# Posts for Tag: coronavirus

### Most COVID-19 deaths in the US could have been avoided

Posted In: Health

#### The US coronavirus death rate is quite high compared to other countries (on a population-corrected basis)

US coronavirus deaths are around 200,000. Many of these deaths could have been avoided if swift action had been taken in February and March, as many other countries did. This graph shows an rough estimate of the number of US deaths that could have been avoided if the US had acted similar to other countries.

This graph takes the rate of coronavirus deaths by country (normalized to their population size) and imagines what would happen if the US had had that death rate, instead of its own. It then applies that reduction (or increase) in death rate to the total number of deaths that the US has experienced. The US death rate is about 600/million people in September 2020 and if a country has a death rate of 60/million people, then 90% of US deaths (about 180,000 people) could have been avoided if the US had matched their death rate. The government response to the pandemic is one of several important factors that determine the number of cases and deaths in a country. Other factors can include the overall health of the population, the population structure (i.e. age distribution of population), ease of controlling borders to prevent cases from entering the country, presence of universal or low-cost health care system, and relative wealth and education of the population.

The graph lets you compare the potential reduction in US deaths when looking at 30 different countries. You can choose those 30 countries based on total population, GDP or GDP per capita. These give somewhat different sets of countries to compare death rates, which is an indication of the effectiveness of the coronavirus response.

A valid criticism of this graph is that testing and data collection is very different in each of the countries shown and the comparisons are not always valid. This is definitely a problem with all coronavirus data but for the most part, the very large differences between death rates would still exist even if data collection were totally standardized. Some of the data from the poorest countries is less reliable, because they have less testing capabilities.

Source and Tools:
Data on coronavirus deaths by country is from covid19api.com and downloaded and cleaned with a python script. Graph is made using the plotly open source javascript library.

### 2020 Stock Market Drop Compared to other Bear Markets

Posted In: Economics

#### 2020’s stock market drop was unprecedented for the speed of the drop and also the speed of the recovery

This graph shows the stock market drops from the 2020 and other bear markets normalized so that the peak is at 100% at day 0. This lets you see the severity and duration of different bear markets from the Great Depression (1929), the Dot Com Bust (2000), and the Financial Crisis (2008) and other drops over 30%.

The coronavirus pandemic has significantly disrupted the global economy. Q2 GDP in the United States declined at an annualized rate of 32% and US unemployment reaching 15% due to coronavirus induced business shutdowns.

However, the stock market drop (represented by the S&P500 index) in late February and early March 2020 has somewhat surprisingly rebounded and reached a new all-time-high in August 2020, even as unemployment and GDP output has continued to falter. There certainly seems to be a disconnect between the fundamentals of the economy and the stock market.

Will the recovery in the stock markets continue or will it begin to align more closely with the fundamentals of the economy?

There are many proposed reasons why this disconnect is happening. The Federal Reserve actions to increase liquidity and prop up the stock market. The heavy weighting of tech in the S&P500 and the pandemic’s boost to many tech company’s business (i.e. Amazon, Zoom, Apple). Whatever the reason, the question of whether the market can continue at this pace or will have a correction is important and one to watch.

Data for the S&P500 price is daily from 1950 onward but before 1950, the data I had available was on a monthly basis. I interpolated this monthly data to create daily data, so not all the data is 100% accurate for any given day before 1950. Data for 2020 will continue to be updated daily.

Source and Tools:
Data on historical S&P500 prices is from Yahoo! Finance and downloaded and cleaned with a python script. Graph is made using the plotly open source javascript library.

### How much will masks reduce coronavirus transmission rate R0?

Posted In: Health

#### It depends on their effectiveness and how many people wear them

R0 is the transmission rate which is defined as the average number of cases that are expected to be produced from a single case in an uninfected population. R0 is dependent on a number of different factors that include transmissibility of a disease (how infectious it is), the amount of social contact and the duration of social contact.

A baseline level of social contact is related to the population density (how often you come into contact with other people) and social distancing (limiting gatherings, not going in to work or school, etc) will reduce the amount of social contact with different people. Given what we know about coronavirus and its transmission, the amount of “contact” can also be influenced by mask wearing. This interactive graph shows the effect of mask wearing and effectiveness on reducing R0 even further.

This graph is a work-in-progress so please feel free to provide suggestions and feedback on issues of scientific concepts as well as for improvements in conveying the concepts/ideas.

##### Methodology

R0 values for different regions and population densities are estimated from Youyang Gu’s machine learning model for spread in Feb and early-March (i.e. before social distancing and mask wearing).

Baseline R0,baseline based on population density – R0 value ranges from about 6 in very high density places like New York City with lots of transit use where you are in close contact with other people for long periods of time to 2 in rural areas with much less contact.

Social distancing factor (SDF) – this is simply a reduction on the baseline R0 based on the amount of social distancing (ranges from 100% (no social distancing) to 33% (high levels of social distancing). This is a reduction in the amount of time and number of people the average person is exposed to compared to baseline levels.

Percent wearing masks (Kmaskfreq) – is simply the percentage of people wearing masks (varies from 0% to 100%). This parameter is shown on the y-axis.

The formula for effective Reffective is:

$R_\mathit{eff}=R_0,baseline \times SDF \times (1-K_{mask\mathit{eff}} \times K_{maskfreq})^2$

where $R_\mathit{eff}$ is the final average transmission value, $R_0,baseline$ is the $R_0$ value based on the population density, SDF is the social distancing factor, $K_{mask\mathit{eff}}$ is the average mask effectiveness and $K_{maskfreq}$ is the percentage of people wearing masks. The squared parameter on the right side of the equation is essentially the average reduction in transmission that is likely due to mask usage and is from a preprint from Howard et al.

As you move up and to the right of the graph, mask use and effectiveness become very high and the transmission of coronavirus declines significantly. If you hover over the graph (on a desktop) or click on the graph (on mobile) you will see a popup that shows the Reff value that results. The lower the Reff value is the better as it dramatically affects the rate of transmission. High numbers will lead to explosive exponential growth while values below 1.0 will eventually reduce coronavirus transmissions to near 0.

For example at R0 of 6 and no social distancing or mask usage, one initial case can lead to approximately 56,000 cases in only 30 days. Whereas an Reff of 0.5 will only lead to a total of ~1 additional case in 30 days.

Please let me know in the comments if you have any questions or suggestions on how the tool works, is structured or presented.

Source and Tools:
The reduction in R0 due to mask effectiveness and usage based on a model from a preprint from Howard et al. Baseline R0 are from Youyang Gu’s machine learning model. Calculations are done in javascript and visualization is done with the open source Plotly javascript graphing library.

### Bay Area Coronavirus Cases

Posted In: Health

#### Compare the Bay Area coronavirus cases with Los Angeles and the rest of California

I wanted to better understand the coronavirus situation in my home region, the Bay Area, and I hadn’t seen any good resources that compared what was happening here to other regions in California. So I decided to make this graph. This page will be updated daily so you can come back regularly to see how the situation is changing (and hopefully improving sometime soon).

The coronavirus lockdowns began in mid-March 2020 and things have been opening up in late May, which corresponded to an uptick in coronavirus cases in the Bay Area and throughout California. While the cases in the Bay Area are increasing, it’s clear that there’s a big difference between the Bay Area and much of the rest of California. Los Angeles is currently leading the state with a large increase in the number of new cases in June as the economy tries to reopen restaurants, bars, gyms and other businesses.

You can toggle between coronavirus cases and deaths and look at the absolute numbers or on a per capita basis (per one million inhabitants). California has 39.5 million residents, while greater LA has 18.7 million residents and the Bay Area has 7.7 million residents. The daily data is shown as well as a five day moving average so you can get a better sense of the trends.

The San Francisco Bay Area was among the first regions to impose restrictions on gatherings and encourage people to stay home to fight the virus. In late February, the city of San Francisco declared an emergency in preparation for the upcoming pandemic and by early March, things became clear that life would not continue on as before.

The Bay Area is defined as the nine-county region consisting of Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano and Sonoma counties.

Greater Los Angeles is defined as the 5 county region consisting of Los Angeles, Orange, Ventura, San Bernardino and Riverside counties.

Data and Tools:
County level data on coronavirus cases and deaths is from the New York Times github. Data is processed in python and javascript and graphed using the plotly open source graphing library.

### US Coronavirus Deaths

Posted In: Health

This visualization is meant to demonstrate the exponential growth of coronavirus deaths in the United States starting in early March when the first confirmed deaths took place. Some reports indicate that the first deaths may have been as early as early February, though that is not shown in this data.

In the animation, each day is about 1 second long so on days with fewer deaths, the figures show up more slowly, while on days with greater deaths, the figures come very, very quickly.

Deaths stop growing exponentially in early April and start to level off and plateau. However, they haven’t yet started to decline significantly so we are still seeing thousands of deaths each day (as of late April).

The data and visualization will be updated daily with data from Covidtracking.com.

For more information about the virus and the disease and data collection, you can find good information on the CDC website.

Sources and Tools:

Coronavirus cases are obtained from covidtracking.com. And the visualization was created using javascript and the font with people figures is called Wee People and was created by Albert Cairo and Propublica.

### Tracking US Coronavirus Cases by State

Posted In: Maps

The coronavirus (SARS-CoV-2) is literally affecting the entire globe right now and changing the way we live our lives here in the US and all over the world.

There are quite a number of different coronavirus-related dataviz out there, but as we shelter-in-place I wanted to add a map that looked at a number of different metrics that tell us about the coronavirus pandemic by US states and look at those metrics on a population basis.

There are a number of data sources that I’ve found that publish data about the coronavirus and the resulting disease (Covid-19) in the United States:

This map is based on the data compiled from covidtracking.com, partly because it has a good API and also lists testing, cases and deaths. The data I’ve included on the map is:

• Numbers of coronavirus cases – i.e. tested positive for virus
• Numbers of coronavirus tests administered
• Numbers of deaths due to coronavirus

Each of these is also calculated per 100,000 population in the state:

• Numbers of coronavirus cases per 100k people- i.e. tested positive for virus
• Numbers of coronavirus tests administered per 100k people
• Numbers of deaths due to coronavirus per 100k people

These latter metrics are important because numbers of cases or deaths can be obscured by small or large populations but per capita data (or per 100k capita data) can point out interesting outliers.

It is important to note that the data is far from perfect. There is probably significant underreporting of tests, cases and deaths. The data is a collection for the various local and state agencies that are working hard to deal with the medical, social and political ramifications of the pandemic, while also collecting data. We don’t know how many Americans have coronavirus because of lack of testing.

Also important is that the number of positive cases is a function of how much testing is taking place so cases does not necessarily represent the exact prevalence of the virus, though there will probably be good correlation between cases and actual coronavirus infections. Luckily it sounds like tests are becoming more widely available so hopefully those numbers will go up sharply.

For more information about the virus and the disease and data collection, you can find good information on the CDC website.

Sources and Tools:

Coronavirus cases are obtained from covidtracking.com. And the visualization was created using javascript and the open source leaflet javascript mapping library.