Doctoral Degrees (Epidemiology and Biostatistics)

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Browsing Doctoral Degrees (Epidemiology and Biostatistics) by browse.metadata.advisor "Nyasulu, Peter Suwirakwenda"

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    Combining sexual behavioural survey data, phylodynamics and agent-based models towards a unified framework for HIV prevention research
    (Stellenbosch : Stellenbosch University, 2021-12) Niyukuri, David; Nyasulu, Peter Suwirakwenda; Delva, Wim; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Global Health. Epidemiology and Biostatistics.
    ENGLISH SUMMARY: Background: Sub-Saharan African countries carry a disproportionate burden of the Human Immunodeficiency Virus (HIV) infection. Thus, beyond estimation tools which are used to produce HIV epidemic estimates, there is a need for simulation tools to understand the structure and the dynamics of sexual networks, and HIV transmission underlying factors. This can help to design and implement effective interventions. These simulation tools should be able to take advantage of existing multi-source data. Furthermore, with such multi-data generation tools, we can be able to assess new methodologies and the accuracy of different inferences made from available real-world data. Methods: We developed a unified simulation framework which combines in one model world the simulation of sexual dynamic network, HIV transmission, and between-host viral evolution for infected individuals. We used that simulation framework to run a benchmark study to infer age-mixing patterns in HIV transmission in different sequence missingness scenarios. We used transmission clusters from phylogenetic trees and compute proportions of pairings between men and women who were phylogenetically linked across different age groups. We assessed the usability of our simulation framework through a calibration study. We focused on fitting the simulation framework to summary features from multiple data sources to increase the accuracy of estimates. The case study was the estimation of determinants of HIV transmission network, namely age-mixing patterns in sexual partnerships, distribution of onward transmission, and temporal trend of HIV incidence. We also used simulated polymerase and protease viral data on same transmission network with Simpact Cyan to check in the phylogenetic results, mainly root-to-tip regression, and transmission clusters. Results: The proof of concept of the appropriateness of the modelling framework was determined by the ability to capture HIV transmission dynamics, and the temporal trends of branching times of a phylogenetic tree built from simulated viral sequence data. For age-mixing patterns in HIV transmission, the results of the simulation suggested that proportions of men/women linked to women/men across different age groups, together with the mean and standard deviation of age difference can unveil age-mixing patterns in HIV transmission networks. For the calibration study, the results showed that the relative errors between true benchmark values and post calibration values of the determinants of HIV transmission network were relatively close in the three calibration scenarios. In post-calibration simulation age-mixing patterns and the distribution of onward HIV transmission had relatively small error values, but the age-gender strata temporal trend of incidence was poorly captured. The root-to-tip regression of phylogenetic trees from protease and polymerase data simulated on the same HIV transmission network showed that the dispersion of the genetic distance with branching and sampling times was explained at 95% and 49% for polymerase and protease data, respectively. For transmission clusters, we could still get at least 90% of individuals within big the size clusters if we use polymerase or protease viral sequence data. This showed that even with the short sequences we could still get useful epidemiological data. Conclusion: The unified framework could be used as a data generation method for benchmark studies. This is so despite the simplistic assumption for HIV viral evolutionary dynamic through consideration of host evolution only. These methods could also help to investigate the effect of sexual dynamic network on HIV transmission and estimate age related individual-level features affecting the HIV transmission dynamic. Furthermore, this simulation framework could i) contribute to the advancement of phylogenetic-based inference methodology; and ii) advance epidemiological methods focusing on combining epidemiological data, sexual behaviour data, viral phylodynamics, and agent-based simulation models.