Active Infection & Immunity Genetics & Molecular Biology

Harnessing genomic epidemiology and machine learning for enhanced poliovirus surveillance and response

Funder Wellcome Charity-funded
Recipient organisation Imperial College London
Funding £825K
Period Oct 2025 — Oct 2030

In plain English

AI plain-English summary

Polio eradication is being undermined by a rare but dangerous side effect of the very vaccine used to fight it—the oral vaccine can mutate and spark new outbreaks of paralysis. The problem is that while every case of polio paralysis is genetically sequenced, those sequences are barely used to guide real-world public health decisions. This project will analyse thousands of unpublished genetic sequences from circulating vaccine-derived poliovirus outbreaks to change that. The researcher will map how the virus spreads across countries, measure whether interventions like wastewater surveillance actually work, and combine genetic data with artificial intelligence to predict where outbreaks will hit next. If successful, this work could transform polio surveillance from a reactive, after-the-fact exercise into a predictive tool. Public health agencies would know where to deploy vaccines before paralysis cases appear, potentially stopping outbreaks before they start. The findings will be fed directly into the Global Polio Eradication Initiative, helping integrate genomic epidemiology into routine outbreak response—a shift that could finally push eradication over the finish line.

View original technical description
The main threat to achieving the eradication of poliovirus is the spread of circulating vaccine-derived poliovirus (cVDPV) – rare event in which the oral poliovirus vaccine becomes capable of causing outbreaks of acute flaccid paralysis (AFP). Since 2016, 4934 cVDPV type 2 cases were reported across 80 outbreaks in 44 countries, especially Africa and Asia, with recent circulation in the USA and Europe. Although every polio AFP case is routinely sequenced, the use of sequences to guide public health strategies remains very limited and underexploited. Here, I will analyze over 4,000 unpublished and novel (2025-2030) genetic sequences to maximize the impact of genomic epidemiology for surveillance and response to cVDPV2 outbreaks. I will apply phylogenetic methods to: (i) reconstruct VDPV spread at different geographic scales and identify key drivers of spread; (ii) extract indicators to inform outbreak response, quantify the impact of interventions and assess the added value of wastewater surveillance; and (iii) couple genetic analysis with artificial intelligence to forecast the spread of VDPVs and inform vaccination campaigns. This will be underlined by knowledge transfer initiatives and findings will be shared regularly with the Global Polio Eradication Initiative to support the integration of genomics epidemiology into routine real-time poliovirus response.

View the original record at the funder ↗

Researchers

Darlan Da Silva Candido (EPMC Awardee)

Related Research

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Original classification

Early-Career Award

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.