Abstract
.We report on and evaluate the process and findings of a real-time modeling exercise in response to an outbreak of measles in Lola prefecture, Guinea, in early 2015 in the wake of the Ebola crisis. Multiple statistical methods for the estimation of the size of the susceptible (i.e., unvaccinated) population were applied to weekly reported measles case data on seven subprefectures throughout Lola. Stochastic compartmental models were used to project future measles incidence in each subprefecture in both an initial and a follow-up iteration of forecasting. Measles susceptibility among 1- to 5-year-olds was estimated to be between 24% and 43% at the beginning of the outbreak. Based on this high baseline susceptibility, initial projections forecasted a large outbreak occurring over approximately 10 weeks and infecting 40 children per 1,000. Subsequent forecasts based on updated data mitigated this initial projection, but still predicted a significant outbreak. A catch-up vaccination campaign took place at the same time as this second forecast and measles cases quickly receded. Of note, case reports used to fit models changed significantly between forecast rounds. Model-based projections of both current population risk and future incidence can help in setting priorities and planning during an outbreak response. A swiftly changing situation on the ground, coupled with data uncertainties and the need to adjust standard analytical approaches to deal with sparse data, presents significant challenges. Appropriate presentation of results as planning scenarios, as well as presentations of uncertainty and two-way communication, is essential to the effective use of modeling studies in outbreak response.
Highlights
Between January 23 and April 4, 2015, 284 cases of measles were identified in Lola, a prefecture of approximately 180,000 people in southeast Guinea within the Nzerekoreregion (Figure 1)
The first was based on the work by Orenstein et al.,[5] which uses the proportion of cases occurring in vaccinated individuals and vaccine efficacy (VE) to estimate the proportion of the population vaccinated (PPV) and immune, based solely on case data
Fitting N’Zoo incidence data for scenarios with R0 of 8, 12, and 18, using the time-series susceptible–infected– recovered model (TSIR) method, we found that assuming a lower R0 led to a prediction of a larger epidemic than that for higher R0 values, as this required a higher level of susceptibility to explain observed cases (Figure 2, box plots [c]–[e])
Summary
Between January 23 and April 4, 2015 (weeks 4–13 of the year), 284 cases of measles were identified in Lola, a prefecture of approximately 180,000 people in southeast Guinea within the Nzerekoreregion (Figure 1). Given healthcare system disruptions caused by the Ebola outbreak, there was concern that reductions in measles vaccination may have increased susceptibility in the younger population.[1] A supplementary immunization activity, aimed at decreasing measles susceptibility, was planned for Guinea in 2014. This campaign was interrupted by the Ebola outbreak and never reached Lola prefecture. Within the Nzerekoreregion of Guinea, measles vaccination coverage has been relatively low (reaching only 61% of children aged 9–59 months in 20122), suggesting that a large proportion of the population aged less than 5 years was susceptible to a measles outbreak. These factors raised concerns that these 284 reported cases heralded a large and potentially deadly measles outbreak (e.g., the estimated case–fatality ratio of measles cases in Africa was 3.7%).[3]
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