In 2019, a measles outbreak infected 47,871 Filipinos and caused 632 deaths [3]. To prevent these casualties in the future, mathematical models of varying components were constructed using the Department of Health (DOH) data to model measles dynamics, help create better models, and give DOH an idea of what to prepare for in the future. By utilizing Mathematica, measles dynamics were modeled using different compartmental, parameter, and population models. The best components of each constructed model were identified. Each model was compared according to the goodness of fit (via Pearson’s correlation coefficient; R2) and the accuracy of predictions (via mean absolute percentage of error; MAPE). Among the parameter models, quadratic had the best fit (R2 = 0.990, 0.992, 0.989, and 0.990) and accuracy (MAPE = 8.951, 8.914, 6.004, and 7.132). Meanwhile, linear population models had unrealistic parameters due to unusually high magnitudes or negative values of the population slope which means that constant population models were more realistic and thus, more accurate. Given the determined best components, the Susceptible-Exposed-Infected-Recovered model with quadratic parameters and constant population (SEIR-QC) is the best SEIR model (R2 = 0.989, MAPE = 6.004) while the Susceptible-Infected-Recovered model with quadratic parameters and constant population (SIR-QC) is the best SIR model (R2 = 0.990, MAPE = 8.951). Using these two models, the reproduction numbers of each were graphed across time to determine measles’ long-term activity and it was determined that the disease did not warrant an epidemic response immediately and health officials may now focus on recovery. Lastly, the effect of quarantine and vaccination to the models were analyzed and showed that conducting quarantines are more effective than vaccinating individuals during an epidemic.
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