This article is from WeChat public account: Xin Zhiyuan (ID: AI_era) , edited by Xiao Lin and Meng Jia, the picture from: IC photo
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Is social isolation really useful? Trump’s decision to extend the quarantine period to April 30 was the result of a review of 12 different statistical models. Stanford University biologist Erin Mordecai and a team of researchers have developed an interactive simulator that simulates the time-varying COVID-19 transmission curve and vividly illustrates the role of social isolation in controlling the outbreak.
After Plague Company is removed, no more epidemic simulation games are available?
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The developers of Plague Inc. have been invited to lecture at the US Centers for Disease Control. It allows players around the world to have a basic knowledge reserve, perceptual knowledge, and crisis awareness of the infection and transmission mechanism of the virus. It is a great popular science game. After the outbreak, it has been removed from the official platforms at home and abroad.
But now there is a more accurate outbreak simulator.
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Of course, you must have good English to play. Students who have over 100 TOEFL can go to the end of the article to find links to adjust the parameters themselves. This article only introduces the research conclusions made by scientists using this model.
Stanford University biologist Erin Mordecai and a team of researchers have developed an interactive website that makes various interventions such as social isolation and quarantine into smoothly adjustable knobs, simulating COVID-19 over time The spread curve allows people to see the dynamic impact of different measures on the number of cases, the number of deaths, and the comparison with the hospital load.
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Stanford University biologist Erin Mordecai
Research Achievements: Visually Demonstrate the Role of Social Isolation
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The above four figures show the direct reduction of the number of hospitalized patients under the implementation of social isolation measures of 0, mild, moderate, and severe. The red line in the picture is the carrying capacity of the hospital.
1. Reduce curve peaks: Many of our health resources have a fixed capacity. If there are too many cases at the same time, we will not be able to take care of everyone who is sick. Exceeding the upper limit of the number of hospitalizations will mean having to prioritize some patients, while others may not be treated, and we have seen this happen in places like Italy. The greater the social isolation we practice, the flatter the curve will be.
2. Delay peaks: Over time, many resources will become more and more abundant. We will be able to produce more resources, such as medications, ventilators, and hospital beds. Practices such as social isolation can help delay the peak of cases and give us time to get the resources we need.
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3. Preventing outbreaks at the end of the curve: Once the number of cases begins to decrease, can we stop social isolation? No. These three graphs show the situation of strong social isolation lasting 3/5/10 months. It is clear that if we remove control too quickly, then we may see the spread of the disease reappear and the number of cases rebound rapidly because many Still susceptible. Desegregation after 3 or 5 months results in a surge in the number of patients and can also lead to an overloaded medical system.
We saw this in the 1918 flu pandemic, when many US cities lifted restrictions after three to eight weeks. As a result, we encountered the second largest flu peak in history.
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To prevent the resurgence of COVID-19, we need to take multiple interventions from 12 months to 18 months, or longer, until effective treatments and / or vaccines are widely used.
An alternative to months of social isolation: the light switch method
We don’t need to be isolated for a full year or more. Experts show that we can keep it relatively low if we use interventions that actively turn on and off, such as the “light switch method” (The Lightswitch Method) Transmission rate allows for greater range of activity while still controlling the epidemic to a level that our healthcare system can manage.
When we turned on the lights, we started to maintain social distance, and the number of cases began to decrease. When we turned off the lights, people could resume socialization to a certain extent and make minor adjustments. Number of casesIt will start to increase a bit, but it will not get out of control before we resume social. We can set the switch time to a certain time based on the data. (three weeks on, three weeks off) or a threshold (eg: open when there are 15 inpatients in a week, and close when there are less than 2 inpatients in a week)
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In this way, we can find a balance between preventing the spread of the new crown and normal life.
Dynamic modeling method for infectious diseases
The basic principles of statistical models are relatively obvious. Epidemiologists divide the population into different sections. The SIR model is a common mathematical model for describing the spread of infectious diseases. The basic assumption is to divide the population into the following three categories:
1. Susceptible people (Susceptible) : refers to those who have not been ill, but lack immunity, Patients are susceptible to infection after contact.
2. Infected people (Infective) : refers to a person infected with an infectious disease, who can spread to susceptible crowd.
3. Remove crowd (Removed) : People who have been removed from the system. (immunity) or died. These people are no longer involved in the process of infection and infection. In the SIR model, there are two conversion relationships between the above three types of people .
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For people who are “exposed” but not yet infected, some models will also add E to become SEIR models. Modelers then formulate variables based on their judgment of the spread of the disease and then run. These variables include how many people an infected person has infected or recovered before death, how long it takes for an infected person to infect another person, and so on.
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SEIR model legend
“Everyone was susceptible at the beginning. The number of infected people was very small. They infected the susceptible people, and the number of infected people began to increase exponentially.” Helen, an infectious disease epidemiologist at Boston University School of Public Health · Jenkins said.
Trump extended the quarantine period to April 30, based on 12 statistical models
On March 29th, at a press conference held by the White House, US President Trump repeated the forecast of the US crown ’s total death data “2.2 million” 16 times. And earlier at the University of Washington Institute of Health Measurement and Evaluation (IHME) A research report published on March 25 predicts that 81,114 people in the United States may die from new coronary pneumonia in the next 4 months. The inflection point of the epidemic was around the second week of April.
IHME is a powerful data processing agency with about 500 statisticians, computer scientists, and epidemiologists. IHME principal Chris Murray said, “Last week’s latest model results showed that the overall situation has improved for the first time, and the death toll statistics have dropped by 20% compared to last week, and the curve has started to flatten.”
From an epidemiological point of view, these death data are derived from mathematical prediction models. White House New Crown Virus Coordinator, Dr. Deborah Birx, said at a press conference that Trump’s decision to extend the quarantine period to April 30 was a combination of a review of 12 different statistical models.
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The 9-cell model used by the Stanford team: S = susceptible E = exposed Ip = pre-symptomatic but infectious state Ia = asymptomatic infectionIs = Severe Im = Mild H = Hospitalized R = Rehabilitation D = Death
Marc Lipsitch’s team at Harvard’s infectious disease epidemiologist also used the SEIR model. The data was adjusted to simulate tightening or relaxation of social isolation measures and possible seasonal changes in Covid-19 infection. (similar to flu) . He changed R0 during the simulation. The basic infection index R0 refers to the average number of people infected by an infected person without external intervention.
In this model, if social isolation in the strict sense is stopped (no measures such as vaccines or treatments are needed) , infection will occur The rate quickly climbed to about two per thousand peopleCritical cases, which is equivalent to 660,000 Americans imminently dying. The team’s model found that even with the most stringent bans in place from April to July, the epidemic would pick up around fall.
Original link:
Stanford-developed interactive model explores how different interventions affect COVID-19’s spread
Simulation model link: https://covid-measures.github.io /
Stanford’s magical source code can be found on Github: https://github.com/morgankain/COVID_interventions
2.2 million people die from new coronary pneumonia? Is Trump’s worst ending reliable?
The Mathematics of Predicting the Course of the Coronavirus
This article is from WeChat public account: New Intel (ID: AI_era) , edited by Xiao Lin, Meng Jia