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Malaria is a serious and life-threatening mosquito-borne disease that every year affects over 200 million individuals and causes 400,00 deaths. An additional 0.5 billion people globally are at risk of malaria infection. The unique role of climate in influencing malaria transmission outcomes across individual communities by acting on multiple dimensions of the malaria vector and parasite ecology has been long recognized. This recognition has led to the development of explicit and implicit climate-driven models of malaria transmission designed to better understand and predict patterns of population vulnerability and uncover potential challenges to malaria control. However, existing implicitly-forced process-based models of malaria have relied on indirectly correlated predictors of malaria transmission, instead of direct relationships among climate, vector entomology and parasite ecology. The lack of biologically-motivated modulation of malaria transmission compromises meaningful interpretation of the ecological role played by climate in malaria transmission. Similarly, the specific influence of climate on vector and parasite dynamics is obscured, limiting the utility of these simple and powerful model forms. This dissertation focuses on elaborating the direct ecological relationships between climate, the malaria vector and parasite to enhance the ecological utility of lower dimensional mathematical models of malaria transmission. In the 2nd chapter of this thesis, a climate-driven entomological modeling framework is developed, consisting of a simple dynamic model that explicitly tracks malaria transmission in human populations and implicitly represents the malaria force of infection through climate-regulation of multiple aspects of the Entomological Inoculation Rate (EIR). The EIR-model construct is found to accurately capture seasonal malaria dynamics under free-simulation, when coupled to local rainfall and temperature climatology across multiple local regions in Rwanda. Furthermore, local rainfall modulation of sub-adult survivorship is found to be a more critical driver of seasonal malaria dynamics than other environmentally-regulated components of EIR. In chapter 3, the model framework is paired with data assimilation methods to dynamically simulate interannual malaria incidence in Rwanda, infer parameters of malaria transmission and validate the malaria model. Results indicate that the implicitly-forced transmission model is able to reproduce interannual and seasonal malaria incidence at regional and local scales. However, accuracy of model description of malaria incidence is more varied at the more resolved local level. Intensified malaria control efforts during the later years of the study are suspected to increase the discrepancy between the vector and parasite dynamics dictated by climate and the observed widespread decline in malaria activity in the region. Nonetheless, the parameters of transmission identified across populations in Rwanda were comparable to existing estimates of malaria, further validating the transmission model and data assimilation approach. For the 4th chapter, a state-of-the-art Bayesian inference forecasting system for the EIR-model framework is developed, as well as a multi-model forecasting system consisting of weighted-average predictions from the dynamic malaria model and historical expectance predictions. Retrospective forecasts of four years of malaria data from 42 regions in Rwanda indicate that the model-inference forecasting system predicts malaria incidence more accurately than historical expectance alone, particularly for predictions with 1-6 weeks lead times. Although slightly less skillful, the multi-model system was found to substantively enhance forecast reliability of the EIR-model system, bolstering the utility of the malaria model as a robust forecaster of malaria in the region. The concluding chapter describes areas for improving the specification of the parsimonious model construct. The need to include malaria control coverage data as exogenous forces of transmission, non-climate drivers and alternate sources of climate exposure that support transmission are highlighted. Future works on forecast calibration needed to improve model performance for real-time prediction are also detailed. In addition, areas for application within information systems for evaluating malaria risk and for advising malaria control efforts, specifically relating to local variability in malaria burden and characterization of entomological drivers of local malaria, are identified and further discussed. The model systems developed in this thesis advance the capabilities of lower dimension dynamic models to connect the ecological drivers of malaria transmission to climate variation. Such process-based formulations could provide better climate-driven descriptions of malaria, while limiting model complexity, without compromising representation of entomological relationships that are potentially valuable for improved understanding and control of malaria transmission.

Development of liposome formulation of an amphiphilic anticancer peptide using the ANTS/DPX leakage assay. The effects of lipid composition on the liposomes' resistance to an amphiphilic cyclic peptide c[KS.S.S.KWL W] were studied by the ANTS/DPX leakage assay. One or more unsaturated acyl chains in the phospholipids, small phospholipid headgroup size, the presence of cholesterol, and the presence of PEG-lipid were demonstrated as critical parameters to stabilize the liposome membrane. A liposome formulation of the peptide comprising POPE/POPC/cholesterol/C16 mPEG 2000 ceramide (20.8:31.2:40:8, mol%) was thereby developed with a peptide-encapsulation efficiency of 47.8%. The liposomal cyclic peptide exhibited dose-dependent toxicity to MCF7 human breast cancer cells and stability under incubation. Design, construction and in vitro characterization of a hydrazone-based convertible liposomal system for anticancer drug delivery. A novel PEG-lipid, PEG2ooo-Hz-DHG, with an acid-labile hydrazone linker between the PEG2ooo head group and the lipidic DHG moiety was synthesized. PEG2000-Hz-DHG was relatively stable at normal physiological pH 7.4, but hydrolyzed more quickly at tumor interstitium pH 6.5-7.0 and endosomal/lysosomal pH 5.0. A novel pH-sensitive "Convertible Liposome System" (CLS) was constructed comprising PEG2ooo-Hz-DHG, positively charged lipid DOTAP, and the zwitterionic phospholipid POPC (8:15:77, mol%). CLS converted from neutrally charged "stealth" liposome to positively charged liposome at tumor interstitual pH owing to the hydrolysis ofPEG2ooo-Hz-DHG. The doxorubicin-encapsulated CLS that had been pre-incubated at pH 6.5 for 30 h exhibited more intensive binding and higher toxicity to Bl6-Fl0 murine melanoma and MDA-MB-435S human breast cancer cells than doxorubicin encapsulated in pH-insensitive stealth liposome.

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The rise of vector-borne diseases transmitted by the Aedes spp. mosquitoes is attributed, in part, to the dramatic rates of contemporary urbanization. Over the past 30 years, scientists have developed a wealth of knowledge around the drivers of heterogeneity in Aedes-borne disease risk within and between cities. However, in current Aedes-borne disease research, characterizations of “urban” are oversimplified, with the built environment and social institutions of the city often relegated to a background context. To mitigate the spread of Aedes-borne diseases, under the dual global pressures of urbanization and climate change, there is an urgent need to incorporate the multi-dimensionality of urban systems in driving Aedes-borne disease risk. This dissertation is anchored in socio-ecological sciences, and tailored to the complexities of urban eco-epidemiological dynamics. Herein, theory and methods from ecology, epidemiology, geography, and urban science are synthesized to develop and implement a novel urban systems approach for Aedes-borne diseases. T he first chapter establishes the theoretical foundation for this approach, integrating concepts from three bodies of knowledge: “cities as complex adaptive systems”, hierarchical patch systems theory, and relational geography. In the framework, cities are conceptualized as hierarchically-structured patches of different land uses and characteristics. Patch composition determines localized disease risk, while patch configuration and connectivity contribute to emergent patterns of disease risk and spread. Complexity is added to the system by considering the cross-scale and dynamical processes occurring within a city. Furthermore, the framework establishes how individual and collective social structures interact with the biophysical landscape to generate risk. The empirical research for this dissertation uses a range of data sets, from open source remotely-sensed environmental data and census-derived socio-economic data to fine-scale household survey and entomological data. Chapter 2 is carried out at the scale of the city, and examines how extreme climate and weather conditions in Colombia differentially affects the onset of peak dengue incidence for urban settlements with varying landscape and socio-economic properties. Using Bayesian spatio-temporal hierarchical models we discovered that extreme temperature anomalies (10–12°C) result in an earlier onset of dengue risk for high-elevation compared to low-elevation settlements, which experience increases in dengue risk two to four months after extreme temperature anomalies. Furthermore, the risk of dengue after extremely dry conditions is higher and extends for a longer duration in highly urban areas compared to areas with a low proportion of the population living in urban settlements. These findings indicate the potential for landscape-specific dengue early warning and forecasting frameworks. Chapter 3 is based in a mid-sized, rapidly growing city (Ibagué) embedded within the Andes Mountains of Colombia, and establishes homogenous urban typologies of dengue risk. Measuring dengue incidence across census block and higher order urban sections, we show that distinctive signatures of incidence can emerge from interactions between heterogeneous socio-environmental composition and configuration. Finally, Chapter 4 is carried out at the household and neighborhood scale in Ibagué, and examines how water governance and neighborhood-based social processes drive household-level dengue risk. We documented the role of collective societal memory of water scarcity in fostering a culture of water storage. We determined that neighborhood-based metrics of social cohesion do not necessarily translate to dengue household preventative practices and that to scale dengue prevention strategies, public health agencies may consider interventions rooted in “place-making” to foster linkages between perceived neighborhood-level versus household-level risk. This dissertation demonstrates how trans-disciplinary research bridges urban science, ecology, and public health research communities, and provides a pathway for mosquito-borne disease interventions to be incorporated into national-level early warning systems as well as community-based initiatives that collectively, set cities on more healthy and sustainable trajectories for the 21st century.

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