DATA CHALLENGE:Characterizing the Clinical Resistome in Malawi: A Vivli AMR Register Analysis to Inform Hospital Wastewater Surveillance Priorities

1. Background & Objective Antimicrobial Resistance is a global threat, and Malawian hospitals face an increasing burden,yet circulating resistant pathogens and genes remain poorly characterized. Hospitals release antimicrobial residues and ARGs, but wastewater surveillance programs lack the baseline clinical data. Using the Vivli AMR Register we will characterize Malawi’s clinical resistome, prevalence, phenotypic resistance molecular to inform hospital wastewater monitoring and treatment. 2. Data & Methods We will request isolate-level data for all available specimens collected in Malawi, with a focus on key variables: year of collection, bacterial species, specimen source such as blood, urine, wound, hospital setting (inpatient/ICU where available), and phenotypic susceptibility results for beta-lactams, fluoroquinolones, aminoglycosides, and last-resort agents. Where molecular data exist, we will map reported antibiotic resistance genes associated with extended-spectrum beta-lactamases (ESBLs) to species and specimen types. Using descriptive and trend analyses, we will identify the most clinically prevalent multidrug-resistant organisms and infer which resistance determinants are most likely to be excreted into healthcare wastewater based on specimen source and hospital location. Should Malawi-specific data be limited, we will expand the analysis to sub-Saharan Africa and contextualize the implications for Malawi 3. Expected Impact This first comprehensive clinical AMR profile of Malawi will link resistance patterns to antimicrobial/ARG burdens in hospital sewage, providing evidence to prioritize wastewater surveillance. Aligned with integrated human-environment AMR action plans, results will be shared as a manuscript and policy brief for Malawian stakeholders. 4. Team & Feasibility Our team comprises 2 members with combined expertise in environmental health, and data science. We are confident in delivering a thorough analysis within the eight-week challenge period using statistical software and adhering to data use agreements. Endorsement: The Lead Applicant will submit this EOI by 26 May 2026 and complete the required Team Form upon data request ID receipt.