Clustering Countries on Development Indicators Reveals Structure Relevant for H5N1 Mortality Analysis

Abstract Infectious diseases are often observed to have different epidemiology in different countries, which arises due to various factors including those that are ecological, socioeconomic, and healthcare-related. Such variability can sometimes be best captured through looking at groups of countries that are similar within-group but variable between-group. In this study we use statistical learning methods to generate data-driven disease-centric groupings of countries rather than those developed for administrative or political reasons by e.g. the WHO, World Bank, and the United Nations. In particular, we apply hierarchical clustering to group countries based on shared disease-relevant characteristics for zoonotic H5N1 influenza. Using statistical methods such as classification and regression trees (CART)-based imputation and dynamic tree cutting, the analysis accounts for missing data and identifies epidemiologically (rather than politically or economically) meaningful clusters. Applying health metric relevant indicators, we cluster the countries of the world and using a Bayesian approach compute CFRs of zoonotic H5N1 influenza before comparing across clusters. We find that countries with stronger healthcare systems and lower poverty rates tend to have lower and more stable CFRs, whereas resource-limited settings face higher fatality risks. Competing Interest Statement The authors have declared no competing interest. Funding Statement MKA was supported by the Schlumberger Foundation Faculty for the Future. TH was supported by the Wellcome Trust (227438/Z/23/Z) and the Medical Research Council (UKRI483). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Statement All data used in this study are publicly available from reputable international databases. Reported H5N1 case and death data (2003–2024) were obtained from the World Health Organization (WHO) Global Influenza Tracker [7], available at WHO H5N1 Case Tracker (2003–2025). Country-level indicators were sourced from the World Bank Open Data repository [8], accessible at https://databank.worldbank.org/source/world-development-indicators. Regional and income-based classifications were derived from the following publicly accessible sources: the WHO regional groupings [9] (https://www.who.int/about/who-we-are/regional-offices), the World Bank income classification system [10] (https://blogs.worldbank.org/en/opendata/world-bank-countryclassifications-by-income-level-for-2024-2025), and the United Nations M49 statistical regions [11] (https://unstats.un.org/unsd/methodology/m49/). All data sources are cited in the manuscript and can be freely accessed for verification and further analysis.