MODEL DETERMINISTIK DINAMIKA PENYEBARAN PENYAKIT CAMPAK

Abstract

It has been done a research to get deterministic model of measles spread dynamic and predict some conditions of population. The research is focused on theory Markov process usage in measles diseases spread model with differential equation. The appropriate model to describe the measles diseases spread dynamic on average of large scale population that count on latent period is deterministic model SEIR (Susceptible, Exposed, Infectious, Recovery). The model could interact between population, susceptible in time t (St), Exposed in time t (Et), Infectious in time t (It) and Recovery in time t (Rt). To find out the counting of population number in each condition and each time period it is used matrix Markov A times with population condition score of prior condition. To get the counting in end of period of model (  ) used matrix   times to prior condition count by mean of Matlab. So it could be thought that the bigger reproduction number the faster the population to steady condition.