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Large-scale Spatial Network Models for modeling disease and information passing for people experiencing homelessness in metropolitan areas - Virtual

Zask Almquist

Recent increases in homelessness in the United States have been described as a nationwide emergency. The negative impacts of homelessness on communities and individuals are well-established, including significant impacts to health, safety, and social and economic equality. To address the effects of increasing homeless populations, particularly in cities on the west coast of the US where numbers are growing rapidly, social scientists must understand the size and distribution of their homeless populations, as well as how information and resources are diffused throughout these communities. Currently, there is limited publicly available information on people experiencing homelessness in the United States. The available information comes largely from the count estimates of homeless across the US gathered annually by the US Housing & Urban Development point-in-time (PiT) survey. While it is theorized in the literature that the networks of homeless individuals provide access to important information for social scientists in areas such as health (e.g. needle exchanges) or access (e.g. information diffusion about the location of new shelters), it is almost never measured and if measured only at a very small scale. In this work I introduce methods for simulating realistic social support and information networks in the homeless population. I then follow this up with new data from Anderson et al (2021) to directly estimate the parameters of this model and estimate the infection and mortality rate of COVID-19 in the homeless population in Nashville, TN. Finally, I will conclude with future directions for this work.

Speaker: Zack Almquist, University of Washington

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Tuesday, 11/09/21


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Berkeley Institute for Data Science

, CA