Category Archives: disease modeling

Recently published article from International Journal of Health Geographics to read:

Read some of the most recently published articles from International Journal of Health Geographics.

Identifying food deserts and swamps based on relative healthy food access: a spatio-temporal Bayesian approach

http://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-015-0030-8

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Filed under disease modeling

Brief survey on sequence classification

hi Abie,

It was great speaking with you. This is the paper I was talking about.

http://dl.acm.org/citation.cfm?id=1882478

Looking forward to know more about each other’s work.

Thanks,

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Filed under disease modeling, machine learning

Book’s got a cover!

An Integrative Metaregression Framework for Descriptive Epidemiology – http://www.amazon.com/Integrative-Metaregression-Descriptive-Epidemiology-Publications/dp/0295991844

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Scientific Constants

A fun pointer I picked up at the eScience institute recently is where to find constants in python: scipy.constants http://docs.scipy.org/doc/scipy/reference/constants.html

or, if you fancy units, astropy.constants http://docs.astropy.org/en/stable/constants/index.html

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To read: Modeling Good Research Practices

I wonder if this will be useful: Modeling Good Research Practices—Overview: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-1 http://www.ispor.org/workpaper/Modeling_Methods/Modeling_Good_Research_Practices_Overview-1.pdf

It has quite a lot of best practices!

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IHME Seminar: Infectious Disease Mapping with Simon Hay

I used to keep up so well with the weekly IHME seminars on this blog. They are still weekly, but now they come too fast for me to capture… I guess it’s me.

There have been a few recently that really need to be mentioned, however, such as last week’s presentation on mapping *all* infectious diseases globally. Audacious project by Simon Hay: http://www.healthdata.org/video/mapping-global-distribution-all-infectious-disease

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Journal Club: Transmission Assessment Surveys (TAS) to Define Endpoints for Lymphatic Filariasis Mass Drug Administration: A Multicenter Evaluation

While I’m catching up on journal club reading, two weeks ago we discussed Chu et al, Transmission Assessment Surveys (TAS) to Define Endpoints for Lymphatic Filariasis Mass Drug Administration: A Multicenter Evaluation, which takes on the question of how to decide when it is safe to stop a massive disease elimination program.  This work must rely on some cool mathematical epi modeling, to say how many years of what level of coverage is necessary before you can hope the LF is gone.

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Filed under disease modeling, global health