As　the　COVID-19　pandemic　continues　to　threaten　various　regions　around　the　world,　obtaining　accurate　and　reliable　COVID-19　data　is　crucial　for　governments　and　local　communities　aiming　at　rigorously　assessing　the　extent　and　magnitude　of　the　virus　spread　and　deploying　efficient　interventions.　Using　data　reported　between　January　and　February　2020　in　China,　we　compared　counts　of　COVID-19　from　near-real-time　spatially　disaggregated　data　(city　level)　with　fine-spatial　scale　predictions　from　a　Bayesian　downscaling　regression　model　applied　to　a　reference　province-level　data　set.　The　results　highlight　discrepancies　in　the　counts　of　coronavirus-infected　cases　at　the　district　level　and　identify　districts　that　may　require　further　investigation.