Using simulated infectious disease outbreaks to guide the design of individually randomized vaccine trials

Madewell, Z. J.; Pastore y Piontti, A.; Zhang, Q.; Burton, N.; Yang, Y.; Longini, I. M.; Halloran, M. E.; Vespignani, A.; Dean, N. E.

Background/Aims: Novel strategies are needed to make vaccine efficacy trials more robust given the uncertain epidemiology of outbreaks. Spatially resolved mathematical and statistical models can help investigators identify sites at highest risk of future transmission and prioritize these for inclusion in trials. Models can also characterize the uncertainty in whether transmission will occur at a site, and how nearby or connected sites may have correlated outcomes. A structure is needed for how trials can use models to address key design questions, including how to prioritize sites, the optimal number of sites, and how to allocate participants across sites. Methods: We illustrate the added value of models using the motivating example of Zika vaccine trial planning during the 2015-2017 Zika epidemic. We used a stochastic, spatially resolved, agent-based transmission model (GLEAM) to generate 1,142 epidemics and site-level incidence at 100 high-risk sites in the Americas. We considered several strategies for prioritizing sites (average site-level incidence of infection across epidemics, median incidence, probability of exceeding 1% incidence), selecting the number of sites, and allocating sample size across sites (equal enrollment, proportional to average incidence, proportional to rank). To evaluate each design, we stochastically simulated trials in each hypothetical epidemic by drawing observed cases from site-level incidence data. Results: When constraining the overall trial sample size, the optimal number of sites represents a balance between prioritizing highest-risk sites and having enough sites to reduce the chance of observing too few endpoints. The optimal number of sites remained roughly constant despite varying the targeted number of events, although it is necessary to increase the total sample size to achieve the desired power. Though different ranking strategies returned different site orders, they performed similarly with respect to trial power. Instead of enrolling participants equally from each site, investigators can allocate participants proportional to projected incidence, though this did not provide an advantage in our example because the top sites had a roughly similar risk profile. Sites from the same geographic region may have similar outcomes, so optimal combinations of sites may be those that are more geographically dispersed, even when these are not the highest ranked sites. Conclusions: Mathematical and statistical models may assist in the design of successful vaccination trials by capturing uncertainty and correlation in future transmission. Although many factors affect site selection, such as logistical feasibility, models can help investigators optimize site selection and the number and size of participating sites.