In　this　paper,　a　mathematical　model　for　sandwich-type　lateral　flow　immunoassay　is　developed　via　short　available　time　series.　A　nonlinear　dynamic　stochastic　model　is　considered　that　consists　of　the　biochemical　reaction　system　equations　and　the　observation　equation.　After　specifying　the　model　structure,　we　apply　the　extended　Kalman　filter　(EKF)　algorithm　for　identifying　both　the　states　and　parameters　of　the　nonlinear　state-space　model.　It　is　shown　that　the　EKF　algorithm　can　accurately　identify　the　parameters　and　also　predict　the　system　states　in　the　nonlinear　dynamic　stochastic　model　through　an　iterative　procedure　by　using　a　small　number　of　observations.　The　identified　mathematical　model　provides　a　powerful　tool　for　testing　the　system　hypotheses　and　also　for　inspecting　the　effects　from　various　design　parameters　in　both　rapid　and　inexpensive　way.　Furthermore,　by　means　of　the　established　model,　the　dynamic　changes　in　the　concentration　of　antigens　and　antibodies　can　be　predicted,　thereby　making　it　possible　for　us　to　analyze,　optimize,　and　design　the　properties　of　lateral　flow　immunoassay　devices.