Global Arbovirus Models Miss Africa, Creating a Critical Surveillance Blind Spot

The recent spread of mosquito-borne viruses such as West Nile and Usutu across Europe has triggered a wave of new surveillance programs, diagnostic investments, and predictive modeling efforts. Yet a perspective published in Frontiers in Tropical Diseases argues that this intensifying focus on data-rich regions may be masking one of the biggest blind spots in global virology: large parts of Africa, despite harboring exceptional mosquito diversity and extensive human-vector contact, remain largely absent from the datasets that shape our understanding of arbovirus emergence.

The perspective, authored by Rosheen S. Mthawanji of the University of Liverpool, contends that global interpretations of arbovirus dynamics are disproportionately informed by a small set of well-surveilled regions, including Thailand, Brazil, Singapore, North America, and Europe. This imbalance is not merely a gap in coverage but a structural problem that distorts ecological inference, disease forecasting, and global risk assessment. In an increasingly interconnected world, where hosts, vectors, and pathogens move across continents, the absence of reliable data from ecologically critical regions carries consequences that extend far beyond Africa itself.

Underrepresentation in this context refers specifically to the scarcity of arbovirus surveillance data and vector competence studies, not to a lack of disease burden or research activity. Africa supports a wide array of mosquito species, including established arbovirus vectors and numerous understudied taxa, alongside dense human populations and extensive wildlife reservoirs. These conditions are highly favorable for viral maintenance and transmission. Yet surveillance across much of the continent remains fragmented, frequently triggered by outbreak response to yellow fever, Rift Valley fever, dengue, or chikungunya rather than by routine longitudinal monitoring. Regional bulletins from Africa CDC and WHO AFRO confirm ongoing reporting of outbreaks, but comprehensive, standardized, and openly accessible surveillance systems remain limited compared with dedicated platforms such as the Pan American Health Organization’s Arbovirus Portal in the Americas.

The consequences of this asymmetry are subtle but profound. Predictive models of arbovirus risk typically rely on correlations between environmental variables and observed transmission events. When such models are trained predominantly on data from Europe and North America, they implicitly assume that these regions represent global dynamics. That assumption is unlikely to hold in ecologically diverse regions of Africa, where vector communities, host availability, and environmental conditions differ substantially. Models calibrated on incomplete data may therefore fail to capture critical transmission pathways, including those involving less-studied vector taxa or complex ecological interactions, and may underestimate both the complexity and the geographic scale of arbovirus circulation.

Perhaps the most provocative argument in the perspective is that apparent arbovirus emergence in Europe may partly reflect better detection rather than genuine ecological change. Enhanced surveillance systems, including coordinated vector monitoring and molecular diagnostics, have increased the probability of detecting viral circulation in regions that already possess strong infrastructure. Without comparable data from Africa, it becomes difficult to distinguish true emergence from increased detection, and equally difficult to determine whether transmission in underrepresented regions has been ongoing but undetected. An increase in arbovirus detection is, in this view, not solely a biological phenomenon but also a function of observation.

This bias also obscures key components of transmission systems that span continents. Migratory birds are known to play a role in the spread of West Nile virus, linking African and European transmission networks, yet limited data on viral circulation within African bird populations restricts the ability to quantify that connection. Human mobility and trade networks may similarly facilitate pathogen movement, but these pathways cannot be modeled without comprehensive surveillance data. The result is a skewed representation of global risk, in which regions that generate more data are more likely to be included in future studies, while lesser-surveilled regions remain marginalized, further entrenching gaps in global understanding.

The perspective also highlights a largely unexplored dimension of the problem: the exclusion of Anopheles mosquitoes from arbovirus surveillance frameworks. Anopheles are among the most intensively studied mosquito genera worldwide, thanks to decades of malaria research supported by sustained investment through programs such as the Global Fund and the President’s Malaria Initiative. Extensive infrastructure exists for monitoring them, including entomological surveillance, insecticide resistance tracking, and intervention programs. Yet despite this detailed knowledge base, Anopheles are rarely included in arbovirus surveillance or predictive models, largely due to assumptions about their vector competence.

That exclusion is difficult to justify from an ecological standpoint. Overwintering studies have reported Anopheles alongside Culex and Aedes mosquitoes in shared hibernation sites, including bunkers, cellars, and other sheltered environments used during diapause, and these studies have occasionally detected arboviruses in overwintering mosquitoes. Experimental work has also demonstrated that some Anopheles species can support arbovirus infection under laboratory conditions, although their vector competence varies. The systematic separation of malaria and arbovirus research systems reinforces this gap, as surveillance programs are typically organized around specific diseases with limited integration across pathogen types. In regions where Anopheles coexist with established arbovirus vectors, the possibility of concurrent transmission or co-infection with Plasmodium parasites remains poorly characterized.

To address these structural biases, the perspective proposes a conceptual framework built on three interconnected components. First, surveillance must expand from reactive, outbreak-driven sampling to systematic, longitudinal monitoring across diverse ecological settings, combining targeted diagnostics with untargeted genomic approaches capable of detecting both known and previously uncharacterized viruses. Second, surveillance must move beyond a narrow focus on established vector species to incorporate broader vector communities, including Anopheles, Mansonia, and other regionally abundant genera, with standardized approaches for assessing vector competence. Third, integrating heterogeneous data sources, combining field surveillance with laboratory-derived vector competence data, environmental variables, Plasmodium interactions, and host distribution information, would enable more robust predictive models, with machine learning and network-based approaches offering promising tools for prioritizing surveillance under data-scarce conditions.

Crucially, the author argues that existing malaria surveillance infrastructure across Africa provides a practical foundation for implementing such integrated systems, offering a scalable and cost-effective strategy for improving global coverage. The perspective is careful to note that integration is not universally cost-free: existing malaria systems were designed around specific epidemiological objectives and may require additional diagnostic capacity, personnel training, sampling modifications, and financial investment. Nonetheless, the core message is clear. Until surveillance inequalities in Africa and other underrepresented regions are addressed, global arbovirus models will continue to provide only a partial representation of transmission risk, and the world will remain dangerously underprepared for the next viral emergence in an increasingly interconnected landscape.

Subject of Research: Geographic bias in African arbovirus surveillance data and its impact on global arbovirus modeling

Article Title: Underrepresentation of African surveillance data in global arbovirus modeling

Article References: Mthawanji, R. S. (2026). Underrepresentation of African surveillance data in global arbovirus modeling. Frontiers in Tropical Diseases, 7, Article 1894025. https://doi.org/10.3389/fitd.2026.1894025

Image Credits: AI Generated

DOI: 10.3389/fitd.2026.1894025

Keywords: arboviruses, surveillance bias, Africa, Anopheles, vector competence, predictive modeling, malaria, West Nile virus, Usutu virus, dengue, data gaps, public health