jlerollblues on Science and surprisingly related topics

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Showing posts from November, 2022

Covid update - 11-4-2022 (model almost ready)

November 04, 2022

Well, I had a wonderful trip to my hometown and the Mayo Clinic. I was surprised that the staff there are still wearing the cheap surgical masks (<15% effective) and not N95 (nearly 80% effective). Those of us with crappy immune systems were about the only ones. I still feel fortunate to go to one of the best medical centers in the world. I returned to the MAPS and see it created some more data for me to process. That is proceeding write now. We now have more ingredients for the spaghetti I served the other day. I am anxious awaiting the final production of initial conditions (ICs) so I can run the quick turnaround forecasts again. But it is over a terabyte of information to chew through right now. Ugghh . I will get these out ASAP. Ending the Pandemic - https://www.nature.com/articles/s41586-022-05398-2 Thanks for reading Dr. Joe's Science Newsletter! Subscribe for free to receive new posts and support my work. ✓ Bottom line : Echos what many of us have been saying for alm

MAPS Model Description

Multiple Algorithm Pandemic Software MAPS is a valuable tool for forecasts or research that simulates pandemics or epidemics. It is written so that for a given virus the use can choose what type of differential equation solver that is most appropriate. The level of sophistication allows the simulation of the common flu to more complicated viral pathways. It is written in modular FORTRAN and can be run in serial (one processor) or parallel (unlimited processors). This provides a platform that can model a single point all the way up to global scales. If run in parallel the domain is decomposed to accommodate the number of processors specified. In this mode the domain is specified by latitude and longitude bounds and a desired grid spacing to produce a mesh, where the differential equations are solved for each square contained in the mesh. The individual points and MPI decomposed sub-domains are not independent. They communicate through ground and air transportation. The model has several solver algorithms, from a simple SI compartment model (flu) to the most sophisticated SEIRDCV (Susceptible Exposed Infected Recovering Death reCovered Vaccinated) compartment model. Variants are all subject to their own characteristic settings, from varying transmission to death rates. They also have settings for hospitalization rates. Hospitalization has its own algorithm that can mimic capacity effects like stress, triage mode, transport to adjacent hospitals, and ICU vs normal beds. Each variant also has its own mask/Shelter In Place settings reflecting country/state/local. mitigation policies including vaccination/booster rates. Output can range from global, country, state, local average files with the S E I R C D V fields or optional spatial output (GrADS, EWB, netCDF, ascii). The model also can be run in a "normal" or "forecast" modes. Post-processing can be run as a simple statistical analysis or a more involved training mode for the forecast system. The assimilation system can ingest observations at multiple scales with a multitude of methodologies. Seperate files are created depending on the number of processors deployed.