What Can Artificial Intelligence Reliably Predict in Vector Biology? A Systematic Review of Analytical Tasks, Data Integration, Explainability, and Reproducibility

INTRODUCTION

Artificial intelligence (AI) is becoming an analytical layer between vector observations and biological or operational interpretation. Image classifiers can assign taxonomic labels, acoustic and optical systems can automate detection, and forecasting models can connect surveillance records with environmental variation. Yet these applications do not constitute one uniform predictive problem. A model identifying an adult mosquito from a curated image addresses a different construct from a model estimating trap abundance, mapping habitat suitability, predicting arbovirus incidence, or comparing intervention scenarios. Machine-learning research in mosquito control spans geospatial, visual, and acoustic pipelines, but evaluation remains heterogeneous and incompletely standardized [1]. The central issue is therefore not whether AI can generate predictions, but what each prediction represents, how it was validated, and whether its stated interpretation exceeds the evidence produced by the underlying task.

This distinction is scientifically important because vector biology links several partially observed processes: vector identity, abundance, spatial distribution, age structure, host contact, infection, intervention exposure, and pathogen transmission. Each process can become an input, target, proxy, or latent assumption within an AI pipeline. AI-based prediction has expanded across vector-borne diseases and pathogens, although disease-, pathogen-, and vector-level outcomes are not interchangeable [2]. A model predicting reported disease incidence may perform well without accurately representing vector abundance, while a classifier detecting infection in mosquitoes does not automatically estimate transmission intensity. Reliability must consequently be evaluated against the biological level at which the output is defined, rather than inferred from a broad label such as “vector-borne disease prediction.”

Time and space further complicate interpretation. Vector and pathogen observations are structured by seasonality, surveillance effort, environmental dependence, human movement, intervention activity, and reporting systems. Randomly partitioned records may therefore place closely related observations in both development and test sets, producing strong internal performance without demonstrating transportability. Temporal and spatiotemporal arbovirus forecasts vary in outcome definition, horizon, and validation design, restricting direct performance comparison [3]. A forecast that reconstructs a familiar seasonal pattern is not equivalent to one that anticipates an outbreak at a useful lead time, and a model calibrated within one surveillance programme cannot be assumed to retain calibration under altered trap designs, climates, reporting practices, or control regimes.

The same boundary applies to emerging multimodal and digital-twin-oriented representations. Combining drone imagery, remote sensing, sensor streams, entomological observations, and epidemiological data may improve system representation, but additional modalities do not automatically produce biologically valid or decision-ready models. Drone-enabled sensing can extend mosquito surveillance, but ecological ground truth determines whether mapped features are operationally meaningful [4]. This review therefore asks what AI can reliably predict across vector identification, automated surveillance, abundance, distribution, transmission, and intervention planning. Its central argument is that reliability is task-, scale-, species-, sensor-, and context-specific. Internal test performance is not external validity; prediction accuracy is not decision usefulness; post-hoc explainability is not biological plausibility; and computational reproducibility is not evidence that performance will recur under distribution shift.

Review questions and eligibility criteria

The review was structured around ten linked questions concerning what the selected evidence establishes, leaves uncertain, or contradicts across vector identification, automated surveillance, abundance and distribution forecasting, transmission prediction, intervention planning, decision support, explainability, reproducibility, and external validity. Additional questions examined which claims were directly demonstrated rather than conceptually inferred, which biological or methodological conditions explained divergent findings, and which apparently similar measurements represented non-equivalent constructs. Eligibility, searching, selection, and synthesis will be reported using the PRISMA 2020 framework [5]. The review questions were operationalized by specifying the entomological target, biological scale, input modality, prediction output, validation design, intended decision, and principal interpretation boundary for each study.

Eligible evidence comprised peer-reviewed journal articles within the prespecified recent-publication window that directly addressed AI in vector biology or supplied a methodological principle necessary for evaluating a named article domain. Primary prediction studies, field-validation studies, systematic reviews, methodological evaluations, and closely relevant analytical perspectives were eligible when they contained sufficient information to judge sampling, predictors, outcomes, validation, applicability, explanation, or reproducibility. Generic machine-learning studies without vector relevance, non-journal sources, conference-only papers, preprints, software descriptions, vendor material, and records without verifiable bibliographic metadata were excluded. The accompanying explanation and elaboration will guide explicit rationale for each eligibility boundary and any protocol deviation [6]. Evidence was not accepted merely because it contained title-related terminology; each source had to support a planned claim, comparison, limitation, table element, figure relation, or research implication.

Outcome eligibility preserved distinctions among identification, detection, abundance, distribution, transmission, infection or trait classification, intervention prediction, and decision utility. Habitat suitability was not treated as observed vector distribution, predicted abundance was not treated as disease transmission, and intervention-relevant traits were not treated as demonstrated intervention effectiveness. Extraction fields covered taxon and life stage, setting, data provenance, model family, output, temporal and spatial scale, validation partition, external testing, explanation method, reproducibility evidence, distribution-shift risk, decision relevance, and deployment boundary. Methodological appraisal examined sampling-unit dependence, predictor measurement, outcome definition, leakage, model tuning, calibration, uncertainty, external validation, and applicability. Missing information was retained as uncertainty rather than converted into a favourable appraisal, and no quantitative synthesis was planned where tasks, outcomes, scales, or validation designs were not meaningfully comparable.

Search strategy, screening, and study classification

Database searches were organized through intersecting concept blocks rather than disconnected repetitions of the article title. The biological block included mosquitoes, disease vectors, and vector-borne systems; the analytical block included artificial intelligence, machine learning, deep learning, neural networks, computer vision, and related predictive methods; and the task block covered identification, surveillance, abundance, distribution, forecasting, transmission, intervention, decision support, explainability, reproducibility, and external validity. Controlled vocabulary was combined with title, abstract, and keyword terms according to database syntax. Searches were restricted using the prespecified publication and source criteria, while supplementary citation searching was reserved for directly relevant journal articles that satisfied the same eligibility rules. Because outcomes and validation designs are non-comparable, synthesis will follow SWiM reporting rather than forced statistical pooling [7].

Records were to be merged through DOI-first deduplication, followed by normalized comparisons of title, publication year, and authorship where DOI data were incomplete. Title-and-abstract screening was followed by full-text eligibility assessment, with exclusions recorded under predefined categories such as absent vector relevance, ineligible source type, unverified article status, no usable predictive task, or inadequate validation information for the planned claim. Disagreements were resolved through discussion and, where necessary, additional adjudication. Included studies were classified by analytical task, biological target, data modality, model family, prediction scale, validation design, external-validity evidence, explanation, reproducibility, decision relevance, and deployment boundary. Prediction studies will be appraised across sampling units, predictors, outcomes, analysis, and applicability using an ecologically adapted PROBAST logic [8].

Classification was designed to prevent evidence from being aggregated merely because studies used similar algorithms. An image classifier, an abundance forecast, and a transmission-risk model could all use a convolutional or neural architecture while differing fundamentally in inputs, targets, sampling units, validation requirements, and decision consequences. Primary studies were therefore separated from reviews and methodological critiques, while internal holdout tests, temporal holdouts, spatial holdouts, device transfer, geographic external validation, and field evaluation were recorded as distinct evidence classes. Explanation methods were documented separately from evidence of biological plausibility, and code or data availability was recorded separately from performance transportability. The review questions, eligibility boundaries, search and screening logic, evidence-classification rules, and bias controls are specified in Table 1.

Table 1. Search Strategy, Screening, and Study Classification: Review Questions, Eligibility Boundaries, Search Logic, Screening Rules, Evidence Classification, and Bias Controls

Vector identification and automated surveillance

Vector identification is among the most developed AI tasks in vector biology because images can be standardized, labelled, and processed using established computer-vision architectures. Nevertheless, the apparent clarity of the classification task can conceal important biological and operational assumptions. Convolutional neural networks can classify selected adult mosquito species from images, but closed-class performance does not establish open-set field validity [9]. Curated image sets may underrepresent damaged specimens, unusual orientations, cryptic taxa, non-target insects, immature stages, and field backgrounds. A multitiered ensemble can add novelty detection to mosquito image classification, yet detecting an unknown class is not equivalent to resolving its taxonomy [10]. Novelty scores can support triage by indicating that a specimen differs from known training classes, but they do not establish the identity, epidemiological importance, or taxonomic relationship of the unrecognized specimen.

Acoustic and optical systems extend automated surveillance beyond static morphology by representing wingbeat-related or flight-dependent signals. Budget smartphones can support distributed acoustic monitoring, but microphone variation, ambient noise, and overlapping flight tones constrain taxonomic certainty [11]. Acoustic frequency can vary with temperature, sex, physiological condition, flight behaviour, recording geometry, and hardware, while unrelated insects may generate overlapping signal structures. Backscattered optical signals are machine-learnable for mosquito identification, although algorithm rankings from one sensing configuration may not transfer to another [12]. Such systems may reduce manual processing and increase observation frequency, but sensor calibration, non-target detection, missingness, maintenance, and changing field conditions become components of the prediction problem. Automated surveillance therefore requires evaluation at both the model level and the complete sensing-system level.

The strongest evidence for generalization comes from studies that move beyond laboratory partitions and test trained systems under field conditions. A laboratory-trained optical system retained useful performance in two field trials for Aedes and Culex classification, providing bounded external-validation evidence [13]. This result is more informative than an additional random internal split because the field trials introduce environmental noise and operational variation. It nevertheless remains bounded by the tested genera, locations, sensor configuration, and sampling conditions. Automated counts may support downstream abundance forecasting, but only if detection error, taxonomic uncertainty, sensor downtime, and surveillance changes are propagated rather than treated as error-free observations. Internal test performance is therefore evidence about a particular evaluation set, not a general licence for autonomous field identification or surveillance replacement.

Abundance, distribution, and transmission forecasting

Abundance forecasting asks whether environmental observations, recent surveillance records, and temporal structure can anticipate changes in vector populations at a defined site and horizon. Neural networks can represent nonlinear Aedes-abundance dynamics, but forecast value remains conditional on horizon, calibration, and surveillance continuity [14]. This evidence supports the feasibility of learning recurrent seasonal and environmental relationships; it does not show that the same relationship will persist when trap design, control activity, climate, urban form, or reporting practices change. Earth-observation variables can support mosquito-abundance prediction, while their ecological meaning remains dependent on spatial scale and local calibration [15]. Vegetation, surface water, temperature, and built-environment measurements may represent vector habitat indirectly, but remote-sensing variables are not themselves mosquito observations. Their predictive contribution may arise through genuine ecological relationships, through correlation with unmeasured social or surveillance processes, or through both.

Distribution forecasting introduces a related but distinct representational problem. A habitat-suitability surface, a probability of occurrence, an estimated population density, and a forecast of trap abundance describe different outcomes. Remote-sensing and weather variables can model dengue-vector populations, but geographic transfer cannot be assumed from retrospective fit [16]. Spatial autocorrelation can make neighbouring development and test locations appear independent while preserving nearly identical environmental conditions. A model may therefore interpolate effectively within a sampled landscape yet fail when transferred to a different ecological zone. Near-term relative-abundance forecasts may improve operational awareness, although relative abundance is neither absolute burden nor an intervention outcome [17]. Operational relevance depends on whether the forecast is calibrated, timely, available at a controllable spatial scale, and demonstrably more useful than seasonal expectations or conventional surveillance alone.

Transmission forecasting is more complex because disease outcomes emerge from interacting vector, pathogen, host, environmental, behavioural, intervention, and reporting processes. A model can predict cases without representing vector mechanisms correctly, particularly when recent incidence dominates the forecast. Conversely, an informative abundance forecast may add little to disease prediction where pathogen introduction, immunity, human mobility, or reporting delay is the principal constraint. Hybrid dynamic and neural models can generate mechanistic hypotheses about meteorological effects, but model-derived mechanisms remain inferential [18]. Mechanistic structure can improve transparency by forcing assumptions about population processes to be stated, yet it does not transform observational associations into experimental evidence. Reliable transmission forecasting therefore requires explicit outcome definitions, prespecified horizons, temporally and spatially appropriate validation, uncertainty calibration, simple comparator models, and assessment of the incremental information contributed by vector data.

Intervention prediction and decision support

Intervention-relevant prediction often begins with estimating biological traits that conventional surveillance measures slowly or incompletely. Spectroscopy and supervised learning can classify vertebrate blood meals, but host-source prediction does not directly quantify transmission contribution [19]. Blood-meal classification can describe contact patterns and support investigation of residual transmission, yet its decision relevance depends on sampling design, digestion stage, host availability, and whether identified feeding patterns indicate an actionable intervention pathway. Diffuse reflectance spectroscopy may predict mosquito age, species, and cuticular resistance under laboratory conditions, but field operational validity remains unestablished [20]. In particular, a spectral distinction associated with a resistant strain may reflect cuticle composition, strain background, rearing conditions, or correlated physiology. It should not be interpreted as a universally validated resistance mechanism without independent populations and field testing.

Prediction of mosquito age and infection status could improve estimation of epidemiologically important population structure. Mass spectrometry with deep learning can estimate the age of field Anopheles mosquitoes, while transfer across sites, populations, and instruments requires separate validation [21]. An age estimate may contribute to surveillance because older mosquitoes have had more opportunity to acquire and transmit pathogens, but predicted age is not a direct measurement of infectiousness or intervention impact. Mid-infrared spectroscopy can predict malaria infection status in field-collected mosquitoes, but infection classification is not equivalent to intervention-effect prediction [22]. The evidence supports a potentially faster surveillance measurement under the evaluated species, location, preparation, instrument, and reference assays. It does not establish that the same model will remain calibrated across parasite prevalence, vector species, instruments, collection methods, or national control programmes.

Decision support extends beyond predicting a state to selecting or prioritizing an action. Spatial models can compare population-replacement scenarios at continental scale, although scenario success is not observed operational effectiveness [23]. Such models are valuable for exposing assumptions about dispersal, fitness, release location, landscape connectivity, and programme coverage, but their outputs remain conditional on those assumptions. Across the evidence, an end-to-end decision pathway can be expressed as multimodal observation, task-specific biological representation, prediction with uncertainty, contextual qualification, and a bounded surveillance or intervention decision. A component should enter this pathway only when its target, validation context, failure modes, uncertainty, and expected contribution to a decision are explicit. Figure 1 classifies artificial-intelligence applications across vector surveillance, identification, forecasting, and intervention planning within the analytical logic developed in this section.