IntroductionThe　immune　responses　play　important　roles　in　the　course　of　disease　initiation　and　progression　upon　virus　infection　such　as　SARS-CoV-2.　As　the　tissues　consist　of　spatial　structures,　the　spatial　dynamics　of　immune　responses　upon　viral　infection　are　essential　to　the　outcome　of　infection.MethodsA　hybrid　computational　model　based　on　cellular　automata　coupled　with　partial　differential　equations　is　developed　to　simulate　the　spatial　patterns　and　dynamics　of　the　immune　responses　of　tissue　upon　virus　infection　with　several　different　immune　movement　modes.ResultsVarious　patterns　of　the　distribution　of　virus　particles　under　different　immune　strengths　and　movement　modes　of　immune　cells　are　obtained　through　the　computational　models.　The　results　also　reveal　that　the　directed　immune　cell　wandering　model　has　a　better　immunization　effect.　Several　other　characteristics,　such　as　the　peak　level　of　virus　density　and　onset　time　and　the　onset　of　the　diseases,　are　also　checked　with　different　immune　and　physiological　conditions,　for　example,　different　immune　clearance　strengths,　and　different　cell-to-cell　transmission　rates.　Furthermore,　by　the　Lasso　analysis,　it　is　identified　that　the　three　main　parameters　had　the　most　impact　on　the　rate　of　onset　time　of　disease.　It　is　also　shown　that　the　cell-to-cell　transmission　rate　has　a　significant　effect　and　is　more　important　for　controlling　the　diseases　than　those　for　the　cell-free　virus　given　that　the　faster　cell-to-cell　transmission　than　cell-free　transmission　the　rate　of　virus　release　is　low.DiscussionOur　model　simulates　the　process　of　viral　and　immune　response　interactions　in　the　alveola　repithelial　tissues　of　infected　individuals,　providing　insights　into　the　viral　propagation　of　viruses　in　two　dimensions　as　well　as　the　influence　of　immune　response　patterns　and　key　factors　on　the　course　of　infection.