As climate change reshapes vector ecology and human mobility fuels global connectivity, understanding the transboundary risk of mosquito-borne diseases becomes increasingly urgent. A recent modelling study published in The Lancet Planetary Health by Poongavanan et al. highlights how Africa, a continent with large areas of vector suitability but limited surveillance capacity, is at substantial risk of dengue virus (DENV) introductions.
Despite the presence of competent vectors (Aedes aegypti and Aedes albopictus) and favorable environmental conditions, dengue remains underreported in Africa due to diagnostic gaps and clinical overlap with diseases like malaria and yellow fever. The study aims to identify African airports and regions at the highest risk of receiving viraemic travelers from dengue-endemic countries, integrating air travel data with climate-based transmission suitability.
To estimate the risk of dengue virus introduction into Africa, the authors combined high-resolution climate, demographic, and mobility datasets into a novel modelling framework. They focused on 2019 as a reference year, collecting air travel data from the International Air Transport Association that tracked passenger flows from 14 dengue-endemic countries in Asia and Latin America and 18 African countries that had reported outbreaks over the past decade. This dataset covered 197 commercial airports across the continent. To address the limitations of sparse and often unreliable case data, they used a climate-based “P index” to estimate transmission suitability—essentially the reproductive potential of an infected mosquito under local environmental conditions. They then enhanced this metric by integrating population density, creating a composite index that reflects both ecological and demographic drivers of local transmission potential.
Using these data, the team developed a “risk flow metric” that calculates the probability of dengue importation into each African airport by combining travel volume, transmission suitability at the point of origin, and temporal alignment with local conditions conducive to dengue spread. This approach allowed them not only to rank countries and cities by relative importation risk but also to assess whether these risks were likely to coincide with seasonal windows of heightened local transmission suitability.
Key Findings
Their results revealed that countries in eastern and southern Africa—such as Kenya, Uganda, Mauritius, Egypt, and South Africa—were most exposed to dengue introductions from Asia, driven largely by travel volume and temporal synchrony with local climatic conditions. In contrast, countries in western Africa, including Senegal and Côte d’Ivoire, faced a greater risk of introductions from within the continent itself, suggesting strong regional transmission dynamics. Interestingly, some of the countries with the highest estimated introduction risk, such as South Africa, had relatively low local transmission suitability, implying a low likelihood of outbreak despite frequent viral introductions. On the other hand, countries like Mauritius and Egypt exhibited both high introduction risk and seasonal windows of high suitability, marking them as potential hotspots for outbreak emergence.
Another key insight from the study was the importance of intra-African connectivity. Patterns of regional travel were found to contribute significantly to dengue risk in both western and eastern Africa, highlighting the need for improved surveillance and early detection efforts not only at international borders but also within the continent. Furthermore, the study emphasized the potential impact of climate change on dengue suitability in areas that are currently considered low-risk, such as the Ethiopian highlands, which may become more vulnerable as temperatures rise.
By combining travel data with climate and population metrics, this modelling approach offers a powerful, data-driven tool to guide dengue preparedness strategies in Africa and beyond, exemplifying how modelling approaches integrating climate, demography, and mobility data can enhance early warning systems. However, the authors also caution that several limitations must be considered. The analysis is based on relative risk estimates derived from ecological and statistical models, rather than direct counts of imported cases. Moreover, due to the lack of comprehensive genomic data and traveller screening records, the model’s predictions have yet to be empirically validated. In regions with more robust surveillance systems, travel histories of infected individuals could help refine such models, and the ongoing expansion of dengue virus genome sequencing may soon enable high-resolution reconstructions of transmission chains.
Additionally, the climate-based “P index” used to estimate transmission suitability does not incorporate vector abundance, mosquito control measures, or behavioral factors, all of which can modulate real-world transmission risk. The reliance on air travel data also underrepresents land-based and informal travel routes—especially between neighbouring countries—which are harder to quantify but may significantly contribute to viral spread. Finally, trade-related pathways, such as the movement of mosquitoes in goods like used tyres, remain outside the scope of this framework. Despite these constraints, the model represents a major step forward in forecasting dengue introduction risk and offers a scalable, adaptable approach for guiding future surveillance strategies—particularly in light of expanding dengue suitability zones under climate change.
Read the full open access here: https://doi.org/10.1016/S2542-5196(24)00272-9
Poongavanan, J., Lourenço, J., Tsui, J. L.-H., Colizza, V., Ramphal, Y., Baxter, C., Kraemer, M. U. G., Dunaiski, M., de Oliveira, T., & Tegally, H. 2024. Dengue virus importation risks in Africa: a modelling study. The Lancet Planetary Health, 8(12), E1043–E1054. 10.1016/S2542-5196(24)00272-9 External Link