Decreasing measles burden by optimizing campaign timing

Significance Measles vaccine is a highly effective healthcare intervention, but getting vaccine to those in need remains a major problem. Complicating the issue, high-burden countries typically have low-quality infrastructure, severely limiting the number of infections detected and therefore limiting our understanding of local epidemiology. Here we show that statistical disease models can be fitted to sparse case data from Pakistan using a fast linear regression approach. This method yields estimates of the effects of past interventions, the seasonal likelihood of measles transmission, and the magnitude of future outbreaks under different intervention policies. We use these models to understand in general when and where vaccine should be distributed, and these results were used to inform Pakistan’s 2018 vaccination campaign planning. Measles remains a major contributor to preventable child mortality, and bridging gaps in measles immunity is a fundamental challenge to global health. In high-burden settings, mass vaccination campaigns are conducted to increase access to vaccine and address this issue. Ensuring that campaigns are optimally effective is a crucial step toward measles elimination; however, the relationship between campaign impact and disease dynamics is poorly understood. Here, we study measles in Pakistan, and we demonstrate that campaign timing can be tuned to optimally interact with local transmission seasonality and recent incidence history. We develop a mechanistic modeling approach to optimize timing in general high-burden settings, and we find that in Pakistan, hundreds of thousands of infections can be averted with no change in campaign cost.