Search

Introduction The Eliminate Yellow fever Epidemics (EYE) strategy was launched in 2017 in response to the resurgence of yellow fever in Africa and the Americas. The strategy relies on several vaccination activities, including preventive mass vaccination campaigns (PMVCs). However, by how much PMVCs are associated with a decreased risk of outbreak to occur has not yet been quantified. Methods and Findings We used the self-controlled case series (SCCS) method to assess the association between the occurrence of yellow fever outbreaks and the implementation of PMVCs at the province level in the African endemic region. As all time-invariant confounders are implicitly controlled for, the SCCS method is an alternative to classical cohort or case-control study designs when the risk of residual confounding is high, in particular confounding by indication. The location and dates of outbreaks were identified from international epidemiological records, and information on PMVCs was provided by coordinators of vaccination activities and international funders. The study sample consisted of provinces that were both affected by an outbreak and targeted for a PMVC between 2005 and 2018. We compared the relative incidence of outbreaks before and after the implementation of a PMVC. The sensitivity of our estimates to a range of assumptions was explored, and the results of the SCCS method were compared to those obtained through a retrospective cohort study design. We further derived the number of yellow fever outbreaks that have been prevented by PMVCs. The study sample consisted of 33 provinces from 11 African countries. Among these, outbreaks occurred during the pre-PMVC period in 26 (79%) provinces versus 7 (21%) occurring in the post-PMVC period. At the province level, the post-PMVC period was associated with a 86% reduction (95% Confidence interval 66% to 94%, p-value A limitation of our study is the fact that it does not account for potential time-varying confounders, such as changing environmental drivers of yellow fever or possibly improved disease surveillance. Conclusion In this study, we provide new empirical evidence of the high preventive impact of PMVCs on yellow fever outbreaks. This study illustrates that the SCCS method can be advantageously applied at the population level in order to evaluate a public health intervention. AUTHOR SUMMARY Why Was This Study Done? Yellow fever is a mosquito-borne vaccine-preventable disease that may cause large urban outbreaks, especially in tropical African regions. Since 2006, Preventive Mass Vaccination Campaigns (PMVCs) have been implemented in many African provinces. These are large scale vaccination campaigns targeting all or most age group in a specific area. The preventive impact PMVCs may have on the risk of yellow fever outbreak has not been quantified yet. What Did the Researchers Do and Find? We used the self-controlled case series (SCCS) method to assess the association between PMVCs and outbreak risk at the province level over 34 African countries between 2005 and 2018. Using this method, we compared pre- and post-PMVC period are compared within each province individually, thus controlling for all possible confounders that do not vary in time, especially the fact that provinces indicated for PMVCs are generally those considered at highest baseline risk of yellow fever (ie confounding by indication). At the province level, we estimated that implementation of PMVCs was associated with an 86% reduction (66% to 94%) in the risk of yellow fever outbreak. Results from a complete cohort analysis provided less reliable results than the SCCS method, likely because of confounding by indication that was not entirely controlled for by adjusting on known drivers of yellow fever. We further estimated that all PMVCs conducted between 2006 and 2018 in Africa may have reduced the total number of yellow fever outbreaks by 34% (22% to 45%). What Do These Findings Mean? These results provide new empirical evidence of the high preventive impact of PMVCs on yellow fever outbreaks. These results may encourage rapid re-scheduling of yellow fever PMVCs that have been postponed due to the COVID-19 pandemic. The SCCS design may be advantageously applied at the population level to assess the impact of public health interventions.

International audience; Yellow Fever (YF) remains a major public health issue in Sub-Saharan Africa and South America, despite the availability of an effective vaccine. In Africa, most YF outbreaks are reported in West Africa. However, urban outbreaks occurred in 2016 in both Angola and the Democratic Republic of Congo (DRC), and imported cases were reported in Chinese workers coming back from Africa. In Central Africa, Cameroon and the Republic of Congo host a high proportion of non-vaccinated populations increasing the risk of urban outbreaks. The main vector is Aedes aegypti and possibly, Aedes albopictus, both being anthropophilic and domestic mosquitoes. Here, we provide evidence that both Ae. aegypti and Ae. albopictus in Cameroon and the Republic of Congo are able to transmit Yellow fever virus (YFV) with higher rates of infection, dissemination, and transmission for Ae. aegypti. We conclude that the potential of both Aedes species to transmit YFV could increase the risk of urban YF transmission and urge public health authorities to intensify their efforts to control domestic vectors, and extend vaccine coverage to prevent major YFV outbreak.

Yellow fever is a vector-borne disease endemic in tropical regions of Africa, where 90% of the global burden occurs, and Latin America. It is notoriously under-reported with uncertainty arising from a complex transmission cycle including a sylvatic reservoir and non-specific symptom set. Resulting estimates of burden, particularly in Africa, are highly uncertain. We examine two established models of yellow fever transmission within a Bayesian model averaging framework in order to assess the relative evidence for each model’s assumptions and to highlight possible data gaps. Our models assume contrasting scenarios of the yellow fever transmission cycle in Africa. The first takes the force of infection in each province to be static across the observation period; this is synonymous with a constant infection pressure from the sylvatic reservoir. The second model assumes the majority of transmission results from the urban cycle; in this case, the force of infection is dynamic and defined through a fixed value of R0 in each province. Both models are coupled to a generalised linear model of yellow fever occurrence which uses environmental covariates to allow us to estimate transmission intensity in areas where data is sparse. We compare these contrasting descriptions of transmission through a Bayesian framework and trans-dimensional Markov chain Monte Carlo sampling in order to assess each model’s evidence given the range of uncertainty in parameter values. The resulting estimates allow us to produce Bayesian model averaged predictions of yellow fever burden across the African endemic region. We find strong support for the static force of infection model which suggests a higher proportion of yellow fever transmission occurs as a result of infection from an external source such as the sylvatic reservoir. However, the model comparison highlights key data gaps in serological surveys across the African endemic region. As such, conclusions concerning the most prevalent transmission routes for yellow fever will be limited by the sparsity of data which is particularly evident in the areas with highest predicted transmission intensity. Our model and estimation approach provides a robust framework for model comparison and predicting yellow fever burden in Africa. However, key data gaps increase uncertainty surrounding estimates of model parameters and evidence. As more mathematical models are developed to address new research questions, it is increasingly important to compare them with established modelling approaches to highlight uncertainty in structures and data., Author summary Yellow fever (YF) is notoriously under reported due to non-specific symptom spectrum and the true burden is highly uncertain as a result of a complex transmission cycle. As such, estimates surrounding YF burden are highly uncertain and the mechanisms behind transmission are often unclear. We assess these mechanisms and the resulting uncertainty by estimating two existing models of YF transmission within a product space framework. This allows us to produce updated estimates of transmission intensity and to compare the relative support for each model given the data. We find strong support for a model assuming a static force of infection, approximating the constant infection pressure from the sylvatic reservoir of YF. We also highlight areas where data is sparse, often the same areas estimated to have especially high transmission intensity. This is the first robust multi-model approach to applied YF modelling and provides a framework that could be extended to other disease models.

Background Aedes-borne diseases as dengue, zika, chikungunya and yellow fever are an emerging problem worldwide, being transmitted by Aedes aegypti and Aedes albopictus. Lack of up to date information about the distribution of Aedes species hampers surveillance and control. Global databases have been compiled but these did not capture data in the WHO Eastern Mediterranean Region (EMR), and any models built using these datasets fail to identify highly suitable areas where one or both species may occur. The first objective of this study was therefore to update the existing Ae. aegypti (Linnaeus, 1762) and Ae. albopictus (Skuse, 1895) compendia and the second objective was to generate species distribution models targeted to the EMR. A final objective was to engage the WHO points of contacts within the region to provide feedback and hence validate all model outputs. Methods The Ae. aegypti and Ae. albopictus compendia provided by Kraemer et al. (Sci Data 2:150035, 2015; Dryad Digit Repos, 2015) were used as starting points. These datasets were extended with more recent species and disease data. In the next step, these sets were filtered using the Köppen–Geiger classification and the Mahalanobis distance. The occurrence data were supplemented with pseudo-absence data as input to Random Forests. The resulting suitability and maximum risk of establishment maps were combined into hard-classified maps per country for expert validation. Results The EMR datasets consisted of 1995 presence locations for Ae. aegypti and 2868 presence locations for Ae. albopictus. The resulting suitability maps indicated that there exist areas with high suitability and/or maximum risk of establishment for these disease vectors in contrast with previous model output. Precipitation and host availability, expressed as population density and night-time lights, were the most important variables for Ae. aegypti. Host availability was the most important predictor in case of Ae. albopictus. Internal validation was assessed geographically. External validation showed high agreement between the predicted maps and the experts’ extensive knowledge of the terrain. Conclusion Maps of distribution and maximum risk of establishment were created for Ae. aegypti and Ae. albopictus for the WHO EMR. These region-specific maps highlighted data gaps and these gaps will be filled using targeted monitoring and surveillance. This will increase the awareness and preparedness of the different countries for Aedes borne diseases. Electronic supplementary material The online version of this article (10.1186/s12942-018-0125-0) contains supplementary material, which is available to authorized users.