The　COVID-19　pandemic　has　led　to　considerable　morbidity　and　mortality,　and　consumed　enormous　resources　(e.g.　energy)　to　control　and　prevent　the　disease.　It　is　crucial　to　balance　infection　risk　and　energy　consumption　when　reducing　the　spread　of　infection.　In　this　study,　a　quantitative　human,　behavior-based,　infection　risk-energy　consumption　model　for　different　indoor　environments　was　developed.　An　optimal　balance　point　for　each　indoor　environment　can　be　obtained　using　the　anti-problem　method.　For　this　study　we　selected　Wangjing　Block,　one　of　the　most　densely　populated　places　in　Beijing,　as　an　example.　Under　the　current　ventilation　standard　(30　m3/h/person),　prevention　and　control　of　the　COVID-19　pandemic　would　be　insufficient　because　the　basic　reproduction　number　(R0)　for　students,　workers　and　elders　are　greater　than　1.　The　optimal　required　fresh　air　ventilation　rates　in　most　indoor　environments　are　near　or　below　60　m3/h/person,　after　considering　the　combined　effects　of　multiple　mitigation　measures.　In　residences,　sports　buildings　and　restaurants,　the　demand　for　fresh　air　ventilation　rate　is　relatively　high.　After　our　global　optimization　of　infection　risk　control　(R0　≤　1),　energy　consumption　can　be　reduced　by　13.7%　and　45.1%　on　weekdays　and　weekends,　respectively,　in　contrast　to　a　strategy　of　strict　control　(R0　=　1　for　each　indoor　environment).　©　2022　Elsevier　B.V.