Developing a Minimum Dataset for a Mobile-based Contact Tracing System for the COVID-19 Pandemic

authors:

avatar Mostafa Shanbehzadeh ORCID 1 , avatar Hadi Kazemi-Arpanahi ORCID 2 , 3 , *

Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran

how to cite: Shanbehzadeh M, Kazemi-Arpanahi H. Developing a Minimum Dataset for a Mobile-based Contact Tracing System for the COVID-19 Pandemic. Shiraz E-Med J. 2022;23(3):e114456. https://doi.org/10.5812/semj.114456.

Abstract

Context:

Contact tracing is a cornerstone community-based measure for augmenting public health response preparedness to epidemic diseases such as the current coronavirus disease 2019 (COVID-19). However, there is no an agreed data collection tool for the unified reporting of COVID-19 contact tracing efforts at the national level.

Objectives:

The purpose of this research was to determine the COVID-19 Contact Tracing Minimal Dataset (COV-CT-MDS) as a prerequisite to develop a mobile-based contact tracing system for the COVID-19 outbreak.

Methods:

This study was carried out in 2020 by a combination of literature review coupled with a two-round Delphi survey. First, the probable data elements were identified using an extensive literature review in scientific databases, including PubMed, Scopus, ProQuest, Science Direct, and Web of Science (WOS). Then, the core data elements were validated using a two-round Delphi survey.

Results:

Out of 388 articles, 24 were eligible to be included in the study. By the full-text study of the included articles and after the Delphi survey, the designed COV-CT-MDS was categorized into two clinical and administrative data sections, nine data classes, and 81 data fields.

Conclusions:

COV-CT-MDS is an efficient and valid tool that could provide a basis for collecting comprehensive and standardized data on COVID-19 contact tracing. It could also provide scientific teamwork for health care authorities, which may lead to the enhanced quality of documentation, research, and surveillance outcomes.

1. Context

Contact tracing is a principal public health practice for containing further propagation of the virus through limiting contacts between infected cases and persons adjacent to them (eg, family members, health care providers, healthcare personnel, etc.) (1-3). Contact tracing is principally significant for the COVID-19 outbreak, where a large number of carriers are silent, pre-symptomatic, or may present only mild symptoms and are thus usually not tested, despite having the potential to promulgate the disease (4). In the context of COVID-19, contact tracing is a public health response to detect and inform those individuals who may have been in close contact with an infected person every day for two weeks (5, 6). Accordingly, if an individual is confirmed positive for COVID-19, every other individual who had possibly been in close contact is tracked and recommended to go into protective self-quarantine for cutting off the transmission chain of the disease in the community (7).

To overcome the limitations of traditional contact tracing, digital-based contact tracing has been adopted (8). One promising type of digital contact tracing is the implementation of mobile-based contact tracing applications (apps). Such apps use mobile devices to promptly detect and alert users who may be in close contact with a confirmed-COVID-19 case (9). Due to the wide accessibility and affordability of mobile devices, employing mobile-based contact tracing apps can lead to making the public health process of contact tracing more efficient on a massive scale (10).

Mobile-based contact tracing systems offer a practical solution to controlling the spread of COVID-19; however, standardized data collection as one of the designing specification criteria to achieve a uniform and mass tracing app acceptance is a great challenge (11, 12). Moreover, from a data management perspective, the novelty of COVID-19 has created major gaps in data harmonization, integration, and unified reporting of disease as a basis for investigating many unfamiliar clinical aspects and outcomes of the disease, characterizing the public health threat, and supporting health authorities’ decisions (13).

The human-to-human spread of COVID-19 requires active case identification, that is, early confinement, timely testing, and treatment, besides detection and future tracking of persons who may be in close contact with infected cases (14). Meanwhile, a large number of reports inflowing the health care systems from varied networks and formats need to be validated. Current surveillance systems are generally not constructed to meet such data requirements. Moreover, vagueness and postponement of surveillance data due to isolated and heterogeneous health information systems are a barrier to data exchange among these systems, which have led to limited consistency of epidemiologic studies (15).

To our knowledge, no comprehensive data collection template currently exists that has been designed to capture high-quality, consistent, and standardized data regarding COVID-19 contact tracing.

2. Objectives

To address this priority, the current study aims to determine a minimum dataset (MDS) as an essential measure before the design and implementation of a digital contact tracing system. Accordingly, we sought to develop a COVID-19 Contact Tracing Minimal Dataset (COV-CT-MDS) based on mobile devices due to their ability to appropriately document contact tracing data during COVID-19.

3. Methods

This was a cross-sectional study conducted in 2020 following a combination of literature review and a broad discussion with a multidisciplinary team of involved healthcare experts, as follows.

3.1. Literature Review

3.1.1. Search Strategy

A systematic review was undertaken to extract the primary data elements to include in COV-CT-MDS. This systematic review was reported according to the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (16). PubMed, Scopus, Web of Science (WOS), Science Direct, and ProQuest databases were reviewed between 1 January 2020 and 20 December 2020 to determine the required data elements, features, and attributes for designing a mobile-based COV-CT-MDS. The following search terms were used (designed using English MeSH keywords) to maximize the output from literature findings: [COVID-19 OR Novel coronavirus OR SARS-CoV-2 OR n-CoV2] AND [Mobile phone OR Smartphone OR Cell phone OR Mobile Apps OR Mobile health] AND [Contact tracing OR Contact tracking].

3.1.2. Study Selection

Two independent researchers (M: SH and H: K-A) reviewed the titles and abstracts of the articles extracted from the initial search, and then full-text articles were obtained for detailed evaluation. Finally, we read the full text of articles and recognized potentially eligible studies to be included in the systematic review.

The following criteria were considered as the inclusion criteria:

(1) Type of a study: Original or review research papers were selected, and newspapers, reports, editorial, letters, posters, and conference papers were not examined.

(2) Date of publication: Papers published between 1 January 2020 to 20 December 2020

(3) Language: English language

(4) Text availability: Full-text papers with the keywords in the title or abstracts

(5) Content analysis: At least two of the following reporting parameters: (1) basic/general, (2) clinical, (3) para-clinical, (4) geo-locational, and (5) contact/exposure data classes.

Finally, the probable data elements to be included in the COV-CT-MDS were recorded in a checklist with two administrative and clinical sections.

3.1.3. Data Extraction

For each eligible research, the following information was extracted based on a designed data extraction form, which included the first author, country, year of publication, study design, and reporting data classes in the two non-clinical and clinical data categories. The results were organized under the following categories: (1) data categories, (2) data classes, (3) data fields, and (4) data features and attributes.

3.2. Delphi Technique

3.2.1. Questionnaire Design

After conducting the necessary literature review and receiving expert advice, we developed a questionnaire. We invited 20 experts, including five infectious diseases specialists, five virologists, five health information management (HIM), and five clinical epidemiologists, in a two-round Delphi survey. The questionnaire included the following parts: (1) demographical data, (2) clinical finding, (3) geolocation location, (4) relocation data, and (5) contact/ exposure data.

3.2.2. Data Analysis

The experts participating in the study were asked to score the tabulated data elements in terms of their importance using a five-point Likert scale (ranging from 1: “very slightly important” to 5: “highly important”). Data fields with less than 50% agreement were excluded in the first round, while those with greater than 75% agreement were included in the primary round. Those with 50% to 75% agreement were surveyed in the second round, and if there was 75% consensus over a subject, it was regarded as a final data field.

4. Results

4.1. Characteristics of Included Studies

A total of 388 articles were retrieved from the literature search. After the removal of duplicate articles and those not meeting the inclusion criteria, 24 articles that satisfied all the inclusion criteria were included in the analysis. Figure 1 summarizes the selection process (PRISMA chart).

PRISMA chart for the study selection process
PRISMA chart for the study selection process

4.2. Identifying the Proposed Data Field

The proposed data fields after the literature review were divided into administrative and clinical data sections, nine data classes, and 198 data fields (Table 1).

Table 1.

Summary of Characteristics of Included Studies in the Systematic Review

First Author (2020)MethodData Classes
AdministrativeClinical
BasicGeolocationOccupationalRelocationContactExposureClinicalManifestationsVital SignsReferral
Bassi et al. (17)Descriptive***
Basu (18)Case study***
Davalbhakta et al. (19)Review****
Ekong et al. (20)Exploratory review****
Hassandoust et al. (21)Developmental*****
Martin et al. (5)Review**
Parker et al. (22)Descriptive***
Rahman et al. (23)Case study**
Shubina et al. (24)Retrospective**
Vuokko et al. (25)Descriptive***
Prabu et al. (26)Exploratory review***
Teixeira and Doetsch(27)Descriptive***
Kondylakis et al. (28)Review****
Nakamoto et al. (29)Developmental****
Altmann et al. (30)Retrospective**
Dar et al. (31)Developmental***
Singh et al. (32)Review**
Urbaczewski and Lee(33)Retrospective****
Whaiduzzaman et al. (34)Developmental*****
Bianconi et al. (35)Descriptive****
Grantz et al. (36)Prospective*
Ming et al. (9)Retrospective*****
Wirth et al. (37)Scoping review****
Nijsingh et al. (10)Descriptive***

Several data fields were excluded after the second round of Delphi. Thus, of the 198 proposed data fields, 117 fields were excluded from the study, and 81 data fields were finalized (Table 2).

Table 2.

Consensus Thresholds

Decision Agreement Rate (%)Frequency
First Round
Inclusion < 7558
Exclusion > 5092
Entering in second round50 - 7548
Second Round
Inclusion < 7525
Exclusion > 7523

The final reporting template is composed of two data sections, nine data classes, and 81 data fields. Table 3 lists the data sections, classes, fields, their formats and values, and corresponding reference SNOMED-CT codes.

Table 3.

Required Data Elements for Contact Tracing

Data ElementFeature ContentFeature FormatSNOMED-CT CategorySNOMED-CT Codes
General Characteristics
Full name (11-16, 34, 36, 38, 39)StringObservable entity371484003
Age (5-9, 12, 14, 18, 34, 38, 40, 41)Forced choiceQualifier value764868004
Gender (2, 4, 9, 10, 12, 15, 21, 36, 40, 41)M: 1 F: 0BinaryClinical finding703118005
National ID number (4, 6, 9, 13, 18, 34, 40)xxx- xxxxxx-xNumericalObservable entity422549004
Citizenship (2, 15, 18, 21, 34, 36, 39, 41)Iranian; Non-IranianBinarySocial concept275595001
Medical record number (4, 5, 7, 12, 13, 16, 18, 21, 31, 40)xx-xx-xxNumericalObservable entity398225001
Level of education (5, 10, 12, 13, 15, 18, 36, 39, 41)Primary; Secondary; TertiaryForced choiceObservable entity224300008
Marital status (5, 7, 9, 12, 13, 16, 18, 34, 36, 39, 40)Single; Married; Widow; OtherForced choiceClinical finding87915002
Monthly income (3, 5, 6, 9, 13, 16, 21, 34, 36, 39, 41)Low: < 120$; Medium: 120$ - 250$; High: > 250$Forced choiceClinical finding424860001
Family relationship to index cases (5, 6, 9, 15, 34)Nuclear family; Extended familyBinarySocial concept394568007
Phone number (4-6, 9, 13, 15, 16, 40)+98 xxx xxx xxxxNumericalObservable entity398198004
Healthcare facility unique ID (5, 6, 15, 18, 21, 40) xxxxxNumericalObservable entity713578002
Frontline health worker ID (4-6, 9, 10, 13, 15, 16, 36) xxxxxNumericalObservable entity713578002
Relationship with the source case (5, 6, 12, 14, 15, 18, 21, 34, 40, 41) Partner / spouse; Family member; OtherForced choiceClinical finding852071000000103
Geolocation Data
Place of birth (6, 14, 15, 18, 21, 40, 41)Geographical location: Province, city, villageStringEnvironment/ location315446000
Resident situation (5, 6, 8, 15, 40)Tenant; Owner; OtherForced choiceEnvironment/ location184097001
Residential address (3, 4, 6, 8, 9, 14, 36, 40)StringObservable entity433178008
Postal code / zip code (3, 6, 8-10, 15, 36, 41)xxxxx-xxxxxNumericalobservable entity184097001
Place of contact (3-6, 10, 11, 14-16, 36, 41)Workplace; Home; Public place; Other; UnknownForced choiceEnvironment/ location257710009
Location case identified (4, 6, 11, 13, 15, 21, 40)Geographical locationStringEnvironment/ location706956001
Origin of travel (5, 6, 15, 16, 34, 41)Geographical locationStringEnvironment/ location224803003
Travel destination (6, 10, 13, 16, 36, 39)Geographical location StringEnvironment/ location224807002
Address of healthcare organization (3, 6, 13, 21, 39-41)StringObservable entity184097001
Isolation/quarantine location (6, 13, 16, 40)Self-isolation at home; Hospital; Long term care facilities; OtherForced choiceProcedure1321131000000109
Clinical Characteristic
Symptom incidence (3, 4, 6, 7, 10, 12, 16, 31, 39, 40) Asymptomatic; Pre symptomaticForced choiceQualifier value264931009
Date of symptom onset (6, 9, 12, 13, 15, 31, 36, 39, 40)yyyy /mm/ ddIntegerObservable entity520191000000103
Days from exposure to symptom onset (8-10, 12, 13, 15, 36, 38, 39) xxNumericalQualifier value307474000
Days from illness onset to first admission (5, 6, 10, 12, 15, 18, 38)xxNumericalQualifier value307474000
Days from diagnosis to treatment (8, 12, 14, 40) xxNumericalQualifier value432213005
Date of diagnosis (10, 12, 14, 18, 40, 41)yyyy /mm/ ddIntegerObservable entity432213005
Covid-19 classification (10, 12, 18) Confirmed; Probable; UnknownForced choiceSituation395098000
Covid-19 status (9, 14, 18)Active; Inactive; RecoveredForced choiceClinical finding110278006
Case finding approachesRandom screening; Symptomatic case referral; Contact tracing; OtherForced choiceClinical findingCountry, Province/ State, City,
Prior hospitalization (3, 5, 9, 10, 12, 14, 34, 38) Yes; NoNumericalClinical finding 314503007
Self Reported Clinical Manifestation
Fever/chill (4-7, 10, 13, 18, 21, 31, 40)Yes; NoBinaryQualifier value14732006
Cough (4, 6, 9, 10, 13, 18, 41)Yes; NoBinaryClinical finding314503007
Dyspnea (6, 10, 12, 14, 18, 31, 38, 39) Yes; NoBinaryQualifier value385432009
Respiratory distress (10, 12, 21, 38, 39)Yes; NoBinaryClinical finding386661006
Myalgia (9, 12, 18, 38)Yes; NoBinaryClinical finding36523521
Headache (10, 14, 18, 38, 39)Yes; NoBinaryClinical finding43724002
Nausea/ vomiting (4, 9, 14, 18, 21, 36)Yes; NoBinaryClinical finding65124004
GI symptoms (4, 10, 15, 16, 39, 40)Yes; NoBinaryClinical finding664563201
Anosmia (12, 16, 21, 34)Yes; NoBinarySituation162298006
Runny nose (12, 13, 15, 34, 39, 41) Yes; NoBinarySituation162062008
Sore throat (4, 12, 13, 16, 21, 34, 40, 41)Yes; NoBinarySituation162104009
Unexpected fatigue (12, 13, 15, 16, 40)Yes; NoBinaryClinical finding93559003
Real-Time Vital Sign Monitoring
Oxygen saturation (SO2) (13, 18, 34) 75 < mmHg; 75 – 100 mmHg; 100 > mmHgForced choiceClinical finding448225001
Heart rate (bit per minute) (10, 12, 13, 18, 34, 36, 41) < 60 bps; 60-100 bps; > 100 bpsForced choiceClinical finding76863003
Blood pressure (mmHg) (10, 12, 14, 16, 31, 39) < 120; 120-139; > 140Forced choiceClinical finding2004005
Body temperature (°C) (2, 3, 12-14, 16, 21) < 37.3; 37.3 – 39; > 39.0Forced choiceClinical finding50177009
Respiratory rate (breaths per min) (2, 3, 12, 16, 21)≤ 24; > 24Forced choiceClinical finding289100008
Occupational Criteria
Employment status (5, 7, 8, 18, 34, 41)Unemployed; EmployedForced choiceClinical finding224363007
Working status (7, 16)Full time; Part timeForced choiceClinical finding160903007
If employed, occupation risks (3, 8, 13, 31, 34, 38, 39)High risk; Medium risk; Low riskForced choiceEvent16090731000119102
Work situation during general quarantine (7, 16, 34, 38)Not working; Working at usual place; Teleworking; OtherForced choiceClinical finding302201002
Work in a patient care setting (3-5, 7, 9, 13, 16, 21, 39-41)Yes; NoBinaryClinical finding302201002
Attending work at the time of symptom occur (4, 9, 10, 14, 34, 40)Yes; NoBinaryClinical finding83408003
Travel/Relocation Data
Recent travel / relocation (4, 6, 8, 10, 13, 15, 18, 34, 36, 40)Yes; NoBinarySituation473087005
Reason for travel (6, 9, 15, 36, 41)Holiday business; Pilgrimage otherForced choiceclinical finding 161091009
Travel type (4, 6, 8, 18)Domestic travel; Foreign travelBinaryObservable entity441969007
Date of departure (3-6, 8, 9, 14, 34, 40)dd/mm/yyIntegerObservable entity810811000000107
Number of travels in the last 7 days (5, 6, 8, 9, 11, 16, 36, 40)None; One - two times a week; Two - four times a week; More than five times a week Forced choiceQualifier value 259083004
Travel to epidemic places (2, 5, 7, 9, 18, 21, 36, 40)Yes; NoBinaryClinical finding506931000000109
Relocation / transfer method (3, 5, 9-11, 13, 16, 36)Public transportation; Personal transportationBinaryProcedure715957006
Duration of travel (3, 7, 13)Daily travel (1 day <); 1 day ≥ BinaryQualifier value69620002
Contact Tracing Data
Prior contact tracing experience (10, 15-18, 22, 25) Yes; NoBinaryProcedure;225368008
If yes, prior contact tracing approach (2, 3, 7, 13)Conventional; AutomaticBinaryClinical finding;52669001
If Automatic, contact tracing technology (2, 3, 5-11, 14, 18, 31, 34, 40, 41)Mobile phone; Implant tools other microcomputersForced choiceQualifier value723991000000105
Contact tracer ID (3, 5, 6, 9, 11, 14, 34, 38, 39)XXXXForced choiceQualifier value118522005
Notification ID (3, 5, 6, 9, 10, 34, 39)XXX /XXXX -XForced choiceObservable entity895571000000108
Contact Data
Contact type (4, 10, 12, 13, 18, 36, 38)Primary: Person-to-person; Secondary: Person-to-surface / animalBinarySocial concept70862002
Contact category (2-4, 14, 21, 31) No contact; Family members; Social contact; otherForced choiceClinical finding381441000000103
Contact risk level (13, 15, 34, 36, 41)Living with an infected/suspected case in the past 14 days; Prolonged direct contact in the past 14 days; Casual and indirect contact in the past 14 days; Not in contactForced choiceSituation76906009
Contact with care facility Yes; NoBinarySituation136569214
Contact frequency (3, 8, 16, 18) Sometimes: ≥ 2 times a day; Always: 2 - 4 times a day; Repeatedly: < 4 times a dayForced choiceQualifier value735269004
Contact list (person) (7, 13, 34) 5 >; 5 - 10; 10 - 30; 30 < Forced choiceSocial context 125676002
Minimal distance of contact (meters) (2-4, 14, 21, 36, 41) 2 >; 2 < BinaryQualifier value421669002
Date of last contact (6, 12, 14, 16, 18, 21, 36, 41) yyyy /mm/ ddInteger
Time between contact and diagnosis (10, 15, 16, 18, 41)NumericalQualifier value 305698526
Total duration of contact (minutes) (3, 7, 8, 13, 34, 36) ≥ 15; < 15BinaryQualifier value356624006
Total duration of contact (day) (3, 7, 8, 13, 34, 36) ≥ 14; < 14BinaryQualifier value258703001

5. Discussion

Contact tracing is known as a crucial surveillance measure in avoiding the spread of epidemic diseases such as the current COVID-19. During this epidemic, contact tracing data should be integrated across healthcare data collection systems at the national level (34). However, data are gathered from stand-alone recording and reporting systems largely manually generated via the contact tracing process. Data collection is a crucial strategic preparation measure for governments and health officials battling the COVID-19 epidemic (36).

The CoV-CT-MDS is a promising tool to meet some of the data necessary for epidemiology contact tracing leading to a validated template for the documentation of active case finding for public health practice and research purposes. Determining a core data set or MDS from a scientific perspective and according to the actual demands of users is the most central prerequisite for the design and development of any information system or app in the healthcare industry (38). It can be advantageous for designers and vendors of health information systems to simplify and accelerate the development of such systems and reduce the possibility of their failure (39). From this point of view, in this study, the CoV-CT-MDS can be used as a basis for the effective collection and management of data related to COVID-19 contact tracing using related information systems or apps.

In the initial months of the pandemic, contact tracing measures were recorded through manual data collection tools (eg, in Excel sheets, spreadsheet), which was a time-intensive, resource-demanding, and error-prone process (18, 40). Additionally, the conventional approaches did not always offer inclusive data about the number of investigated contacts, the nature of the relationship between cases and contacts, the number of contacts, who in turn, become cases, and the first and last days of follow-up surveillance (21, 41). To cope with these issues, it is essential to develop a contact tracing system that enables standardized data recording and accelerates the surveillance of contacts and outbreak paths (31, 42). This system allows intervallic analyses for the creation of standard reports and offers detailed epidemiological analysis for the identification of high-risk exposures and targeting of contact tracing efforts (21, 41, 43).

Implementing an active and responsive contact tracing strategy would be a valuable containing measure for avoiding the transmission of COVID-19. In this context, mobile technology enabling self-reports and smartphone applications for virtual contact tracing could be used to control disease outbreak and detect as well as quarantine COVID-19 cases and those who may have been exposed to the virus (44). For this purpose, a contact tracing system including timely and accurate data collection process and a unified case reporting template are proposed to guide healthcare authorities for proper interventions (20, 32, 36). There is, therefore, a pressing need for a unified data collection template to swiftly and prospectively collect high-quality data related to recent exposure and mobility patterns of confirmed and suspected individuals (45, 46).

The novelty of COVID-19 with frequent mutations of the virus demands numerous and unknown aspects to be investigated in prospective studies, and thus, studies related to COVID-19 contact tracing are limited at the time of writing this article (Decembers 2020). Hence, the main limitation of this living systematic review is the scarcity of available related resources and lack of data enrichment. Review of only English-language articles is another limitation of the study. However, multiple scientific databases were broadly reviewed. Future modifications, along with a Delphi survey is recommended to augment the COV-CT-MDS.

5.1. Conclusions

An effective COVID-19 contact tracing system requires reliable and timely information to guide fully informed decisions to contain the further spread of the disease by taking early preventive actions. For developing the CoV-CT-MDS, we performed an extensive literature review and expert view to identify the proposed contact tracing data fields and corresponding variables from an evidence-based perspective. The COV-CT-MDS as a unified data collection tool is the first step for developing a mobile-based contact tracing system. This template can provide valuable information for clinicians, health policymakers, and researchers for integrating the COVID-19 contact tracing efforts across Iran’s healthcare system. Given the prominence of reliable, accurate, and comprehensive data on COVID-19 surveillance measures, it is suggested that different countries design and implement a comprehensive national MDS for COVID-19 contact tracing.

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