The Remote Emerging Disease Intelligence-NETwork

The Remote Emerging Disease Intelligence-NETwork (REDI-NET) consortium aims to develop a global collaborative network of excellence among domestic and international partner institutions to address surveillance needs to effectively detect, predict and contain potentially emergent zoonosis (diseases that can spread between animals and humans) of human importance and improve the accuracy and timeliness of the ‘data-to decision’ pipeline. The gold reach-back support labs (University of Notre Dame, Naval Medical Research Center and Walter Reed Biosystematics Unit) have developed field-ready, robust standard operating procedures for standardization of data collections, specimen storage, preparation, and processing. Silver labs conduct field work and laboratory testing to characterize naturally occurring pathogens in temperate (Florida), tropical forest (Belize), and tropical grassland sites (Kenya).

Mpala hosts the REDI-NET silver laboratory which implements field surveillance and laboratory testing to characterize naturally occurring pathogens on tropical grasslands in Kenya. The Silver lab team includes Dr. Maureen Kamau, the team lead; Dr. Janerose Mutura, field veterinarian; Griphin Ochieng, research technician; and Rashid Lebunge, research assistant. The gold lab at the Walter Reed Biosystematics Unit led by Dr. Yvonne Linton provides reach-back support and training for the silver lab team at Mpala.

REDI-NET fieldwork activities in Kenya include monthly collection of ticks and water samples at selected permanent and high wildlife and livestock traffic watering holes across Laikipia County. The silver lab team will also collect tick samples at targeted tick sampling sites to assess the presence and absence of tick species and pathogens they carry. The REDI-NET pathogen portfolio also includes opportunistic vertebrate DNA testing of samples collected from non-consortium members whose testing outputs are aligned with REDI-NET project deliverables. The Mpala silver lab team members have received training from the gold lab at WRBU-WRAIR and are now able to conduct field collections, process a variety of sample types: environmental samples (water, soil); invertebrate samples (leeches, ticks) and vertebrate samples (swabs, blood, feces). The team can also complete nucleic acid extractions and nanopore library preparations and sequencing.

The REDI-NET project activities are centered around addressing critical gaps that still exist to effectively mitigate emergent or re-emerging pathogens on local, national, and global scales. First, disease surveillance efforts are often narrow in scope (targeting a predetermined number of biological samples and testing for known pathogens). Second, lack of capacity at the source of sampling to identify invertebrates and process various sample types is problematic as surveillance reports can be seriously delayed while samples are sent to overloaded reach-back laboratories, where it can take weeks or months to be processed with data release occurring many months later. A lack of appropriate epidemiological surveillance tools and adequately trained staff to carry out surveillance activities also exist. Finally, a uniformed approach to data warehousing and analysis for risk assessments is also critical to improving the predictability and reliability of disease forecasting.

The standardization of data collection, specimen preparation, storage, and processing on the REDINET project addresses limitations in training and the absence of suitable epidemiological approaches for disease surveillance. For broad-spectrum pathogen detection, a metagenomic approachーallows for the identification and characterization of organismsーwill be used for pathogen detection facilitating the discovery of known, emerging and unknown pathogens. To address the specimen identification gap, the collected ticks will be identified using morphological keys and images of these ticks taken. This data will be used to develop a high throughput automated AI technology for morphological tick species identification, facilitating distance and real-time tick identification. A key deliverable of the REDI-NET is a custom designed electronically merged (e-MERGE) data pipeline and alert dashboard that integrates remotely captured data with state-of-the-art metagenomic next-generation sequencing technology. This pipeline incorporates data generated from field and laboratory best practices, to provide health decision-makers with a centralized, timely, and rigorous database to efficiently search interdisciplinary and heterogeneous data sources necessary to alert, prepare and mitigate health threats. The e-MERGE pipeline, once fully established, will be a flexible, scalable, and expandable tool for varied health applications.

With the REDINET project permits recently issued, the silver lab team has embarked on monthly field collections, as well as processing and testing of the collected samples. Accurate prediction of zoonotic spillover events requires a detailed understanding of baseline pathogens circulating in differing global environments. By characterizing the diversity and determining the natural baseline of pathogens in Laikipia’s tropical grasslands, any concerns regarding this balance can be detected, leading to estimates of risk for emerging diseases.