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Pharmaceutical interventions (PI) and non-pharmaceutical interventions (NPI) are relevant for controlling and mitigating the transmission of respiratory tract infectious diseases such as influenza and COVID-19. The impact of these strategies on children, workers, and low-middle-income countries was explored (chapter 2-6). Chapter 2 used a scoping review to describe the application of digital technology in improving hand hygiene among children in educational settings. Three main types were identified, including computer games (n=2), videos (n=8), and video cameras (n=2) which were found to be effective when used over a short time. Chapter 3 was a systematic review to assess the evidence of economic evaluations against influenza among workers or the workplace setting. Out of the twenty-four articles included, 21 were cost-benefit analyses, and 23 used PIs (primarily vaccination). All but two studies reported that interventions at the workplace were cost-saving and cost-effective. Chapter 4 characterized the transmission dynamics of COVID-19 in Ghana by estimating the time-varying reproduction number (Rt) at the national and regional levels using publicly available data from March 2020 to December 2021. The association between Rt and implemented interventions was also explored. Ghana experienced four pandemic waves with epidemic peaks in July 2020 and January, August, and December 2021. The epidemic peak was the highest nationwide in December 2021, with Rt ≥2. Rt decreased from above 1 when schools reopened in January 2021 to below 1 after vaccination rollout in March 2021. Chapter 5 utilized a homogenous-mixing compartmental model to simulate the transmission dynamics of COVID-19 and assessed the impact of increasing vaccination rate to achieve 50% coverage. The findings suggest

Measuring selective pressures shaping the evolution of viral populations is important for preventing and controlling the spread of disease, as well as for understanding evolutionary processes in general. Traditional methods for detecting and quantifying selection assume that a single segregating allele is under constant selection in a population of constant size. However, viruses frequently violate these assumptions due to (i) their high mutation rates and (ii) their complex epidemiological dynamics. We examine the effect of these factors using computational models describing evolution at protein-coding regions, under various population dynamics. In Chapter 2 we show, assuming population sizes are constant, that linkage-induced interference between segregating mutations distort commonly used statistics such as dN/dS and the McDonald-Kreitman (MK) statistic. We propose three alternative statistics to detect the effect of background selection, hitch-hiking and clonal interference. In Chapter 3, we examine selection acting in the context of an epidemiological multi-strain SIRS model, by explicitly modelling the effect of cross-immunity between related strains, competition for susceptible hosts, and decaying host immunity. By studying the probability of antigenic reversion, we show that time-varying antigenic selection mediated by host immunity has a qualitatively different effect from constant selective constraint, which is observable from changing frequencies at antigenic sites over time. In Chapter 4, we apply both of these methods to avian influenza, demonstrating their utility in comparing selection between different lineages. In combination, these methods allow us to distinguish between different forms of selection, which may allow us to discriminate between potential biological mechanisms shaping viral populations.