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Tang, T., Y. Zhu, Y.-Y. Zhang, J.-J. Chen, J.-B. Tian, Q. Xu, B.-G. Jiang, et al. 2024. The global distribution and the risk prediction of relapsing fever group Borrelia: a data review with modelling analysis. The Lancet Microbe. https://doi.org/10.1016/s2666-5247(23)00396-8

Background The recent discovery of emerging relapsing fever group Borrelia (RFGB) species, such as Borrelia miyamotoi, poses a growing threat to public health. However, the global distribution and associated risk burden of these species remain uncertain. We aimed to map the diversity, distribution, and potential infection risk of RFGB.MethodsWe searched PubMed, Web of Science, GenBank, CNKI, and eLibrary from Jan 1, 1874, to Dec 31, 2022, for published articles without language restriction to extract distribution data for RFGB detection in vectors, animals, and humans, and clinical information about human patients. Only articles documenting RFGB infection events were included in this study, and data for RFGB detection in vectors, animals, or humans were composed into a dataset. We used three machine learning algorithms (boosted regression trees, random forest, and least absolute shrinkage and selection operator logistic regression) to assess the environmental, ecoclimatic, biological, and socioeconomic factors associated with the occurrence of four major RFGB species: Borrelia miyamotoi, Borrelia lonestari, Borrelia crocidurae, and Borrelia hermsii; and mapped their worldwide risk level.FindingsWe retrieved 13 959 unique studies, among which 697 met the selection criteria and were used for data extraction. 29 RFGB species have been recorded worldwide, of which 27 have been identified from 63 tick species, 12 from 61 wild animals, and ten from domestic animals. 16 RFGB species caused human infection, with a cumulative count of 26 583 cases reported from Jan 1, 1874, to Dec 31, 2022. Borrelia recurrentis (17 084 cases) and Borrelia persica (2045 cases) accounted for the highest proportion of human infection. B miyamotoi showed the widest distribution among all RFGB, with a predicted environmentally suitable area of 6·92 million km2, followed by B lonestari (1·69 million km2), B crocidurae (1·67 million km2), and B hermsii (1·48 million km2). The habitat suitability index of vector ticks and climatic factors, such as the annual mean temperature, have the most significant effect among all predictive models for the geographical distribution of the four major RFGB species.InterpretationThe predicted high-risk regions are considerably larger than in previous reports. Identification, surveillance, and diagnosis of RFGB infections should be prioritised in high-risk areas, especially within low-income regions.FundingNational Key Research and Development Program of China.

Lopes, D., E. de Andrade, A. Egartner, F. Beitia, M. Rot, C. Chireceanu, V. Balmés, et al. 2023. FRUITFLYRISKMANAGE: A Euphresco project for Ceratitis capitata Wiedemann (Diptera: Tephritidae) risk management applied in some European countries. EPPO Bulletin. https://doi.org/10.1111/epp.12922

Ceratitis capitata (Wiedemann), the Mediterranean fruit fly or medfly, is one of the world's most serious threats to fresh fruits. It is highly polyphagous (recorded from over 300 hosts) and capable of adapting to a wide range of climates. This pest has spread to the EPPO region and is mainly present in the southern part, damaging Citrus and Prunus. In Northern and Central Europe records refer to interceptions or short‐lived adventive populations only. Sustainable programs for surveillance, spread assessment using models and control strategies for pests such as C. capitata represent a major plant health challenge for all countries in Europe. This article includes a review of pest distribution and monitoring techniques in 11 countries of the EPPO region. This work compiles information that was crucial for a better understanding of pest occurrence and contributes to identifying areas susceptible to potential invasion and establishment. The key outputs and results obtained in the Euphresco project included knowledge transfer about early detection tools and methods used in different countries for pest monitoring. A MaxEnt software model resulted in risk maps for C. capitata in different climatic regions. This is an important tool to help decision making and to develop actions against this pest in the different partner countries.