36 studies compared to Garske et al.However, all studies are located in Africa, the high vaccination coverage in many of the provinces render conventional tests of seroprevalence uninformative in terms of assessing background infection. There are further issues that may arise in using seroprevalence. We take a positive serology test to indicate exposure to the disease but also protection, adhering to the conventional assumption that immunity to yellow fever is acquired after infection or vaccination and remains for a lifetime. However, there have been recent studies suggesting that this may not be the case in children. Domingo et al., 2019 found that immunity against yellow fever waned in children following vaccination. If these results are representative of infant and child vaccination across the regions and time, our estimates of population immunity may need to be readdressed.

Modelling yellow fever is inherently uncertain. We have attempted to quantify this uncertainty through a Bayesian framework and ensemble model predictions. However, there are still elements that we have not captured. Uncertainty in demography and vaccination are not propagated through our model results and yet both will be influential. Demography is captured from UNWPP and scaled according to Landscan. We assume relatively static age structures and, whilst UNWPP goes some way to accounting for population movements, we do not include them explicitly. Population movements not only effect the model directly but will also influence the resulting vaccination coverage estimates. In a similar way, vaccination activities are collated from a number of sources with all efforts made to ensure completeness. Yet there are activities that may have been omitted or not correctly parameterised could affect the results of this study. A dominant area of uncertainty is in the symptom spectrum of yellow fever. In our model, we estimate infections and then scale these to arrive at severe infections or cases and, finally, deaths. In this we use the estimates of Johannsson et al. to capture the uncertainty in the proportion of infections considered severe etc.; however, this remains an area of contention for yellow fever as previous estimates of case fatality ratio (CFR) have varied substantially and are significantly larger than other flaviruses such as dengue (Johansson et al., 2014; Oo et al., 2017). The estimates of CFR we use also differ from those in the global burden of disease study leading to substantial differences in burden estimates between the two, although under-reporting is also addressed differently in the global burden of disease study (GBD) (Compare, 2019).

We have refined an established model but have also inherited some of its limitations, one of which is constancy. We assume that dynamics do not change substantially over the observation period in each province. As such, the variation over time is dominated by changes in demography and vaccination. In reality, the epidemiology of yellow fever is likely to change and has seen changes over recent years. Brazil experienced some of the largest outbreaks in its history with yellow fever in 2017 and 2018; this was suggested to have been caused by changing patterns of human behaviour, such as urbanisation and movement, or changes in epidemiology in the sylvatic cycle; however, the full list of causes remains unclear (Couto-Lima et al., 2017; Moreira-Soto et al., 2018; Chen et al., 2019; Possas et al., 2018; Saúde, 2019). Spillover is also inherently stochastic, whereas, due to the focus on long-term burden, we assume a constant risk of spillover. As such, the model will not capture outbreak dynamics over a short time window but may highlight areas at most risk of outbreaks. The resulting estimates of burden and vaccine impact are thus the potential number of deaths given the conditions in each province and each country given the environmental conditions but may vary year on year due to outbreaks and stochastic spillover events.

Public repository data: Vaccination cov erage: coverage is available to download from the PoLiCi shiny app (https://shiny.dide.imperial.ac.uk/polici/). Serology surveys: There are seven published surveys used, available at DOI:10.1016/0147-9571(90)90521-T , DOI:10.1093/trstmh/tru086 , DOI: 10.1186/s12889-018-5726-9 , DOI: 10.4269/ajtmh.2006.74.1078 , PMID: 3501739 , PMID: 4004378 , PMID: 3731366. Demographic data: Population level data was obtained from UN WPP (https://population.un.org/wpp/), this was disaggregated using Landscan 2017 data (https://landscan.ornl.gov/landscan-data-availability). Environmental data: This was obtained from LP DAAC (https://lpdaac.usgs.gov/) and worldclim (http://www.worldclim.org/). Non-human primate occurrence: This was obtained from the IUCN red list (https://www.iucnredlist.org/resources/spatial-data-download). Mosquito occurrence: This was obtained from The global compendium of Aedes aegypti and Ae. albopictus occurrence (https://doi.org/10.1038/sdata.2015.35). Yellow fever outbreaks: These were compiled from the WHO weekly epidemiologic record and disease outbreak news (https://www.who.int/wer/en/ and https://www.who.int/csr/don/en/). Compiled dataset for Africa available from https://github.com/kjean/YF_outbreak_PMVC/tree/main/formatted_data (copy archived at https://archive.softwareheritage.org/swh:1:rev:14703d7c5c7f63df6de04b81d5a48751604a906a/). Data elsewhere: The data from the WHO YF surveillance database and from recent serological surveys from WHO member states in Africa underlying the results presented in the study are available from World Health Organization (contact: William Perea, pereaw@who.int or Laurence Cibrelus, cibrelusl@who.int or Jennifer Horton, jhorton@who.int). Data from the occurrence of YF in Brazil were obtained from the Brazilian MoH (contact: Daniel Garkauskas Ramos). Code is available from: https://github.com/mrc-ide/YFestimation (copy archived at https://archive.softwareheritage.org/swh:1:rev:a352fe9369c4e1ec5915d63f0b36c8cfcc18894b/) for estimation and https://github.com/mrc-ide/YellowFeverModelEstimation2019 (copy archived at https://archive.softwareheritage.org/swh:1:rev:38f62f18a9261be7b987e042402331b6aa514233/) for specific analyses.

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Author details

1. Katy AM Gaythorpe

   WHO Collaborating Centre for Infectious Disease Modelling, MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   k.gaythorpe@imperial.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3734-9081

2. Arran Hamlet

   WHO Collaborating Centre for Infectious Disease Modelling, MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, United Kingdom

   Contribution

   Data curation, Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Kévin Jean

   Maître de conférences, Laboratoire MESuRS - Cnam Paris, Paris, France

   Contribution

   Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6462-7185

4. Daniel Garkauskas Ramos

   Secretariat for Health Surveillance, Brazilian Ministry of Health, Brasilia, Brazil

   Contribution

   Data curation, Validation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

5. Laurence Cibrelus

   World Health Organisation, Geneva, Switzerland

   Contribution

   Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

6. Tini Garske

   WHO Collaborating Centre for Infectious Disease Modelling, MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Supervision, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

7. Neil Ferguson

   WHO Collaborating Centre for Infectious Disease Modelling, MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, London, United Kingdom

   Contribution

   Supervision, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

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