Efficacy Data Modelling Assistant - Antimicrobial materials

The BioEffective Surfaces for Transport (BEST) Network (see thebestnetwork.org) is a UK wide, multidisciplinary EPSRC funded initiative led by Manchester Metropolitan University. Its mission is to advance the development, testing and deployment of antimicrobial materials (AMMs) across the transport network and wider built environment, with the goal of improving hygiene, safety and infection resilience. The network unites scientists, engineers, microbiologists, transport professionals, regulators, and industry partners.  

A key challenge identified by the network is that evidence for AMM performance is highly fragmented across laboratory standards and real world field studies (e.g., buses, rail vehicles, door hardware). Many AMMs show good performance in lab settings but variable or diminished effectiveness in real world environments where factors such as soiling, material type, abrasion, cleaning regimens, and environmental conditions strongly influence outcomes. 

The purpose of this project is to build a structured, unified model of antimicrobial materials efficacy evidence by: 

• Mapping and integrating laboratory testing standards,  

• Capturing and standardising real world field studies from public transport and built environments,  

• Producing a prototype evidence model and dataset that BEST members can build upon, 

• Creating a transparent, practical evidence grading framework to support deployers, decision makers, and policy audiences, 

• Developing visualisation outputs (evidence maps/dashboards) to make the evidence accessible. 

This placement will provide a student with hands on experience in data modelling, evidence synthesis, and applied research translation, contributing directly to BEST’s objective to ensure that AMM efficacy assessment is fit for purpose and supports future deployment, guidance and policy development. 

Key Tasks  

1  Orientation & Landscape Review 

• Get up to speed on the background of the BEST Network objectives and project progress to date.  Review documented challenges relating to AMM deployment and testing.  Compile a bibliography of key standards and field studies, to prepare a short summary of the current evidence landscape and known gaps.

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2  Development of the Evidence Model

• Define entities and fields for a data model capturing laboratory and real world AMM evidence. Create a crosswalk comparing testing standards and specify required data fields, to produce a draft evidence model (schema) and accompanying extraction codebook.

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3  Evidence Extraction & Data Population 

• Extract and standardise data from 30–50 studies covering both laboratory and field contexts.  Include key field exemplars e.g photodynamic coatings, copper surfaces, QAC based coatings and longlasting sprays.  Produce a populated prototype dataset.

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4  Evidence Grading Framework Development 

• Develop a structured rubric with evidence levels (e.g., lab wet → lab dry → simulated → field), add applicability modifiers. 

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5  Evidence Visualisation 

• Build an interactive evidence map or dashboard with slicers for setting, microbe, material, standard, and outcome.  Create visual summaries of laboratory vs. field evidence patterns and gaps. Produce a visual dashboard, accompanying short guide and also a summary of key findings suitable for a non-academic audience (e.g. transport and policy).

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6  Stakeholder Input (Light Engagement) 

• Conduct a short consultation with a BEST Steering Group member/s to confirm decision relevant fields and interpretive needs, updating the model and rubric accordingly.

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7  Write Up & Handover 

• Produce a concise technical note, 2 page executive summary and suggested next steps. Compile a README file for dataset reuse. Outline a backlog for future expansion. 

Skills and Experiences to be developed  

• Understand the role of antimicrobial materials within transport hygiene strategies and built environment infection control. 

• Compare and interpret key laboratory standards and identify their assumptions and limitations.  

• Analyse real world AMM performance evidence from transport and built environment studies, recognising key factors. 

• Apply evidence synthesis skills to standardise heterogeneous data into a structured format. 

• Produce a prototype evidence model, grading rubric, and visual dashboard. 

• Communicate findings clearly to non academic audiences, including BEST network members and potential policy, transport or industry stakeholders.

Skills Needed: 

• Ability to synthesise scientific information 

• Interest in laboratory and field evidence 

• Strong organisational skills and attention to detail 

• Comfortable working with structured data and analytical tasks 

• Clear written communication skills for technical and nontechnical audiences 

• MS Office skills, especially Excel  

• Desirable - Visualisation and presentation skills, e.g Canva or Adobe 

Suitable for students studying and interested in: 

• Microbiology / Infection Biology / Public Health 

• Materials Science / Surface Engineering / Chemistry / Nanotechnology 

• Biomedical Science / Biochemistry 

• Data Science / Informatics / Statistics / Evidence Synthesis 

• Chemical, Mechanical or Biomedical Engineering