Browsing by Author "Azam, James Mba"

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    Assessment of a measles outbreak response vaccination campaign, and two measles parameter estimation methods
    (Stellenbosch : Stellenbosch University, 2018-03) Azam, James Mba; Pulliam, Juliet R. C.; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences.
    ENGLISH ABSTRACT : Measles is highly transmissible, and is a leading cause of vaccine-preventable death among 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 that the 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 successful in averting many potential cases.
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    Modelling outbreak response intervention strategies for decision-making
    (2022-03-07) Azam, James Mba; Pulliam, Juliet R.C.; Ferrari, Matthew J.
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    Socioeconomic inequalities in food insecurity and malnutrition among underfive children : within and between-group inequalities in Zimbabwe
    (BioMed Central, 2020-08-04) Lukwa, Akim Tafadzwa; Siya, Aggrey; Zablon, Karen Nelwin; Azam, James Mba; Alaba, Olufunke A.
    Background: Food insecurity and malnutrition in children are pervasive public health concerns in Zimbabwe. Previous studies only identified determinants of food insecurity and malnutrition with very little efforts done in assessing related inequalities and decomposing the inequalities across household characteristics in Zimbabwe. This study explored socioeconomic inequalities trend in child health using regression decomposition approach to compare within and between group inequalities. Methods: The study used Demographic Health Survey (DHS) data sets of 2010\11 and 2015. Food insecurity in under-five children was determined based on the WHO dietary diversity score. Minimum dietary diversity was defined by a cut- off point of > 4 therefore, children with at least 3 of the 13 food groups were defined as food insecure. Malnutrition was assessed using weight for age (both acute and chronic under-nutrition) Z-scores. Children whose weight-for-age Z-score below minus two standard deviations (− 2 SD) from the median were considered malnourished. Concentration curves and indices were computed to understand if malnutrition was dominant among the poor or rich. The study used the Theil index and decomposed the index by population subgroups (place of residence and socioeconomic status). Results: Over the study period, malnutrition prevalence increased by 1.03 percentage points, while food insecurity prevalence decreased by 4.35 percentage points. Prevalence of malnutrition and food insecurity increased among poor rural children. Theil indices for nutrition status showed socioeconomic inequality gaps to have widened, while food security status socioeconomic inequality gaps contracted for the period under review. Conclusion: The study concluded that unequal distribution of household wealth and residence status play critical roles in driving socioeconomic inequalities in child food insecurity and malnutrition. Therefore, child food insecurity and malnutrition are greatly influenced by where a child lives (rural/urban) and parental wealth.