Ambulance Vehicle Routing under Pandemic with Fuzzy Cooperative
Game via Smart Contracts
Alexander Smirnov and Nikolay Teslya
a
SPC RAS, 14
th
line, 39, St. Petersburg, Russia
Keywords: Decision Support, Optimization, Fuzzy Game, Pandemic, Smart Contacts, Transportation, Ambulance,
Vehicle.
Abstract: The pandemic caused by COVID-19 virus has posed a challenge for healthcare systems in many countries.
One of the important tasks facing after a sick person detection is the timely patient’s transportation to a
hospital. When making a decision on transportation to the hospital, it is necessary to account for many
parameters, including beds space availability, availability of hospital staff and medicines required by the
treatment protocol, diagnostic equipment on ambulance team, the distance to the hospital, ambulance vehicle
locations, as well as hospital and ambulance staff psychophysical state, and patient’s reaction to
hospitalization. Some of them can be gathered through smart city sources, like city databases or operational
systems, but most of them require access to medical services. It is proposed to consider hospitals as
participants of a cooperative game, whose overall goal is to ensure the maximum of cured patients. To describe
the psychophysical state of the personnel, as well as to ensure greater variability of the resulting solution, the
game parameters are proposed to be set using fuzzy sets and fuzzy logic. To implement the game rules, it is
proposed to use smart contracts in blockchain technology. The blockchain could also be used to provide access
to data from medical services, store and distribute the current state of hospitals, and save processing results
for later analysis and model refinement.
1 INTRODUCTION
Coronavirus pandemic has proven to be a major
challenge for healthcare systems in many countries.
One of the problems was the patient’s timely
hospitalization, given the limited number of
ambulance vehicles and bed space in hospitals.
When discussing the problem sources and
possible solutions, a large number of factors must be
considered (Patel et al., 2020); at that, four large
groups can be distinguished, associated with the main
actors: hospital, patient, ambulance, a dispatcher
(distribution center). Each of them, due to the
increased load, must constantly monitor the current
situation and promptly form a solution suitable for all
participants in the process (see Fig. 1) (Xiong et al.,
2020). The complexity of the problem of patient
distribution is the stochasticity of their admission and
a high rate of change in the operational situation.
High load causes an increased impact on the
psychological and emotional state of staff and
a
https://orcid.org/0000-0003-0619-8620
patients. This leads to distress increase and errors’
number increase during decision making that can
reduce the care speed and quality (Patel et al., 2020;
Xiong et al., 2020).
The paper proposes considering a narrower
problem associated with the patients’ hospitalization
under a pandemic. A methodology for using
information technologies to support decision-making
for ambulance vehicle routing during hospitalization
is proposed. It is based on the division of the problem
into two tasks: 1) making a decision on
hospitalization with the selection of an ambulance
vehicle and 2) the selection of a hospital to which the
patient must be transported with the selected vehicle.
For each task, several parameters are estimated that
describe both the quantitative characteristics of the
task, such as the availability of vacant places (beds)
in hospitals, the average transportation time, the
number of ambulance vehicles, the cost of treatment,
and the qualitative parameters associated with the
psychoemotional load on patients, dispatchers, and
medical staff.
538
Smirnov, A. and Teslya, N.
Ambulance Vehicle Routing under Pandemic with Fuzzy Cooperative Game via Smart Contracts.
DOI: 10.5220/0010455605380545
In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2021), pages 538-545
ISBN: 978-989-758-513-5
Copyright
c
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Figure 1: Decision making on ambulance vehicle routing for patient treatment.
The choice of a hospital is carried out through the
formation of a cooperative game in which hospitals
are participants. The cooperative game characteristic
function estimates benefit of cooperation as the
number of patients cured under the constraints
imposed by each participant under current conditions,
like, the supply of medicines, the availability of
equipment, the probability of successful treatment
and psycological state of medical staff. To take into
account the probabilistic characteristics and
psychological state of medical personnel, it is
proposed to use fuzzy logic and fuzzy sets.
To quickly track the current situation in this work,
it is proposed to use blockchain and smart contracts.
Blockchain capabilities allow to save patient medical
records and permit access to them only by authorized
staff. It is also possible to store the current state
according to the main parameters that can describe
the current situation. Smart contracts in the form of
decentralized applications can be used to compute the
outcome of the coalition game and support decision-
making during a hospitalization.
The paper is structured as follows. Section 2
describes the current state of research on the use of
information technology to support decision-making
in healthcare. Section 3 describes the decision-
making methodology for hospitalization under a
pandemic. Section 4 contains a description of
describes the hospitalization process as a fuzzy
cooperative game and the parameters that should be
taken into account when making an appropriate
decision. The work conclusion provides general
conclusions and directions for further work.
2 RELATED WORK
Currently, the application of information
technologies to healthcare is an active and promising
research area. The range of their applications is quite
wide: from storing medical records, containing all
parameters of a patient and her/his health history are
saved (Dalianis, 2018), to the use of artificial
intelligence for identifying diagnoses and searching
for medicines for a specific disease of a particular
patient (Topol, 2019; Yu et al., 2018).
It should also be noted that the use of information
technology in healthcare is focused on the processing
of information that is usually classified as personal
data and medical secrecy. This requires
corresponding levels of security for the subsystems
responsible for processing, storing, transferring, and
providing access to the information. In the event of an
epidemic, however, it is necessary to provide online
access to this information (Raskar et al., 2020). The
recent advanced studies suggest an application of the
distributed ledger technology based on the blockchain
concept to solve this problem, as well as smart
contracts technology that expands its capabilities. The
research efforts are focused on developing scalable
systems for collecting readings from sensors that
Smart city
Hospital
Ambulance
vehicle
Patient
Traffic
Population density
Pharmacies location
Hospital location
Free beds
Treatment protocols
Staff competences
Equipment
Dispatcher
center
Location
Staff competences
Equipment
Disease symptoms
Initial diagnosis
Patient transportation
Medical
diagnostics
center
Decision
Decision
Ambulance Vehicle Routing under Pandemic with Fuzzy Cooperative Game via Smart Contracts
539
monitor the patient's condition (Jamil et al., 2020;
Satamraju & Malarkodi, 2020), creating distributed
systems for accessing patient data during routine
examinations, and making diagnoses in case of illness
(Satamraju & Malarkodi, 2020; S. Wang et al., 2018;
Z. Wang et al., 2020).
It is also worth noting existing interdisciplinary
research combining the technologies of the Internet of
Things, supply chains, and the distributed ledger in
healthcare. Thus, in (Islam & Young Shin, 2020), a
system is proposed that collects data from patients
using unmanned aerial vehicles furnished with
special equipment. The collected data are encrypted
and moved to the nearest server, where they are
decrypted and put onto a medical record, access to
which is organized by calling functions of smart
contracts implemented using the Solidity language in
the Ethereum blockchain platform. This scheme
provides remote diagnostics using professional
equipment, with full protection of all patient’s
personal data from the measurement moment to the
medical doctor submission.
Another study suggests using blockchain to
control the vaccine supply chain (Yong et al., 2020).
Within the framework of the developed system, the
control of the stages from the creation of a vaccine to
its application by a doctor is provided. This enables
tracking the distribution process, covering the
population with the vaccine, and is compliant with the
conditions and terms of the vaccine transportation and
storage, which allows for avoiding negative
consequences caused by damage of the vaccine due
to any violation at any stage of delivery. Additionally,
the system includes a recommendation module based
on a neural network of the LSTM (long short-term
memory) model for assessing the vaccine demand
based on the results of the previous vaccination.
3 DECISION SUPPORT ON
AMBULANCE VEHICLE
ROUTING UNDER A
PANDEMIC
3.1 Common Requirements
Since the resources used to overcome the
consequences of epidemics are limited, it is assumed
that their distribution should be directed towards
solutions that are most effective in the current
situation. Agents (participants) form coalitions to use
these resources in which the competencies of each
agent correspond best to the particular problem being
solved. The contribution of each member of the
coalition is estimating as a payoff for task solving
based on the benefit that coalition will gain after
successful task solving. For example, in the case of
hospitalization task the benefit will be the ratio of
cured patients to the number of deaths. It is proposed
to evaluate the effectiveness of socially-oriented
decisions as to the ratio of funds spent on treatment to
potential losses from deaths, while it should be borne
in mind that losses significantly exceed any spent
funds.
The gain of the coalition in this case can be
estimated as the difference between the potential
losses and the funds spent. To motivate agents when
working in a coalition, the entire total payoff (or part
of it) can be distributed among them. This model
allows for applying fuzzy cooperative games to form
a coalition and distribute the payoff between the
members of the coalition. Fuzziness in this problem
allows for operating with fuzzy values of efficiency
and gain, which provides flexibility and variability of
the solutions obtained, as well as to account for
retrospective estimates of the level of uncertainty
distress among participants, including those arising
under the influence of competing for external
regulators of behavior.
3.2 Social and Psychological
Characteristics for Decision
Support
It is proposed to use a mathematical formalism to
describe human behavior. For example, in (Kleiner et
al., 2018), a model of psychological factors of
economic behavior is considered, that includes the
systemic structure of an agent (person), presented in
the work as a set of four interacting subsystems:
intentional (agent's intentions), anticipatory (agent's
expectations), cognitive (agent's perception of the
surrounding world) and functional (agent's behavior).
Analysis and compilation of behavior models are also
used to predict human actions, for example, in
tourism (Gretzel et al., 2015) and marketing (Stalidis
et al., 2015). In this case, the description can be based
on various formalizations, for instance, first-order
logic, description logic, classical algebraic formulas,
consistent with the proposed methodology.
In studies conducted in the context of the 2020
pandemic, it is noted that the incompleteness of a
threatening situation produces a major effect on
people, and the cumulative nature of the stress effect
is recorded (Xiong et al., 2020). Unfavorable
background factors for all participants in the decision-
making process are anxiety, fear of infection, forced
VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems
540
isolation from family members (Shah et al., 2020),
anxiety, and depressive states increase (Montemurro,
2020). The behavior of patients is influenced by the
stress, fear, depression caused by the disease itself
and its severity; and by the stress associated,
according to foreign scientists, with a “health crisis”
(Garfin et al., 2020), which is largely due to the
dissemination of threatening, emotional, and
sometimes redundant information about an invisible
threat, which in isolation leads to the constant appeal
of people to its sources, doubts that in these
conditions one can count on a full and high-quality
medical service. Lack of protective equipment,
problems with adequate treatment, as well as a purely
human factor - fatigue, tension, anxiety, and
professional burnout of medical personnel (Li et al.,
2020; Mo et al., 2020; Montemurro, 2020), induce
negative emotional states in the hospitalized, attempts
to influence the decisions of medical personnel.
Decision-making by medical workers,
management personnel, operational workers is
carried out against a background of fatigue, stress,
and distress. Uncertainty distress has become a key
factor in the impact on medical staff in COVID-19
(Freeston et al., 2020). The uncertainty of the
diagnosis and prognosis of treatment is associated
with the lack of reliable and complete information
about the disease and the forms of its manifestation,
treatment methods, considering the individual
characteristics of patients. These features of the
diagnosis are the characteristic of the current situation
of a pandemic, in which the diagnosis is not always
defined timely, it is formulated vaguely and
unclearly, the disease manifests itself variably, and a
high variety of symptoms is demonstrated.
As several studies have shown (Buheji et al.,
2020), stress occurs when a person is sensitive to the
perception of environmental stimuli, while her/his
resources are aimed at correcting the impact of the
environment or changing it are insufficient. In the
context of the ongoing pandemic, methods are being
developed to express diagnostics of anxiety, states of
uncertainty in large samples, and attempts are being
made to assess the emotional state of individual social
groups or society as a whole (Ahorsu et al., 2020;
Pakpour & Griffiths, 2020).
Formalization of stress indicators requires high
variability with the possibility of states’ fuzzy
description. The most effective tool in this case is
fuzzy sets. Within the framework of the methodology,
fuzzy sets can be used together with the mathematical
apparatus of fuzzy logic and fuzzy cooperative
games, which provide the formalization of the patient
and doctor's state and also form constraints for the
optimization problem being solved.
The use of fuzzy logic and fuzzy cooperative
games in describing the interaction of coalition
participants is a relatively new approach that has
shown, however, its effectiveness in problems of
supply chain configuration (Sheremetov, 2009) and
coalition formation (Mohebbi & Li, 2015). From the
medical point of view, the apparatus of fuzzy logic
and cooperative games can be used to assess the
effectiveness of hospitals (Omrani et al., 2018). The
effectiveness in this work is assessed by many
parameters, including the number and quality of staff
(doctors, nurses, support staff), the number of beds,
the number of operations, costs of treatment and
maintenance, etc. The mechanism of coalition games
was used after the division of the country (in this
study, Iran) into separate regions according to
economic and demographic parameters, and the
assumption that each hospital works in cooperation
with other hospitals in the region. This allowed to
calculate the total gain for the region and track the
contribution of each hospital to the total gain.
4 FUZZY COOPERATIVE GAME
USAGE FOR AMBULANCE
VEHICLE ROUTING
To formulate the ambulance vehicle routing problem,
an analysis of open sources and official documents of
the Russian Ministry of Health was carried out.
According to the model, two related tasks can be
distinguished that must be solved during the
hospitalization process.
4.1 Initial Diagnosis
The first task is to determine the need for
hospitalization and the selection of the ambulance
vehicle from the vehicle pool (Fig. 2).
Figure 2: Initial call dispatching problem.
Upon receipt of a call from a patient, a short
Patient
Emergency
service
Ambulance
vehicle
call
assign
inspect
Ambulance Vehicle Routing under Pandemic with Fuzzy Cooperative Game via Smart Contracts
541
survey is conducted to estimate the probability of a
particular disease. The survey, beside questions to
identify the main symptoms, may contain questions
about concomitant diseases, gender, age, and recent
contacts. Additionally, the location of the patient and
his/her medical insurance is being specified.
After collecting the information, the dispatcher
service must estimate the need for hospitalization (at
least half of the symptoms of the disease has to be
confirmed). Then, based on the current demand and
availability, an ambulance is selected, which has the
necessary equipment and medicines for preliminary
examination and potential hospitalization.
As part of the developed methodology,
dispatchers are offered a decision support system
evaluating the need for hospitalization and selecting
an appropriate ambulance vehicle. The set of
parameters to be used for decision support includes:
Global Parameters:
Healthcare system priorities;
General epidemiological situation.
Information Gathered from the Dispatcher:
Number of calls for a day;
The average severity of patients who require
hospitalization;
The average distance from the patient's location
to the hospital (ambulance service radius);
Vacant beds in hospitals;
Average queue time for ambulance
appointments in case of hospitalization.
Information from Ambulance Cars:
Location;
Staff quality (Average decision time, number of
errors (the patient returns to the place of
residence));
Ambulance equipment (medical drugs, tools,
tests);
Staff working time (fatigue level).
The problem of choosing an ambulance for
hospitalization is proposed to be formulated as a
choice problem. Moreover, if an ambulance is already
with the patient, then it is only necessary to solve the
problem of choosing a hospital. Otherwise, after
choosing a hospital, it is necessary to be guided by the
parameters listed above, as constraints in the selection
problem.
4.2 Hospital Selection
The second task is to choose the hospital, the patient
needs to be transported to (Fig. 3).
Figure 3: Process of patient transportation to hospital.
After an ambulance is assigned to a patient, the
ambulance staff is responsible for the state of the
patient. They need to conduct an initial examination
using their equipment and decide whether the patient
needs an urgent hospitalization or transportation to a
sorting center or for additional testing. For example,
in the case of COVID-19, computer tomography of
the lungs may be necessary to confirm the diagnosis
and the need for hospitalization.
When deciding on hospitalization, it is necessary
to choose a hospital that has vacant beds and all the
necessary equipment to treat the patient with the
given severity of the disease. For this purpose, it is
also necessary to contact the dispatcher, who, based
on the assessment of the current situation, decides on
the place of additional examination or hospitalization.
When solving this problem, it is also necessary to
consider many parameters, including:
Patient and Ambulance Parameters:
Patient condition (Condition description sheet
(20 parameters and general assessment),
comorbidities, age);
Location.
Dispatcher Parameters:
Healthcare system priorities;
Road traffic;
Sorting and laboratory parameters;
Service time for one patient;
Service queue;
Resources (tests, disinfectant materials);
Free seats;
Cleaning and disinfection time after service.
Hospital Parameters
Service time for one patient;
Queue length;
Bed amount;
Available treatment protocols and scripts;
Available resources (medicines, equipment);
Ambulance vehicle
with patient
Emergency
service
Hospital
Sorting centre
and computer
tomography
request
assign
assign
VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems
542
Bed quality (availability of additional functions
and/or equipment to ensure comfort and relief
of the patient's condition);
Staff qualification and competences.
This problem is proposed to be formulated using
the formalism of a cooperative game. The hospitals in
the game are the players among whom it is needed to
assign patients. Also, the players are diagnostic
centers and distribution centers. A coalition can be
formed between hospitals (in the absence of full
compliance with the treatment protocol), between
hospitals, and diagnostic centers.
Let us introduce a formalization of the coalition
game for choosing a coalition of hospitals. Each of
the players aims at providing patient care P
(transferring him from the "sick" state to the "healthy"
state), guided by the treatment protocol (strategy)
using own resources
. Hospitals can form coalitions
to pool their resources to achieve a goal within
existing strategies. When solving a coalition game, a
set of coalition participants is formed, to which the
patient is assigned to (1). In this case, the coalitions
of one participant are possible if he has the necessary
resources to achieve the goal.







(1)
Where is the coalition,
is a characteristic
function that is calculated based on the the current
parameters of patient and player of the
coalition ,

is the strategy of the player of the
coalition . It is proposed to formalize stress levels,
the psychological and physical state of hospital staff
and patients through linguistic variables such as
, where is the name of the variable, is the
set of values and their membership functions, is the
carrier of the fuzzy set.
5 SMART CONTRACTS
UTILIZATION
When organizing the exchange of information within
the framework of the methodology, it is proposed to
use the capabilities of a distributed digital ledger. The
rationale for this decision is the need to provide
trusted access for various services to electronic health
records to speed up the exchange of information
about the patient's condition, to save the history of
changes in the state. Using the smart contract
mechanism, a distributed ledger can be configured to
control access to electronic health records.
Smart contracts are used as a distributed
application. The contracts implement the logic of
solving the coalition game. They take the current state
of the players as input and form a decision that is
stored in the distributed ledger. According to the
decision received, the appointment of the hospital is
carried out and, after approval by the dispatcher, the
process of transporting the patient to the hospital is
started.
Figure 4: Decision making through with fuzzy cooperative game via smart contracts.
Smart
city
Smart city
services
Ambulanc
e vehicle
service
Ambulanc
e vehicle
service
Ambulance
vehicle service
Patient
Dispatcher
center
Medical
diagnostics center
service
Medical
diagnostics center
service
Medical diagnostics
center service
Hospit
al
servic
Hospit
al
servic
Hospital
service
Distributed digital ledger
(Hyperledger Fabric)
Electronic Health Records
Free beds in Hospitals
Hospitals equipment, staff and drugs
Ambulance vehicles distribution
Ambulance vehicles equipment and staff
Current tasks
Fuzzy cooperative
game rules
Ambulance Vehicle Routing under Pandemic with Fuzzy Cooperative Game via Smart Contracts
543
The system for decision making on ambulance
vehicle routing is presented in Figure 4. Sources of
information about the current state of the system are
represented by services in the information space.
Each service is associated with a distributed ledger
and can transfer data through blocks in the ledger.
When requested by a patient, the dispatch center can
quickly receive current information and
recommendations on where to transport the patient.
The recommendation is formed by calling a smart
contract containing the rules of a cooperative game.
By placing contracts in the digital ledger, all blocks
can be accessed from a smart contract, which allows
to quickly get the current state of the system and form
a coalition composition that corresponds to maximum
benefit (to cure a sick person).
The Hyperledger Fabric platform is used to form
a distributed ledger (Androulaki et al., 2018). Its
choice is due to the possibility of forming separate
chains of blocks corresponding to hospitals or
electronic patient records. Using built-in capabilities
for the implementation of smart contracts, access
control to medical records can be controlled.
Complex contracts can also be implemented to
calculate the outcome of a coalition game.
The example of smart contract for core calculation
is presented at listing 1.
Listing 1: Example of a chaincode for coalition
calculation.
var hospitals []Hospital // Hospital
list
var patient Patient // Patients list
var K, K_prev // coalitions
func coalitionCalc(stub
shim.ChaincodeStubInterface, args
[]string) (string, error) {
hospitals[i], patient = args[i],
args[j]
for h in hospitals {
K = f(patient, h)
if K > K_prev
K = K_prev
}
}
stub.PutState(K)
}
Preliminary experiments had shown that
information exchange between coalition participants
through smart contracts requires about 20 ms for each
exchange transaction that includes information
transfer, store in digital ledger and sharing between
all coalition participants. The test stand had the
following configuration: CPU Intel i7 7700k, RAM
16 Gb, SSD M2, 240 Gb.
6 CONCLUSIONS
The work shows the formalization of the
transportation process using a fuzzy cooperative
game problem that takes into account the main
parameters of the hospitals and patients as the values
of the objective function, and the limitations of the
health care system as the limitations of the function.
The main impact on the proposed methodology is
concentrated on taking into account the economic
effect and human behavior with an increase in the
level of stress in the methodology. All parameters are
formalized with the fuzzy sets and their consideration
in the objective function is done using the fuzzy logic.
For the exchange of data on the current situation,
the use of a digital distributed ledger is proposed. The
decision of the target function and decision making,
in this case, can be implemented in the form of smart
contracts in the ledger. This approach ensures the
consistency and immutability of data in the decision-
making process and the accumulation of statistics for
subsequent objective analysis of the pandemic.
ACKNOWLEDGEMENTS
The reported study was funded by RFBR according
to the research project 20-04-60054 and by
Russian State Research No. 0073-2019-0005.
REFERENCES
Ahorsu, D. K., Lin, C. Y., Imani, V., Saffari, M., Griffiths,
M. D., & Pakpour, A. H. (2020). The Fear of COVID-
19 Scale: Development and Initial Validation.
International Journal of Mental Health and Addiction,
19. https://doi.org/10.1007/s11469-020-00270-8.
Androulaki, E., Manevich, Y., Muralidharan, S., Murthy,
C., Nguyen, B., Sethi, M., Singh, G., Smith, K.,
Sorniotti, A., Stathakopoulou, C., Vukolić, M., Barger,
A., Cocco, S. W., Yellick, J., Bortnikov, V., Cachin, C.,
Christidis, K., De Caro, A., Enyeart, D.,Laventman,
G. (2018). Hyperledger Fabric: A Distributed Operating
System for Permissioned Blockchains. Proceedings of
the Thirteenth EuroSys Conference on - EuroSys ’18, 1,
115. https://doi.org/10.1145/3190508.3190538.
Buheji, M., Jahrami, H., & Dhahi, A. S. (2020). Minimising
Stress Exposure During Pandemics Similar to COVID-
19. International Journal of Psychology and
Behavioral Sciences, 2020(1), 916. https://doi.org/10
.5923/j.ijpbs.20201001.02.
Dalianis, H. (2018). Clinical text mining: Secondary use of
electronic patient records. In Clinical Text Mining:
Secondary Use of Electronic Patient Records.
https://doi.org/10.1007/978-3-319-78503-5.
VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems
544
Freeston, M., Tiplady, A., Mawn, L., Bottesi, G., &
Thwaites, S. (2020). Towards a model of uncertainty
distress in the context of Coronavirus (Covid-19).
https://doi.org/10.31234/osf.io/v8q6m.
Garfin, D. R., Silver, R. C., & Holman, E. A. (2020). The
novel coronavirus (COVID-2019) outbreak:
Amplification of public health consequences by media
exposure. Health Psychology: Official Journal of the
Division of Health Psychology, American Psychologi-
cal Association, 39(5), 355357. https://doi.org/10.
1037/hea0000875.
Gretzel, U., Werthner, H., Koo, C., & Lamsfus, C. (2015).
Conceptual foundations for understanding smart
tourism ecosystems. Computers in Human Behavior, 50,
558563. https://doi.org/10.1016/j.chb.2015.03.043.
Islam, A., & Young Shin, S. (2020). A blockchain-based
secure healthcare scheme with the assistance of
unmanned aerial vehicle in Internet of Things.
Computers and Electrical Engineering, 84, 106627.
https://doi.org/10.1016/j.compeleceng.2020.106627.
Jamil, F., Ahmad, S., Iqbal, N., & Kim, D. H. (2020).
Towards a remote monitoring of patient vital signs
based on iot-based blockchain integrity management
platforms in smart hospitals. Sensors (Switzerland),
20(8). https://doi.org/10.3390/s20082195.
Kleiner, G. B., Rybachuk, M. A., & Ushakov, D. V. (2018).
Psychological factors of economic behavior: a systemic
view. Terra Economicus, 16(1), 2036.
Li, W., Yang, Y., Liu, Z. H., Zhao, Y. J., Zhang, Q., Zhang,
L., Cheung, T., & Xiang, Y. T. (2020). Progression of
Mental Health Services during the COVID-19
Outbreak in China. In International journal of
biological sciences (Vol. 16, Issue 10, pp. 17321738).
NLM (Medline). https://doi.org/10.7150/ijbs.45120.
Mo, Y., Deng, L., Zhang, L., Lang, Q., Liao, C., Wang, N.,
Qin, M., & Huang, H. (2020). Work stress among
Chinese nurses to support Wuhan for fighting against
the COVID-19 epidemic. Journal of Nursing
Management. https://doi.org/10.1111/jonm.13014.
Mohebbi, S., & Li, X. (2015). Coalitional game theory
approach to modeling suppliers’ collaboration in supply
networks. International Journal of Production
Economics, 169, 333342. https://doi.org/10.1016/
j.ijpe.2015.08.022.
Montemurro, N. (2020). The emotional impact of COVID-
19: From medical staff to common people. In Brain,
Behavior, and Immunity. Academic Press Inc.
https://doi.org/10.1016/j.bbi.2020.03.032.
Omrani, H., Shafaat, K., & Emrouznejad, A. (2018). An
integrated fuzzy clustering cooperative game data
envelopment analysis model with application in hospital
efficiency. Expert Systems with Applications, 114, 615
628. https://doi.org/10.1016/j.eswa.2018.07.074.
Pakpour, A. H., & Griffiths, M. D. (2020). The fear of
CoVId-19 and its role in preventive behaviors. Journal
of Concurrent Disorders.
Patel, U., Malik, P., Mehta, D., Shah, D., Kelkar, R., Pinto,
C., Suprun, M., Dhamoon, M., Hennig, N., & Sacks, H.
(2020). Early epidemiological indicators, outcomes,
and interventions of COVID-19 pandemic: A
systematic review. Journal of Global Health, 10(2),
020506. https://doi.org/10.7189/jogh.10.020506.
Raskar, R., Schunemann, I., Barbar, R., Vilcans, K., Gray,
J., Vepakomma, P., Kapa, S., Nuzzo, A., Gupta, R.,
Berke, A., Greenwood, D., Keegan, C., Kanaparti, S.,
Beaudry, R., Stansbury, D., Arcila, B. B., Kanaparti, R.,
Pamplona, V., Benedetti, F. M., Werner, J. (2020).
Apps Gone Rogue: Maintaining Personal Privacy in an
Epidemic. http://arxiv.org/abs/2003.08567.
Satamraju, K. P., & Malarkodi, B. (2020). Proof of concept
of scalable integration of internet of things and
blockchain in healthcare. Sensors (Switzerland), 20(5).
https://doi.org/10.3390/s20051389.
Shah, K., Kamrai, D., Mekala, H., Mann, B., Desai, K., &
Patel, R. S. (2020). Focus on Mental Health During the
Coronavirus (COVID-19) Pandemic: Applying
Learnings from the Past Outbreaks. Cureus. https://
doi.org/10.7759/cureus.7405.
Sheremetov, L. B. (2009). A model of fuzzy coalition
games in problems of configuring open supply
networks. Journal of Computer and Systems Sciences
International, 48(5), 765778. https://doi.org/10.1
134/S1064230709050116.
Stalidis, G., Karapistolis, D., & Vafeiadis, A. (2015).
Marketing Decision Support Using Artificial
Intelligence and Knowledge Modeling: Application to
Tourist Destination Management. Procedia - Social
and Behavioral Sciences, 175, 106113.
https://doi.org/10.1016/j.sbspro.2015.01.1180.
Topol, E. J. (2019). High-performance medicine: the
convergence of human and artificial intelligence.
Nature Medicine, 25(1), 4456. https://doi.org/10.
1038/s41591-018-0300-7.
Wang, S., Wang, J., Wang, X., Qiu, T., Yuan, Y., Ouyang,
L., Guo, Y., & Wang, F. Y. (2018). Blockchain-
Powered Parallel Healthcare Systems Based on the
ACP Approach. IEEE Transactions on Computational
Social Systems, 5(4), 942950. https://doi.org/10.1
109/TCSS.2018.2865526.
Wang, Z., Luo, N., & Zhou, P. (2020). GuardHealth:
Blockchain empowered secure data management and
Graph Convolutional Network enabled anomaly
detection in smart healthcare. Journal of Parallel and
Distributed Computing, 142, 112. https://doi.org/
10.1016/j.jpdc.2020.03.004.
Xiong, J., Lipsitz, O., Nasri, F., Lui, L. M. W., Gill, H.,
Phan, L., Chen-Li, D., Iacobucci, M., Ho, R., Majeed,
A., & McIntyre, R. S. (2020). Impact of COVID-19
pandemic on mental health in the general population: A
systematic review. In Journal of Affective Disorders
(Vol. 277, pp. 5564). Elsevier B.V. https://doi
.org/10.1016/j.jad.2020.08.001.
Yong, B., Shen, J., Liu, X., Li, F., Chen, H., & Zhou, Q.
(2020). An intelligent blockchain-based system for safe
vaccine supply and supervision. International Journal of
Information Management, 52(November 2019), 102024.
https://doi.org/10.1016/j.ijinfomgt.2019.10.009.
Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial
intelligence in healthcare. Nature Biomedical
Engineering, 2(10), 719731. https://doi.org/10.1038/
s41551-018-0305-z.
Ambulance Vehicle Routing under Pandemic with Fuzzy Cooperative Game via Smart Contracts
545