Preventing the spread of coronavirus | Mobile activity and network

coronavirus-preventing-mobile-activity

Travel information is now more accurately available than at any time in history – primarily from telecom services. Symbolic photography information is now more accurately available than at any time in history – primarily from telecom services. At least 10 new statistical models are being developed around the world every day to accurately calculate how the symbolic coronavirus will spread and through whom.

The problem is that the protection of public life and property is not possible with just a computer simulation. It requires thorough information, appropriate evidence, and transparency of application. How do we get these materials in such a short time? The answer is very straightforward. The simplest solution is on our mobile phones.

How does our mobile phone work? Mobile companies have built thousands of towers across the country. When you make a call using a SIM card, the call communicates with the nearest tower. So the question is, what happens when you travel? Then your call is transferred from one tower to another like a football pass. Every mobile company collects this information at every moment. The question is, how does mobile work, what is the relationship of coronavirus with it? When office, court, garments holidays were announced a few weeks ago, the tide of ordinary people going home was down. We know the matter is harmful. Because, if any of them have coronavirus, it will now spread effortlessly all over Bangladesh. The level of movement of the people going out of Dhaka can be easily determined by going from one tower of their mobile phone to another.
Other movements can also be detected using this technology. For example, if a person infected with the coronavirus travels long distances on a regular basis, those areas can be identified and immediately made risk-free. The virus can spread long-distance as well as travel to the nearest hospital or bank in case of emergency.

Just as travel of different distances or levels does not play an equal role in the spread of the disease, so does the importance and role of all areas. Areas that are in contact with many areas will naturally be at higher risk, as viruses from different sources can get there. In this way, it is possible to determine the future nature of the spread of the virus by using information on infections in different areas and the movement between them. And network science can play a great role in this.

It is also possible to diagnose who is at higher risk of spreading the disease among people infected with the virus. Let’s look at a small example. Suppose you want to reach out to people in your neighborhood. A schoolteacher in the area may play a leading role, as the teacher may have met a lot of people in the area, and there is a relationship of trust. Suppose again, you want to spread the word about your product. Maybe someone shared the ad on social media, which is followed by people of different ages, different professions. Science says the video is more likely to go viral. Notice, the role of the second person is different from that of the teacher. Where the teacher was playing a role within the same area, the second is helping to spread information among different areas / ages / professions. Without knowing one’s occupation, education, gender, standard of writing or any other information, it is possible to mathematically determine from what kind of work a person can play a pioneering role from the ‘network’ information with whom he communicates online or offline.

It is also very easy to extract aggregate or area information from individuals using the network. We know that there are 13 upazilas in Sylhet in Bangladesh. Assuming that each upazila has only a border connection, it maintains contact with neighboring upazilas. It is only from this information that it can be deduced that Sylhet Sadar is one of the most important upazilas, as there are border connections of six upazilas with Sadar. The picture shows such important upazilas with green circles. If we have the information about the trade communication or roads between the upazilas, it is possible to determine the relative importance and role of the areas more accurately.

We can get supplementary information from various sources to prevent the spread of coronavirus. For example, what is the prevalence in an area, what is the distribution of age according to the census, or what is the inter-area communication. By integrating this information, such network-based analysis can provide an easy and quick solution to emergency policy formulation. This is not a risky computer simulation. Rather the utilization of the information collected using information technology.

The Bangladesh government has taken various initiatives with the help of information technology to tackle COVID-19. One of them is to try to show on the map how many people in an area are showing signs of being infected with the coronavirus by knowing from the public in various ways like phone calls/text messages/apps. With the ICDR infection information is there. A glance at the information on the map reveals what kind of action needs to be taken in an area.

However, there are now a number of identified or suspected areas, and it is not clear from the data alone what exactly the pace of the virus’s spread is going to be. In that case, it can be associated with information obtained from another important source: travel or mobility.

The country is now in lockdown, communication is more limited than usual. However, people may have to go to the bank or the market, may have to go to the hospital in an emergency, so it is not uncommon to have some commute. When two people come close or come in contact, the risk of spreading coronavirus between them increases. We can say that the more people commute between the two areas on a daily basis, the greater the risk of transmission from one area to another. That ‘area’ could be a police station or a village, or a coverage area of ​​a mobile service provider’s tower.

Let’s give an example. Suppose, from government infection calculations and symptom data collected from the public, we know that infections are more prevalent in areas A and B. In the first picture it is shown in red. No such information was available from the rest of the area, so they were shown in seemingly low-risk green. On the other hand, inter-area travel data can tell us that there has been a lot of travel between areas A, B and C in recent times (shown in bold in the second figure). From this data, we can infer that the risk of future infections in C area is much higher. As a result, it is possible to take immediate action by alerting the C area, before any infection is reported there.

And this travel information is now more accurate than at any time in history — primarily from telecom services. As mentioned above, exactly which SIM cards are receiving service from a tower and where exactly those phones are, the information is monitored with great efficiency, in the interest of ensuring high-quality service. Suppose today a total of 20 people from my area went to the next area and came back at the end of the day. Mobile phone service providers are able to convey that information very quickly.

In this way, if we take together the travel information between different areas of the country, we will get a ‘network’. From that network we can easily calculate just how powerful an area is capable of spreading the virus, what the risk is in an area regardless of the degree of detection and where it is important to take action. It is possible to diagnose the future spread of the virus by integrating the data of identified / suspected patients. At the same time it is possible to verify the accuracy of information, even to draw knowledge from incomplete information. Scientific analysis using travel data is not new. If this information is not used to diagnose the spread of coronavirus in Bangladesh, it can be taken into account on an urgent basis.

Some of the most advanced technologies, such as graph signal processing, graph neural networks, and statistical network analysis, are now in our hands. These technologies are capable of creating quick and reliable knowledge by using travel and symptom information to deal with Covid-19. As a result, they can be very helpful in taking effective action in this emergency. It is possible to take advantage of the above technologies as well as misuse them. Therefore, it is necessary to determine the analysis of travel information with appropriate policies. Failure to do so can lead to a number of abuses, including loss of personal liberty, which need to be addressed.

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