With the growing concern about the spread of new respiratory infectious diseases, several studies involving the application of technology in the prevention of these diseases have been carried out. Among these studies, it is worth highlighting the importance of those focused on the primary forms of prevention, such as social distancing, mask usage, quarantine, among others. This importance arises because, from the emergence of a new disease to the production of immunizers, preventive actions must be taken to reduce contamination and fatalities rates.
From the emergence of new infectious diseases, new research studies are also being carried out in order to contribute to their treatment motivated not only because of health crisis, but also social and economic impacts. However, until new medications or vaccines are produced, preventive measures are recommended by health organizations in order to reduce transmission among the population, such as social distancing, mask usage, isolation and quarantine [8,9,10][1][2][3].
Being a topic of considerable importance, especially due to the social, health and economic impacts to society, studies focused on the application of technology in the primary forms of prevention of new infectious diseases have attracted much attention and concern from institutions and researchers.
The scope of this SLR was to identify relevant studies that adopt information technology solutions in the primary ways of preventing respiratory infectious diseases transmission/spread.
From the findings, it was possible to identify six application domain categories in which there was a greater trend in studies related to pandemic planning and, among the support mechanisms adopted, data and mathematical application-related solutions received greater attention.
ID | Quality Assessment Question | Yes | Partially | No | |
---|---|---|---|---|---|
Category | Studies | ||||
Q1 | Are the study objectives and goals clearly specified? | 218 (99.5%) |
1 (0.5%) |
0 | |
CD1: Healthcare and Clinical management (59) | (0.0%) | ||||
S4, S11, S14, S21, S24, S25, S27, S31, S37, S38, S39, S40, S56, S57, S58, S59, S60, S61, S64, S70, S77, S80, S88, S89, S92, S93, S95, S103, S104, S112, S123, S126, S127, S133, S137, S139, S140, S141, S150, S151, S157, S158, S160, S168, S171, S181, S182, S183, S187, S191, S192, S197, S202, S203, S205, S206, S207, S218, S219 | Q2 | Is the study context clearly defined? | 113 (51.6%) |
89 | |
CD2: Infection Testing/Screening (14) | (40.6%) | 17 (7.8%) |
|||
S26, S35, S65, S82, S118, S122, S128, S148, S159, S163, S177, S185, S193, S198 | Q3 | Does the research design support the objectives/goals of the study? | 135 (61.6%) |
71 (32.4%) |
13 (5.9%) |
CD3: Mask Detection (16) | S6, S8, S47, S63, S74, S75, S76, S106, S107, S108, S121, S142, S143, S144, S152, S178 | Q4 | Does the study have an adequate description of the analysis of the data? |
Level | Classification | Description | ||||
---|---|---|---|---|---|---|
96 | ||||||
(43.8%) | 67 | (30.6%) |
56 (25.6%) |
|||
CD4: Pandemic Planning (75) |
S1, S2, S3, S5, S7, S15, S17, S23, S28, S29, S32, S36, S41, S43, S44, S45, S46, S48, S51, S53, S54, S55, S62, S66, S68, S71, S78, S81, S83, S85, S91, S96, S97, S98, S99, S100, S101, S102, S105, S111, S113, S116, S117, S120, S124, S125, S129, S130, S132, S138, S145, S146, S147, S153, S155, S156, S161, S164, S165, S169, S170, S173, S180, S186, S188, S189, S190, S199, S200, S201, S204, S209, S211, S215, S216 | Q5 | Does the study present a clear statement of the findings and provide enough data to support them? | 79 (36.1%) |
81 (37.0%) |
59 (26.9%) |
CD5: Quarantine/isolation/containment/social distancing (24) | S10, S16, S19, S22, S30, S50, S52, S69, S79, S84, S90, S109, S110, S119, S131, S135, S167, S172, S174, S179, S184, S194, S195, S213 | Q6 | Do researchers critically examine potential bias and/or influence in the study? | |||
CD6: Tracking, surveillance, and Contact tracing (31) | S9, S12, S13, S18, S20, S33, S34, S42, S49, S67, S72, S73, S86, S87, S94, S114, S115, S134, S136, S149, S154, S162, S166, S175, S176, S196, S208, S210, S212, S214, S217 | 3 |
0 | No evidence | No evidence was presented regarding evaluation or validation | ||
(1.4%) | ||||
33 | (15.1%) | 183 (83.6%) |
||
Q7 | Study limitations are discussed explicitly? | 51 (23.3%) |
66 (30.1%) |
102 (46.6%) |
Category | Sub-Category | Support Mechanism | Studies |
---|---|---|---|
1 | |||
AnyLogic, Django Framework (1) | |||
S2 | |||
ArcGIS (3) | |||
S28, S62, S208 | |||
Autodesk Revit/Meshmixer, Rhino3D, AutoCAD, Grasshopper (4) | |||
S83, S110, S188, S199 | |||
AWS—Amazon Web | |||
S25, S31, S39, S47, S74, S86, S109, S114, S121, S123, S134, S136, S137, S139, S185, S191, S192, S197, S198, S207, S219 | |||
Printer and scan devices (3) | |||
S110, S174, S218 | |||
Spray/Dispenser devices (6) | |||
S11, S82, S168, S191, S205, S219 | |||
UV technology (e.g., UVC, UV Chip, UV Led, UV Light, UV ray) (7) | |||
S11, S24, S38, S95, S127, S133, S148 | |||
Example or demonstration | |||
Robot/Drones | |||
Robot/Drones/Unmanned Aerial Vehicles (UAV) (14) | |||
S18, S22, S43, S80, S90, S127, S140, S167, S173, S183, S187, S198, S205, S218 | |||
CS4: Blockchain | |||
* | |||
1 | |||
Blockchain (7) | |||
S43, S67, S71, S117, S162, S173, S186 | |||
Objective | Item | Objective | Item |
---|
Evidence Level | Context | |||||||
---|---|---|---|---|---|---|---|---|
Academic (121) | Industrial (98) | |||||||
CS1: Data and Mathematical Application Related Solutions | Algorithms, Theories, Mathematical/Statistical Models | Bootstrap Method (1) | S170 | |||||
General Data | Title | RQ5 | Context | |||||
Q2 | ||||||||
0: No evidence (13) |
S3, S11, S20, S25, S53, S56, S70, S124, S126, S141, S150, S177, S181, | *1 | Dijkstra Algorithm (1) | S16 | ||||
Author(s) | Q1 | Discrete Fourier Transform (DFT) model (1) | S65 | |||||
Description of the Context | ||||||||
Objective of the Study | ||||||||
Services (e.g., software, and load Balancer, elastic container, lambda, Greengrass, others) (3) | ||||||||
S12, S62, S137 | ||||||||
1: Example or demonstration (36) |
S1, S5, S9, S12, S26, S30, S31, S33, S36, S50, S67, S69, S79, S81, S82, S84, S87, S101, S107, S127, S133, S134, S140, S146, S163, S165, S166, S175, S179, S185, S191, S193, S197, S208, S213, S214, | *1 | Application description is provided with an example to aid its description | Publication Year | General Algorithms, mathematical models/equations (12) | |||
2 | Specialists Notes | Qualitative or textual assessments are provided. Example: advantages and disadvantages contrasts/comparation | S9, S34, S49, S52, S69, S94, S102, S130, S156, S162, S210, S214 | |||||
Venue | ||||||||
Bootstrap, Adobe Photoshop (1) | ||||||||
2: Specialists Notes (7) | S14, S47, S71, S72, S86, S174, S194,Q3 | |||||||
S204 | ||||||||
Description of the Research Project | ||||||||
3 | Experiment in laboratory | |||||||
Business Model Canvas (BMC), Service Blueprint (1) | ||||||||
* | 1 | |||||||
3: Experiment in laboratory (117) |
S2, S7, S8, S10, S13, S16, S18, S34, S40, S43, S44, S49, S51, S52, S54, S59, S62, S64, S66, S75, S80, S89, S90, S91, S94, S96, S98, S99, S100, S102, S103, S104, S105, S109, S112, S115, S119, S121, S122, S129, S132, S142, S145, S151, S153, S156, S158, S159, S161, S162, S164, S171, S172, S173, S180, S182, S186, S190, S200, S206, S209, S210, S211, S215, S219 | S4, S6, S17, S23, S37, S38, S39, S41, S60, S61, S65, S68, S74, S76, S77, S92, S95, S106, S108, S110, S111, S113, S116, S117, S118, S120, S123, S125, S128, S136, S138, S143, S147, S148, S152, S155, S160, S167, S170, S183, S184, S187, S188, S189, S195, S196, S198, S199, S202, S204, S216, S218 | Results are reached from simulations with artificial data in real experiments. Evidence collection is performed formally or informally. | K-nearest Neighbor Algorithm, Nearest-neighbour distance (2) | Paper Summary | Q4 | Analysis of the Data | S124 |
4: Empirical Investigation S13, S167 | ||||||||
(24) |
*1 | |||||||
4 | Empirical Investigation | Markov Model, Spatial Temporal Method, Graph Theory, NHPP, Monte Carlo (19) | S17, S18, S23, S34, S36, S44, S51, S53, S62, S85, S99, S101, S111, S116, S129, S149, S154, S215, S216 | |||||
RQ1 | Approach | Multi-agent (Model/simulation), Equation-based model (13) | S2, S3, S4, S5, S7, S91, S96, S99, S100, S125, S132, S160, S211 | |||||
Multiple Signal Classification (MUSIC) Algorithm (1) | S128 | |||||||
Ethereum (1) | S67 | |||||||
Q5 | Google Cloud Platform (2) | S62, S68 | ||||||
Hadoop (1) | S42 | |||||||
Hyperledger Fabric (1) | S117 | |||||||
Conclusions Presentation | IOTA Tangle Platform (1) | S71 | ||||||
S19, S22, S27, S28, S29, S32, S46, S55, S73, S83, S85, S88, S93, S130, S131, S135, S139, S154, S169, S176, S203, S205, S207, S212, | Optimal Control Theory (1)Kibana (Elasticsearch) (1) | S62 | ||||||
Real context investigation of the behavior of the proposed approach | MATLAB (1) | |||||||
5: Strict analysis (22) | S113 | |||||||
Microsoft Azure Cloud (2) | S70, S78 | |||||||
NetLogo (1) | S4 | |||||||
5* | S161 | |||||||
Regression models, Short-term Prediction, RMSE, MAE (5) | S15, S68, S128, S147, S169 | |||||||
SEIR model, Grey Prediction Model, DSGE Algorithm, SLIR, SIS, SIR (24) | NLTK—Natural Language Toolkit (1) | S158 | ||||||
Node.js (2) | S62, S137 | |||||||
Node-RED and Grafana (1) | S136 | |||||||
1 | Strict analysis | Evaluation/validation of the study is performed using a formal methodology. Example: questions and variables definition for analysis after the application of the approach | OpenCV (2) | S109, S151 | ||||
Ultimaker Cura (1) | S110 | |||||||
Unity Platform (e.g., WebGL, 3D) (3) | S83, S131, S179 | |||||||
RQ2 | Application Domain | Q6 | Critical Analysis Description | |||||
RQ3 | Adopted Support Mechanisms | Q7 | Description of Limitations and Bias | |||||
RQ4 | Level of Evidence | S21, S32, S55, S66, S91, S96, S97, S98, S116, S117, S129, S130, S145, S146, S153, S155, S161, S189, S190, S199, S200, S201, S209, S215 | ||||||
Self-Propelled Entity Dynamics (SPED) model, LDS—Low Discrepancy Sequence (1) | S164 | |||||||
Artificial intelligence, Deep learning, Machine Learning, Big Data and Data mining | Big Data (5) | S1, S42, S81, S123, S147 | ||||||
Decision Tree, Regression Tree, CART (5) | S40, S41, S46, S123, S180 | |||||||
DBSCAN—Density-Based Spatial Clustering of Applications with Noise (1) | S172 | |||||||
Fuzzy Logic (3) | S125, S171, S192 | |||||||
Heterogeneous Diffusion Network (1) | S154 | |||||||
K-means (5) | S33, S44, S180, S214, S217 | |||||||
LLA—Lexical Link Analysis (1) | S138 | |||||||
Logistic Regression (10) | S27, S45, S46, S55, S104, S149, S154, S169, S170, S177 | |||||||
Maximum Entropy Model (1) | S105 | |||||||
Naive Bayes (2) | S27, S41 | |||||||
NLP—Natural Language Processing (3) | S103, S137, S158 | |||||||
Neural network (CNN, MTCNN, MobileNet, others), Feature Enhancement Module (FEM), Spatial Separable Convolution, SSD (41) | S6, S8, S10, S20, S40, S41, S45, S47, S48, S55, S57, S58, S59, S60, S61, S63, S64, S65, S70, S74, S75, S76, S88, S90, S106, S107, S108, S109, S112, S120, S121, S142, S143, S144, S152, S178, S182, S184, S185, S201, S206 | |||||||
Random Forest, iForest (5) | S27, S33, S40, S41, S46 | |||||||
Support Vector Machine (8) | S3, S27, S40, S41, S45, S46, S61, S104 | |||||||
Vector Space Model (2) | S123, S141 | |||||||
CS2: Software/Systems/Apps/Programing languages | Market Software/Platform (Proprietary or Free/Open Source) | Android Studio (1) | S71, S135WeChat, WhatsApp, WhatsApp Bot (4) | S93, S109, S111, S117 | ||||
Wireshark Dumpcap (1) | S195 | |||||||
Zoom Platform (1) | S207 | |||||||
Mobile, Desktop, WEB or Cloud Application/Framework proposed as study contributions | Cloud Application (3) | S12, S31, S35 | ||||||
Desktop Application (4) | S51, S83, S96, S113 | |||||||
Mobile Application (24) | S12, S20, S33, S63, S70, S72, S73, S84, S92, S111, S115, S119, S123, S135, S150, S151, S165, S166, S175, S192, S194, S203, S204, S214 | |||||||
Web Application/Framework (21) | S2, S14, S28, S29, S37, S41, S42, S62, S67, S68, S78, S82, S93, S123, S131, S158, S165, S166, S176, S204, S214 | |||||||
Programming Languages | C#, C++ (2) | S83, S96 | ||||||
Java (J2EE, J2ME, JNI, Hibernate) (5) | S14, S28, S37, S73, S92 | |||||||
JavaScript Libraries/ API (e.g., jQuery, ReactJS, AJAX, Google Web Toolkits, Google Maps) (9) | S2, S67, S28, S29, S72, S151, S166, S196, S204 | |||||||
PHP (2) | S166, S204 | |||||||
Python (6) | S2, S83, S96, S158, S199, S216 | |||||||
Visual Basic (1) | S51 |
Diseases | Studies | |
---|---|---|
Infectious diseases in general (using or not some disease as examples) (62) |
S1, S3, S4, S5, S13, S21, S23, S28, S29, S36, S37, S42, S49, S53, S76, S85, S96, S101, S103, S105, S111, S113, S117, S118, S122, S123, S124, S126, S128, S129, S130, S132, S133, S146, S147, S149, S150, S151, S154, S156, S159, S160, S161, S162, S164, S165, S168, S172, S173, S177, S181, S182, S186, S187, S196, S197, S204, S208, S211, S213, S215, S219 | |
COVID-19 (139) |
S6, S8, S9, S10, S11, S12, S14, S15, S16, S18, S19, S20, S22, S24, S25, S26, S27, S30, S31, S32, S33, S34, S35, S38, S39, S40, S41, S43, S44, S45, S46, S47, S50, S52, S54, S55, S56, S57, S58, S59, S60, S61, S62, S63, S64, S65, S66, S67, S68, S69, S70, S71, S72, S73, S74, S75, S77, S78, S79, S80, S81, S82, S83, S84, S86, 87, S88, S89, S90, S92, S93, S94, S97, S98, S104, S106, S107, S108, S109, S110, S112, S119, S120, S121, S127, S131, S134, S135, S136, S137, S138, S139, S140, S141, S142, S143, S144, S145, S148, S152, S153, S157, S158, S163, S166, S167, S169, S170, S171, S174, S175, S176, S178, S179, S180, S183, S184, S185, S188, S189, S191, S192, S193, S194, S195, S198, S199, S200, S202, S203, S205, S206, S207, S209, S210, S214, 216, S217, S218 | |
Influenza (H1N1, H5N1, and others) (17) | ||
Data Base Management System | ||
Firebase (4) | ||
S71, S73, S92, S194 | ||
Influx DB (1) | ||
S136 | ||
MongoDB (2) | ||
S2, S42 | ||
MYSQL (3) | ||
S78, S123, S204 | ||
MS Access (1) | ||
S51 | ||
Oracle (1) | ||
S29 | ||
PostgreSQL (2) | ||
S37, S208 | ||
S2, S7, S17, S48, S51, S91, S99, S100, S102, S114, S115, S116, S125, S155, S190, S201, S212 | ||
Neo4j (1) | ||
S12 | ||
SQLite (1) | ||
S83 | ||
CS3: Internet of Things and Hardware | ||
* | ||
1 | ||
Wearable devices (e.g.,smartwatches, smartphones, smartbelt, and others) (17) | ||
S19, S27, S40, S54, S56, S69, S70, S79, S111, S115, S139, S157, S163, S175, S181, S195, S213 | ||
Sensors (mobile or fixed), Cameras, RFID (Radio Frequency Identification) |
Cameras—photo and video (Fixed and mobile) (11) |
S30, S74, S82, S106, S109, S118, S121, S122, S128, S193, S194 |
Environment Sensors (e.g., Passive Infrared (PIR) Sensor, and others) (26) | S11, S16, S21, S30, S38, S47, S50, S54, S74, S77, S82, S89, S109, S114, S121, S122, S126, S127, S135, S136, S172, S177, S185, S191, S192, S193 | |
Klebsiella pneumoniae (1) | RFID (Radio Frequency Identification) devices (9) | S13, S27, S30, S35, S50, S182, S185, S192, S196 |
Wearable and/or mobile body sensors (e.g., temperature, cough, oxygen, pressure, heart rate measurement) (14) | S21, S25, S26, S27, S31, S35, S44, S69, S86, S87, S118, S157, S159, S181 | |
Others (e.g., Printers, Spray, Chips, GPS/GSM/Bluetooth devices, WIFI routers, UV tech, WBAN, and others) | Bluetooth/WIFI/GPS/Wireless devices (e.g., module, routers, access point, receivers, SMS gateways, GPS chips, and others) (21) | S9, S18, S26, S31, S44, S50, S84, S86, S123, S124, S134, S136, S142, S148, S172, S175, S194, S196, S207, S212, S219 |
Desktops, Laptops, and computer accessories (e.g., memory cards, processors, and other boards) (21) |
S195 |
S15, S21, S24, S35, S42, S45, S48, S57, S58, S63, S78, S97, S114, S137, S144, S149, S157, S168, S178, S192, S201, S217 |