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<span style="font-size:10.0pt;font-family:"Arial",sans-serif"><a href="https://emails.illinois.edu/newsletter/2099284528.html" title="https://emails.illinois.edu/newsletter/2099284528.html">Click here to see this online</a></span><o:p></o:p></p>
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<h1 style="margin:0in;line-height:25.2pt"><strong><span style="font-size:18.0pt;font-family:"Open Sans"">Geospatial Fellows Webinar Series: Monday, 4/26</span></strong><o:p></o:p></h1>
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<p style="margin-bottom:15.75pt;line-height:18.0pt"><strong><span style="font-size:12.0pt;font-family:"Open Sans"">Topic:</span></strong><o:p></o:p></p>
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<li class="MsoNormal" style="line-height:18.0pt;mso-list:l0 level1 lfo3"><span style="font-family:"Open Sans"">A Bottom-up Approach to Epidemic Modeling: Conceptual Framework, Implementation, and Case Study</span><o:p></o:p></li></ul>
<p style="margin-bottom:15.75pt;line-height:18.0pt"><strong><span style="font-size:12.0pt;font-family:"Open Sans"">Speakers:</span></strong><o:p></o:p></p>
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<strong><span style="font-size:11.5pt;font-family:"Open Sans"">Date:</span></strong><span class="apple-converted-space"><span style="font-size:11.5pt;font-family:"Open Sans""> </span></span><span style="font-size:11.5pt;font-family:"Open Sans"">Monday, April
 26, 2021<span class="apple-converted-space"> </span></span><o:p></o:p></p>
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<strong><span style="font-size:11.5pt;font-family:"Open Sans"">Time:</span></strong><span class="apple-converted-space"><span style="font-size:11.5pt;font-family:"Open Sans""> </span></span><span style="font-size:11.5pt;font-family:"Open Sans"">4:00 - 5:00
 pm U.S. Central Time<span class="apple-converted-space"> </span></span><o:p></o:p></p>
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<p class="MsoNormal" align="center" style="text-align:center"><span style="color:white"><a href="https://urldefense.com/v3/__https:/aag-geospatialfellows-series.secure-platform.com/a/solicitations/16/sessiongallery/246__;!!DZ3fjg!uDmd3EEXPH3ZtQly5qWYJ1oR38r17tpaVt5aL7rp18WRhFli9A_psP2n5QlA-9BG$" target="_blank" title="https://urldefense.com/v3/__https:/aag-geospatialfellows-series.secure-platform.com/a/solicitations/16/sessiongallery/246__;!!DZ3fjg!uDmd3EEXPH3ZtQly5qWYJ1oR38r17tpaVt5aL7rp18WRhFli9A_psP2n5QlA-9BG$"><span style="font-family:"Open Sans";color:white;border:solid #0E405F 1.0pt;padding:8.0pt">Register
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<p style="margin-bottom:15.75pt;line-height:18.0pt"><span style="font-size:11.5pt;font-family:"Open Sans"">The Geospatial Software Institute conceptualization project (<a href="https://gsi.cigi.illinois.edu" title="https://gsi.cigi.illinois.edu/">https://gsi.cigi.illinois.edu</a></span><span style="font-size:12.0pt">)
 funded by the National Science Foundation is holding a webinar series to showcase the findings and outcomes of the projects of the Geospatial Fellows for advancing COVID-19 research and education. These webinars are scheduled on selected Mondays between 4-5
 pm Central Time through August 2021. Check out the webinar series here:<span class="apple-converted-space"> </span><a href="https://urldefense.com/v3/__https:/aag-geospatialfellows-series.secure-platform.com/__;!!DZ3fjg!t0-DzfCngyWGgt2eQRQleP0tZ5dluc8ceaC_DR5HpjDax5MTKC06mZU67K5Pq58m-ovAE2istopciuKZ$" title="https://aag-geospatialfellows-series.secure-platform.com/">https://aag-geospatialfellows-series.secure-platform.com/</a>.<span class="apple-converted-space"> </span></span><o:p></o:p></p>
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<p style="margin-bottom:15.75pt;line-height:18.0pt"><span style="font-size:11.5pt;font-family:"Open Sans"">The next webinar in this series will be held on April 26, 2021 at 4 pm CT. During this webinar,<span class="apple-converted-space"> </span><strong><span style="font-family:"Open Sans"">Dr.
 Xun Shi</span></strong>, Professor at Dartmouth College will showcase his project findings as a Geospatial Fellow for advancing COVID-19 research and education.<span class="apple-converted-space"> </span><strong><span style="font-family:"Open Sans"">Dr. Sara
 L. McLafferty</span></strong>, Professor at University of Illinois Urbana-Champaign, will serve as a discussant for the webinar. We hope you will be able to join this webinar and learn from their insights.<span class="apple-converted-space"> </span></span><o:p></o:p></p>
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<h2><span style="font-size:15.0pt;color:#F8FAFC">A Bottom-up Approach to Epidemic Modeling: Conceptual Framework, Implementation, and Case Study</span><o:p></o:p></h2>
<p style="margin-bottom:15.75pt;line-height:18.0pt"><strong><span style="font-size:12.0pt;font-family:"Calibri",sans-serif;color:#F8FAFC">Xun Shi</span></strong><span style="font-size:12.0pt;color:white"><br>
</span><span style="font-size:12.0pt;color:#F8FAFC">Dartmouth College</span><o:p></o:p></p>
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<p style="margin-bottom:15.75pt;line-height:18.0pt"><span style="font-size:11.5pt;font-family:"Open Sans"">Most current modeling works of COVID-19 are based on the classic SIR model, typically at the population level and adopting top-down strategies. They are
 sensitive to modeling setting and parameter values that feature subjectivity. Their aggregate data also mask variation across people and space. An alternative approach is to model at the individual level and to resort to a bottom-up strategy, which takes advantage
 of increasingly available big data of individuals’ mobilities and high-performance computing capabilities. It anticipates general and high-resolution spatiotemporal patterns of the epidemic to emerge from the modeling process. We have been developing a bottom-up
 approach to epidemic modeling for years. The components of the approach include: 1)<span class="apple-converted-space"> </span><strong><span style="font-family:"Open Sans"">disaggregating</span></strong><span class="apple-converted-space"> </span>available
 human mobility data to obtain individual-level trajectory data, using a Markov Chain Monte Carlo (MCMC) process; 2)<span class="apple-converted-space"> </span><strong><span style="font-family:"Open Sans"">tracing contacts</span></strong><span class="apple-converted-space"> </span>among
 individuals based on their daily activity trajectories; 3)<span class="apple-converted-space"> </span><strong><span style="font-family:"Open Sans"">building an</span></strong><span class="apple-converted-space"><b> </b></span><strong><i><span style="font-family:"Open Sans"">Epidemic
 Forest</span></i></strong><span class="apple-converted-space"> </span>model that represents transmission relationships among actual disease cases; and 4)<span class="apple-converted-space"> </span><strong><span style="font-family:"Open Sans"">predicting</span></strong><span class="apple-converted-space"> </span>future
 epidemics based on the epidemic characteristics derived from the<span class="apple-converted-space"> </span><em><span style="font-family:"Open Sans"">Epidemic Forest</span></em><span class="apple-converted-space"> </span>model, as well as data depicting intervention
 scenarios through agent-based modeling. The process has been preliminarily implemented within an ArcGIS environment and is being migrated to the CyberGISX platform. It has been applied to case studies of COVID-19 in China.<span class="apple-converted-space"> </span></span><o:p></o:p></p>
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<p style="margin-bottom:15.75pt;line-height:18.0pt"><strong><span style="font-size:11.5pt;font-family:"Open Sans"">Xun Shi</span></strong><span class="apple-converted-space"><span style="font-size:11.5pt;font-family:"Open Sans""> </span></span><span style="font-size:11.5pt;font-family:"Open Sans"">is
 a Professor of Geography at Dartmouth College. He has been highly active in the area of health-related geospatial research. His research covers disease mapping, disease-environment association detection, communicable disease modeling, healthcare accessibility
 assessment, and data infrastructure for health-GIS. He received funding support from NIH, NSF, CDC, and other sources. He published more than 70 research papers in international journals, and developed ArcHealth, a software package serving spatial analytical
 functions particularly requested by health-related studies and practices. He served as the Chair of the Health and Medical Geography Specialty Group of the American Association of Geographers (AAG) during 2015-2016, and as an editor of AAG Annals during 2008-2018.<span class="apple-converted-space"> </span></span><o:p></o:p></p>
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<p style="margin-bottom:15.75pt;line-height:18.0pt"><strong><span style="font-size:11.5pt;font-family:"Open Sans"">Sara L. McLafferty</span></strong><span class="apple-converted-space"><span style="font-size:11.5pt;font-family:"Open Sans""> </span></span><span style="font-size:11.5pt;font-family:"Open Sans"">is
 a Professor at University of Illinois Urbana-Champaign. Her current research investigates place-based inequalities in health and well-being and access to health services for women, immigrants and racial/ethnic minorities in the United States. Her ongoing work
 examines the impacts of increasing economic inequality and residential segregation on women's commuting times and modes and on maternal and infant health outcomes. She also uses and develops GIS and spatial analysis methods for examining health and social
 issues in cities and planning public health interventions. She is currently an Associate Editor of<span class="apple-converted-space"> </span><em><span style="font-family:"Open Sans"">Health and Place</span></em><span class="apple-converted-space"> </span>and
 serves on the editorial boards of<span class="apple-converted-space"> </span><em><span style="font-family:"Open Sans"">Geographical Analysis</span></em>, and<span class="apple-converted-space"> </span><em><span style="font-family:"Open Sans"">Spatial and Spatiotemporal
 Epidemiology</span></em>.<span class="apple-converted-space"> </span></span><o:p></o:p></p>
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