Note: The concept below is an incomplete draft. Feedback is welcome!'
Matrix by Methods and Topic
| Mathematical Modeling | Statistical Modeling | Mobile Phone "Big" Data | Clinical Data |
Machine Learning |
|
| Malaria | 1, 2 | ||||
| Cholera | 7, C | ||||
| Ebola | 5, C | 8 | 8 | ||
| Hepatitis A | 5 | A | A, B | B | |
| Other* | 3, 5 | 9, 10 |
* Includes yellow fever, splenomegaly, wound botulism, and global health surveillance
Ongoing Research
A: Hepatitis A and Homelessness. Using data from the 2018-2018 outbreak of hepatitis A in San Diego, we demonstrate that people experiencing homelessness are at 2-4 times elevated odds of infection with hepatitis A virus and severe outcomes including hospitalization and death due to the illness. Methods include multivariate models and the novel use of a test-negative case control study design for risk factor analysis. {Under Review}
B: Hepatitis A Case Classification and Machine Learning. We assess modifications to the national hepatitis A case definition by varying quantitative cutoffs and compare performance to classification algorithms derived from machine learning techniques including recursive partitioning, random forests, and support vector machines. {In Preparation}
C: Impact of the Incubation Period on Spatial Predictability of Outbreaks. We perform a comparative epidemiologic study of cholera and ebola outbreaks in Sierra Leone and develop a mathematical model to support the hypothesis that the duration of the disease incubation period is a strong driver of spatial predictability and outbreak synchrony, with important implications on ring vaccination strategies. {In Preparation}
Published Research
Numbers correspond to work as described in my CV.