Decreasing measles burden by optimizing campaign timing

Abstract

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.

Keywords: mathematical model; measles elimination; time series; vaccine.

Fig. 1.

Measles transmission seasonality in Pakistan. Laboratory-confirmed cases from 2012 to 2017 aggregated by month are plotted as gray bars. The corresponding inferred force of infection (red trace, SD cloud) shows that transmission varies by as much as $50\%$ throughout the year, with a low-transmission season (blue line) from May through September, Pakistan’s summer rainy season.

Fig. 2.

Testing model performance. (Lower) Semimonthly (red) and 6-y (black) model extrapolations are compared with laboratory reporting-rate scaled cases demonstrating that the model predicts outbreak timing and magnitude. (Upper) The underlying susceptible population (blue) corresponding to the long-term projection highlights the potentially strong effect of SIAs (gray dashed lines). For all traces, shaded regions indicate $95\%$ CIs.

Fig. 3.

Optimizing SIA timing in Pakistan. (A) Comparing total expected infections in 2018–2021 (black, SE shading) under different SIA policies shows that November minimizes measles burden by taking advantage of susceptible buildup over the low-transmission season (blue region). As a result however, delays into the 2018–2019 high-transmission season (red, SE shading) are costly. (B) Model projections for pre– (April) and post–low-transmission season (November) SIAs (black dashed lines) demonstrate the tradeoff between 2018 and 2020 outbreak control. As a result, 2017 measles burden also plays a significant role in timing optimization. (C) Extending the model to the province level allows us to compare April and November SIA timing subnationally. Preference for April is mapped in red while preference for November is mapped in purple; gray provinces [Federally Administered Tribal Areas and Azad Kashmir, representing less than $5\%$ of Pakistan’s total population (20)] are inaccessible to health workers while white areas indicate disputed territory. Heterogeneity in the 2017 laboratory-confirmed measles cases per 100,000 (indicated) is reflected in the timing optimization.