West Nile virus and Usutu virus are closely related flaviviruses maintained in an enzootic cycle between birds and mosquitoes, primarily Culex species that feed on avian hosts. Humans and horses are incidental, dead-end hosts: they can develop severe neuroinvasive disease, including meningitis, encephalitis, and flaccid paralysis, but they do not produce enough virus in their blood to infect feeding mosquitoes. Both viruses have expanded their footprint across Europe in recent decades, causing increasingly frequent outbreaks in temperate regions. Usutu virus, in particular, has been linked to significant declines in blackbird and other wild bird populations, while West Nile virus has become a recurring public health concern in the Mediterranean basin, the Balkans, and central Europe.

The problem, as the researchers behind the new study emphasize, is that the vast majority of human infections with both viruses are asymptomatic or produce only mild, nonspecific febrile illness. In France, the existing surveillance framework is largely reactive, triggered by clinical reports from hospitals, veterinary alerts from equine cases, or detections in captive birds. By the time such signals accumulate, viral circulation in the environment has typically been underway for weeks or months. That delay matters: vector control measures, such as larvicide treatments and public health messaging, are most effective when deployed before the spillover from birds to humans accelerates. The new study set out to test whether environmental sampling could close that gap.

The team, led by Julien Mocq and Yannick Simonin, implemented their surveillance system in southern France during the 2024 and 2025 transmission seasons. Their strategy combined two complementary sampling streams. The first was molecular xenomonitoring of mosquito excreta: rather than testing mosquito bodies, which requires laborious trapping, sorting, and pooling, the researchers collected the liquid waste that live mosquitoes deposit after feeding. Because female mosquitoes excrete surplus fluid and viral RNA can be shed in these droplets, excreta offers a sensitive, noninvasive readout of infection in the local vector population. The second stream targeted environmental water itself, sampling wetlands and larval habitats where Culex mosquitoes breed and where viral particles shed by infected birds or mosquitoes may accumulate.

Detection relied on a multiplex reverse transcription digital PCR workflow, a technique that partitions each sample into thousands of tiny reactions and counts individual amplification events, allowing absolute quantification of viral RNA without standard curves. Digital PCR is particularly well suited to environmental surveillance because it tolerates the low viral loads and inhibitors commonly found in water and excreta samples. Crucially, the team rigorously characterized the analytical performance of their assay, establishing the limits of blank, detection, and quantification for each target. This formal characterization distinguishes true low-level positives from background noise, a critical step when results will inform public health decisions. Positive findings at low concentrations were further confirmed by Sanger sequencing, providing independent verification that the detected RNA genuinely corresponded to West Nile or Usutu virus rather than artifacts or cross-reactivity.

The results were striking. Repeated detections of both viruses revealed previously unrecognized, cryptic circulation in the study area, with West Nile virus RNA appearing in environmental samples as much as nineteen weeks before the first human cases were reported and before the equine alert that would normally have triggered the official response. In practical terms, the environmental signal preceded the conventional surveillance system by more than four months, offering a window during which vector control operations, blood safety measures, and community warnings could have been deployed. The repeated nature of the detections, rather than isolated single positives, strengthened confidence that genuine viral circulation was underway rather than sporadic contamination.

This early-warning capability represents what the authors describe as a paradigm shift for the surveillance of Culex-borne arboviruses. Traditional xenomonitoring, which involves grinding and testing pooled mosquito samples, is constrained by the logistics of trapping enough vectors and by the dilution effect of pooling uninfected with infected individuals. Environmental sampling inverts that logic: instead of chasing the virus inside its vector, surveillance teams simply collect the traces the virus leaves in shared habitats. Water from wetlands and larval sites can be gathered with simple field protocols, and excreta collection can be integrated into existing mosquito trap deployments, since trapped live mosquitoes naturally deposit excreta on collection surfaces. Both approaches are compatible with routine, repeated sampling across a transmission season, building a time series that reveals trends rather than snapshots.

The One Health framing of the study is central to its significance. West Nile and Usutu viruses sit at the intersection of wildlife, veterinary, and human health, and their dynamics are shaped by bird migration, mosquito abundance, temperature, and rainfall. An environmental surveillance system that samples wetlands, the very habitats where these ecological drivers converge, provides an integrated signal that no single clinical or veterinary stream can match. The authors argue that such proactive detection is a key enabler of effective One Health responses, allowing public health authorities, veterinarians, and wildlife managers to act on the same early evidence. In regions where West Nile virus has become endemic but unpredictable, and where Usutu virus continues to spread largely beneath the radar of clinical systems, that integration could be decisive.

Several technical considerations will shape how the approach scales. Digital PCR instrumentation remains more expensive and less widely distributed than conventional quantitative PCR, although multiplexing multiple viral targets in a single reaction improves throughput and cost efficiency. Environmental matrices also demand careful handling: organic matter in wetland water can inhibit amplification, and the limits of blank and detection established by the team provide the statistical foundation for interpreting borderline results. The confirmation of low-level positives by Sanger sequencing adds a layer of rigor that will be essential as environmental detections begin to carry operational weight, since a false alarm triggers real costs in vector control and public communication, while a missed signal forfeits the entire advantage of early warning.

For now, the French study demonstrates that the concept works under real field conditions across two consecutive transmission seasons, and that its analytical framework can distinguish meaningful signals from noise. As climate change extends the season and geographic range of mosquito-borne viruses in Europe and beyond, the interval between silent avian circulation and human spillover becomes an increasingly precious resource. Reading viral RNA in wetland water and mosquito excreta turns that hidden interval into actionable time, and the nineteen-week lead reported here suggests that environmental surveillance may become a standard front line in the defense against emerging arboviruses.

Subject of Research: Early-warning environmental surveillance of West Nile and Usutu viruses using digital PCR

Article Title: Early-warning surveillance of West Nile and Usutu viruses in wetland and larval habitat waters and in mosquito excreta using digital PCR

Article References: Mocq, J., Raymond, J., Bolloré, K., Fossot, A., Beaubaton, R., Lepeule, A., Gruet, C., Durandet, F., Hanin, J., Lacour, G., Fontaine, A., Courot, O., Mignotte, A., & Simonin, Y. (2026). Early-warning surveillance of West Nile and Usutu viruses in wetland and larval habitat waters and in mosquito excreta using digital PCR. PLOS Neglected Tropical Diseases, 20(10), e0014799. https://doi.org/10.1371/journal.pntd.0014799

Image Credits: AI Generated

DOI: 10.1371/journal.pntd.0014799

Keywords: West Nile virus, Usutu virus, digital PCR, molecular xenomonitoring, mosquito excreta, environmental surveillance, arboviruses, Culex mosquitoes, One Health, wetlands, early warning, public health

The core of the modeling effort is a multi-component simulation framework that links several distinct biological and economic modules. On the vector side, the model tracks Aedes and Culex mosquitoes using Susceptible Exposed Infectious dynamics, capturing the biology of the two genera that drive transmission in nature. Aedes mosquitoes, whose eggs can survive dry periods and hatch when flooding rains arrive, are classically responsible for initiating outbreaks, while Culex species amplify transmission among animals once an epizootic is underway. On the host side, livestock populations follow Susceptible Exposed Infectious Recovered dynamics, layered on top of a herd demography module that accounts for births, deaths and the gradual turnover of animals that changes the susceptible fraction of the population between outbreaks.

What distinguishes this framework from many earlier RVF models is the explicit coupling of these infection dynamics to end-market processes and demand-side behavior. Livestock producers in pastoral systems such as Ijara County do not operate in a vacuum: when disease strikes, buyers retreat, prices fall and household incomes contract, and these economic signals feed back into decisions about animal holdings and sales. By incorporating demand shocks and behavioral feedback directly into the simulation, the authors allow the economic consequences of an outbreak to unfold endogenously rather than being imposed as fixed external costs. This design reflects the study’s central premise that livestock diseases present systemic threats to animal health, rural livelihoods and national economies, and that nonlinear feedbacks, time lags and cross-sectoral interactions must be captured to evaluate policy honestly.

The simulation runs over a ten-year horizon with a daily time step, a resolution fine enough to represent the rapid course of mosquito-borne transmission and the timing of vaccination campaigns with realistic precision. Parameterization draws on both primary and secondary data, including El Niño associated rainfall patterns that historically precede RVF epizootics in East Africa and the results of producer surveys conducted in the study area. The heavy rains associated with El Niño events create the flooded habitats in which Aedes eggs hatch and Culex populations explode, so anchoring the model’s environmental forcing to these observed patterns helps ensure that simulated outbreaks resemble real ones. The authors report that the model reproduces key outbreak features consistent with historical RVF events, an important credibility check before using it to compare policies that have never been implemented at scale.

With this validated platform in place, the researchers compared alternative vaccination strategies against a business-as-usual baseline and a no-intervention scenario. The business-as-usual strategy corresponds to the reactive posture that has often prevailed in practice: vaccination is delayed until an outbreak is already underway, constrained by the logistical realities of mobilizing vaccines, cold chains and veterinary teams in remote pastoral areas. The preventive alternatives include annual vaccination of susceptible animals, biannual campaigns and triennial schedules, each representing different assumptions about budget, coverage and the frequency with which herds are refreshed with newly born, susceptible stock.

The results are striking in their asymmetry. Under business-as-usual delayed reactionary vaccination, the model shows only marginal improvements in herd recovery and producer income relative to doing nothing at all. By the time a reactive campaign takes effect, the explosive early phase of an RVF epizootic has typically already run its course through the most susceptible animals, so late vaccination buys little additional protection and recovers little of the lost income. In contrast, annual preventive vaccination with sufficient coverage of susceptible animals prevents simulated outbreaks entirely within the model horizon. Biannual and triennial strategies occupy an intermediate position: they reduce outbreak severity but do not fully eliminate risk, because gaps in coverage allow susceptible animals to accumulate and transmission to rekindle when vector populations surge.

Beyond the comparison of fixed schedules, the model illuminates the decisive importance of timing itself. Shortening the delay between the onset of an outbreak and the initiation of vaccination substantially reduces livestock losses and improves the trajectory of income recovery for producers. This finding quantifies a principle that outbreak responders have long understood intuitively: in a fast-moving vector-borne epidemic, every week of delay translates into animals infected that timely vaccination could have protected. The system dynamics formulation makes the cost of delay explicit, translating it into herd-level mortality and household-level economic outcomes rather than leaving it as an abstract warning.

For policymakers in Kenya and other RVF-endemic countries, the practical implications are considerable. Preventive vaccination is expensive and competes with other demands on limited veterinary budgets, and in years without outbreaks it can appear to be money spent for nothing. The model’s ex-ante comparisons offer a way to weigh that recurring cost against the episodic, catastrophic losses of uncontrolled epizootics, and to demonstrate that proactive programs outperform reactive ones in both disease control and economic resilience. The authors position the model as a decision-support tool for livestock health policy, one that can be rerun under different assumptions about rainfall, coverage, vaccine efficacy and market behavior to stress-test strategies before committing public resources to them.

Methodologically, the study illustrates why system dynamics approaches are gaining traction in animal health economics. Because the framework integrates vector ecology, host infection dynamics, herd demography and market processes in one coupled structure, it can reveal behaviors that emerge from interactions among components rather than from any single module. Time lags between rainfall triggers, mosquito population booms, livestock exposure and policy response are represented explicitly, as are the nonlinear thresholds that separate a contained incursion from a runaway epizootic. Conventional static impact assessments, which typically multiply observed losses by fixed prices, cannot capture these dynamics, and the authors argue that this limitation has systematically distorted the apparent value of preventive investment.

The research also speaks to the broader challenge of preparing for climate-sensitive disease threats. RVF outbreaks in East Africa are tightly associated with flooding events whose frequency and intensity may shift with a changing climate, and models of this kind provide a template for evaluating how preparedness investments perform across a range of environmental futures. For the pastoral communities of Ijara County and similar regions, where livestock constitute both income and a form of savings, the difference between a prevented outbreak and a delayed response is measured in household welfare as much as in veterinary outcomes. By demonstrating that annual preventive vaccination with adequate coverage can hold the virus at bay within the simulated decade, while reactive campaigns barely improve on inaction, the study offers a quantified case for shifting RVF policy from reaction to anticipation, and a modeling architecture that other endemic countries can adapt to their own epidemiological and economic circumstances.

Subject of Research: System dynamics modeling of Rift Valley fever vaccination strategies in Kenya

Article Title: Integrating epidemiological and economic dynamics to assess Rift Valley fever vaccination strategies: A system dynamics model from Kenya

Article References: Bahta, S., Wanyoike, F., Bett, B., & Rich, K. M. (2026). Integrating epidemiological and economic dynamics to assess Rift Valley fever vaccination strategies: A system dynamics model from Kenya. PLOS Complex Systems, 3(6), e0000101. https://doi.org/10.1371/journal.pcsy.0000101

Image Credits: AI Generated

DOI: 10.1371/journal.pcsy.0000101

Keywords: Rift Valley fever, system dynamics, vaccination strategy, Kenya, livestock health, epidemiological modeling, economic impact, Aedes mosquitoes, Culex mosquitoes, SEIR model, preventive vaccination, pastoral systems

The significance of the advance lies partly in the mosquito genus involved. Culex species have received far less attention from spectroscopy-based methods than Anopheles mosquitoes, which dominate malaria research, despite the fact that Culex vectors are increasingly important players in the transmission of orthoflaviviruses and filarial parasites. Because these pathogens typically infect multiple host species, including birds, mammals and humans, mapping the feeding patterns of Culex mosquitoes is essential for predicting where and when spillover into human populations is likely to occur. Traditional blood meal identification relies on techniques such as PCR-based assays or sequencing, which are accurate but costly, time-consuming and progressively less reliable as the blood meal is digested inside the mosquito. A method that is rapid, inexpensive and robust to digestion time could transform the scale at which blood meal surveillance is feasible, particularly in the low-resource settings where mosquito-borne disease burdens are often highest.

The experimental design was straightforward but rigorous. The team reared Culex quinquefasciatus mosquitoes in the laboratory and fed them blood from six different host species: human, pig, chicken, cattle, dog and horse. This panel deliberately spans the range of hosts that matter most for multi-host pathogens in many endemic regions, from domestic animals that amplify viruses to humans who suffer the consequences. At four time points after feeding, namely 6, 12, 24 and 30 hours, the researchers rolled the abdominal contents of the mosquitoes onto filter paper to create dried bloodspots, a simple and field-friendly way of preserving samples without refrigeration or elaborate cold chains. Each bloodspot was then scanned using a Bruker ALPHA Fourier-transform infrared spectrometer, an instrument that measures how the sample absorbs mid-infrared light across a spectrum of wavelengths.

The underlying physics is what gives the method its power. Mid-infrared light interacts with the vibrational modes of chemical bonds in proteins, lipids, carbohydrates and nucleic acids, producing a complex absorption spectrum that acts as a molecular fingerprint of the sample. Because the blood of different host species differs subtly in its biochemical composition, those fingerprints carry enough information to distinguish a chicken blood meal from a dog blood meal, or a human meal from a horse meal. The raw spectra, however, are far too intricate for visual interpretation, which is where machine learning enters. The researchers trained a multilayer perceptron, a type of artificial neural network, on the spectra from laboratory mosquitoes with known blood meal origins, allowing the algorithm to learn the spectral patterns that discriminate between host species and then apply that learned knowledge to unknown samples.

The results were striking. On laboratory-reared mosquitoes, the trained model predicted host species with 93.6 percent accuracy, a dramatic improvement over the 16.7 percent expected from random classification among six host species. Even more importantly for real-world application, accuracy declined only modestly with digestion time, falling by just 6 percent at the 30-hour mark. This resilience matters because mosquitoes caught in surveillance traps are rarely fresh; they may have fed a day or more earlier, and conventional molecular methods lose accuracy as digestive enzymes break down the host DNA they rely on. A technique that remains reliable across a full day of digestion therefore captures a much larger and more representative fraction of the blood meals collected in the field, reducing the bias that plagues traditional approaches.

The true test came with field-collected mosquitoes. When the laboratory-trained model was applied to bloodspots from wild-caught Culex mosquitoes, accuracy dropped to 67.9 percent. The authors attribute this gap to differences between laboratory and field conditions rather than any fundamental flaw in the approach. Wild mosquitoes feed directly on living hosts, whereas the laboratory mosquitoes were fed through artificial feeders, and the blood used in the laboratory was treated with anticoagulants that wild blood meals naturally lack. Wild blood meals also reflect the true biochemical diversity of free-ranging animals, whose diet, health and physiology vary far more than those of the standardized blood sources used in the laboratory. In other words, the model was trained on a simplified version of the world and then asked to interpret a messier one, and the performance drop is an expected consequence of that mismatch.

Crucially, the team found a practical way to manage this uncertainty. By applying a probability threshold to the model’s predictions, accepting only classifications in which the algorithm expressed high confidence, accuracy on field samples rose to 78 percent, at the cost of discarding 21 percent of samples that the model could not confidently assign. This trade-off is a familiar one in applied machine learning, and for surveillance purposes it is often the right one. A public health programme that needs to know which hosts are driving transmission can afford to leave a fifth of samples unclassified if the remaining classifications are substantially more trustworthy. The threshold approach effectively lets users dial between coverage and confidence depending on the question at hand, a flexibility that rigid molecular assays do not offer.

The economic and logistical implications are considerable. Once the spectrometer is in place, the marginal cost of scanning a bloodspot is minimal, and the analysis requires no reagents, no cold chain and no highly specialized molecular biology laboratory. Filter paper bloodspots can be collected in remote villages, shipped at ambient temperature and analysed centrally, which makes the method particularly attractive for large-scale surveillance programmes in low-resource settings. The study’s field collections in the Philippines, supported by the Department of Health and local partners, alongside work in Australia’s Northern Peninsula Area, demonstrate that the sampling workflow is feasible in exactly the kinds of environments where Culex-borne pathogens impose their greatest burden. The work was funded by the Wellcome Trust, the European Research Council and several university and institutional grants, reflecting a collaboration spanning the University of Glasgow, the University of Sydney, NSW Health Pathology and the Ifakara Health Institute.

There are, of course, limits to what the current model can do. The laboratory training set covered only six host species, whereas real mosquitoes bite a far wider menagerie, including wild birds, rodents, bats and marsupials, and the model cannot classify hosts it was never taught to recognize. Expanding the library of host species, incorporating spectra from wild-fed mosquitoes into the training data, and refining the preprocessing of spectra to correct for the chemical differences introduced by anticoagulants and artificial feeders are all obvious next steps. The authors note that further development using samples from field-collected mosquitoes could enhance classification accuracy and broaden the range of identifiable hosts, and the open-access publication of the work should accelerate those efforts across the research community.

Even in its present form, however, the study marks a meaningful shift in how blood meal analysis could be conducted. By demonstrating that a benchtop infrared spectrometer and a neural network can recover host identities from a day-old blood meal with near-laboratory accuracy, the researchers have opened a path toward blood meal surveillance at a scale that PCR-based methods could never economically achieve. For diseases that move between animals and humans along cryptic transmission chains, that scale is precisely what has been missing. If subsequent studies confirm and extend these results across more host species and more field settings, the humble dried bloodspot, read by a beam of infrared light, may become one of the standard tools of vector-borne disease intelligence, helping health authorities see not just which mosquitoes are present, but whose blood they are carrying and which transmission pathways need to be interrupted first.

Subject of Research: Mid-infrared spectroscopy and machine learning for identifying host species of Culex mosquito blood meals

Article Title: Rapid host species identification from dried bloodspots of Culex mosquito blood meals using mid-infrared spectroscopy

Article References: Rapid host species identification from dried bloodspots of Culex mosquito blood meals using mid-infrared spectroscopy. (n.d.). https://doi.org/10.1186/s13071-026-07703-8

Image Credits: AI Generated

DOI: 10.1186/s13071-026-07703-8

Keywords: Culex mosquitoes, blood meal analysis, mid-infrared spectroscopy, machine learning, multilayer perceptron, vector-borne disease, orthoflaviviruses, lymphatic filariasis, dried bloodspots, disease surveillance, one health, entomology