Abstract

Parasites are organisms that adversely affect their host, either by modifying specific physiological functions or by multiplying and developing large populations within their host. The aim of this study is to demonstrate the role of ectoparasites of zoonotic rodents in the transmission of infectious diseases. 200 H.B. Sherman type traps, with dried fish as bait, were used to capture rodents, either in lines or spaced 4 metres apart. Rodents were identified using the classic method of Rosevear, D.R., Wilson, D.E. and Reeder, D.M. PCR and RT-PCR were carried out on samples of blood, organs, mite shreds and insects. The study took place from April 2020 to August 2022; 8 prefectures were chosen according to their ecosystems (scrubland, agricultural fields, villages, orchards, bushes, warehouses, riverbanks, etc.). A total of 1,265 rodents, divided into 18 species, were the subject of our work. The species most frequently encountered were: Rattus rattus (n=437), Mus musculus (n=185), Mus spp. (n=150) and Cricetomys gambianus (n=92). A total of 412 ectoparasites were identified, comprising 7 species including 3 mites and 4 insects. Analyses detected 2 cases of Mammarenavirus lassa, 42 cases of Borrelia spp. 5 cases of Anaplasma spp. 4 cases of Ehrlichia spp. 4 cases of Leptospira spp. and 1 case of Coxiella burnetii. Analysis of the results shows that N'Zérékoré and Kindia are the prefectures most at risk.

Keywords

  • AI
  • data engineering
  • real-time fraud detection
  • digital ecosystems
  • machine learning
  • artificial intelligence
  • cybersecurity
  • big data
  • predictive analytics
  • anomaly detection
  • fraud analytics
  • neural networks
  • blockchain
  • financial fraud
  • e-commerce security
  • deep learning
  • AI ethics
  • digital security
  • behavioral biometrics
  • fraud prevention
  • cloud computing
  • edge computing
  • data lakes
  • data pipelines
  • event stream processing
  • fraud detection models
  • supervised learning
  • unsupervised learning
  • reinforcement learning
  • cyber fraud
  • financial technology
  • fintech
  • online transaction security
  • pattern recognition
  • anomaly scoring
  • real-time processing
  • dynamic rule generation
  • distributed systems
  • AI pipelines
  • fraud risk management
  • multi-layered security
  • hybrid fraud models
  • real-time monitoring
  • contextual fraud detection
  • behavioral analysis
  • fraud scenarios
  • streaming analytics
  • predictive modeling
  • advanced analytics
  • AI-based solutions
  • risk analysis
  • AI scalability
  • fraud detection algorithms
  • feature engineering
  • data integration
  • cyber resilience
  • algorithm optimization
  • system scalability
  • proactive fraud management
  • automated fraud detection
  • AI governance
  • data privacy
  • ethical AI
  • big data analytics
  • data preprocessing
  • continuous learning
  • fraud response systems
  • adaptive systems
  • anomaly thresholds
  • cybersecurity trends
  • machine learning pipelines
  • real-time data ingestion
  • sensor data
  • hybrid AI approaches
  • multi-cloud systems
  • identity verification
  • credential theft detection
  • phishing scams
  • anomaly patterns
  • event correlation
  • fraud signals
  • transaction data analysis
  • credit card fraud detection
  • AI-enhanced systems
  • secure APIs
  • risk mitigation

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