Contact tracing during Phase I of the COVID-19 pandemic in the Province of Trento, Italy: key findings and recommendations

  1. Pirous Fateh-Moghadam
  2. Laura Battisti
  3. Silvia Molinaro
  4. Steno Fontanari
  5. Gabriele Dallago
  6. Nancy Binkin
  7. Mariagrazia Zuccali

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Abstract

Introduction

Contact tracing is a key pillar of COVID-19 control. In response to the COVID-19 epidemic in the Autonomous Province of Trento (Italy) a software was developed to standardize data collection and facilitate surveillance of contacts and outbreaks and map the links between bases and contacts. In this paper, we present the results of contact tracing efforts during Phase I of the epidemic (March-April, 2020, mostly under lockdown), including sociodemographic characteristics of contacts who became cases and of the cases who infected one or more contact.

Methods

A contact tracing website was developed that included components for geolocation and linking of cases and contacts using open source software. Information on community-based confirmed and probable cases and their contacts was centralized on the website. Information on cases came directly from the central case database, information on contacts was collected by telephone interviews following a standard questionnaire. Contacts were followed via telephone, emails, or an app.

Results

The 2,812 laboratory-diagnosed community cases of COVID-19 had 6,690 community contacts, of whom 890 (13.3%) developed symptoms. Risk of developing symptomatic disease increased with age and was higher in workplace contacts than cohabitants or non-cohabiting family or friends. The greatest risk of transmission to contacts was found for the 14 cases <15 years of age (22.4%); 8 of the 14, who ranged in age from <1 to 11 years) infected 11 of 49 contacts. Overall, 606 outbreaks were identified, 74% of which consisted of only two cases.

Discussion

The open-source software program permitted the centralized tracking of contacts and rapid identification of links between cases. Workplace contacts were at higher risk of developing symptoms. Although childhood contacts were less likely to become cases, children were more likely to infect household members, perhaps because of the difficulty of successfully isolating children in household settings.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.16.20127357: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    To standardize data collection and to build a system capable of creating a database for epidemiological analysis and accessible from the public health point of view, a contact tracing website, based on the Django framework, with extensions in Python(2), was developed.
    Django
    suggested: (Django, RRID:SCR_012855)
    Python(2)
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study has several limitations. First, becoming infected and being identified as a case are not synonymous. Contacts were not routinely tested and in most instances, determining if they had become a case was based on symptoms plus an epidemiological link. It could be, for example, that children and young adults may be less likely to have symptoms than adults and we may have under-estimated secondary attack rates in the younger age groups. Second, in our evaluation of contagiousness, the number of cases among children was relatively small, as was the number of contacts because schools were closed during all but one week of our study interval. Nonetheless, the findings are intriguing and merit further analyses in settings where comprehensive data on cases and contacts can be adequately linked. Although diligent efforts were made to trace contacts, it should be noted that these efforts did not succeed fully in controlling community spread and became increasingly difficult as the epidemic peaked. By April 30th, the province had cumulative case rate of 761 cases/100 000, more than twice the national rate of 341/100 000. Fortunately, the province was able to increase ICU beds by 20%, and existing structures were converted into COVID-19 hospitals, allowing the health care system to cope despite the high case rates (13). The ongoing spread in the face of good follow up of contacts could potentially be attributed to the importance of asymptomatic cases in disease dissemination, the...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. NCRC

    Our take

    This is a contact tracing study based in Trento, Italy, published as a preprint and thus not yet peer reviewed. There were 2,812 cases and 6,690 contacts. There was an overall secondary attack rate of 13.3%, with the highest secondary attack rate occurring among contacts over the age of 75 years. However, index cases who were between the age of 0-14 years had the highest percentage of contacts who became infected (22%). There was no routine testing of contacts, so most of them were identified as a case through being symptomatic; thus, there is likely an underrepresentation of the secondary attack rates. However, the finding that the youngest age group of index cases had the highest transmission among their contacts is helpful to note when policy makers are making decisions on reopening schools.

    Study design

    cross-sectional;ecological

    Study population and setting

    The provincial agency for health services (APSS) in Trento, Italy conducted contact tracing from March to April, 2020 using a contact tracing website. Data on cases was provided by the central local health unit database while data on contacts of cases was collected by telephone interviews contact tracers from each local health district. A contact was defined as anyone who had contact with a confirmed or probable case within 48 hours prior or 14 days after symptom onset.

    Summary of main findings

    There were 2,812 reported cases, with almost half having up to three contacts each, for a total of 6,690 contacts (890 of whom developed symptoms). Prior to the lockdown on March 10, 2020, (consisting of shutting down schools, universities, and businesses except for grocery stores, pharmacies, and newsstands), the majority of contacts were non-cohabitating family or friends (~37%); however, after March 10, the majority of contacts became household contacts (67%). Ultimately, household contacts comprised 56% of all contacts and non-cohabitating family or friends comprised 27%. The secondary attack rate steadily increased with age (e.g. 18.9% among those 75 year and older vs. 8.4% among those 0-14 years). However, the youngest age group (0-14 years) were more likely to spread infection than any other age group, as 22% of their contacts became infected.

    Study strengths

    This study has a large sample of cases and respective contacts. The contact tracing website also provides a centralized resource for data on cases and contacts that can be helpful for future analyses.

    Limitations

    Classifying a contact as a case was determinant on being symptomatic and having an epidemiological link as there was no routine testing conducted among contacts. Thus, the study is likely reporting an underrepresentation of how many contacts became infected (especially among younger age groups as these groups are more likely to exhibit mild to no symptoms).

    Value added

    As schools are opening up in the United States and other countries, the fact that secondary infection was more likely to occur in the youngest age group in this study suggests a potential for high levels of transmission both in schools and households if there are not protocols in place to reduce transmission while children are in school.

  3. ScreenIT

    SciScore for 10.1101/2020.07.16.20127357: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variableThe number of males and females were virtually iden cal.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: We did not find any issues relating to colormaps.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. ScreenIT

    SciScore for 10.1101/2020.07.16.20127357: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variableThe number of males and females were virtually iden cal.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: We did not find any issues relating to colormaps.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  5. ScreenIT

    SciScore for 10.1101/2020.07.16.20127357: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variableThe number of males and females were virtually iden cal.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: We did not find any issues relating to colormaps.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  6. ScreenIT

    SciScore for 10.1101/2020.07.16.20127357: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variableThe number of males and females were virtually iden cal.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Materials and methods To standardize data collec on and to build a system capable of crea ng a database for epidemiological analysis and accessible from the public health point of view, a contact tracing website, based on the Django framework, with extensions in Python(2), was developed.
    Django
    suggested: (Django, SCR_012855)
          <div style="margin-bottom:8px">
        <div><b>Python(2</b></div>
        <div>suggested: None</div>
      </div>
    </td></tr></table>

    Data from additional tools added to each annotation on a weekly basis.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  7. Version published to 10.1101/2020.07.16.20127357v1 on medRxiv