Abstract
To investigate the feasibility of using freeware to model and forecast disease on a local scale, we report the results of modeling measles using a spatial patch model centered around 73 clinics in the North West London Borough of Ealing. MMR1 and MM2 immunization data was extracted for three cohorts, age 1-3, 4-6 and 7-19 and patient population was estimated using general practice profile records.
We designed the measles model using the open source Spatiotemporal Epidemiological Modeler (STEM), extending a compartmental disease model to include both maternal immunity and delays in antibody response after immunization. Individuals above age 19 are not included in the modeling.
Next, we generate an approximate 20-year model of vaccination coverage for Ealing. In England, children are immunized between age 1 and 2, then again at around age 5; hence immunization events are modeled for the age 1-3 and age 4-6 cohorts. Parameter values were based on measles research literature; transmission coefficients were estimated using the Polymod contact data and also fitted to 2011-2012 case reporting data for Ealing.
To examine possible effects of policy change, we create two scenarios A and B. In A, we increase vaccination coverage by 10% for all clinics; in B, we focus only on the bottom 10% of the poorest performing clinics (8 clinics total) and equivalently improve their coverage. Scenario A reduces measles from an initial level of 60 cases per year (2011) to 26 cases per year in 2017 (a 58% reduction), compared to the status quo which declines to 45 cases per year. Scenario B reduces measles by 44%, or to 34 cases per year in 2017.
We conclude that local scale modeling is possible, and that the transparency of analysis provided by an open source application lends credence to the output of the models.