Personal privacy VS. public safety: A hybrid model of the use of smart city solutions in fighting the COVID‐19 pandemic in Moscow

Abstract

Technological advancements and big data have brought many improvements to smart city infrastructure. During the COVID‐19 outbreak, smart city technologies were considered one of the most effective means of fighting the pandemic. The use of technology, however, implies collecting, processing personal data, and making the collected data publicly available which may violate privacy. While some countries were able to freely use these technologies to fight the pandemic, many others were restricted by their privacy protection legislation. The literature suggests looking for an approach that will allow the effective use of smart city technologies during the pandemic, while complying with strict privacy protection legislation. This article explores the approach applied in Moscow, Russia, and demonstrates the existence of a hybrid model that might be considered a suitable tradeoff between personal privacy and public health. This study contributes to the literature on the role of smart city technologies during pandemics and other emergencies.

1. INTRODUCTION

Along with effective urban management, smart city technologies are useful for effective emergency management (Ekman, 2019; Liu & Li, 2020). The COVID‐19 outbreak and the associated government restrictions triggered a significant increase in the use of smart city technologies to fight the pandemic (Inn, 2020; Markotkin, 2021). According to the literature (Inn, 2020; WHO, 2019), smart city technologies can be useful in identifying, tracking, and forecasting outbreaks through big data analytics, enhancing public security via improved facial recognition and infrared technologies, delivering supplies, and assisting surveillance. The investment in smart city technologies improved the quality of planning, preparation, and forecasting during the pandemic (Sharifi et al., 2021).

The benefits of using smart city technologies are evident, however, the municipalities of many countries can not use such technologies to the fullest to fight the pandemic because they raise concerns about the “erosion of privacy” and could violate privacy protection regulations (Kummitha, 2020). That is why such countries have to apply the restrictions on using these technologies, applying “a human‐driven” approach (Kummitha, 2020; Kupferschmidt & Cohen, 2020). Some other countries were able to immediately and forcefully activate the full capacity of smart city technologies to keep the pandemic under control by applying “the techno‐driven” approach. The literature describes the pros and cons of these approaches when using smart city technologies during pandemics but advises looking for a tradeoff between personal privacy and public safety (Kitchin, 2020). The article demonstrates one of those tradeoffs represented by a hybrid approach applied in Moscow. Based on the above and considering that Moscow has the most advanced smart city technologies in Russia and strict privacy regulations at the same time, the article answers the following research question: How did Moscow use its smart city technologies to fight against COVID‐19?

To answer the research question, the author studied the literature, government reports and decrees, WHO reports, newspaper articles, other websites, and tested the functionality of the federal and Moscow authorities' mobile applications. The key finding of the research is that Moscow adopted a hybrid approach that combines the features of both the human‐driven and techno‐driven approaches. That approach has not been described in the literature and could be considered as a potential compromise between the concerns about the privacy of individuals and public safety. This approach could be applied by other countries with strict privacy regulations. The article also proposes a qualification matrix, which can be used to define the type of approach applied by a municipality.

The article is organized as follows. The second section contains a literature review aimed at developing qualification criteria to distinguish between human‐driven and technology‐driven approaches to using smart city technologies and define which smart city devices support specific types of government measures (active surveillance, issuing warnings, identification of the infected, isolation, lockdown, and quarantine). The third section describes the research methodology, while the fourth section describes the results (the approach adopted by Moscow authorities). The fifth section contains the overall discussion and considers the theoretical and practical implications of this research and suggests areas for future study. The sixth section provides conclusions and limitations.

2. LITERATURE REVIEW

2.1. Techno‐and human‐driven approaches during a pandemic: Criteria for qualification

A smart city requires collecting and integrating data obtained from sensors, physical devices, software applications, personal cameras, the Internet, smartphones, and similar devices (Quijano‐Sánchez et al., 2020) for further analysis using artificial intelligence algorithms. It requires opening the data for public consideration to increase the transparency on the virus outbreak, which would decrease privacy (Janssen & van den Hoven, 2015). In countries with advanced privacy protection regulations, the use of smart city technologies to track people during the pandemic were perceived as a significant increase in digital control (Markotkin, 2021) and a form of government overreach (The Wall Street Journal, 2020). The literature suggests that the free flow of information and data collection makes the technology work effectively (Kummitha, 2020), but “the challenge is how much data is enough”? (The New York Times, 2020a). When looking for trade‐offs, this is one of the critical questions to be theoretically explored and practically addressed using liberty‐friendly principles of the adoption of technologies (Kitchin, 2020).

In line with the difference between “technology‐push” and “demand‐pull” theories of social change and technological innovation (Kim & Lee, 2009), Kummitha (2020) suggested the key differences between the two approaches to using smart city technologies during a pandemic (Table 1).

TABLE 1.

The key differences between Techno‐ and Human‐driven approaches during a pandemic: criteria for qualification

Techno‐driven	Human‐driven
Immediate	Time‐distributed
Forceful	Flexible
Full capacity of the technologies	Selective use

The approach to using smart city technologies during a pandemic represents a type of the decision the authorities need to make. That means, within the same technological equipment the decision made (the approach applied) could be different. The techno‐driven approach suggests the immediate and forceful use of the full capacity of smart city technologies to keep the pandemic under control. It requires the synchronization and replication at all levels of government at once (Kummitha, 2020) as well as the collection and the use of citizens' data (Cabestan, 2020). China was the first country that used smart city technologies to track citizens, which was solely aimed at fighting the pandemic (Selinger, 2020). The activation of all the technologies available in smart cities allowed the Chinese government to apply effective non‐pharmaceutical measures to stop the spread of COVID‐19 (Kummitha, 2020). As reported by The Wall Street Journal, “in South Korea, investigators scan smartphone data to find within 10 min people who might have caught coronavirus from someone they met. Israel has tapped its Shin Bet intelligence unit, usually focused on terrorism, to track down potential coronavirus patients through telecom data”. (The Wall Street Journal, 2020). The techno‐driven approach requires citizens to follow the protocols and does not consider the context (Janssen & Kuk, 2016). This is one of the reasons why it raises concerns about “erosion of privacy” and freedom (The Wall Street Journal, 2020). The literature doubts that the techno‐driven approach adopted in China could be replicated anywhere else in the world (Kupferschmidt & Cohen, 2020).

Western democracies adopted a human‐driven approach (Kummitha, 2020) (Table 1). This approach adopted when the government has restrictions on the collection and use of citizens' data (personal data protection and privacy laws) (Kupferschmidt & Cohen, 2020) and therefore has to take the context into account and be very selective when using technologies because of the many sensitive limitations (Kummitha, 2020).

Thus, the approach chosen by municipalities depends on a number of factors: the extent to which the regulations allow tracking citizens, collecting personal data, and addressing an individual if specifically required for public safety (Table 2).

TABLE 2.

Techno‐and human‐driven approaches during a pandemic: additional criteria for qualification

The approach	Data collection	Data processing	Data sharing and contacting the infected individuals
Techno‐driven	At an individual level (data from surveillance cameras, geolocation, temperature screening systems, QR‐codes, etc.)	At an individual level (resident's profile, travel history/paths, infection status, etc.)	a) Make publicly available data on the travel history/paths of infected residents
			b) Contact individuals who are infected or possibly infected (by text messages, social networks, QR‐code notification, etc.)
Human‐driven	At an individual level (with restrictions on collecting complete data and rather resort to anonymous data)	Aggregated, anonymous data	a) Share aggregated data
			b) No direct contacts are assumed while processing the aggregated data

As shown in Table 2, the techno‐driven approach implies data collection and processing at an individual level, while the human‐driven approach deals with aggregated data. The literature points out that the techno‐driven approach is more effective during a pandemic than the human‐driven approach (Kummitha, 2020; WHO, 2019), because, for example, relying on anonymous data may not be as effective as collecting data from individuals (Stamati et al., 2015). Relying on quantitative analysis, Yang and Chong (Yang & Chong, 2021) concluded that the investment in smart cities decreases the number of COVID‐19 cases. That is one of the reasons why “technology” is considered a key factor in predicting and controlling a pandemic such as COVID‐19 (Yang & Chong, 2021). However, along with the benefits, the use of AI and Big Data (for the techno‐driven approach) could raise concerns, because increased transparency (making the collected data publicly available) may violate privacy (Janssen & van den Hoven, 2015). The literature also specifies other reasons why technology alone could not be an effective solution in the public sector (Kuziemski & Misuraca, 2020). While the opportunities are well‐described, the literature points out that “the risks and downsides are given less attention” and “the effects are hard to predict and accountability requires both the curation of data and algorithm” (Janssen & Kuk, 2016, p. 376). That is why “evidence‐based policies are not a panacea for many reasons” (Nam, 2020, p. 1). The literature does not conclude which approach (techno‐ or human‐driven) is preferable for smart city authorities during a pandemic and suggests tailoring it to the local needs and resources available (Inn, 2020). Considering that each approach has its pros and cons, Kitchin (2020) suggests that governments should try to respect both civil liberties (not to collect, process, and share personal data—the human‐driven approach) and public health (collect, process, and share personal data—the techno‐driven approach). Therefore, further research of possible trade‐offs is required to make the use of smart city technologies during a pandemic more liberty‐friendly.

2.2. Types of measures municipalities may implement during a pandemic

Based on WHO‐recommended strategies for the prevention and control of communicable diseases (WHO, 2001), which is aligned with the epidemic theory (Patten & Arboleda‐Flórez, 2004), there are four types of measures that municipalities can implement during a pandemic: (1) active surveillance and issuing warnings; (2) identification of the infected; (3) isolation; and (4) lockdown and quarantine.

Active surveillance and warnings are measures aimed at preventing citizens from being infected. Identification is the measure aimed at identifying possibly infected citizens for further tests and decisions on isolation and quarantine. Once an infected person is identified, they need to be isolated from society. Isolation is a process of instructing a person on the next steps toward quarantine. Lockdown and quarantine are the measures aimed at preventing infected persons from infecting others.

These four measures could be supported by smart city technologies to a different extent. Table 3 demonstrates that the “Active surveillance and issuing warnings” type of measures relies on the largest number of smart city devices: “As advised by WHO and learned from the Chinese context, early surveillance is the most effective strategy available for the prevention of transmission” (Kummitha, 2020, p. 8).

TABLE 3.

Available smart city technologies to support the four types of measures during a pandemic

Smart city data sources (IoT devices a )		Active surveillance and issuing warnings	Identification of the infected	Isolation	Lockdown and quarantine
1. Surveillance cameras		(Kummitha, 2020), (Editorial, 2020)	(Liu & Li, 2020)	(Kummitha, 2020)	(Mansfield‐Devine, 2020), (The New York Times, 2020a, 2020b)
2. Camera‐equipped drones		(Kummitha, 2020)		(Kummitha, 2020)	(Kummitha, 2020)
3. Robots		(CNN, 2020)
4. Temperature screening systems (sensors)		(Kummitha, 2020)
5. Mobile phones	Applications that track personal and geolocation data, data from mobile operators	(Kupferschmidt & Cohen, 2020), (360 TV, 2020), (Reuters, 2020)	(Liu & Li, 2020)		(Mansfield‐Devine, 2020) (The New York Times, 2020a, 2020b), (Kummitha, 2020)
	SMS	(Lee & Lee, 2020)		(Shaw et al., 2020)	(RIA Novosti, 2020b)
	QR‐codes authorization	(Kummitha, 2020)		(Shaw et al., 2020)
6. Bank card transactions		(The New York Times, 2020a, 2020b)
7. Websites and applications to share the information about the travel history of infected residents, other online services for sharing information on virus transmission for the prevention of the disease)		(Vedomosti, 2020a)	(Lee & Lee, 2020)
8. Emergency call‐center big data		(RIA Novosti, 2020a), (BBC, 2020b)	(RIA Novosti, 2020a), (BBC, 2020b), (The website of the Moscow's Mayor and Moscow's Government, 2020)	(BBC, 2020a)
Total number of smart city components used		8	4	4	3

^a IoT—Internet of Things.

Therefore, the approach municipalities choose is based on:

• Whether a municipality activates all available smart city components to fight the pandemic, or uses them selectively because of some limitations;

• Whether a municipality uses smart city components to support the four types of measures or only some of them.

I will elaborate upon the above in Section 4 to explore the approach adopted by the Moscow authorities.

3. RESEARCH APPROACH

This section describes the data selected for the study, the methods, and the analytical approach adopted. This paper is a part of a more extensive research project1 that focuses on the analysis of changes in public administration driven by digital technologies.

3.1. Case selection

As mentioned, while the techno‐driven approach was predominantly used in China (Kupferschmidt & Cohen, 2020), Western countries adopted a human‐driven approach (Kummitha, 2020). Russia is geographically located between China and Western countries and has an advanced smart city infrastructure and strict privacy regulations at the same time. Russia was one of the most severely affected countries in the world (as of May 8, 2020, Russia had the third‐largest number of new coronavirus cases identified in the world (Worldometer, 2020)). Considering the above, the author decided to study Russia's experience in using smart city technologies in fighting the COVID‐19 pandemic. The study analyzes Moscow's experience for three main reasons.

Firstly, being the capital city, Moscow has the most advanced smart city technologies in Russia, and the article aims to explore whether the authorities were able to use the technologies in line with the privacy protection laws in place. In terms of smart city devices, nowadays, Moscow authorities collect data from surveillance cameras (installed on public buses, the subway, and at traffic lights, 193,000 cameras in total) (Forbes, 2020a; Moscow Department of Information Technology, 2020), taxi and car‐sharing services, transport (transport card transactions), GLONASS sensors, and Caesar‐Satellite anti‐theft systems (BBC, 2020b). The AI system allows for finding a person's location in the city based on a photo. The main source of photos has been doctors, who are required to take pictures of infected (quarantined) citizens when visiting them (BBC, 2020b). The cameras are also used to identify the elderly who left their home. However, the system has reportedly had some problems identifying individuals wearing face masks (BBC, 2020b). Since 2015, Moscow authorities have had access to geolocation data from mobile providers and have been collecting voice samples of citizens calling the city hotline (BBC, 2020b). Free Wi‐Fi points, the mos. ru, and other city services were used as data sources; and since 2017, even if a person turned on the incognito mode in a browser, the system would recognize the person and collect data (BBC, 2020b). It was declared that Moscow authorities have no direct access to the bank transaction history of the residents; but the authorities have access to the data on citizen's property and related payments (BBC, 2020b). In some countries, such as South Korea, the government monitored both the phones and credit cards of the infected and quarantined citizens after informing them about these measures (Lee & Lee, 2020). Moscow is equipped with similar types of smart city devices as China and South Korea, except for robots and temperature screening systems (sensors) for public places.

Secondly, due to the high population concentration, Moscow had the largest number of the infected in Russia and there was an urgency to use any available means to combat the spread of the disease.

Finally, at first glance, it was hard to determine whether the approach applied by the Moscow authorities was techno‐driven or human‐driven. During the outbreak, the Russian government did not introduce equal and synchronized measures for all Russian cities. However, the Moscow authorities experimented with measures and technologies to keep the pandemic under control. Upon the introduction of certain measures in Moscow, other cities and regions adopted some of the measures as well (Vedomosti, 2020b).

3.2. Identification of the data

The following data sources were used to answer the research question: scholarly articles, news articles, government reports and decrees, mobile applications of the Moscow and federal authorities, and WHO reports.

WHO reports were found using Google, by filtering the search results of trusted sources. Research articles about the role of technologies in tackling COVID‐19 transmission were found using a search engine in Scopus on May 8, 2020, and updated on May 12, 2021, with search phrases such as “coronavirus OR COVID‐19 AND “smart technology” OR “smart city”” (1688 documents in Scopus, 49,576 results in Science Direct), ““Smart City” AND Moscow AND COVID‐19 OR pandemic” (23 documents in Scopus, 83,269 results in Science Direct). The results were narrowed down by using filters, searching within search results, using recommended and cited articles to find information on the article's scope. To find newspaper articles on the devices that were used in smart cities around the world, I looked through news items that covered the first 5 months of 2020. The search was conducted in Google and Yandex News, and the results were limited to popular and trusted media sources. The search for the use of specific devices of the smart city system was performed using Google, with search phrases such as “Moscow coronavirus temperature sensors public places”. Government reports (both nationwide and Moscow‐specific), and decrees on the measures during the pandemic in Russia were found on the relevant government websites or trusted law databases using Google. Google and the App Store were used to explore websites, infection maps, and applications. In total 59 sources (articles, newspapers, websites) were selected and cited in this paper.

3.3. Analysis

To assess whether a techno‐ or human‐driven approach was applied by Moscow, I considered Kummitha's (2020) definition of the key differences between the approaches (Table 1). I defined three key criteria for determining the approach to using smart city technologies during a pandemic based on the literature (Table 2). Also based on the literature, I distributed the types of smart city components of the four types of municipal measures during a pandemic that they could support (Table 3). Using the qualification matrices to explore the approach described in Tables 2 and 3, I would conclude that the approach used by Moscow authorities was technology‐driven, if:

1. It was characterized by the immediate and forceful activation of all2 available smart city devices for all3 types of measures.

2. The authorities collected the personal data of citizens (data from surveillance cameras, geolocation, temperature screening systems, QR‐codes, etc.).

3. The authorities openly shared data processing results (the data on the travel history/paths of the infected citizens), based on that the authorities contacted those who got infected or possibly got infected to apply the measures.

I would conclude that the approach applied by Moscow authorities was human‐driven, if:

• 4It was characterized by selective and time distributed activation of smart city devices.
• 5The authorities did not collect the personal data of citizens, and would rather collect anonymous data.
• 6The authorities were not openly sharing data after processing; the authorities predominantly were focused on sharing anonymous aggregated data on the infected citizens and warnings to society.

In order to determine the approach adopted by Moscow authorities, I explored the smart city components that were available in Moscow and the extent to which they had been used to fight the pandemic (Tables 4 and 5). Based on the results, I made a conclusion on the type of the approach that was adopted by Moscow authorities and suggested the qualification matrix to define the approach applied by the authorities during the pandemic (Table 6).

TABLE 4.

Smart city technologies of Moscow in action during the pandemic

Smart city data sources (IoT elements)		Active surveillance and issuing warnings	Identification of the infected	Isolation	Lockdown and quarantine
Surveillance cameras		– c	–	–	(Vedomosti, 2020b)
Camera‐equipped drones d		–	–	–	–
Mobile phones	Applications that track personal and geolocation data, data from mobile operators	–	–	–	(Vedomosti, 2020b), (The website of the Moscow Mayor and Moscow Government, 2020b)
	SMS	–	–	(RIA Novosti, 2020b)	(Vedomosti, 2020b)
	QR‐codes authorization	a (Ministry of DDCMM of the Russian Federation, 2020), (The website of the Moscow Mayor and Moscow Government, 2020a)	–	–	–
Websites and applications to share information about infected people and their travel history, other online services for sharing information on the virus transmission for prevention of disease		b (Coronavirus‐monitor, 2020), (Mash, 2020)	–	–	–
Bank card transaction history		–	NA	NA	NA
Total		2	0	1	3

^a To save movement history only.

^b Only websites with the number of the virus detections shown on a map.

^c No information was found on the use of these data for fighting the pandemic.

^d Offered by a drone business, rejected by the authorities.

TABLE 5.

The approach of the Moscow authorities to use smart city technologies during the outbreak of COVID‐19

Techno‐driven approach		Human‐driven approach
The approach was characterized by
	Immediate And forceful activation of all available smart city devices for all a types of government measures	✔	Selective and time distributed activation of smart city devices
Personal data of residents
✔	Were collected by authorities (data from surveillance cameras, geolocation, temperature screening systems, QR‐codes, etc.)		Were collected by authorities selectively; authorities collect anonymous data
The authorities
	Openly Shared the data on the travel history/paths of infected residents	✔	Predominantly were focused on sharing anonymous aggregated data on infected residents
✔	Contacted those who got infected or were possibly infected		Warnings to society, not individual infected residents defined by AI

^a Where applicable.

TABLE 6.

Qualification matrix for the approach applied by the authorities during the pandemic

The approach of the authority	Type	Main subject to
		Data collection	Processing	Apply measures
Technology‐driven approach	Immediate and forceful	At an individual level (personal data of residents via different technologies) a	At an individual level (resident profiles, travel history/paths, infection status, etc.)	At an individual level:
				a) Share travel history/paths of infected residents;
				b) Contact infected or potentially infected individuals with instructions
Human‐driven approach	Time distributed and selective	At an individual level (with restrictions to collect complete data in favor of anonymous data)	Aggregated, anonymous data	At the societal level:
				a) Share aggregated data, issue warnings
The hybrid approach adopted by Moscow authorities	Time distributed and selective	At an individual level (data from surveillance cameras, geolocation, QR‐codes, etc.).	No information was found	At the societal level: Share aggregated data on official websites and city maps;
				At an individual level: Infected or potentially infected residents were informed by text messages on the need to be isolated and quarantined and to install the government's app.

^a The choice of Moscow authorities is colored with bold.

4. RESULTS

The first infected person in Russia was identified in Moscow (Ministry of DDCMM of the Russian Federation, 2020) and Moscow was the city with the largest number of infected citizens in Russia. In this section, I explore the extent to which Moscow used its smart technologies to fight COVID‐19. I describe the key features of the approach used by the Russian government and focus on the comparison between the available and utilized components of Moscow's smart city system.

4.1. COVID‐19 outbreak: Key features of the government's approach

There are two main features of the approach adopted by the Russian government during the COVID‐19 outbreak. First, a State of Emergency was not declared, lockdown or quarantine measures were not introduced in Russia; secondly, each region could introduce its own measures (Vedomosti, 2020b). When the federal government launched a mobile application designed for self‐identification and for using QR passes, it was not mandatory for use by citizens or regional governments (Ministry of DDCMM of the Russian Federation, 2020). As a result, the application was not widely and systematically used. The literature points out that the transfer of measures from the national to municipal level was one of the success factors in fighting the pandemic in other countries (Huynh et al., 2020).

When the situation began to deteriorate, Moscow authorities introduced a self‐isolation regime for all citizens. Moscow authorities developed several scenarios for COVID‐19 transmission in Russia and introduced measures for each possible scenario; all other regions considered the Moscow's experience the best practice (Vedomosti, 2020a). Moscow Mayor Sobyanin signed a decree on regulation and restrictions, but despite the pandemic, a State of Emergency was not introduced in Moscow, along with the lockdown and quarantine measures. At the same time, quarantine was introduced for elderly people in Moscow — adults 65 or older being most vulnerable (Forbes, 2020b). The common protocol of the identification of infected people in Moscow was self‐identification. Moscow authorities distributed instructions (the websites of Moscow's Mayor and Moscow's Government, 2020) stating that if a citizen has SARS or seasonal allergy symptoms, they should call a doctor for further instructions. Moscow authorities were quite transparent in sharing the information daily through a special website and in the media (The official portal of the Moscow's Mayor and Moscow's Government, 2020).

4.2. Smart city components used during the pandemic in Moscow

The Smart City concept is quite popular in Russia. Based on the IQ Index of Russian cities4 (covering 191 cities), Moscow has the highest urban digitalization index, followed by Kazan and Saint Petersburg (Russian newspaper [Rossiyskaya Gazeta], 2020). The Smart City concept of Moscow is similar to other projects all around the world and is aimed at “the development of urban management by increasing the efficiency and transparency of urban management; improving the life quality of the Moscow population by the large‐scale use of information and communication technologies in the social sphere, in the sphere of ensuring the integrated security of the city of Moscow and in other spheres of city administration in Moscow, as well as in the citizens' everyday life” (Ruzina, 2020). Moscow authorities have been implementing smart city technologies since 2010 based on the Singapore model (International Telecommunication Union, 2018). Moscow was ranked 72nd in the Smart City Index 2019 (The IMD World Competitiveness Center, 2019), which ranks 102 cities worldwide and measures how citizens perceive the impact of policies on their daily lives. Singapore holds the 1st place, Zurich and Oslo are in 2nd and 3rd, respectively.

When the pandemic began and the self‐isolation regime was introduced, Moscow authorities announced that they would use the current smart city system to monitor citizens (RBC, 2021). The wide use of smart city technologies to fight the pandemic was possible due to changes in federal legislation that were introduced in April 2020 (Markotkin, 2021). These changes allowed the Moscow authorities to conduct experiments involving the use of smart city technologies to improve the life of the citizens and the efficiency of governance (Markotkin, 2021), however, these new amendments guaranteed the protection of privacy during the experiments.

However, there was no forceful and immediate activation of available smart city technologies for active surveillance, identification, and isolation of infected persons—the approach was time distributed and selective (not all available smart city technologies were used, the measures differ from the ones in other regions of Russia) (Table 4).

For instance, high alert mode was put into place in Moscow on March 16 (Moscow Government, 2020). A “social monitoring” application to track infected citizens was launched on April 2, 2020 (Mjerija Moskvy Gotova Primenjat’ QR‐Kody Dlja Kontrolja Rezhima Izoljacii [The Moscow City Government Is Ready to Apply QR Codes to Control the Mode of Isolation], 2020), and from April 13, 2020, the QR‐pass system was introduced for those who needed to leave their residence (Sobjanin Ob'javil o Vvedenii v Moskve Specpropuskov. Chto Jeto Znachit [Sobyanin Announced the Introduction of Special Passes in Moscow. What Does It Mean], 2020).

Not all available smart city technologies were activated in Moscow to keep the virus under control. The technologies were mostly focused on the isolation and quarantine of infected citizens and less focused on active surveillance to issue warnings and tracking to identify potentially infected persons to isolate them for further lockdown and quarantine. Evidence of the wide use of the technologies (cameras, for instance) for active surveillance and issuing warnings for COVID‐19‐related purposes in Moscow has not been found. However, these technologies were used in urgent situations: surveillance cameras were used to track potentially infected citizens when the first infected person was identified (Vedomosti, 2020a). Based on surveillance camera records and geolocation data from mobile phones, all persons that the infected person contacted (including family members) were identified and quarantined. Text messages were used to inform potentially infected citizens of their status and the need to self‐isolate (RIA Novosti, 2020b). This demonstrates that the data were collected and processed on an individual level and were not anonymized. However, smart city technologies were used in cases of emergency. Moscow authorities were sharing aggregated (anonymized) data on Internet websites only and never published the data on the travel history of the infected citizens. Publicly available maps for Russia (Ministry of Health of the Russian Federation, 2020) and Moscow (Mash, 2020) show the addresses from where infected persons were taken to a hospital.

The approach introduced by Moscow authorities was issue‐based—smart city technologies were used only based on evidence (when an infected person was identified). The most active measure of the authorities to prevent the disease among citizens was to issue a recommendation to self‐isolate. Self‐identification mode was required for infected citizens (who had symptoms and were suspected of being infected). While WHO recommendations and the Chinese experience show that “governments need to impose lockdowns as early as possible” (Kummitha, 2020, p. 8), lockdowns and quarantines were not imposed in Moscow.

The business community tried to contribute by offering technologies to the authorities. For instance, AT Consulting VOSTOK developed a solution to identify infected citizens (including the potentially infected) (ComNews, 2020). Drone producers suggested using drones for monitoring public places (to avoid crowds) and infrastructure during an emergency shutdown of enterprises for alerting the citizens about antiviral activities, for the expedited delivery of medical tests, samples, vaccines, and drugs, and for applying antiseptics and disinfectants to potentially infected areas (RBC, 2020). However, none of those suggestions were accepted by Moscow authorities.

As mentioned in Section 4.1, no uniform anti‐COVID‐19 measures were implemented in all Russian regions (Vedomosti, 2020b). Moscow was leading the way in introducing the regulation to get COVID‐19 under control, and the regions were adopting some of them. For instance, in Tatarstan, it was required that citizens receive a text message with a permit to leave their homes. The permit was valid for only 1 hour, and it could be obtained only twice a day. QR‐code identification was launched in the Nizhny Novgorod region (The website of the Moscow Mayor and Moscow Government, 2020). In many other regions of Russia, AI systems were used to quickly collect data on the number of free hospital beds, ambulance crews online, etc (RIA Novosti, 2020a). These data were used to make forecasts and arrange medical assistance effectively. Using AI allows the reduction in the number of people involved in the collection and analysis of information (from 100 to 200 specialists at a call‐center to 10–15).

In order to assess people's perception of smart technologies applied by the Moscow authorities during the pandemic, a number of surveys were conducted. One survey reported, “The population does not believe in an easy solution, as in H.G. Wells' The War of the Worlds; on the contrary, the more the screws are tightened, the quieter the crisis is perceived” (translated by the author) (How Do Russians Respond to the Epidemic? Polling Stories [Kak Rossijane Reagirujut Na Jepidemiju? Istorii Oprosov], 2020). Citizens experienced issues with some of the technologies, for instance, with the Social Monitoring application aimed at tracking home‐treated infected citizens (issues with downloading the app, registration, issues with uploading a photo, etc.) even when mobile devices were provided to the infected by the mayor's office (“Social Monitoring”: How Moscow Mocks the Quarantined Sick [“Social'nyj Monitoring”: Kak Moskva Izdevaetsja Nad Zapertymi v Karantin Bol'nymi], 2020)). As a consequence, people were fined for violating the regulations and were not happy about it (gave the application low scores and negative reviews) (Mobile Application “Social Monitoring” Reviews, 2021).

5. DISCUSSION

5.1. The approach of the Moscow authorities: Neither techno‐driven nor human‐driven

The approach of the Moscow authorities could be qualified as human‐driven because it was selective and time distributed, and the authorities shared aggregated (anonymized) data on the infected citizens (Table 5).

In other regions of Russia, AI systems were used to quickly collect data to make forecasts and arrange medical assistance effectively (RIA Novosti, 2020a). However, this practice was not common for all Russian regions. The absence of harmonized country‐wide measures to fight the pandemic (Vedomosti, 2020b) also proves that the approach of the authorities was rather selective.

However, the approach introduced by Moscow authorities also has the attributes of the technology‐driven approach (Table 5), because it allows for collecting the personal data along with the ability to contact infected or potentially infected individuals when required (based on the data processing results). For instance, the Moscow authorities were collecting the personal data from surveillance cameras, mobile phones, and so on, and were using them when they needed to find, track, or inform (by a text message) the infected or potentially infected persons.

Thus, I concluded that Moscow authorities adopted a hybrid model that combines features of the techno‐driven and human‐driven models (Table 5). Smart city technologies in Moscow were used selectively and were mostly focused on the isolation and quarantine of the infected and less focused upon active surveillance to issue warnings, identify potentially infected persons and to isolate them for further lockdown and quarantine. These technologies allowed the authorities to collect the personal data and use them when there was a need to find, track, or inform the infected or potentially infected person, but it was shared only in an anonymized form. A State of Emergency was not declared, lockdown and quarantine were not introduced in Russia, and there were no uniform country‐wide measures in place (each of the regions was able to introduce their own measures to fight the pandemic).

5.2. Theoretical and practical implications

“Human history has always been about keeping up with technological advances to make life more comfortable (fire), easier (the wheel), more productive (the printing press, steam power), and more mobile (the car)”. (Done, 2012, p. 53). Humanity has achieved fantastic results in the development of technology, but during the pandemic, it faced the paradox of the inability to use it at full capacity. This is because along with the development of the technologies, humanity was developing the concept of key civil rights and liberties, which resulted in the implementation of legislation such as the European Convention on Human Rights (Glas, 2013) or General Data Protection Regulation (Otto, 2018). “But in emergencies like pandemics, privacy must be weighed against other considerations, like saving lives”, said Mila Romanoff, the data and governance lead for United Nations Global Pulse (The New York Times, 2020a). “I am more and more convinced the greatest battle of our time is against the “religion of privacy”. It literally could get us all killed”, said the former Portuguese Europe Minister Bruno Macaes (BBC, 2020a).

Authorities around the world were not ready for the COVID‐19 outbreak and when it happened, they used the means that were available in each specific country or municipality. As demonstrated in Section 2.1, the available literature defines the techno‐driven and human‐driven approaches used by the authorities during the pandemic. The techno‐driven approach is considered more effective in fighting the pandemic (Kummitha, 2020; WHO, 2019), but it cannot be replicated in countries with strict privacy regulations (Kupferschmidt & Cohen, 2020). The active use of technologies during the pandemic was criticized for overreach and the “erosion of privacy” (The Wall Street Journal, 2020) because “the increased surveillance and health data disclosures have also drastically eroded people's ability to keep their health status private” (The New York Times, 2020a). The governments were also expected to find ways to use technologies while complying with data protection laws at the same time, and to reconsider the balance between personal privacy and public safety (The New York Times, 2020a). The technologies are developing very rapidly and the literature suggests that a trade‐off model is needed to harmonize civil liberties and public health (Kitchin, 2020).

In this regard, the article demonstrates the existence of a hybrid model that could represent a new generation of approaches aimed at finding a meaningful balance between privacy and public safety, using the benefits of technology. The literature shows that technology alone could not be an effective solution in the public sector (Kuziemski & Misuraca, 2020) and a hybrid model of the use of smart city technologies significantly resonates with this statement. The model relies on the strength of the technology and acknowledges its role in fighting the pandemic, allowing the authorities for temporary tracking of the infected persons for the sake of public safety. However, using such a model might require amending the legislation in time to make it work, which might be quite difficult to do in some countries. This is one of the limitations of the hybrid model. The existence of emergency protocols for the use of smart city technologies could be a solution for such countries. The hybrid model is selective in using technologies (not all available technologies are used at all stages of fighting the pandemic, and the protocols used could differ from one region to the next) and it is cautious with data collection (for many reasons). For instance, Russia “lacks the vast troves of user data possessed by China” (Goode, 2020, p. 1).

The existence of hybrid models is important for several reasons. First, from a theoretical point of view, the hybrid model adopted in Moscow demonstrates the existence of alternative models other than the two main model types identified in the literature (Table 5). Further research could focus on developing a classification of hybrid models and analyzing the factors that shape them in different countries. Based on the demand for the trade‐off between civil rights and public safety, hybrid models need to be explored further. At the same time, the findings of the article contributes to the studies of the public administration model in Russia.

Secondly, from a practical point of view, the hybrid model will allow governments to have a third option and use smart city technologies effectively while meeting the requirements of local regulations on privacy. That means that authorities do not need to choose one of the two main approaches but could consider a hybrid model (Table 5). There are clear practical intentions from the countries that were not satisfied with the human‐driven models to find such a hybrid approach. “These are strange times. Germany, perhaps the most privacy‐conscious nation on Earth, is considering a mobile phone app that would trace the contacts of anyone infected with COVID‐19” (BBC, 2020a). During the emergency, former New York Governor Cuomo “got the unlimited authority to rule by executive order during state crises like pandemics and hurricanes” (The New York Times, 2020a). Another example of such an approach comes from Israel where the government was allowed to use mobile provider data of infected people within 30 days: “We have to maintain the balance between the rights of the individual and needs of general society, and we are doing that”, said former Israeli Prime Minister Benyamin Netanyahu at the time (The New York Times, 2020b). Nevertheless, when looking for a balance the authorities would need to decide on how much data is enough, and further research and practical experiments should help in answering this question.

Thirdly, the existence of a hybrid model is important from a political point of view, because using a techno‐driven approach that violates freedoms could negatively affect the political reputation of governors even if it is successful from a healthcare point of view. The use of a flexible and meaningful approach could bring many benefits for the politicians who could, for instance, arrange public participation in choosing the extent of using the technologies in emergency situations.

The results described in Table 5 could be presented as a Qualification matrix for the approach applied by the authorities during a pandemic (Table 6).

The Qualification matrix could be useful for the theoretical analysis of models applied in other countries (regions) and classifying them. The matrix is also useful for a self‐audit and policy development within a region and a country. “The pandemic may, finally, humanize the use of high‐tech in cities. The smart city models of a generation ago were all about regulation and control—the state online. What's emerging in this pandemic are good programs and protocols which create community”, stated Richard Sennett, Professor of Urban Studies at MIT (Digital Leaders, 2020). Therefore, the exploration of new hybrid models of a government approach to pandemics, including the limitations and new trade‐offs, could be popular for some time, because many questions remain to be answered both in theory and in practice.

6. CONCLUSION

Many countries implemented smart city technologies, but during the COVID‐19 outbreak in 2020, some countries were able to use its full capacity (the techno‐driven approach), while others could do this only selectively (the human‐driven approach) because of strict privacy protection legislation. The literature suggests that along with these two approaches, an alternative model would add value. The Russian Federation has advanced smart city infrastructure and strict legislation on privacy protection simultaneously. This paper explored the approach of the Moscow authorities to using smart city technologies during the COVID‐19 outbreak in 2020 and concluded that the authorities used a hybrid approach which demonstrates the features of both human‐driven and techno‐driven approaches. The author developed a Qualification matrix to define the approach used by authorities during the pandemic.

This research was based on publicly available sources of information and did not rely on any internal data of the authorities that could potentially influence the findings. For instance, only publicly available data were used when assessing whether smart city devices were utilized for the specific government measure. That may mean that other devices can also be used, but no information about such devices was available via the open sources. This is the main limitation of this research. As the next step, the results of the research could be validated through interviews with the managers of the smart city system of Moscow.

CONFLICT OF INTEREST

This article is a part of a research project implemented as part of the Basic Research Program at HSE University. The research was undertaken independently by the author.

Revyakin, S. A. (2022). Personal privacy VS. public safety: A hybrid model of the use of smart city solutions in fighting the COVID‐19 pandemic in Moscow. Public Administration and Development, 42(5), 281–292. 10.1002/pad.1997

DATA AVAILABILITY STATEMENT

Data derived from public domain resources.