Heterogeneity　exists　in　the　case　information　of　an　emergency,　and　case　retrieval　plays　a　crucial　role　in　generating　an　emergency　response　alternative.　Despite　advancements,　fusing　heterogeneous　case　information　and　consid-ering　decision　maker　preferences　remain　challenging　issues　to　be　solved.　To　address　this,　a　novel　heterogeneous　multi-attribute　case　retrieval　method　is　proposed　and　applied　to　COVID-19　emergency　decision-making　in　this　study.　To　overcome　uncertainties　and　heterogeneities　in　case　information,　a　new　interval　evidential　reasoning　(IER)-based　case　similarity　measurement　is　developed　to　better　protect　data　intergrity　and　improve　case　retrieval　accuracy.　Next,　the　best-worst　method　and　information　entropy　are　used　to　obtain　subjective　and　objective　weights　of　evaluated　attributes,　respectively,　and　subsequently　these　weights　are　integrated.　Furthermore,　a　novel　interval　gained　and　lost　dominance　score　(GLDS)　method　based　on　TODIM　(Portuguese　acronym　for　interactive　multi-criteria　decision-making)　is　developed　to　derive　the　comprehensive　utilities　of　the　similar　historical　case.　This　method　simultaneously　considers　the　fairness　of　the　decision-making　process　and　the　psy-chological　behavior　of　decision　makers.　Finally,　a　simulation　example　with　large-scale　data　and　two　illustrated　examples　of　generating　emergency　response　altenatives　are　provided　to　demonstrate　the　applicability　of　the　proposed　method.　Sensitivity　and　comparative　analysis　are　conducted　to　verify　the　validity　of　the　method.