Accurate, reliable, and timely estimates of pathogen variant risk are essential for informing effective public health responses to infectious diseases. Despite decades of use for influenza vaccine strain selection and PCR-based molecular diagnostics, data on pathogen variant prevalence and growth advantage has only risen to its current prominence during the SARS-CoV-2 pandemic. However, such data are still often sparse: novel variants are initially rare or a region has limited sequencing. To ensure real-time estimates of risk are available in these types of data-sparse conditions, we develop a hierarchical modeling approach that estimates variant fitness advantage and prevalence by pooling data across geographic regions. We apply this method to estimate SARS-CoV-2 variant dynamics at the country-level and assess its stability with retrospective validation. Our results show that more stable and robust estimates can be obtained even when sequencing data are sparse, as compared to established, single-country estimation approaches. We discuss how this method can inform risk assessment of novel emerging variants and provide situational awareness on currently circulating variants, for a range of pathogens and use-cases.