I wanted to find out if the same age cutoff of cases can also signal the presence of Covid19 Delta variant in Thailand. For this experiment, I roped in my nephew, Jantzen Ang and sought the help of Chris Sobrepena. He is a Pinoy stationed in Thailand. Chris provided the link to the source data and context — since he is living in Thailand.

Data source: https://data.go.th/dataset/covid-19-daily

The most significant 3rd wave started on April 3, 2021 which is around the 13th week of 2021.

Officially, Delta Variant was detected in mid May :

The Delta variant first hit Thailand…

In an earlier post, I theorized that we might be able to identify areas with Delta variant using data. Now I wanted to show that it is POSSIBLE to anticipate where Covid19 DELTA variant will strike next.

LGUs can control the spread of epidemics like Covid when they can visualize the migration pattern of people. This is patterned after Haiti earthquake and Ebola outbreak in Africa. There, the governments used migration patterns to channel aid, to set up road blocks/control points.

My basic (simple) theory:
Covid is carried by people. So naturally, wherever infected people go, there goes the Delta…

My friend Dennis Uy is an avid runner. On one of his jaunts to Sante Fe Ridge (pictured below), he used a Garmin Fenix 3 wrist watch to collect trail data.

Garmin website can show these:

Several sources have published that the Indian Covid variant B1617 appears to affect children more. Thanks to Alex Timbol for the link: https://spiral.imperial.ac.uk/handle/10044/1/89629.

We can use this finding to help Philippine Local Governments to track Delta variant in their midst. This compliments the genomic testing that provides definite proof, but may be limited and slow.

Using Philippine Department of Health (DOH) data, let us show which regions have comparatively more Covid cases involving younger adults. This chart shows the rate of growth of cases for Regions where younger patients (0 -19 years old) cases spiked:

Interestingly, under certain criteria, we…

Our goal is to visualize ProjectBASS collected bandwidth data into a Map. With the brangay level areas color coded for median bandwidth. The output looks like this:

First we create a new QGIS project. We add in PhilGIS barangay shapefile and also use Open StreetMaps/ Stamen Terrain. Then we import the CSV file named: BASSSubmissionsOutput.csv.

This BASSSubmissionsOutput.csv is processed from Tableau using the ProjectBASS dataset. It is further processed by Tableau Prep to clean the data. Here is a screenshot of the data structure:

Pasay City Mayor Emi Calixto-Rubiano said on April 18, 2021 that “they have seen a drop in coronavirus disease (COVID-19) cases from three digits down to two digits in the past three weeks due to the city’s contact tracing efforts.”

So let’s unpack the claims into two parts. First is the claim that Pasay’s cases have gone down.

“197 a day noon…pero ngayon po bumababa po kami ng 80, 70, 60”

Here, the DOH data confirms this. In the last 3 months starting from February, the case counts were 1,895, 7,244 and the MID-April figure shows much lower numbers at…

We start with the Co-INFORM risk Maps generated by PSPHP.org. The areas in Red are at the highest risk, while areas in GREEN are at the lower risk levels. Why are these areas harder hit than the rest? What can explain this?

Let us use the Dept of Health’s Data Drop to see if we can find clues to what is causing the spike. The DOH data includes the over 648,000++ covid positive cases. We hope to shed light on the recent spike in PH Covid Cases (7,999) as of today.

We select some of the Regions that were harder…