Henri Edouard Zefack Tonnang

Plant pests threaten 10-40% of global food production, resulting in $55-220 billion in annual economic losses. Despite these escalating risks, biosecurity remains largely reactive, lacking anticipatory frameworks that integrate pest-specific drivers governing transboundary spread. We present GIRAF 1.0 (Global Invasion Risk Assessment Framework), the first quantitative, data-driven system that unifies pest-specific multi-host landscapes, abiotic suitability, and global trade networks with international phytosanitary policies. We applied GIRAF to four globally devastating pests - ranging from viral to insect taxa - to reconstruct a century of transcontinental spread and generate the first multiscale atlases of future invasion potential. GIRAF reveals that 22-37% of Earth's land surface can contain host communities that largely overlap with environmentally suitable hotspots. Over 115 countries are highly vulnerable to trade-mediated pest introductions despite adopted phytosanitary policies. GIRAF provides a foundation for proactive surveillance and pandemic preparedness, offering a scalable path for transnational biosecurity agencies and global food industries.

Malaria remains a major public health concern in Kenya, where changing climatic conditions, insecticide resistance, and mosquito behavioral adaptations continue to challenge control efforts. This study develops and analyzes a climate‐sensitive malaria transmission model that incorporates mosquito behavior, insecticide resistance, and vector–human ecological dynamics to identify optimal control strategies for the Kenyan context. The model’s well‐posedness was established, the basic reproduction number ( R ₀ ) computed using the next‐generation matrix method, and the stability of equilibrium points assessed. Spatial analysis of R ₀ was performed using temperature and rainfall raster data to map transmission risk across Kenya under varying insecticide use scenarios. Results indicate that regions with climatic conditions favorable for vector survival, particularly parts of the Eastern and Western regions, experience higher transmission intensity. Simulations show that while insecticide use (deltamethrin or permethrin) substantially reduces malaria prevalence, resistance ( δ _v = 0) results in R ₀ > 1 and sustained transmission. An optimal control problem incorporating personal protection, antimalarial treatment, and vaccination was formulated and solved using Pontryagin’s Maximum Principle. The most effective reduction in malaria prevalence was achieved when all three interventions were applied simultaneously, with infections declining to zero within 10 days. These findings highlight the importance of integrating vector behavior, climate variability, and resistance dynamics into malaria models and support the use of combined intervention strategies to enhance control efforts in Kenya.

Data mining, particularly multivariate time series data analysis, is crucial in extracting insights from complex systems and supporting informed decision-making across diverse domains. However, assessing the similarity of multivariate time series data presents several challenges, including dealing with large datasets, addressing temporal misalignments, and necessitating efficient and comprehensive analytical frameworks. A novel integrated computational approach, Multivariate Time series Alignment and Similarity Assessment (MTASA) is proposed to address these challenges. MTASA is built upon a hybrid methodology designed to optimise time series alignment, complemented by a multiprocessing engine that enhances the utilisation of computational resources. This integrated approach comprises four key components, each addressing essential aspects of time series similarity assessment, offering a comprehensive framework for analysis. To evaluate the effectiveness of MTASA, we conducted an empirical study focused on assessing agroecological similarity, a key aspect of climate smart agriculture, using real-world environmental data. The results from this study highlight MTASA’s superiority, achieving approximately 1.5 times greater accuracy and twice the speed compared with existing state-of-the-art integrated frameworks for multivariate time series similarity assessment. It is hoped that MTASA will significantly enhance the efficiency and accessibility of multivariate time series analysis, benefitting researchers and practitioners across various domains. Its capabilities in handling large datasets, addressing temporal misalignments, and delivering accurate results make MTASA a valuable tool for deriving insights and aiding decision-making processes in complex systems.

Edible caterpillars are among the most traded commodities in Cameroon, yet there is limited data on their species diversity and host trees. The study addressed this gap through semi-structured interviews with local experts from 11 localities in the bimodal forest zone of Cameroon. The experts provided local names of edible caterpillars and their host trees. Between August 2020 and September 2021, 15 edible caterpillar specimens were collected and identified through morphological and molecular methods. The identified species belong to the Erebidae, Notodontidae, and Saturniidae families. The study recorded over 22 host trees, with Entandrophragma cylindricum ‘Sapelli’, Baillonella toxisperma ‘Moabi’, Erythrophleum ivorense ‘Tali’, and Petersianthus macrocarpus ‘Essia’ being the most common. The trees, from diverse plant families, are heavily exploited for timber, creating a dual pressure on both timber and non-timber resources, including edible caterpillars. Gonimbrasia oyemensis and Cirina forda emerged as the most vulnerable species due to their monophagy and reliance on highly exploited host trees. The Maximum Entropy model successfully identifies suitable habitats for these host trees beyond Cameroon. The model’s high accuracy and predictive power offer valuable insights for conservation efforts to preserve edible caterpillars and their host plants across Africa. The research provides critical data on edible insects in Cameroon, highlighting the cultural significance of their host plants and offering a robust model for sustainable exploitation and conservation management.

BACKGROUND: The cotton jassid, Amrasca biguttula , a dangerous and polyphagous pest, has recently invaded the Middle East, Africa and South America, raising concerns about the future of cotton and other food crops including okra, eggplant and potato. However, its potential distribution remains largely unknown, posing a challenge in developing effective phytosanitary strategies. We used an ensemble model of six machine‐learning algorithms including random forest, maxent, support vector machines, classification and regression tree, generalized linear model and boosted regression trees to forecast the potential distribution of A. biguttula in the present and future using presence records of the pest and bioclimatic predictors. The accuracy of these algorithms was assessed based on the area under the curve (AUC), correlation (COR), deviance and true skill statistic (TSS). RESULTS: All algorithms showed good performance in forecasting the distribution of A. biguttula (AUC ≥ 0.91, COR ≥ 0.72, TSS ≥ 0.77 and deviance ≤ 0.65). Mean temperature of wettest quarter, mean temperature of driest quarter and precipitation of the wettest month were the key variables that contributed to predicting A. biguttula occurrence. Projection of the model showed that cotton production areas in Asia, sub‐Saharan Africa, and South America are at threat of invasion by A. biguttula under the current climatic scenario. Additionally, range expansion for A. biguttula is projected in the future in sub‐Saharan Africa, South America and China, indicating a suitable ecological niche for A. biguttula to thrive. CONCLUSION: Our results provide early warning and decision‐making information that can guide efforts to prevent this pest's further spread and invasion into new areas. © 2025 Society of Chemical Industry.

The One Health (OH) concept has evolved significantly in recent decades, emerging as a key framework guiding international research and policy in managing new infectious diseases, chiefly zoonoses. While its initial conception revolved around managing zoonotic diseases as they traverse the interface between animals and humans through the environment, this concept has transformed beyond its origins as a collaboration solely between veterinary and public health stakeholders. Notably, the past decade has ushered in a new era of addressing complex issues in a novel manner. Emerging evidence has led to a fresh theoretical framework, highlighting interconnected terrestrial and aquatic ecosystems. Understanding these links is crucial in tackling emerging issues and resultant health challenges within these systems under what we call One Health 2.0. The current paper describes Climate-Smart One Health (CS-OH) and Climate-Smart Integrated Pest Management (CS-IPM) approaches, emphasizing holistic perspectives and practical tools. The One Health (OH) 2.0 concept applies to the agricultural sector and more specifically to agrifood systems exposed to climate change impacts. It is meant to address, in a comprehensive manner, soil, water, plant, animal, rural and urban farmers and farming communities, and consumer health issues. The One Health (OH) 2.0 concept is embodied in the Climate-Smart One Health (CS-OH) approach. The latter is designed for applications in agrifood systems. Pathways for the deployment of both CS-OH and CS-IPM interventions are proposed in this paper. A Ghanaian case is discussed.

The current knowledge on insects feeding on fruits is limited, and some of the scarce existing data on the fruit‐associated insects are secluded within the host institutions. Consequently, their value is not fully realized. Moreover, in countries like Kenya, the integration of biocollections data within a digital framework has not been fully exploited. To address these gaps, this article presents a description of the development of a web‐based platform for data sharing and integrating biodiversity historical data of wild plants, fruits, associated insects, and their molecular barcodes (WiPFIM) while leveraging data science technologies. The barcodes corresponding to the biocollections data were retrieved from BOLD database. The platform is an online resource about fruit–insect interactions that can be of interest to a worldwide community of users and can be useful in building innovative tools. The platform is accessible online at https://test‐dmmg.icipe.org/wpfhi.