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The Center for Systems Science and Engineering (CSSE) at JHU

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who we are

The Center for Systems Science and Engineering (CSSE) is a research collective housed within the Department of Civil and Systems Engineering (CaSE) at Johns Hopkins University (JHU).  Our faculty, researchers, and students work on a range of complex and interdisciplinary problems, united by the goal to better understand and improve societal, health, and technological systems for everyone.

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Energy

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public health

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healthcare

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transportation

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space

Tracking COVID-19

We are tracking the COVID-19 spread in real-time on our interactive dashboard with data available for download. We are also modeling the spread of the virus. Preliminary study results are discussed on our blog.

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Tracking Measles Cases in the U.S.

The project is a collaborative, interdisciplinary effort conducted by a group of researchers at Johns Hopkins University who are tracking and modeling the risk of measles in the U.S. It reflects contributions from the Center for Systems Science and Engineering (CSSE) at the Whiting School of Engineering, the International Vaccine Access Center (IVAC) at the Bloomberg School of Public Health, and the Bloomberg Center for Government Excellence. The team is led by Lauren Gardner, Shaun Truelove, and William Moss.

Link to Tracker

A set of female individuals with the above criteria were considered. Further demographic and diet considerations (in order to select similar patients) led to selecting 11 different individuals’ one day of intake as the initial dataset for the model. In another setting, we only considered people that have consumed a reasonable amount of sodium and water. We consider these two nutrients as the main constraints in the DASH diet. 



In order to compare different potential data and their performance with the model, we used different data groups from the NHANES database. A group of middle-aged women with certain similar characteristics and a group of people with certain attributes in their diets. In the first group, we did not consider how the individual’s daily diet is reflecting on the constraints that the forward problem had and we relied on their own personal answer to questions regarding hypertension and also how prone they thought they were to type-2 diabetes. The result was a sparse set of variables and an inconclusive optimal solution in regards to the preferences. In the second group, we tried to obtain sub-optimal data. We prioritized the maximum sodium intake constraint and the water intake constraints as our main and most important constraints.