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Geography and Environmental Science

Southampton Research Fellow wins Commonwealth prize

Published: 6 August 2018
Dr Chigozie Utazi
Dr Chigozie Utazi

Chigozie Utazi, Research Fellow at the University of Southampton, has won the 2018 Taylor & Francis Commonwealth Scholar Best Journal Article Prize. He was awarded the prize for his article 'High resolution age-structured mapping of childhood vaccination coverage in low and middle income countries', which was published in Vaccine in 2018.

We caught up up Chigozie to ask him more about his article and the prize that he was been rewarded.

What led to your article?

Vaccination coverage is an important indicator used to assess the performance of vaccination programmes. However, coverage levels and numbers covered continue to be measured through national or large area statistics which mask epidemiologically-relevant heterogeneities and ‘coldspots’ of low coverage that may lead to sustained disease circulation even when overall coverage levels are high. The VaxPop project within the WorldPop group, funded by the Gates Foundation, aims to address this problem by providing estimates of coverage at a high spatial resolution to enhance program assessment and inform efficient use of limited resources in low- and middle-income countries. This article led by Dr Utazi represents an output of the project.

How does this fit with your academic interests?

My research interest has always been in the development and application of novel spatial and spatiotemporal statistical methodology. Being the Lead on the VaxPop project has afforded the opportunity to gain some valuable experience with real world problems in this research area.

Congratulations on your prize! What does it mean to you?

I am delighted that the impact of our research is being recognized with this prize. This is a significant progress in my career and I feel greatly honoured and motivated by it.

Can you tell us a bit more about your article?

The article features a novel application of Bayesian geostatistical techniques implemented via Markov chain Monte Carlo (MCMC) methods to map measles vaccination coverage at 1x1 km spatial resolution for varying age groups of children under five years using cluster-level Demographic and Health Surveys data and a suite of publicly available geospatial covariates. Using measles vaccination as an example, the output 1 x 1 km maps for the pilot countries of Nigeria, Mozambique and Cambodia, revealed significant heterogeneities in vaccination coverage in these countries which were not captured by large area statistics, among other important findings.

 

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