Population-Based Surveillance for Invasive Pneumococcal Disease in Homeless Adults in Toronto

BACKGROUND: Identification of high-risk populations for serious infection due to S. pneumoniae will permit appropriately targeted prevention programs. METHODS: We conducted prospective, population-based surveillance for invasive pneumococcal disease and laboratory confirmed pneumococcal pneumonia in homeless adults in Toronto, a Canadian city with a total population of 2.5 M, from January 1, 2002 to December 31, 2006. RESULTS: We identified 69 cases of invasive pneumococcal disease and 27 cases of laboratory confirmed pneumococcal pneumonia in an estimated population of 5050 homeless adults. The incidence of invasive pneumococcal disease in homeless adults was 273 infections per 100,000 persons per year, compared to 9 per 100,000 persons per year in the general adult population. Homeless persons with invasive pneumococcal disease were younger than other adults (median age 46 years vs 67 years, P<.001), and more likely than other adults to be smokers (95% vs. 31%, P<.001), to abuse alcohol (62% vs 15%, P<.001), and to use intravenous drugs (42% vs 4%, P<.001). Relative to age matched controls, they were more likely to have underlying lung disease (12/69, 17% vs 17/272, 6%, P = .006), but not more likely to be HIV infected (17/69, 25% vs 58/282, 21%, P = .73). The proportion of patients with recurrent disease was five fold higher for homeless than other adults (7/58, 12% vs. 24/943, 2.5%, P<.001). In homeless adults, 28 (32%) of pneumococcal isolates were of serotypes included in the 7-valent conjugate vaccine, 42 (48%) of serotypes included in the 13-valent conjugate vaccine, and 72 (83%) of serotypes included in the 23-valent polysaccharide vaccine. Although no outbreaks of disease were identified in shelters, there was evidence of clustering of serotypes suggestive of transmission of pathogenic strains within the homeless population. CONCLUSIONS: Homeless persons are at high risk of serious pneumococcal infection. Vaccination, physical structure changes or other program to reduce transmission in shelters, harm reduction programs to reduce rates of smoking, alcohol abuse and infection with bloodborne pathogens, and improved treatment programs for HIV infection may all be effective in reducing the risk

Looking at Community Issues Through the Lens of Mathematical Modeling

Mathematical modeling is an important tool for analyzing and understanding the world around us. Throughout history, it has been instrumental in advancements made in the sciences. Recently, mathematical modeling has been used to gain insight in the social sciences, specifically social issues. Thus, the overall goal for this dissertation is to build up the mathematical modeling toolkit for addressing social issues, especially by applying and developing techniques in network theory. We accomplish this by looking at three social issues facing our communities. In Chapter 2, we use flow networks to develop an alternative method for calculating and analyzing the basic reproductive number for infectious disease outbreaks, which is the expected number of secondary infections produced by a typical infectious person in a susceptible population. We developed this method to be more accessible to non-mathematical audiences, as the basic reproductive number is important for public health officials and the general population in understanding infection risk and responding to outbreaks. We show our method is equivalent to traditional methods and provide instructions on its implementation. Chapter 3 applies immuno-epidemiological modeling techniques to study violence spread through exposure. Recently, there has been a push to understand violence as a public health issue. We expand this analogy between violence and infectious diseases to formulate a susceptible-exposed-infectious model. We then provide stability and equilibrium analysis and run example numerical simulations to show that the insights gained from a mathematical model might help identify effective interventions. The fourth chapter uses network modeling to explore how public transportation can help connect food desert residents to grocery stores. According to the USDA, food deserts are census tracts that experience high poverty rates and limited grocery store access. We select five cities and formulate network models where the food deserts and grocery stores are the nodes and transit lines are the edges. We analyze these networks through centrality measures and provide policy suggestions to improve public transit use in increasing food access. In total, this work presents examples of how novel mathematical models (especially using network theory) can help address societal problems and effect meaningful change

The Mycobacterium tuberculosis regulatory network and hypoxia

We have taken the first steps towards a complete reconstruction of the Mycobacterium tuberculosis regulatory network based on ChIP-Seq and combined this reconstruction with system-wide profiling of messenger RNAs, proteins, metabolites and lipids during hypoxia and re-aeration. Adaptations to hypoxia are thought to have a prominent role in M. tuberculosis pathogenesis. Using ChIP-Seq combined with expression data from the induction of the same factors, we have reconstructed a draft regulatory network based on 50 transcription factors. This network model revealed a direct interconnection between the hypoxic response, lipid catabolism, lipid anabolism and the production of cell wall lipids. As a validation of this model, in response to oxygen availability we observe substantial alterations in lipid content and changes in gene expression and metabolites in corresponding metabolic pathways. The regulatory network reveals transcription factors underlying these changes, allows us to computationally predict expression changes, and indicates that Rv0081 is a regulatory hub