Jo Lines

Background: Reports of arboviral outbreaks are increasing, yet there are acknowledged shortfalls in surveillance data and outbreak preparedness on the African continent. The second part of this scoping review features Aedes vector–pathogen interactions, bionomics and species distribution. Methods: We reviewed studies published between 1980 and 2023 to synthesize existing information and identify knowledge gaps to guide future Aedes research and control in Africa. Results: 877 full-text articles were reviewed, 70 featured vector–pathogen interactions, 230 vector bionomics and 394 species distribution. Vector competence studies were limited to a handful of countries, highlighting knowledge gaps regarding urban, bridge and sylvatic vector species competence for major arboviruses. Aedes albopictus was highly competent for chikungunya virus with the E1-226V mutation, which has been implicated in epidemics where Ae. aegypti was displaced. Vector competence was dependent on virus lineage and mosquito population. Ae. aegypti competence to dengue virus was highly variable, whereas Ae. albopictus was an efficient vector of Zika virus for African but not Asian lineages. Discarded tires were the most frequently reported positive breeding habitats, ranking among the top three in 55% of publications, followed by other discarded items (cans, bottles, broken plastic items) and water storage containers; however, the relative importance varied by location. The pupal index was reported less frequently than traditional indices (container, Breteau and house indices), which co-occurred in 84% of studies reporting indices. As pupal counts are a better proxy for adult mosquito production, their wider use is recommended to identify locally important breeding sites for targeted control. Adult vector monitoring has shifted away from human landing catch towards BG-Sentinel and CDC-LT traps which may be less accurate proxies of human-vector contact, but efficiency can be improved using attractants. Ae. aegypti was highly anthropophilic and had a major biting peak before sunset. However, findings of Ae. aegypti biting at night-time and being zoophilic in some locations demonstrate the need for periodic monitoring, given changing environmental conditions. Conclusions: This review highlights how Aedes populations vary in their competence for key arboviruses, their breeding habitats, and biting behaviors across Africa. Understanding these patterns can guide targeted vector control and improve preparedness for arbovirus outbreaks.

Aedes (Ae.) aegypti and Ae. albopictus are important vectors of arboviruses. Yet their blood-feeding pattern remains poorly characterised in Africa, including Cameroon. In this study, we characterised the blood-meal sources in both species collected from vegetation, household surroundings, and animal cages across four urban sites, one rural site, and a zoo-botanical garden where humans and animals in captivity are the main hosts. Overall, Aedes mosquitoes represented about half of 10,054 female mosquitoes collected, with Ae. albopictus strongly dominating Ae. aegypti among 5001 Aedes females, and only 5.95% of females visibly blood-fed. Sequencing a 748 base pairs (bp) fragment of the cytochrome oxidase I gene from 156 blood-fed abdomens yielded 126 high-confidence host assignments, of which 98.25% were humans, indicating a strong anthropophagic pattern in both species. Unpredictably, two Ae. albopictus individuals had fed on a baboon (Papio anubis) and a frugivorous bat (Pteropodidae), as confirmed by bio informatic analyses, highlighting the species’ opportunistic blood-feeding nature and providing preliminary molecular evidence consistent with a potential bridge-vector role in this setting. Despite the extreme anthropophagy of both species observed, results indicate that Ae. albopictus could also serve as a bridge vector enabling spillover of enzootic viruses to humans, including urbanised settings where wild animals are present. These findings emphasise the urgent need for enhanced arbovirus surveillance in Central Africa using a One Health approach.

Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d’Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.

Land-use practices such as agriculture can impact mosquito vector breeding ecology, resulting in changes in disease transmission. The typical breeding habitats of Africas second most important malaria vector Anopheles funestus are large, semipermanent water bodies, which make them potential candidates for targeted larval source management. This is a technical workflow for the integration of drone surveys and mosquito larval sampling, designed for a case study aiming to characterise An. funestus breeding sites near two villages in an agricultural setting in Côte dIvoire. Using satellite remote sensing data, we developed an environmentally and spatially representative sampling frame and conducted paired mosquito larvae and drone mapping surveys from June to August 2021. To categorise the drone imagery, we also developed a land cover classification scheme with classes relative to An. funestus breeding ecology. We sampled 189 potential breeding habitats, of which 119 (63) were positive for the Anopheles genus and nine (4.8) were positive for An. funestus. We mapped 30.42km² of the region of interest including all water bodies which were sampled for larvae. These data can be used to inform targeted vector control efforts, although its generalisability over a large region is limited by the fine-scale nature of this study area. This paper develops protocols for integrating drone surveys and statistically rigorous entomological sampling, which can be adjusted to collect data on vector breeding habitats in other ecological contexts. Further research using data collected in this study can enable the development of deep-learning algorithms for identifying An. funestus breeding habitats across rural agricultural landscapes in Côte dIvoire and the analysis of risk factors for these sites.

Zoonotic bacterial infections are a health hazard for people who are in regular contact with livestock at the farm level. Improved biosecurity can limit zoonotic pathogen transmission within farms. The aim of this review was to summarize the effectiveness of farm‐level biosecurity interventions in reducing bacterial transmission from animals to people who lived, worked in or visited farms. A systematic literature review was conducted using Embase, Ovid Medline and Agris databases, which were searched on 7^th of July 2019, limited to English language papers but with no time exclusion criteria. A narrative synthesis was undertaken utilizing the Centre for Reviews and Dissemination approach, reported in accordance with Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines. Risk of bias within and across the included studies was performed using established checklists. Out of 869 studies retrieved through database searches, 11 studies were selected. In addition, three studies were found through study reference lists. Fourteen studies were therefore included in this review. Biosecurity interventions were grouped into five categories: hand washing, sanitization and hygienic measures (six studies); personal protective equipment (five studies); vaccination (two studies); other interventions (e.g. air ventilation flap) (four studies); and routine farm activities (two studies). Across studies that investigated odds of human colonization or infection (three studies), odds were seen to both be increased and decreased through use of tested biosecurity measures. Large confidence intervals that often crossed the threshold of an odds ratio equal to 1 were found. Most of the studies' overall risk of bias was ‘medium risk’ (11 studies), with selection bias domains generally being scored ‘medium risk.’ Biosecurity interventions are potentially beneficial in reducing bacterial transmission from animals to humans. However, more high‐quality evidence is needed to increase certainty in which interventions, in which contexts, are most effective from the human health perspective.

Current published guidance on how to evaluate antibiotic resistance (ABR) from a One Health perspective has focussed on the evaluation of intervention design and of the intervention implementation process. For efficient resource allocation, it is also important to consider quantitative measures of intervention impact. In particular, there has been little discussion of how to practically evaluate ABR-related agri- and aquaculture interventions from a public health perspective. Lessons can be learned from other One Health and cross-sectoral intervention impact evaluations. WebofScience, EconLit, PubMed and grey literature were searched for literature quantitatively evaluating interventions across humans, animals and/or the environment. The review included 90 studies: 73 individual evaluations (from 72 papers) and 18 reviews, all including some measure of human impact, but only 29 papers covered all three One Health perspectives (human, animal and environmental). To provide decision makers with expected outcome estimates that are related to their objective functions, evaluations should provide outcome estimates from different perspectives. These include individual, microeconomic and/or macroeconomic perspectives across the One Health system. Based on the methods found in this review, a multi-level compartmental modelling approach for ABR-related intervention evaluation is proposed. The outcomes of such models can then feed into multi-criteria-decision analyses that weigh respective impact estimates alongside other chosen outcome estimates (for example equity or uncertainty). It is key that future quantitative evaluation models of ABR-related interventions are shared (for example through open source code sharing websites) to avoid duplication of effort and to enable more comprehensive estimates of intervention impact to be modelled in the future.