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Author: Wu hequan, scholar, China Engineering College and Internet Society of China Consulting Committee Director
Original Publisher: 中国信通院CAICT Date: February 24, 2020 Source:

The following is a translated transcript of a public presentation and lecture delivered by Wu Hequan, scholar, China Engineering College and Internet Society of China Consulting Committee Director.

Phones as the second personal ID card

Smart phones have become another form of personal identification card for us. Our mobile communication penetration rate in 2018 was 112%, compared to 106% globally. The penetration rate of independent mobile communication users, that is, after deducting more than one person, is 82%, which is close to the level of developed countries and higher than the global average.

It can be said that almost all people with independent activities in our country possess a mobile phones, and since China implements a real-name system for mobile phone users we know the identity of each mobile phone user.

Phone can be associated with the location of the holder

Generally, when the mobile phone is in standby, the user will move from one cell tower to another. At this time, the mobile phone must continuously receive the measurement signals from the base station. Read the re-selected cell parameters issued by the base station, select the optimal cell, and actually switch over when it is not in standby. Because the user may be moving and the cell radius is dense, the update time should be faster. The current update is in seconds, which can be said to be real-time.

How is the positioning of the phone calculated? Base stations are mainly used, and there are several ways to improve base station positioning. If you use a better method, the positioning accuracy can be tens of meters, at the moment it generally is currently up to one or two hundred meters. 5G base stations are more dense, and the positioning accuracy is higher.

Global Navigation Satellite + Digital Maps Improve positioning accuracy

In addition to base station positioning we can use global navigation satellites and digital maps to locate users. The proportion of smart phones is now very high, accounting for 80 to 90% of mobile phone users, and smart phones are all equipped with global navigation satellite reception capabilities. The positioning accuracy is generally accurate up to tens of meters, and it the best way to achieve meter level accuracy—but it cannot locate indoor users. Additionally, digital map companies have some methods to scan streets, so its positioning can even reach every building .Generally speaking, if a mobile phone user downloads this digital map app, turns on the device and enables the positioning function, it will send a new position to the GPS as the location changes. So it knowns where you are. Of course, this is much better than the base station method. However, it is limited to users with digital map apps. We have a relatively well-known digital map company that at present it has 700 million users (downloads[probably referring to Baidu Maps]), accounting for less than half of the country's mobile users, only 43%, so coverage is limited. However, although telecom operators cover more and more comprehensive positioning users, their accuracy is not necessarily better than that of digital map companies.

Using the big data map of a digital map company we can traced where the nearly 5 million people who left Wuhan before the lock down went to. Some decent trends can be asserted here.

With these data, how do you analyze the spread of the epidemic? Internationally, a model called SEIR is usually used. Which divides people into the following classifications I = people already infected E= close contact S=currently healthy R=total recovered population.

It has a set of rules and Chinese scholars have proposed an improved C-SEIR, adding P and Q, where P = suspected population and Q = the population with confirmed diagnostic tests. Right now our country is also divided into four categories, from which models for epidemic spread can be calculated.

At present China is in the process of revising the model to take into account the measures taken by the government and the public’s awareness of epidemic prevention. At the moment all models are still based on the number of infected and uninfected in the city, and does not take into considering the the inflow and outflow of people from the city. Using the telecommunications data we can take into account the inflow and outflow of some people which can make this model more accurate.

Big Data Visualized