The hottest Stanford released the simulator lighti

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Stanford released the "epidemic prevention company" simulator: "lighting switch method" or the optimal solution of American social isolation

,: Xiaolin, Mengjia, 36 krypton are authorized to release

source: Stanford et al.

[introduction to Xinzhiyuan] is social isolation really useful? Trump's decision to extend the quarantine period to April 30 was synthesized after reviewing 12 different statistical models. Erin Mordecai, a biologist at Stanford University, and a group of researchers developed an interactive simulator to simulate the propagation curve of covid-19 over time, vividly demonstrating the role of social isolation in controlling the epidemic

after the "plague company" was taken off the shelves, you no longer have games to play to simulate the epidemic

the developers of plague company have been invited to the CDC to give lectures. It enables players around the world to have basic knowledge reserves, perceptual knowledge and crisis awareness of the infection and transmission mechanism of the virus. It is a great popular science game. After the outbreak of the epidemic, it has been removed from official platforms at home and abroad

now, you have a more accurate epidemic simulator

of course, you have to be good at English to play. Students with a TOEFL of more than 100 can go to the end of the article to find a link to adjust their own parameters. This article only introduces the research conclusions made by scientists using this model

Stanford University biologist Erin Mordecai and a group of researchers have developed an interactive station, which makes various interventions such as social isolation and quarantine into knobs with a smooth degree of adjustment, simulating the propagation curve of covid-19 over time, People can intuitively see the dynamic impact of different measures on the number of cases and deaths, as well as the comparison with the hospital load

Stanford University biologist Erin Mordecai

research results: visually show the role of social isolation

the above four figures show the intuitive reduction of the number of hospitalized patients under the implementation of social isolation measures at 0, mild, moderate and severe levels. The red line in the figure is the carrying capacity of the hospital

1. Reduce the peak of the curve: many of our health resources have fixed capacity. If there are too many cases at the same time, we will not be able to take care of every sick person. Exceeding the upper limit of the number of hospitalizations will mean that we have to determine which patients to give priority to, while others can only be treated. We have seen that this situation occurs in a relatively simple and convenient way. The greater the social distance we practice, the flatter the curve will be

2. Delay peak: with the passage of time, many resources will become more and more abundant. We will be able to produce more resources, such as treatment, ventilators and hospital beds. Social alienation and other practices can help delay the time when the number of cases reaches the peak, and win us the time that we must strictly implement the input data to obtain the required resources

3. Prevent outbreaks at the end of the curve: can we stop social isolation once the number of cases begins to decrease? no These three figures show the situation of strong social isolation lasting for 3/5/10 months. It is obvious that if we remove control too quickly, we may see the re emergence of disease transmission and the rapid rebound in the number of cases, because many people are still susceptible. After 3 or 5 months, the number of patients will soar and even significantly exceed the carrying capacity of the medical system

we saw this in the 1918 influenza pandemic, when many American cities lifted restrictions after 3 to 8 weeks, and saw the second influenza peak

in order to avoid the resurgence of covid-19, we need to take a variety of interventions within 12 to 18 months or more until effective treatments and/or vaccines are widely used

an alternative to social isolation for months: lighting switch method

we don't need to be highly isolated for a whole year or more. Experts have shown that if we use active on and off interventions, such as the LightSwitch method, we can maintain a relatively low transmission rate, allow a larger range of activities, and still maintain the epidemic at the level that our health care system can manage

when we "turn on the light", we start to maintain social distance, and the number of cases begins to decrease; When the lights are turned off, people can resume social intercourse to a certain extent and make minor adjustments. The number of cases will start to increase a little, but will not get out of control before we resume social distance. According to the data, we can set the switching time to a certain time (three weeks on, three weeks off) or a certain threshold (for example, when there are 15 inpatients in a week, and when it is lower than 2 inpatients in a week)

in this way, we can find a balance between preventing the spread of covid-19 and normal life

The basic principle of statistical model is relatively obvious. Epidemiologists divide the population into different segments. Among them, SIR model is a common mathematical model to describe the spread of infectious diseases. Its basic assumption is to divide the population into the following three categories:

1 susceptable: refers to the people who do not have the disease, but lack immune ability, and are vulnerable to infection after contact with the infected people

2 infected: refers to the infected person, who can spread it to susceptible people

3 removed: people removed from the system. A person who recovers from illness (has immunity) or dies. These people are no longer involved in the process of infection and infection. In SIR model, there are two transformational relationships among the above three groups

for "exposed" but not yet infected people, some models will also add e to become SEIR models. Then, modelers formulate variables based on their judgment of disease transmission, and then run. These variables include: how many people are infected after an infected person recovers or before death, how long it takes for an infected person to infect another person, and so on

seir model legend

"at the beginning, everyone is a susceptible population. The number of infected people is very small. They infected the susceptible population, and the infected people began to grow exponentially." Said Helen Jenkins, an infectious disease epidemiologist at Boston University School of public health

Trump extended the quarantine period to April 30. Based on 12 statistical models,

at a press conference held at the White House on March 29, U.S. President trump repeated the predicted total death data of "2.2 million" of the COVID-19 in the United States 16 times. An earlier research report published by the Institute of health measurement and evaluation (IHME) at the University of Washington on March 25 predicted that 81114 people in the United States may die of covid-19 in the next four months. The inflection point of the epidemic is about the second week of April. IHME is a powerful data processing organization with about 500 statisticians, computer scientists and epidemiologists. Chris Murray, head of IHME: according to the latest model results last week, the overall situation has improved for the first time. The death toll has decreased by 20% compared with last week, and the curve is gradually flattening

from the perspective of epidemiology, the prediction of these death data comes from mathematical prediction models. Dr. Deborah birx, the White House novel coronavirus Response Coordinator, said at the press conference that Trump's decision to extend the quarantine period to April 30 was based on a comprehensive review of 12 different statistical models

the 9-cell model used by Stanford team: s= susceptibility e= exposure ip= pre symptom but infectious state

ia= asymptomatic infection is= severe im= mild disease h= hospitalization r= rehabilitation d= death

the team of Marc lipsitch, an epidemiologist of infectious diseases at Harvard University, also used SEIR model, and the adjustment of data was used to simulate the tightening or loosening of social isolation measures, And the possible seasonal changes of covid-19 infection (similar to influenza). He changed R0 during the simulation. The basic infection index R0 refers to the average number of people who can infect an infected person without external intervention. In this model, if social isolation in the strict sense is stopped (without taking measures such as vaccines or treatment), the infection rate will rapidly rise to about two critical cases per thousand people, which is equivalent to 660000 Americans who are about to become critically ill. The team's model found that even if the strictest ban measures were continuously implemented from April to July, the epidemic would pick up around the autumn

original link:

simulation model link:

the source code of Stanford magic can be found on GitHub:

HT bike TPS://

2.2 million people died of COVID-19? What problems should trump pay attention to when installing the tensile testing machine? Is the worst outcome reliable:

Dynamic Modeling and parameter identification of Wuhan novel coronavirus based on SIR model (with Python code):

the mathematics of predicting the course of the coronavirus:

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