An underappreciated aspect of the pandemic is the point at which each state and country started. Some areas began with low infection rates and relatively small problems to solve; other areas started with astronomical infection rates that implied once-in-a-lifetime challenges. The starting point in New York was not the same as the starting point in Wyoming.

Opinions vary about how preemptively each area should have responded to the pandemic before the first death, but everyone agrees that areas should have been taking strong corrective action by the time the first death in each area occurred.

To provide a common basis for evaluating each area’s response, I’ll review how effectively each American state and European country controlled the virus relative to its specific starting point. …

Every day I hear someone say, “We should have managed the pandemic the way Europe did.”

No we shouldn’t have. Europe is having a total pandemic meltdown, and the data shows it.

The graph below shows the number of Covid-19 deaths per million people during the past 2 weeks on the vertical axis. Population is shown on the horizontal axis. Orange points are European countries. Blue points are American states. The data used to create the graph is from Johns Hopkins University and was current as of November 1, 2020.

Democratic strategists believe an unrelenting focus on the pandemic will propel Joe Biden to the presidency. Polls of swing state voters are released daily that favor Biden, but nagging doubts about voters’ real intentions remain because of the near-universal failure of polls to predict Donald Trump’s victory in 2016.

Real Clear Politics currently lists 22 states as leaning slightly toward Biden, leaning slightly toward Trump, or tossups. In total, 268 electoral votes are uncertain. When tossups are decided using aggregated polling data, RCP projects Biden to win 357 to 181.

Which way will the election go if the polls are once again wrong, Democratic strategists are correct, and battleground states are decided on the basis of Covid-19? …

I wrote a series of much longer articles on Covid-19 fatality rates, including one on the effect of comorbidities. Here’s a graph that summarizes the conclusions of that article:

The full article provides details and explains some of the ifs, ands, or buts about the numbers on this graph. But the graph is basically it, including the fat lines that are intended to show that the fatality rates are approximate.

The figure below highlights the risk for a 74 year old (Trump) vs. a 77 year old (Biden). …

You might think that raw data is more accurate than smoothed data. But in the case of the Covid-19 pandemic, smoothed data reduces reporting anomalies and is a more accurate representation of timing than the raw data is. But only if the smoothing is done correctly.

Wrong Way #1: Not Using Smoothed Data to Expose Trends

Raw state-level data is noisy, and it’s difficult to see trends in raw data. The example below shows the current raw data report from Hawaii. The light blue lines represent positive tests, and the red lines represent deaths.

Are tests going up or down? …

As one of the contributors to the CDC’s Covid-19 “Ensemble” forecast model, I update a set of state and national graphs several times a week on my Covid-19 Spin Free Data Center. I include the charts that I personally find useful in understanding the status and trends of the pandemic.

National Graphs

The most foundational graph is the one that shows the raw data on daily positive tests and deaths, as shown below. The blue lines represent positive tests, and the red lines represent deaths. The axis is scaled so that the positive test scale is 10 times the deaths scale.

The main thing I look for in this graph is unexpected noise in the data. We typically see a weekly reporting pattern of underreporting on Sunday and Monday and overreporting on Tuesday and Wednesday. You can see that in the “hills and valleys” of this graph. Sometimes there are exceptions, such as severe underreporting around holiday weekends. If you look at the blue lines around Labor Day, you can see a conspicuous dip followed by a spike. …

On Friday, the Institute for Health Metrics and Evaluation (IHME) released three new fatality forecasts. Their most likely scenario stated that 410,000 people will die from Covid-19 in the US by January 1. These forecasts have already been covered extensively by major news media (CNBC, NBC, NPR, USA Today, San Jose Mercury News, etc.).

There’s only one problem. IHME’s forecasts in the past have been inaccurate by as much as several hundred percent. In many cases, their forecasts have literally been worse than no forecasts at all because they created such misleading ideas about where the pandemic was headed.

IHME’s forecasts for January are no better. Indeed, they are so completely unrealistic that I wonder whether they are really based on science, or whether the science has become secondary to some other agenda. …

Note from the editors: Towards Data Science is a Medium publication primarily based on the study of data science and machine learning. We are not health professionals or epidemiologists, and the opinions of this article should not be interpreted as professional advice. To learn more about the coronavirus pandemic, you can click here.

A comorbidity is a condition that a person already has before they contract Covid-19. This is also known as a preexisting condition. Common comorbidities include diabetes, obesity, heart disease, hypertension, dementia, and cancer.

You’ve probably read statements like, “90% of Covid-19 deaths involve comorbidities,” and I have too. Those statements leave me wondering, “How does that affect me?” Because I don’t have any comorbidities, they give me a general sense that I’m at lower risk. …