<p dir="ltr">Infectious diseases are caused by a multitude of organisms, ranging from viruses to bacteria, from parasites to fungi, and can be passed directly or indirectly from one person to another. Further, they continue to be a leading cause of death, especially in low-resource countries, thereby emphasizing the need for continued investigation. Understanding transmission of such diseases is vital as management or prevention of outbreaks through detection, reporting, isolation, and case management are ever-evolving. One way by which scientists can study infectious diseases is through a combination of epidemiological, genomic, and evolutionary biology approaches. This doctoral research occurred precisely at this interface, spanning across the fields of genomics, molecular biology, and epidemiology, as applied to the study of infectious disease dynamics of two separate pathogen systems (protozoan and virus).</p><p dir="ltr">The first half of this research (Chapters 1 + 2) involved the implementation of SARS-CoV-2 genomic sequencing and surveillance at Purdue University. Through this investigation in a university setting (Chapter 1), this work identified relevant variants of concern in hundreds of newly sequenced viral genomes and compared variant temporal trends with other similar university settings using publicly available data. Further phylodynamic analysis of Gamma (P.1) genomes from campus revealed multiple introductions into the Purdue community, predominantly from states within the United States. A second study (Chapter 2) assessed the transmission of variants over the course of an entire academic year from 2021-2022 in Purdue’s highly vaccinated community. This research described the rapid transition from Delta to Omicron variants and investigated variant introduction events into the campus. This comprehensive analysis showed that robust surveillance programs coupled with viral genomic sequencing and phylogenetic analysis can provide critical insights into SARS-CoV-2 spread and can help inform mitigation strategies for future pandemics.</p><p dir="ltr">The latter half of this body of research (Chapters 3 + 4) focused on malaria, which is a disease caused by <i>Plasmodium </i>species<i> </i>parasites and transmitted to humans through the bites of infected mosquitoes. The first investigation explored diagnostic accuracy metrics across a malaria transmission gradient in Zambia through a comparison of the diagnostic performance of Rapid Diagnostic Tests (RDT) and Light Microscopy (LM) with photo-induced electron transfer polymerase chain reaction (PET-PCR) as the gold standard using 2018 Malaria Indicator Survey (MIS) data. Results suggested that RDTs and LM both performed well across a range of transmission intensities, but low parasitaemia infections can affect accuracy. This suggests that more sensitive tools should be utilized to identify the last cases as Zambia moves towards malaria elimination. In addition to diagnostic metrics, preventing disease is also crucial for infectious diseases, and vaccines present one mechanism by which this can be done. Research to develop a malaria vaccine with sustained high efficacy has spanned decade. However, the process has proven to be challenging, with several vaccine candidates having advanced to early-stage trials, but only a few demonstrating sustained efficacy in clinical testing. The goal of the last investigation (Chapter 4) was to shed light on the diversity of <i>Plasmodium falciparum </i>antigens which could be considered when developing future malaria vaccines. Results of evolutionary and genomic analyses of Whole-Genome-Sequences from Zambia and other countries in Africa suggest that conserved merozoite antigens and/or transmission-blocking antigens should be prioritized when developing future malaria vaccines.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/24729924 |
| Date | 06 December 2023 |
| Creators | Ilinca I Ciubotariu (17552118) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/INVESTIGATING_INFECTIOUS_DISEASE_DYNAMICS_USING_PATHOGEN_GENOMICS_IN_APPLIED_PUBLIC_HEALTH_SETTINGS/24729924 |
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