1 Introduction
In December 2019, an outbreak of contamination by a new zoonotic agent, later identified as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), was observed in Hubei province in China, and the disease as a result of this new coronavirus was called of corona virus disease 2019 (COVID-19), and since then the disease has reached more than 200 countries and territories spread all over the world, with more than 36 million confirmed cases and more than 1 million deaths.[1–3]
As it is a new disease, many issues are not fully clarified and situations that pose challenges for monitoring the course of the pandemic are also identified, such as reduced laboratory testing capacity, frequent delays in disseminating results, variability in the course of the disease in different territories and the lack of clarity in communication in some nations. Such situations have hampered the development of policies and interventions focused on resource allocation. At a time when conventional health surveillance capabilities are limited, electronic social media is an important strategy for identifying an emerging outbreak.[4]
In this scenario, research in surveillance, called infovigilance or infodemiology, presents itself as a strong expectation in the use of internet data to track the development of health problems in real time, with greater attention and more precise time.[5–8]
The growing production and use of data based on increasingly powerful and specialized digital technologies has enabled the emergence of new forms of knowledge production based on sophisticated computational modeling and algorithms. In this new context, data gains value and importance through diverse mobilizations that involve negotiations, social, political and economic interests. In times of COVID-19 pandemic, due to the urgent need to respond quickly to the challenges posed by the introduction of a new etiological agent and the peculiarity of the disease, which poses risks to people's lives and health, the use of personal data from different sources being required to explore scientific issues based on the characteristics of the population, laboratory and hospital data, among others, as long as it is guided by an ethical and legal basis.[9]
The use of data-based monitoring and surveillance applications has been used as a means of combating coronavirus. Use of technologies based on facial recognition, installation of thermometers at airports, applications that track where infected people have walked are examples of some applications of digital surveillance in combating the pandemic.[10]
So, this study will identify the use of digital technologies to monitor, identify contacts and track transmission chains from COVID-19 worldwide.
2 Methods and analysis
2.1 Protocol and registration
This systematic review was recorded in the International prospective register of Systematic reviews (PROSPERO) on Sep 14, 2020 under the number CRD42020211744. Available at: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020211744
2.2 Selection process
The design and development of this systematic review and meta-analysis will be in accordance with the statement of Preferred Reporting Items for Systematic Reviews and Meta-Analyses.[11] Figure 1 .
Figure 1: Flow diagram. Adapted from PRISMA-P. PRISMA-P = preferred reporting items for systematic reviews and meta-analyses protocols.
2.3 Search strategy
As keywords for search strategy will be considered: Technology; digital technology; Mass Screening; Mobile Applications; Epidemiological Monitoring; Public Health Surveillance ; Disease Transmission, Infectious; COVID-19; Coronavirus Infections; Humans; Pandemics.
The keywords search will be based on Medical Subject Headings and the search strategy described will be adapted and customized for each database to be searched using Boolean operators, truncations and proximity operators, as appropriate.
2.4 Inclusion criteria
Studies that report the use of digital technologies to monitor suspected and confirmed cases and to track transmission chains of COVID-19 will be included, regardless of the type of study, the type of institution that proposes/executes the tool (government institutions, government agencies, government agencies or research institutions), language and place of use. Table 1 .
Table 1 -
PICO description.
PICO
Abbreviation
Elements
Participants
P
Digital Technologies
Exposition
E
Identification of cases and suspects and tracking of transmission chains of COVID-19.
Control
C
No control needed
Outcome
O
Technologies for monitoring suspected and confirmed cases of COVID-19.
COVID-19 = corona virus disease 2019.
2.5 Exclusion criteria
We will exclude studies that do not report the use of digital technologies to monitor suspected and confirmed cases and to track transmission chains of COVID-19.
2.6 Data collection process
Data extraction will be taken independently by 2 reviewers in 2 stages: Title and abstract screening and full-text screening.
The studies will be imported by Mendeley software[12] [13]
In the second stage, the full-texts of the studies selected in the previous stage will be analyzed.
If any disagreement remains on eligibility, it will be discussed between the first 2 reviewers and the third review. Disagreements will be resolved by discussion in all stages.
Final selection will be always based on the full-text of the publication. Excluded full-text studies will be listed with the reason for their exclusion, and a preferred reporting items for systematic reviews and meta-analyses flow-diagram of included studies will be produced.
Two independent reviewers (1R and 2R) will collect data from the selected articles. After this stage, they will crosscheck the retrieved information. If there are any disagreement will be discussed between them and the third reviewer. If necessary, the expert becomes involved to make a final decision.
The data will be extracted using a standardized data-extraction form created a priori.
The information to be extracted will be:
Study information: Title, author(s), year, journal, location; geographic setting, inpatient/outpatient
Study design and duration
Study participants: number of participants, sample characteristics (age, gender)
Phenomenon of interest: description of the specifics of the monitoring tools; Type of technology used
Comparators: details of control conditions (not mandatory)
Evaluation: Different interventions, outcomes, effectiveness/importance will be assessed by evaluating the majority of study's findings.
2.7 Risk of bias assessment
There is no immediate need to individually assess the quality of each study. The studies will be characterized according to their type and study design in relation to the ease in demonstrating the technologies used and the type of information produced.
Selection bias and publication bias will be minimized by using independent validation of coding decisions and by comprehensive research from unpublished sources in journals.
2.8 Data synthesis
If quantitative synthesis is appropriate, a method of meta-analysis will be performed using Review Manager 5.3 (RevMan 5.3, The Nordic Cochrane Centre, Copenhagen, Denmark). Heterogeneity will be calculated by inconsistency indexes (I-squared), and a value greater than 50% was considered an indicator of substantial heterogeneity between studies. The significance level was set at 5.0%.[14]
2.9 Confidence in cumulative evidence
The grading of recommendations, assessment, development, and evaluation approach will be used to assess the quality of evidence that will be included in this review
2.10 Ethics and dissemination
As it is a systematic review protocol and there will be no patient participation, it is not necessary to conduct an ethical review of the study. The results will be presented at congresses and published in a peer-review journal. The results of this review may support the development and improvement of digital technologies that enable the monitoring of COVID-19 and assist in the reduction.
3 Discussion
This study will be the first study to compile results about the development, use of digital technologies for the monitoring and screening of COVID-19. Its development will allow knowledge of how these technologies were applied according to each territory and their effectiveness in reducing cases of COVID-19. We hope that the results of this review can reveal the importance of modern technologies for reducing cases of COVID-19 and that these measures can be adopted by governments, organizations, and for everyone.
Author contributions
Conceptualization: Talita Araujo de Souza, Arthur de Almeida Medeiros, Isabelle Ribeiro Barbosa.
Data curation: Talita Araujo de Souza.
Formal analysis: Arthur de Almeida Medeiros, Isabelle Ribeiro Barbosa.
Funding acquisition: Arthur de Almeida Medeiros, Gilson de Vasconcelos Torres.
Investigation: Talita Araujo de Souza, Isabelle Ribeiro Barbosa.
Methodology: Talita Araujo de Souza, Arthur de Almeida Medeiros, Isabelle Ribeiro Barbosa, Gilson de Vasconcelos Torres.
Project administration: Arthur de Almeida Medeiros, Isabelle Ribeiro Barbosa, Gilson de Vasconcelos Torres.
Supervision: Isabelle Ribeiro Barbosa, Gilson de Vasconcelos Torres.
Validation: Talita Araujo de Souza.
Writing – original draft: Talita Araujo de Souza.
Writing – review and editing: Talita Araujo de Souza, Arthur de Almeida Medeiros, Isabelle Ribeiro Barbosa, Gilson de Vasconcelos Torres.
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