| dc.description.abstract | The geographic distribution of infectious diseases has received considerable attention after several dramatic emergence events around the world. Here, I took the full advantages of several approaches available in a single toolbox to examine geographic distribution and spread of several neglected and zoonotic diseases across the world. These approaches included geographic information system, remote sensing, ecological niche modeling, and phylogeography of disease outbreaks. The results assessed and evaluated several diseases based on their public health importance, data availability, and geographic dimension. These diseases included major neglected tropical diseases of potential public health worldwide (e.g. mycetoma, and leishmaniasis), zoonosis (e.g. Rift Valley Fever), and livestock diseases (e.g. Bluetongue). In 2013, the World Health Organization (WHO) recognized mycetoma as one of the neglected tropical conditions due to the efforts of the mycetoma consortium. This same consortium formulated knowledge gaps that require further research. One of these gaps was that very few data are available on the epidemiology and transmission cycle of the causative agents. Previous work suggested a soil-borne or Acacia thorn-prick-mediated origin of mycetoma infections, but no studies have investigated effects of soil type and Acacia geographic distribution on mycetoma case distributions. In chapter 1, the study mapped risk of mycetoma infection across Sudan and South Sudan using ecological niche modeling (ENM). I developed ENMs based on case occurrences, and digital GIS data layers summarizing soil characteristics, land-surface temperature, and greenness indices to provide a rich picture of environmental variation across Sudan and South Sudan. ENMs were calibrated in known endemic districts and transferred countrywide; model results suggested that risk is greatest in an east-west belt across central Sudan. Visualizing ENMs in environmental dimensions, mycetoma occurs under diverse environmental conditions. The study also compared niches of mycetoma and Acacia trees, and could not reject the null hypothesis of niche similarity. This study revealed contributions of different environmental factors to mycetoma infection risk, identified suitable environments and regions for transmission, signaled a potential mycetoma-Acacia association, and provided steps towards a robust risk map for the disease. In chapter 2, I studied another neglected tropical disease in Libya where political instability prevent active surveillance of cutaneous leishmaniasis (CL). CL ranks among the tropical diseases least known and most neglected in Libya. World Health Organization reports recognized associations of Phlebotomus papatasi, Psammomys obesus, and Meriones spp., with transmission of zoonotic cutaneous leishmaniasis (ZCL; caused by Leishmania major) across Libya. Here, the study map risk of ZCL infection based on occurrence records of L. major, P. papatasi, and four potential animal reservoirs (Meriones libycus, Meriones shawi, Psammomys obesus, and Gerbillus gerbillus). Ecological niche models identified limited risk areas for ZCL across the northern coast of the country; most species associated with ZCL transmission were confined to this same region, but some had ranges extending to central Libya. All ENM predictions were significant based on partial ROC tests. As a further evaluation of L. major ENM predictions, the study compared predictions with 98 additional independent records provided by the Libyan National Centre for Disease Control (NCDC); all of these records fell inside the belt predicted as suitable for ZCL. The study tested ecological niche similarity among vector, parasite, and reservoir species and could not reject any null hypotheses of niche similarity. Finally, I tested among possible combinations of vector and reservoir that could predict all recent human ZCL cases reported by NCDC; only three combinations could anticipate the distribution of human cases across the country. Further in chapter 3, I developed a comprehensive occurrence data set to map the current distribution, estimate the ecological niche, and explore the future potential distribution of BTV globally using ecological niche modeling and based on diverse future climate scenarios from general circulation models (GCMs) for four representative concentration pathways (RCPs). The broad ecological niche and potential geographic distribution of BTV under present-day conditions reflected the disease’s current distribution across the world in tropical, subtropical, and temperate regions. All model predictions were significantly better than random expectations. As a further evaluation of model robustness, I compared our model predictions to 331 independent records from most recent outbreaks from the Food and Agriculture Organization Emergency Prevention System for Transboundary Animal and Plant Pests and Diseases Information System (EMPRES-i); all were successfully anticipated by the BTV model. Finally, I tested ecological niche similarity among possible vectors and BTV, and could not reject hypotheses of niche similarity. Under future-climate conditions, the potential distribution of BTV was predicted to broaden, especially in central Africa, United States, and western Russia. Finally, in chapter 4, I used phylogenetic analyses to understand the demographic history of RVFV populations, using sequence data from the three minigenomic segments of the virus. I used phylogeographic approaches to infer RVFV historical movement patterns across its geographic range, and to reconstruct transitions among host species. Results revealed broad circulation of the virus in East Africa, with many lineages originating in Kenya. Arrival of RVFV in Madagascar resulted from three major waves of virus introduction: the first from Zimbabwe, and the second and third from Kenya. The two major outbreaks in Egypt since 1977 possibly resulted from a long-distance introduction from Zimbabwe during the 1970s, and a single introduction took RVFV from Kenya to Saudi Arabia. Movement of the virus between Kenya and Sudan, and CAR and Zimbabwe was in both directions. Viral populations in West Africa appear to have resulted from a single introduction from Central African Republic. Finally, host transition analysis identified both humans and livestock as natural hosts of RVFV. The overall picture of RVFV history is thus one of considerable mobility, and dynamic evolution and biogeography, emphasizing its invasive potential, potentially more broadly than its current distributional limits. The results raised by all these analyses offered the potential capacity of ecological modeling and phylogeographic approaches to understand the potential distribution and spread of different disease systems and open the possibilities for their applications in understanding disease epidemiology for surveillance and control efforts of several other disease systems emerged recently across the world. | |
