The pandemic of COVID-19 showed the humanity is vulnerable to threats of epidemic emergent infections. Hence, the challenge of creating a safety system of the population from these threats at territory, national and international levels. The challenge poses a problem in the area of ICT consisting of that developing principles and techniques for engineering flexible decision-making systems. The paper presents a vision of an approach to solving the problem epidemic process, social process, spatial population flow, emergent infection, control of a
process, anti-epidemic measure
social processes. are the necessity of:
The pandemic of COVID-19 taught us a lesson that humanity is vulnerable to threats of epidemics
emergent infections. It caused the understanding of the necessity of creating a system for providing
safety of the population from such a kind of threats at different levels, territorial, national, and
international. The challenge for developing such a system consists in the complexity of one. The
complexity is a consequence of the necessity to take into account not only the epidemic process in
progress but also the progress of related social processes. Moreover, we need to consider also the nature
of the correlation of epidemic and social processes. This complexity is also associated with the fact that
we need to make decisions in conditions of significant information uncertainty, which is caused both
by the emergent nature of the infection and the difficulties of organizing the monitoring epidemic and
The COVID-19 pandemic has shown that the factors that make it difficult to control these processes
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continuous monitoring of public opinion aimed at preventing the development of negative
social phenomena and ensuring the effectiveness of anti-epidemic measures [
2021 Copyright for this paper by its authors.
Note that from the point of view of mitigating the consequences, an epidemic of an emergent infection goes through two phases. The first of these phases is the initial phase that is characterized by a low level of knowledge about the infection and by a lack of effective drugs. The second of these phases is the phase of the controlled epidemic process.
The initial phase operates with various strategies of non-pharmaceutical interventions during the spread of infection. For controlling pandemic COVID-19, different countries have used different policies for planning and using prevention and anti-epidemic measures following their socio-cultural, political and epidemic features. However, the main ones are providing physical distance; introduction of obligatory observance of hygiene and safety measures, as well as personalized measures of selfisolation and quarantine based on information about possible contacts with carriers of infection; providing adequate medical care; informing the public, risk assessment and activation of emergency management services; transport restrictions on both long-distance travel within the country and border crossings.
The World Health Organization (WHO) has been monitoring the pandemic on an ongoing basis (see
Coronavirus disease (COVID-19), Weekly Epidemiological Update and Weekly Operational Update
[
These and similar issues are relevant not only in the context of the COVID-19 pandemic but also for
any emergent infection. Therefore, we need tools to assess the reliability of responses to such an
infection, based on estimating the interaction parameters between the structure, quality and
effectiveness of preventive and anti-epidemic measures in emergent pandemic pathogens and also
factors such as culture, political and legal system, economic status, social atmosphere etc., ensuring a
balance between the effectiveness of preventive and anti-epidemic measures [
This paper proposes some architectural vision of an ICT-based technological framework to control the processes related to the challenges posed by the spread of an emergent infection.
The key prerequisite of the paper is the recognition of epidemic and related social processes as the single complex socio-epidemic process.
Let us consider this prerequisite in detail.
The following diagram in Fig. 1 summarizes further discussion
Firstly, a generally accepted assumption is the assumption that the mechanism of the spread of any infectious disease is related to direct or possibly indirect contacts between people. The direct consequence of the assumption is a strategy of restricting the free walking of people for mitigating epidemic progress.
Secondly, the contact intensity of people depends on the intensity and structure of their social activity. Thus, social activity is an important constituent of a socio-epidemic process. Moreover, at the initial phase of the socio-epidemic process, we can impact the progress of its epidemic-constituent only with an influence on its socio-constituent.
Note. It seemed productive to use digital contact tracing technology to identify the sources of
infection spread in the initial phase of the process, but the COVID-19 pandemic showed those
expectations are inflated. The key reason is the serious contradiction between the information needs of
anti-epidemic authorities and the legal guaranty of personal data protection. Unfortunately, no
satisfactory solution for this contradiction has founded. A detailed discussion of the problem can be
found here [
Thirdly, the above discussion shows to consider spatial population flows for recognizing hot spots for infection spreading. Taking into account the mentioned above contradiction, we need to be restricted by aggregated geolocation data, for example, data about the number of gadgets located in each spatial compartment in each time slot. Of course, such data cannot indicate contacts directly but can be used for estimating the number of contacts.
Fourthly, the efficiency of anti-epidemic measures depends on the commitment of the population to agree to the relevant restrictions, or, in other words, on approving these restrictions by public opinion. Of course, we also need legal media tools to influence public opinion to provide the necessary degree of community approvement of the anti-epidemic restrictions.
Another important prerequisite caused by the need to use concurrent threads to build and refine models of the socio-epidemic process, monitor the process or measure its parameters, as well as make a decision aimed at determining anti-epidemic measures being relevant to the current socio-epidemic state.
Our approach to developing a reference architecture of a control system socio-epidemic processes of emergent infections is to refine some basic architecture of complex object control systems using specific features of socio-epidemic processes. The origin point of our development is some basic architecture of control systems, the structural model of which is represented in Fig. 2. The core component of this architecture solution is Decision Support System. It computes a decision based on the observed state of Control Object and predicted one. The components Monitoring System and Control Object Model provides the observed and predicted states respectively.
The system being described operates cyclic, loop-by-loop, and the corresponding control loop is shown in Fig. 3.
Let us refine the components of this model considering the above discussion (see Fig. 1). The refinement is as follows. The model of Control Object includes now four components, namely, Spatial Population Flow Model, Epidemic State Model, Public Opinion Model, and, finally, Interaction Model of these components. Of course, each of the first three models includes a model of its dynamics. In the time, Interaction Model ensures orchestrating these dynamics.
Similarly, Monitoring System should now provide tools for collecting data of three different kinds namely tools for collecting epidemiological and sociological data and the special tool for receiving aggregated geolocation data.
Taking into account the need to provide concurrent threads for refining models and decision making,
we can use the strategy of reinforcement learning [
So, we are now ready to describe the general decision-making model regarding anti-epidemic measures aimed at mitigating the course of the epidemic of emergent infection, which ensures the fulfilment of the assumptions of Sec. 2.
Firstly, let us remind that we consider epidemic and related social processes as a single complex socio-epidemic process. Secondly, our aim is to provide a concurrent running building and refining the model of the process, monitoring (measuring) its parameters, and decision making epidemic measures. Implementation of concurrent execution is provided by the fact that we distinguish two layers of models.
Our vision of implementation of this concept is represented with UML 2 activity diagram shown in Fig. 4. Of course, loop B–B is the necessary adaptation of the general schema (see Fig. 3) to the context of controlling the socio-epidemic process of an emergent infection. Note, the proposed schema of decision making is open for implementing new knowledge about the controlled infection (see the port located in the bottom-right corner of the diagram in Fig. 4).
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The authors expect the construction of the described above framework provides a scientifically sound reference model and a set of mathematical, epidemiological, sociological and software tools that generally form a decision support system aimed at ensuring the controllability of socio-epidemic processes of emergent infections. Such a decision support system should admit the consideration of the features of a particular socio-epidemic process and administrative territorial unit. The core of such a system is a mathematical model of spatial population flows of the administrative-territorial units, with appropriate algorithmic tools to adapt the model to the conditions of a particular administrativeterritorial unit and applied anti-epidemic and preventive measures. The system includes models of contact occurrence, which are adapted to study the dynamics of contacts in the spatial compartments of the model of spatial flows, which provides modelling of the general epidemic dynamics based on the compartmental approach. Appropriate simulation models are tools for forecasting the dynamics of socio-epidemic processes, including in the context of the introduction of new or weakening of existing anti-epidemic and preventive measures. A set of scientifically based methods of epidemiological and sociological analysis, as well as algorithms for accumulation and data processing of aggregate digital footprint, provides observation (measurement) of parameters to assess management criteria, in terms of adapting the model to the real socio-epidemic situation.
We expect results of the implementation of an appropriate complex of research and development provide • taking into account different points of view on the socio-epidemic process to ensure the effectiveness of management decisions and control the level of negative consequences of anti-epidemic measures; stratification of the solution into system-wide and specific layers, which allows accumulating in the system information about the positive and negative experiences of epidemic management; tools for planning and implementing dynamic monitoring of the socio-epidemic process in critical areas to ensure the rational use of available resources; recognizing drivers of the epidemic process of emergent infection; the ability to take into account when making management decisions specific to different phases of the socio-epidemic process tasks and drivers of a particular epidemic process; the ability to integrate solutions with e-government tools.
The study was funded by the Ministry of Education and Science of Ukraine in the framework of the research project 0121U109814 on the topic “Sociological and mathematical modeling of the effectiveness of managing social and epidemic processes to ensure the national security of Ukraine”. 7. References