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And government of India has started increasing taxes. Central Govt increased tax on Petrol & Diesel by ₹8 per liter, that's roughly 15% increase. State Governments will increase their share as well.

 

Additional tax on Liquor was imposed which is sorta understandable but on Fuel is fucking ridiculous, as that will increase transportation cost, which leads to higher inflation.

 

All this because Government was ruining economy from last 7 years.

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30 minutes ago, AndyLL said:

Texas has had a big uptick in cases since May 1st.... before the state opened.  It'll be interesting to see where they are in 2 weeks:

 

According to this website:

https://docs.google.com/spreadsheets/u/2/d/e/2PACX-1vRwAqp96T9sYYq2-i7Tj0pvTf6XVHjDSMIKBdZHXiCGGdNC0ypEU9NbngS8mxea55JuCFuua1MUeOj5/pubhtml#

 

Numbers look like this (except for the 3 may with a strange dig it seem to just due to a up tick in testing volume), the first 2 days of May seem to be the 2 days with the most test by a large amount for May 2:

Date New pos New test %
20200505 1037 19812 5%
20200504 784 16838 5%
20200503 1026 9912 10%
20200502 1293 28873 4%
20200501 1142 21475 5%
20200430 1033 15510 7%
20200429 883 14406 6%
20200428 874 9867 9%
20200427 666 14496 5%
20200426 858 13205 6%
20200425 967 20269 5%
20200424 862 17469 5%
20200423 875 8295 11%
20200422 873 11384 8%
20200421 738 15005 5%
20200420 535 7684 7%
20200419 663 6471 10%
20200418 889 6703 13%
20200417 916 10989 8%
20200416 963 6737 14%
20200415 868 5343 16%
20200414 718 13241 5%
20200413 422 8693 5%
20200412 923 4000 23%
20200411 890 4815 18%
20200410 1441 9584 15%
20200409 877 9876 9%
20200408 1091 7609 14%
20200407 986 3292 30%
20200406 464 14419 3%
20200405 702 7187 10%
20200404 780 7987 10%
20200403 661 5085 13%
20200402 672 2822 24%
20200401 731 4865 15%

 

 

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4 minutes ago, charlie Jatinder said:

Additional tax on Liquor was imposed which is sorta understandable but on Fuel is fucking ridiculous, as that will increase transportation cost, which leads to higher inflation.

Isn't just using the margin created by a reduction of the fuel cost or Indian oil industry/import didn't had much lower cost than usual like the rest of the world ?

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Posted (edited)
19 minutes ago, Barnack said:

Isn't just using the margin created by a reduction of the fuel cost or Indian oil industry/import didn't had much lower cost than usual like the rest of the world ?

The latter but this government hasn't supplied the benefit of cost reduction to public in last 6 years by increasing taxes on the fuel to keep prices level or even increase.

 

And Irony being, this government came to power protesting for higher fuel prices when Crude Oil was very expensive and no one talks about Fuel anymore in India because Media which is lapdog of Government is busy in fighting people over religion.

Edited by charlie Jatinder
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Only the best people. 
 

 

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wow, page 600,... that seems quite a lot for how few people in relation to pre virus times...

 

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33 minutes ago, charlie Jatinder said:

And government of India has started increasing taxes. Central Govt increased tax on Petrol & Diesel by ₹8 per liter, that's roughly 15% increase. State Governments will increase their share as well.

 

Additional tax on Liquor was imposed which is sorta understandable but on Fuel is fucking ridiculous, as that will increase transportation cost, which leads to higher inflation.

 

All this because Government was ruining economy from last 7 years.

You know I am no fan of the current government but what you wrote is somewhat incorrect. While excise and taxes have been increased which will lead to more revenue for the government, the actual price for consumers will remain the same. Oil companies who had been eating all the profits from lower oil prices will instead give up the profits and take on this additional cost. 
 

What is reprehensible about this government is that even at a time like this, when oil prices are low and when the common man really needs some savings, they are not passing on the benefits to the people but keeping all the money for themselves. Even our neighbours in Pakistan have passed on the benefits to their people. 
 

28 minutes ago, Barnack said:

Isn't just using the margin created by a reduction of the fuel cost or Indian oil industry/import didn't had much lower cost than usual like the rest of the world ?

Yup the government is using the reduced oil prices to fill its coffers. Instead of passing on the benefit of reduced prices to consumers they are instead making up the difference with taxes and duty. Consumers pay the same price as before, government gets all the benefits. 
 

Also thanks to all our taxes and duties, oil in India has always been fairly expensive when compared to some similar countries. 

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16 minutes ago, ZeeSoh said:

While excise and taxes have been increased which will lead to more revenue for the government, the actual price for consumers will remain the same.

My bad. I went with tweets on TL pointing the increase in price. Besides yesterday Delhi had increased 'price' so thought Centre followed.

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41 minutes ago, charlie Jatinder said:

My bad. I went with tweets on TL pointing the increase in price. Besides yesterday Delhi had increased 'price' so thought Centre followed.

That's done by Arvind Kejriwal. States are in huge loss. Plus Kejriwal habit of giving everything free is now biting him back. 

 

 

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1 hour ago, DeeCee said:

Only the best people. 
~snip~

 

 

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16 minutes ago, Jason said:

 

 

 

I am not sure to understand what is going on, trying to read about it some article say:

 

https://www.businessinsider.com/white-house-economic-adviser-hassett-model-coronavirus-deaths-zero-10-days-2020-5

 

The White House is relying on a model prepared by a controversial White House economic advisor that shows coronavirus deaths dropping to zero by May 15 to help guide their decision-making.

 

While the picture clearly show the latest version of the model having non zero death in early august (if I understand american dating nomenclature correctly), looking at the bigger version of the model:

https://covid19.healthdata.org/united-states-of-america

 

May 15 is expected to have between 800 and 3,000 death, not 0......

 

The text in the twitt is not saying that their model is based on a cubic, that it is for data visualization purpose and something just made with the observed data not for the projection.

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Posted (edited)
59 minutes ago, Barnack said:

 

I am not sure to understand what is going on, trying to read about it some article say:

 

https://www.businessinsider.com/white-house-economic-adviser-hassett-model-coronavirus-deaths-zero-10-days-2020-5

 

The White House is relying on a model prepared by a controversial White House economic advisor that shows coronavirus deaths dropping to zero by May 15 to help guide their decision-making.

 

While the picture clearly show the latest version of the model having non zero death in early august (if I understand american dating nomenclature correctly), looking at the bigger version of the model:

https://covid19.healthdata.org/united-states-of-america

 

May 15 is expected to have between 800 and 3,000 death, not 0......

 

The text in the twitt is not saying that their model is based on a cubic, that it is for data visualization purpose and something just made with the observed data not for the projection.


Hassett's model is different from the one prepared by IMHE or the ones prepared by the CDC which Trump didn't like very much.

Washington Post article:

Quote

Even more optimistic than that, however, is the “cubic model” prepared by Trump adviser and economist Kevin Hassett. People with knowledge of that model say it shows deaths dropping precipitously in May — and essentially going to zero by May 15.


There's no reason to create a cubic fit even for the purposes of visualization, except to deliberately deceive. You can make the fit arbitrarily close over a region of curvature with a local maximum or minimum, outside that region the extrapolated curve will be absolutely meaningless.

I'm going to keep reposting this tweet, because it illustrates how insanely ridiculous it is to be using cubic fits for non-cubic functions:
 

 

Edited by Jason
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13 hours ago, doublejack said:

That push is having consequences. The much maligned  IHME model doubled their forecast for deaths yesterday, to what I feel is still a very low and optimistic 134k. Yes, that's right. That's optimistic and low, sad to say. 

That's because they took away the presumption of social distancing throughout May.

 

It no longer makes any sort of reference to social distancing on the main page.


Which, fair enough as states pretty much stated lifting them in earnest this week/late last week.

 

From their update notes:

 

Quote

COVID-19: What’s New for May 4, 2020

Main updates on IHME COVID-19 predictions since April 29, 2020

 

UPDATED IHME COVID-19 PROJECTIONS: PREDICTING THE NEXT PHASE OF THE EPIDEMIC

Since our first release of COVID-19 projections on March 26, we have sought to update and advance our modeling strategies alongside the world’s rapidly evolving understanding of the pandemic. Processing new types and more routinely collected data, and then revising modeling approaches as the evidence base expands, is foundational to any scientific endeavor. Its importance becomes dramatically higher when a new disease is affecting millions throughout the world.

 

Our initial COVID-19 modeling strategy drew from the evidence on death reporting earlier in the global pandemic to inform predicted trajectories of deaths and hospital resource needs in the US, Puerto Rico, Canada, and European Economic Area (EEA) countries. Initially, our goal was to predict the peak of the epidemic, both in terms of when the number of deaths would peak, and also when health systems would experience the greatest surge in demand. These projections were informed by early response to the COVID-19 epidemic: the adoption of various social distancing policies to slow and ultimately contain the virus’s rapid spread. Some locations enacted such measures swiftly – Australia and New Zealand, among others – and appear to have been successful in curbing their epidemics. Other locations were slower to implement distancing mandates but instituted strict policies like curfews, while some primarily issued behavioral change recommendations to reduce infection risk. It is increasingly clear that COVID-19 epidemic trajectories – and corresponding responses – are highly variable throughout the world.

 

Globally, data on COVID-19 and key epidemic drivers have markedly improved since the end of March. In addition to an expanded data universe on reported COVID-19 deaths, cases, and hospital resources, we have much more information on COVID-19 hospitalizations and testing. Much more data on human mobility patterns have become available, a critical contributor to heightened exposure and potential transmission of the novel coronavirus. Our team has actively sought to incorporate both new and updated data into our models as soon as they become available, enabling regular updates of COVID-19 predictions for an increasing number of locations.

 

We, collectively, are now entering a new phase of the COVID-19 pandemic. More locations are easing previously implemented social distancing policies, and human mobility patterns are trending upward – even in places where distancing measures remain in place. Testing has scaled up in many parts of the world, but such progress has been uneven and is not keeping pace with the growing demand for lifting business and gathering restrictions. Carefully tracking what is happening today as locations move to “re-open” will provide vital information for potential COVID-19 trajectories in the coming weeks and months.

 

Today we launch a major update to our COVID-19 estimation framework: a multi-stage hybrid model. This modeling approach involves estimating COVID-19 deaths and infections, as well as viral transmission, in multiple stages. It leverages a hybrid modeling approach through its statistical component (deaths model), a new component quantifying the rates at which individuals move from being susceptible to exposed, then infected, and then recovered (known as SEIR), and the existing microsimulation component that estimates hospitalizations. We have built this modeling platform to allow for regular data updates and to be flexible enough to incorporate new types of covariates as they become available. Last, by relating transmission parameters to predictions of key drivers of COVID-19 epidemic trends – temperature, the percentage of populations living in dense areas, testing per capita, and human mobility – this new modeling approach will allow for a more comprehensive examination of how COVID-19’s toll could unfold in the coming months, taking into account these underlying drivers. This is particularly important as many locations ease or end prior distancing policies without having a clear sense of how these actions could potentially affect COVID-19 trajectories given current trends in testing and mobility, among others. With our new modeling framework, we aim to provide a venue through which different COVID-19 epidemic scenarios and responses can be explored by location.

 

We summarize this new modeling strategy below, as well as the data which have made these modeling innovations possible. The results can be explored online: https://covid19.healthdata.org/projections. We would like to highlight that the SEIR model has been incorporated for the US to date; more countries and locations will be added soon..

 

At IHME, our guiding principle is to produce the best possible predictions given what we know today – and to continually improve these estimates to support further gains against COVID-19 tomorrow. We will be updating our projections in the coming days and weeks to incorporate the world’s evolving evidence base on COVID-19.

 

Much more at the link.

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Posted (edited)
12 minutes ago, Porthos said:

That's because they took away the presumption of social distancing throughout May.

 

It no longer makes any sort of reference to social distancing on the main page.


Which, fair enough as states pretty much stated lifting them in earnest this week/late last week.

 

From their update notes:

 

Much more at the link.

A compare and contrast with the COVID alternative model I mentioned on Fri.

 

IHME (Aug 4) :           95,092 - 134,401 - 242,259

Youyang Gu (Aug4):  95,093 - 159,518 - 276,293

 

Same lower bound that is almost certainly not going to happen.  IHME is slightly more optimistic on the main number, but both are currently projecting a lot of death on the high number, though the Youyang model is projecting even more.

 

Edited by Porthos
Decided to re-word and refine my comment to be clearer

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8 minutes ago, Porthos said:

That's because they took away the presumption of social distancing throughout May.

 

It no longer makes any sort of reference to social distancing on the main page.


Which, fair enough as states pretty much stated lifting them in earnest this week/late last week.

 

From their update notes:

 

Much more at the link.

That doesn't take into account people's own instinct to look after themselves now they know what to do.

 

Sweden's R had been below 1 for quite a while now without any strict social distancing laws or lockdown. They just told the people what to do and by and large, they do it.

 

Not everyone will do it, but it doesn't matter, if enough people do...it still works.

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8 minutes ago, AndyK said:

That doesn't take into account people's own instinct to look after themselves now they know what to do.

Quote

Driver 4: Changes in human mobility and its relationship to social distancing policies

  • Sources: We currently use up to four data sources on human mobility and then construct a composite mobility indicator (described next). Two sources – Google’s COVID-19 Community Mobility Reports and Facebook’s Data for Good initiative – have mobility information for all currently included locations, while two other sources (Descartes Labs and SafeGraph) focus on the US only. Subsequently, all four sources inform the mobility composite indicator for US locations, while Google and Facebook are sources informing composite mobility outside of the US.

    Each source has a slightly different way of capturing mobility and recent changes. For Google, change in mobility is captured for six categories based on movement to places (e.g., workplaces, residential) and is benchmarked against median values of corresponding days of the week from January 3 to February 6, 2020. For Facebook, change in mobility is based on trips from different start and end locations relative to the median for the 45-day period preceding the first day Facebook had data for that location. For SafeGraph, change in mobility is based on the percentage change in devices not “completely home” relative to a baseline of February 8 to 14, 2020. And for Descartes Labs, change in mobility is based on median of the maximum distance traveled for samples in a given location relative to a baseline of February 17 to March 3, 2020.
     

  • Processing: Before constructing a composite mobility indicator, we implement a few processing steps to standardize these different data sources. For Facebook, we take the mean percentage change from the Google mobility source and apply this to each location’s baseline period in the Facebook dataset. This step is necessary to account for time series where social distancing measures have already been implemented (and thus could result in a skewed percentage change in mobility). We calculate a seven-day moving average for each data source to account for fluctuations in mobility over different days of the week, and calculate the ratio of between each of the mobility sources per location over time. For US locations, we model this ratio before and after March 3 as the Descartes Lab source more abruptly decreases at this time. Per location, we use these ratios to impute missing data points for each source and thus generate a complete time series of changes in mobility for each source. We then take the average across data sources and calculate the variance over time using a Gaussian process regression. This synthesis produces a single, composite time series of change in mobility for each location; based on the latest available data, this covers a time period of January 1 to April 28, 2020.
     
  • Predictions: We run a meta-regression Bayesian regularized trimmed model (MR-BRT)with random effects by location on the composite mobility indicator to estimate the effects of social distancing policies on changes in mobility. In addition to the six distancing measures we track (i.e., mass gathering restrictions, any business closure, school closures, stay-at-home orders, broader non-essential business closures, and severe travel restrictions), we also included a covariate one week prior to the first mandate implementation. This “anticipatory effect” was meant to capture how mobility changed in many places prior to any formal mandate implementation. For locations where particular measures had been eased or ended, we “switched off” – treated them as 0 – those policies at the time point of policy easing or ending. On the basis of the MR-BRT model, we generated predictions of mobility by location from January 1 to August 4, 2020. For locations where distancing policies have been eased or clear plans have been instituted for their easement, we used those dates for the predictions. In the absence of identified easing plans, we assumed policies would remain in place through August. We calculated the residuals between our predicted composite mobility time series and input composite time series, and then applied ARIMA (autoregressive integrated moving average) models to fit residuals by location. These ARIMA fits then were used to predict residuals from January 1 to August 4, which were then added to the mobility predictions to produce a final time series: “past” changes in mobility from January 1 to April 28 and projected mobility from April 29 to August 4, 2020.
     

 

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Now as Matt Yglesias is quick to point out, using mobility data might be slightly flawed as it doesn't really differentiate driving a long way to a secluded park versus driving a long way to a crowded beach.  Also really doesn't differentiate going to a crowded neighborhood supermarket that's very close to a home versus driving a fair amount of distance to go to a less crowded supermarket.

 

But when all you have is a hammer...  Well, there are worst tools to use.

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24 minutes ago, Porthos said:

That's because they took away the presumption of social distancing throughout May.

 

It no longer makes any sort of reference to social distancing on the main page.


Which, fair enough as states pretty much stated lifting them in earnest this week/late last week.

You support this model so much that I'm now wondering if you work for them. :D

 

They could not even get day after a reported result anywhere close to correct.

 

Their uncertainly started high and went down over time which never made sense to me.

 

If you have actual data for today (plus history) you should be reasonably accurate at predicting tomorrow.  As the timeline goes on uncertainly should grow because more variables go into play.

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