Assessment of a measles outbreak response vaccination campaign, and two measles parameter estimation methods

[摘要] ENGLISH ABSTRACT : Measles is highly transmissible, and is a leading cause of vaccine-preventable deathamong children.Consequently, it is regarded as a public health issue worldwide and has been targeted for elimination by 5 out of the 6 WHO regions by 2020, the exception being the WHO Africa region. The hope of achieving this target, however, seems bleak as regular outbreaks continue to occur. Data from these outbreaks are useful for pursu- ing important questions about measles dynamics and control. This thesis is structured to investigate two questions: the first is on how well the time series susceptible-infected- recovered (TSIR) model and removal method perform when they are used to estimate parameters from poor quality data on measles epidemics. We simulate stochastic epidemics for four spatial patches, resembling data that are collected in low-income coun- tries where resources are limited for properly collecting and reporting data on measles epidemics. We then obtain from the simulated data sets, the size of the initial susceptible population S0, and the basic reproductive ratio R0 - for the TSIR; and S0, and either the effective reproduction number Re f f , or the basic reproductive ratio R0 - for the re- moval method, depending on the simulation assumptions. To assess performance, we quantify the biases that result when we tweak some of the simulation assumptions and modify the data to ensure it is in a form usable for each of the two methods. We find thatthe performance of the methods depends on the assumptions underlying the data gen-eration process, the degree of spatial aggregation, the chosen method of modifying the data to put it in a form usable for the estimation method, and the parameter being fitted. The removal method S0 estimates at the patch level are almost unbiased when the pop- ulation is naive, but are biased when aggregated to the population level, whether the population is initially naive or not. Furthermore, the removal R0 and Re f f estimates are generally biased. The TSIR model, on the other hand, seems more robust in estimating both S0 and R0 for non-naive populations. These findings are useful because they give us an idea of the biases in the fits of these methods to actual data of the same nature as the simulated epidemics.For the second question, we assess the impact of an outbreak response vaccination campaign which was organised in reaction to a measles outbreak in an all-boys high school in Stellenbosch, South Africa. We achieve this by formulating a discrete stochastic susceptible-exposed-infected-recovered (SEIR) model with daily time-steps, ignoring births and deaths. Using the model, we analyse multiple scenarios that allow us to estimate the cases averted, and to predict the cases remaining until the epidemic ended, and the time frame within which those cases would occur. Summarizing across scenarios, we estimate that a median of 255 cases (range 60 − 493) were averted. Also, a median of 15 remaining cases (range 1 − 33), and a median of 4 remaining weeks (range 1 − 16) were expected until the epidemic ended. We conclude that the campaign was successfulin averting many potential cases.

[发布机构] Stellenbosch University