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{
- "hash": "d0621187aa1c7584f983d8a1fa065317",
+ "hash": "04574a51c38aabbc6dcaa22ef2f6970c",
"result": {
- "markdown": "---\npage-layout: full\nformat:\n html:\n toc: false\n---\n\n\n## Research\n\n\n\n::: {.cell}\n\n:::\n\n\n:::: {.grid}\n\n\n::: {.g-col-6}\n\n
Data4Action (COVID-19 at Penn State)
\n
\n\nA longitudinal cohort study, comprising of two geographically related cohorts, that aims to examine the effect of Penn State University students on the SARS-CoV-2 incidence in the surrounding community.\nWe have recently published a the interim serological results in Nature Scientific Reports, which can be found [here](https://doi.org/10.1038/s41598-022-12499-5).\nThe second part of the project focuses on relating the differential exposures observed in the \nstudent cohort to latent risk profiles, examining the potential impact of interventions to reduce infections.\n\n
\n
Measles in the DRC
\n
\n\nAn ongoing collaboration with Médecin Sans Frontières' (MSF) Epicentre unit to analyze measles seroprevalence in the ex-Katanga region of the Democratic Republic of Congo (DRC).\nThe intial project examined the role of new laboratory testing facilities on the speed of diagnosis and outbreak response decisions.\nThe second project examines the impact of Supplemental Immunization Activities in the ex-Katanga region; principally, characterizing the spatial and age-specific seronegativity, and examining the relationship between optical density (OD) distributions and seropositivity thresholds using finite mixture models.\n\n
\nClick to view prior projects
\n\n:::: {.grid}\n\n\n::: {.g-col-6}\n\n
Waning Measles Immunity Among Infants in Canada
\n
\nA prospective cross-sectional serology study of Canadian newborns and mothers\nassessing the rate of waning measles, varicella, and mumps antibodies in infants, \nincluding subgroup analysis by vaccination status. A retrospective analysis of \nwaning measles antibody titers (PRNT) in stored sera from a separate cohort of \nCanadian newborns was published as part of the study, and can be found \n[here](https://doi.org/10.1542/peds.2019-0630)\n
\n\n\n## Teaching\n:::: {.grid}\n\n\n::: {.g-col-6}\n\n
An Introduction to Git and GitHub
\n
\n\nDesigned and create a 2-hour workshop on the basics of Git and GitHub to graduate students, postdocs, and faculty at the Center for Infectious Disease Dynamics at Penn State University.\nFocusses on building a conceptual understanding of Git and GitHub, and how it can be used to improve research workflows by providing clear examples that are directly relevant to infectious disease researchers.\n\n
\n
SISMID Modeling Infectious Diseases
\n
\n\nCreated the website and rewrote the teaching materials for the 2023 SISMID module 2 (Mathematical Models of Infectious Diseases), that introduced students to the basics of mathematical modeling of infectious diseases.\nAdditionally, I wrote and delivered the lecture on understanding heterogeneity in models.\n\n
\n
Julia for Epidemiologists
\n
\n\nI am currently in the process of (slowly) writing a book on using Julia for epidemiological research.\nThe book is aimed at epidemiologists with no prior programming experience, and will cover the basics of Julia, and how to use it for epidemiological research.\nThe [EpiRHandbook](https://epirhandbook.com/en/) is an excellent resource for new and experienced epidemiologists, but Julia has many advantages over R (and certainly some disadvantages), and I hope this book will serve a similar purpose and help epidemiologists thinking about the transition to Julia.\n\n
\n
Data4Action (COVID-19 at Penn State)
\n
\n\nA longitudinal cohort study, comprising of two geographically related cohorts, that aims to examine the effect of Penn State University students on the SARS-CoV-2 incidence in the surrounding community.\nWe have recently published a the interim serological results in Nature Scientific Reports, which can be found [here](https://doi.org/10.1038/s41598-022-12499-5).\nThe second part of the project focuses on relating the differential exposures observed in the \nstudent cohort to latent risk profiles, examining the potential impact of interventions to reduce infections.\n\n
\n
Measles in the DRC
\n
\n\nAn ongoing collaboration with Médecin Sans Frontières' (MSF) Epicentre unit to analyze measles seroprevalence in the ex-Katanga region of the Democratic Republic of Congo (DRC).\nThe intial project examined the role of new laboratory testing facilities on the speed of diagnosis and outbreak response decisions.\nThe second project examines the impact of Supplemental Immunization Activities in the ex-Katanga region; principally, characterizing the spatial and age-specific seronegativity, and examining the relationship between optical density (OD) distributions and seropositivity thresholds using finite mixture models.\n\n
\nClick to view prior projects
\n\n:::: {.grid}\n\n\n::: {.g-col-12 .g-col-md-6}\n\n
Waning Measles Immunity Among Infants in Canada
\n
\nA prospective cross-sectional serology study of Canadian newborns and mothers\nassessing the rate of waning measles, varicella, and mumps antibodies in infants, \nincluding subgroup analysis by vaccination status. A retrospective analysis of \nwaning measles antibody titers (PRNT) in stored sera from a separate cohort of \nCanadian newborns was published as part of the study, and can be found \n[here](https://doi.org/10.1542/peds.2019-0630)\n
\n\n\n## Teaching\n:::: {.grid}\n\n\n::: {.g-col-12 .g-col-md-6}\n\n
An Introduction to Git and GitHub
\n
\n\nDesigned and create a 2-hour workshop on the basics of Git and GitHub to graduate students, postdocs, and faculty at the Center for Infectious Disease Dynamics at Penn State University.\nFocusses on building a conceptual understanding of Git and GitHub, and how it can be used to improve research workflows by providing clear examples that are directly relevant to infectious disease researchers.\n\n
\n
SISMID Modeling Infectious Diseases
\n
\n\nCreated the website and rewrote the teaching materials for the 2023 SISMID module 2 (Mathematical Models of Infectious Diseases), that introduced students to the basics of mathematical modeling of infectious diseases.\nAdditionally, I wrote and delivered the lecture on understanding heterogeneity in models.\n\n
\n
Julia for Epidemiologists
\n
\n\nI am currently in the process of (slowly) writing a book on using Julia for epidemiological research.\nThe book is aimed at epidemiologists with no prior programming experience, and will cover the basics of Julia, and how to use it for epidemiological research.\nThe [EpiRHandbook](https://epirhandbook.com/en/) is an excellent resource for new and experienced epidemiologists, but Julia has many advantages over R (and certainly some disadvantages), and I hope this book will serve a similar purpose and help epidemiologists thinking about the transition to Julia.\n\n