Previous investigations into human infectious diseases have revealed RNA viruses as major etiological agents. Given the recent rate of newly detected human-infective RNA viruses such as severe acute respiratory syndrome coronavirus (SARS-CoV), SARS-CoV-2, Middle East respiratory syndrome coronavirus, and Bundibugyo ebolavirus, targeting virus discovery in high-risk regions, characterizing viruses with the greatest likelihood of spreading and establishing sustained infection in humans would benefit better preparedness for future outbreaks. There is a lack of evidence on determinants of spatio-temporal patterns in the discovery of human-infective RNA viruses, though previous studies have attempted to identify hotspots of emerging infectious diseases caused by various pathogens. There are also no quantitative studies exploring predictors of geographical extent and the disappearance for all currently known human-infective RNA viruses. This thesis aimed to address the following gaps. 1. Identifying predictors discriminating between areas with and without discovery of human-infective RNA viruses and predicting discovery hotspots, at both global and regional scales. Predictors identified include socio-economic, climatic, land use, and biodiversity variables. 2. Prediction of the geographical extent and the disappearance of human-infective RNA viruses, using features such as taxonomy, virus structure, transmission mode, host range, origin, and clinical presentation. 3. Taking SARS-CoV-2 as an example, investigating how predictors related to demographics, socioeconomics, travel, healthcare, co-morbidities, readiness, geography, COVID-19 testing, and interventions have affected the epidemic of the disease it caused-coronavirus disease 2019 (COVID-19)-between countries in the WHO African Region. In order to address the gaps outlined above, I firstly geocoded the first reports of 223 human-infective RNA viruses at the global scale. Using a Poisson boosted regression tree (BRT) model, I identified GDP growth, GDP, and urbanization as top predictors of virus discovery, and predicted discovery hotspots including both historical hotspots-eastern North America, Europe, central Africa, eastern Australia, and north-eastern South America, and new hotspots-East and Southeast Asia, India, and Central America. Stratified analyses suggested discovery of vector-borne viruses and strictly zoonotic viruses was more correlated with climatic variables and biodiversity, whereas the discovery of non-vector-borne viruses and human-transmissible viruses was more strongly correlated with GDP and urbanization. Next, I focused on comparisons of the specific predictors of RNA virus discovery in three different regions with different GDP-United States, China, and Africa. A similar methodology as the global analysis was used on each region separately, the results showed that predictors such as GDP and land use continued to be top predictors in three regions, but climate and biodiversity variables were consistently less important predictors than at a global scale. To identify predictors of the geographical extent and the disappearance (no record of infection in the literature for the past ten years or more), I collated information for 223 human-infective RNA viruses on their geographical extents and persistence in causing human infections from peer-reviewed literature. By fitting Bernoulli BRT models, I observed that viral features that predicted wide geographic extent included transmissibility between humans, a +ssRNA genome, narrow host range [i.e. infecting humans only or humans and other non-human primates (NHP) only], and having a reservoir host in a NHP. Viruses were more likely to disappear if they were incapable of transmission between humans, have had a localised geographic extent, a dsRNA genome, were non-pathogenic and non-fatal, were firstly discovered through active discovery programmes rather than passive investigation of the aetiology, and were transmitted by vectors and direct contact. Results for both geographical extent and virus disappearance did not change after factoring out reporting effort. I concluded that multiple characteristics determined the geographical extent and disappearance of human-infective RNA viruses; however, transmission mode and structure were consistently the most important predictors of the geographical extent and disappearance of human-infective RNA viruses. Host range was an important predictor of geographical extent, though less important for disappearance. Geographical extent, clinical presentation and discovery process all contributed to the probability of a virus disappearing. To understand the differences between epidemics of COVID-19 between countries of the WHO African Region, I selected the timing of the first case and the mortality rate in the first and second waves as the three outcomes. By applying a series of statistical models including Cox proportional hazards regression models, generalized linear mixed models and multinomial logistic regression models, I found that COVID-19 in Africa arrived earlier and caused greater mortality in countries with more pre-pandemic international connectivity and a more urban population. Mortality was exacerbated by high HIV prevalence. The stringency and timing of government restrictions on behaviour were not associated with a lower per capita mortality rate. A more urban population and a higher infectious disease resilience score were associated with more stringent restrictions and/or a higher per capita mortality rate. The predictor set for the first and second waves were similar, and first wave per capita mortality was a significant predictor of second wave per capita mortality. In summary, studies in this thesis showed that there were variations in predictors of discovery both between virus types and geographical regions, and identified high-risk regions for virus discovery beyond their historical extent. The studies also provided proof-of-principle for the prediction of attributes such as mortality, geographical extent, and disappearance for new human-infective RNA viruses. These results help identify priority regions for investment in surveillance systems for new human-infective viruses, and to make risk assessments once they have emerged.