This exploration and treatment shows why the stay-at-home and social-distancing policies has been put in place. The most pessimistic but realistic assumption is that a vaccine won't become available for some time if ever , therefore the best strategy to maximize survival is to act in a way that doesn't overwhelm the health care system.
The reasoning is that with proper care, the mortality rate may be small, but if the health care system breaks down, the mortality rate could be much higher than it would be if proper care were available. A secondary reason to slow the infection rate i. This is another reason to slow the rate at which people become infected. For both these reasons — to keep from overwhelming the health care system and to minimize the infection rate while a vaccine is developed — the stay-at-home, no-nonessential-travel, social-distancing measures are the best approach to minimizing the harm this virus can do.
Until recently, few public policies have been guided by mathematics, but this pandemic is a perfect candidate for a quantitative what-if analysis using easily understood equations. Since this page was first published and predicted a surge in infections as sequestration ended, that prediction has come true worldwide. This outcome shouldn't surprise anyone — all responsible health researchers made the same advance prediction.
To date Covid policy has been steered by politics, not science, and as long as that remains true, we can look forward to more "unexpected" outcomes. I originally created the graphs and animations for this YouTube video , and as a code template for this Web page, using Python scripts like this one:.
The script assumes a a Linux environment, b the presence of Python 3, and c the presence of ffmpeg to convert a series of still images into a video.
This application has evolved quite rapidly since its basic algorithm was imported from Python, and the sequestration feature was developed and tested in JavaScript and back-ported to Python scripts above. It is now much easier to test assumptions and perform experiments than in the earlier Python incarnation.
This application now saves user entries between visits, so visitors can abandon a work session and the interface will preserve their entries for the next visit.
This is implemented using a relatively new browser feature called local storage — cookies are not used — and the storage is purely local, no data is transmitted anywhere else or used in any way. Using Calculus to analyze a pandemic — P. Katie Fogel, social media editor: polling our IG audience on this now Pat: The equation is not written according to ISO standards , leaving ambiguity of interpretation and the real answer is we need to teach better math writing.
Morgan: aka Andrew Daniels, how-to editor: honestly, we could post this slack thread word for word and then get a scholar to chime in and school us. Or do they go away once you solve the mini equation inside the parens first. I say they do not go away. I am on team 1. Trevor Raab, photographer: My question is to what real world scenario would this apply to. Trevor: ahh the classic learn to do math to learn to do more math.
Taylor: You've got, what, 11 years to perfect it. Bobby: she likes to claim she's good at math. She may come to rue the day she bragged about that.
Pat: "This won't help me win millions of dollars playing Fortnite tho". Tyler Daswick, associate features editor: secretly the best answer here. Andrew, minutes later: why is no one reacting appropriately to this news.
Andrew: yes! It was mathematical modelling based on airline passenger numbers and the number of infected people detected outside China that gave us early estimates of the size of the outbreak in the city of Wuhan. Similarly, modelling was used to quickly identify which countries were most at risk of experiencing imported cases, accurately predicting almost all countries that now have cases of the virus.
Models have also been made available as interactive tools to help authorities ascertain likely patterns of global spread , and whether border screening would be effective in detecting imported cases. This circumstance is rare and applies only to a new bacteria or virus like this one to which the population has no immunity from past infections or vaccination.
Several teams around the world have used models to estimate R0 from available case data. Depending upon the methods used, estimates of R0 for the new coronavirus have ranged from 1. Early estimates indicate that the new coronavirus has a serial interval of around seven days, which is substantially longer than for influenza which sits at around three or four days. So even if the new coronavirus has a higher R0 than seasonal influenza, it may spread more slowly. The severity of symptoms caused by this disease will be another key factor that determines whether the outbreak can be contained.
Paradoxically, while it may seem that a more severe disease would be of greater concern, it may actually be easier to control. When symptoms are severe, infected people are more visible, making them easier to identify, quarantine and treat.
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