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. 2022 Dec 7;16:100471. doi: 10.1016/j.onehlt.2022.100471

One health system supporting surveillance during COVID-19 epidemic in Abruzzo region, southern Italy

Alessio Di Lorenzo 1, Iolanda Mangone 1,⁎, Patrizia Colangeli 1, Daniela Cioci 1, Valentina Curini 1, Giacomo Vincifori 1, Maria Teresa Mercante 1, Adriano Di Pasquale 1, Simona Iannetti 1
PMCID: PMC9726647  PMID: 36507072

Abstract

The Istituti Zooprofilattici Sperimentali (IZSs) are public health institutes dealing with the aetiology and pathogenesis of infectious diseases of domestic and wild animals. During Coronavirus Disease 2019 epidemic, the Italian Ministry of Health appointed the IZSs to carry out diagnostic tests for the detection of SARS-CoV-2 in human samples. In particular, the IZS of Abruzzo and Molise (IZS-Teramo) was involved in the diagnosis of SARS-CoV-2 through testing nasopharyngeal swabs by Real Time RT-PCR. Activities and infrastructures were reorganised to the new priorities, in a “One Health” framework, based on interdisciplinary, laboratory promptness, accreditation of the test for the detection of the RNA of SARS-CoV-2 in human samples, and management of confidentiality of sensitive data. The laboratory information system – SILAB – was implemented with a One Health module for managing data of human origin, with tools for the automatic registration of information improving the quality of the data. Moreover, the “National Reference Centre for Whole Genome Sequencing of microbial pathogens - database and bioinformatics analysis” – GENPAT – formally established at the IZS-Teramo, developed bioinformatics workflows and IT dashboard with ad hoc surveillance tools to support the metagenomics-based SARS-CoV-2 surveillance, providing molecular sequencing analysis to quickly intercept the variants circulating in the area. This manuscript describes the One Health system developed by adapting and integrating both SILAB and GENPAT tools for supporting surveillance during COVID-19 epidemic in the Abruzzo region, southern Italy. The developed dashboard permits the health authorities to observe the SARS-CoV-2 spread in the region, and by combining spatio-temporal information with metagenomics provides early evidence for the identification of emerging space-time clusters of variants at the municipality level. The implementation of the One Health module was designed to be easily modelled and adapted for the management of other diseases and future hypothetical events of pandemic nature.

Keywords: Bioinformatics analysis, COVID-19 surveillance, Laboratory information system, GIS, Metagenomics, One health

Highlights

  • •

    The dashboard provides timely information to MoH about epidemic evolution in Abruzzo.

  • •

    The dashboard has proven to be very useful for daily surveillance at municipality level.

  • •

    GIS based monitoring allow quick reactions in case of new rapidly spreading lineages.

  • •

    The dashboard contributes on the re-assessment of risk of COVID-19 in Abruzzo region.

  • •

    The one health system presented can be easily adapted to future pandemic events.

1. Introduction

The novel coronavirus (CoV) called SARS-CoV-2 is responsible for the coronavirus disease 19 (COVID-19) causing the current pandemic [1]. According to the International Committee on Taxonomy of Viruses, SARS-CoV-2 belongs to the species severe acute respiratory syndrome-related virus, in a clade within the order Nidovirales, the family Coronaviridae, genus β-coronavirus, subgenus Sarbecovirus [2], together with other human infecting viruses, like the SARS CoV virus (2003) [3] and Middle East respiratory syndrome coronavirus (MERS-CoV) (2013). The scientific community greatly agrees on the possible animal origin of these viruses [1,2]. In the spillover to humans, bat species are considered the natural animal reservoir of these beta-coronaviruses, with the possible role of intermediate hosts [4,5].

COVID-19 was first reported in December 2019 in humans in connection with the Huanan seafood wholesale market where various species of farmed and wild animals are usually sold (Wuhan, Hubei Province, China) [[5], [6], [7], [8]]. Since 31 December 2019 and as of week 2022–22, 531,470,423, cases of COVID-19 (in accordance with the applied case definitions and testing strategies in the affected countries) were reported, including 6,318,391 deaths [9]. In the absence of effective drugs and a vaccine, in a fully susceptible population, from the starting of the epidemic in less than one year, SARS-CoV-2 resulted in >30 million confirmed cases (2 million in Europe) of infection worldwide and over 900,000 deaths (185,000 in Europe) [10,11]. On 18th February the first Italian case of COVID-19 due to secondary transmission outside China was identified in Codogno, Lombardia region, northern Italy [12]. The unexpected pandemic of COVID-19 caused a never-seen-before disaster in terms of hospitalizations and deaths, overloading the National Health Care System (Servizio Sanitario Nazionale, SSN), particularly in the north of the country, where the epidemic spread with more intensity. From 31 December 2019, >290,000 confirmed cases of SARS-CoV-2 infection and >35,000 deaths were reported in nine months [13]. Besides harmful impacts on workload and organization of hospitals [14] and medical clinics [15], as well as on health [16] and employment [[17], [18], [19]] of populations, the SSN had to perform thousands of daily tests [7,8]. Lockdown and other emergency measures, such as restriction on mobility, social distancing and the closure of all non essential services, were applied.

COVID-19 surveillance in Italy was officially established by the MoH starting from 22 January 2020, setting out the first criteria and methods for reporting cases of SARS-CoV-2 infection [20]. In the Abruzzo region, Ordinance no. 104 of 25 November 2020 [21], obliged the analysis laboratories to use the Swabs Tracing Application of the Abruzzo Region (ATTRA), with the aim of better coordinating surveillance activities and tracing contacts.

In this framework, the Italian Ministry of Health (MoH) appointed the Istituti Zooprofilattici Sperimentali (IZSs), to carry out diagnostic tests for the detection of SARS-CoV-2 in human samples with the aim of supporting the SSN by increasing the total capacity of the analysis laboratories [22].

In central-southern Italy, the IZS of Abruzzo and Molise (IZS-Teramo, headquarter), was involved in the diagnosis of SARS-CoV-2 in the territories under its jurisdiction (Abruzzo and Molise regions) through testing nasopharyngeal swabs by Real Time RT-PCR, under the authority of the Italian MoH.

By late March 2020, Villa Caldari, a small village of the municipality of Ortona (Chieti province, Abruzzo region), registered an incidence rate of COVID-19 cases ten times greater than the overall municipality and was declared a red area [23]. The IZS-Teramo in collaboration with the Local Health Authority (LHA) of Chieti supported in diagnostics and epidemiological investigations, obtaining the suspension of the emergency measures within one month [23].

To face these challenges, the IZS-Teramo reorganised the activities and infrastructures to adapt them to the new priorities, within a broader approach, beyond animal health and food safety.

The IZS-Teramo supported COVID-19 surveillance also as “National Reference Centre for Whole Genome Sequencing of microbial pathogens: database and bioinformatics analysis” (GENPAT). The Decree of the MoH 30 May 2017 formally established the GENPAT at the IZS-Teramo headquarter [24], with the main objective of developing a national platform dedicated to whole genome sequencing (WGS) of microbial pathogens.

In response to the on-going global pandemic, requiring fast graphical assessment of SARS-CoV-2 epidemiological clusters from large numbers of samples, the GENPAT was reorganised in units dedicated to wet-lab, dry-lab, bioinformatics and information technology (IT). During COVID-19 burden, GENPAT contributed to SARS-CoV-2 surveillance by developing bioinformatics workflows, metagenomics databases and informatics systems.

Basic and analytical applications of GIS in epidemiology can help in visualising and analysing geographic distribution of diseases through time, thus revealing space-time trends, patterns, and relationships that would be more difficult or obscure to discover in tabular or other visualization formats [25]. During epidemic and pandemic emergencies, real-time mapping of cases is critical in tracking and controlling the spread of infection. When disease can spread quickly, information has to move even faster. This is where map-based dashboards become crucial [26] to efficiently monitor the spread of infection at a variety of suitable scales [25] and promptly inform decision-makers about the spatiotemporal development of disease outbreaks [27].

This manuscript describes the adaptation and integration of the laboratory information and management system (LIMS) of IZS-Teramo, named SILAB, with the development of a “One Health” module, specially implemented for the management of samples of human origin delivered for the diagnosis of COVID-19 and the implementation of ad hoc tools for collecting human data and information, to support surveillance activities during COVID-19 epidemic. Besides, the manuscript present GENPAT implementations in terms of wet-lab, dry-lab and IT dashboard to observe the geographical spread of the infection for each lineage, in the framework of a unified shotgun metagenomics-based dashboard helping SARS-CoV-2 surveillance in Italy.

2. Materials and methods

2.1. Sample flow and infrastructure reorganisation

During the epidemic, most of the samples came from hospitals of the Abruzzo region, including Teramo, Pescara, and L'Aquila. Usually, control and identification of samples take place in a dedicated laboratory, named Sample Acceptance and Control Unit, at the IZS-Teramo, where the samples delivered are checked for analytical activities, and specialised data entry operators carry out the correct recording in SILAB of the data reported on the samples submission forms and identify the samples with a barcode label in order to make it anonymous. At the Sample Acceptance and Control Unit, COVID-19 samples were delivered by special couriers in triple-wrapped cases, with the sample submission forms outside the cases. For safety reasons, only the operation of data entry was carried out upon delivery of the samples, while samples were checked and identified directly in the Biosafety Level 3 laboratory (BSL3), where each test tube containing the swab was individually identified in biosecurity with a barcode label.

The significant increase of the amount of samples analysed required a significant expansion of the IZS IT infrastructures, i.e. dedicated servers, hardware and hard disks, as well as extension of space for all the acknowledgements of certified email.

2.2. SILAB implementation

SILAB was adapted for collecting data on human samples, by developing a “One Health” module, specially implemented for the management of samples of human origin delivered for the diagnosis of COVID-19. This module was focused on the aspects of privacy and confidentiality, as required by current legislation [28], and on the reduction of the time for the operation of data entry, at the moment of sample acceptance, and of results reporting, given the state of emergency. This “One Health” module was activated in a parametric way (i.e. each item of the system can be modelled to be adapted to new needs), starting from a dedicated area of SILAB to collect data on human health, reserved for appropriately profiled operators, authorised to manage this data.

Moreover, the format of the final test report elaborated by SILAB was appropriately revised, adapting it to show human data and health information (i.e. priority level), as well as processing it in English language, in case of request.

Three ways to automate the sample acceptance process were identified:

  • 1.

    Prior acceptance: a function for quickly entering in SILAB the data of the patients, by collecting most of them automatically in a single form, reading them from parameter tables (Fig. 1). After confirmation, the sample itself was automatically assigned to the analysis to be performed.

Fig. 1.

Fig. 1

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Prior acceptance: simplified acceptance form. The figure shows the form used for insert a sample in SILAB. Mandatory data are indicated using the * symbol.

This way was experienced in the first phase of the epidemic, and during the epidemiological investigations carried out in Villa Caldari.

  • 2.

    Automatic data entry through interoperability between Regional portal and SILAB: after the issuance of the Regional Ordinance [29], according to which the laboratories must use the ATTRA, the connection between this application and SILAB was ensured via Web Service by a unique code assigned to each sample by ATTRA itself. The data of the samples delivered to the IZS-Teramo (i.e. name and surname, date of birth, address of residence and fiscal code of the patient], identified with the ATTRA code, were acquired automatically in SILAB by reading the ATTRA code through a barcode reader. In the same way, the test reports were sent to ATTRA using Web Service technology.

  • 3.

    Massive acceptance from file: given the large number of samples conveyed to the IZS-Teramo from Bergamo, it was necessary to implement a function of massive acceptance. An Excel file structured with a defined template was populated by the public health authorities of Bergamo with all data required for sample acceptance. This file was uploaded by the operator in a specific section of SILAB, and each row was automatically checked and loaded producing a registration number for each sample and its assignment to the laboratory tests to be carried out.

As for test results, regardless of the ways of sample data collection and recording, described above, in order to speed up results acquisition, and to grant quick response times, the laboratory results were acquired and selected from files in a parametric way. In particular, the results of the Real Time RT-PCR were entered by the laboratory staff in an appropriately prepared sheet of an Excel® file (named “schema RT-PCR Sars-CoV-2 - geni ORF1ab, N protein e S protein”], using Excel formulas the final result in terms of positive/ negative/ doubt to the Real Time RT-PCR was calculated, taking into account each specific exam related to the test for the detection of the Sars-CoV-2 (ORF1ab, S protein, N protein]. At the same time, another sheet of this Excel file produced, through appropriate formulas, as rows as there were the samples, each of them containing the sample acceptance number, type of sample, species, date of starting and ending of the analysis, and the result to the Real Time RT-PCR. This latter sheet was uploaded in SILAB, all rows were automatically loaded and checked to verify their consistency and, if no error occurred, the final test report for each tested sample was then elaborated and confirmed.

2.3. Samples processing and sequencing

All the samples were processed and analysed using a specific Real Time RT-PCR test as described in Lorusso et al. 2020. SARS-CoV-2 samples positive with PCR threshold cycle (Ct] <25 were sequenced for genomic surveillance, essential for monitoring the emergence and global spread of viral variants. RNA purified was processed by means of several methods. The first included a metagenomics approach by the combination of the sequence-independent single-primer amplification (SISPA) with Nextera DNA Flex Library Prep (Illumina Inc., San Diego, CA, USA) [8]. The second protocol provided for a targeted approach by the enrichment of some SISPA libraries using myBaits Expert Virus—SARS-CoV-2 kit (Arbor Biosciences, Ann Arbor, MI, USA). Other samples were processed by the targeted-amplicon approach “ARTIC protocol” [30], according to which the cDNA was synthesised with random hexamers and amplified using two Artic v3 primer pools specific for whole SARS-CoV-2 genome [31]. Library preparation was carried out using DNA Flex Library Prep (Illumina Inc., San Diego, CA USA) Finally, an amplicon-based commercial protocol namely Illumina COVIDSeq Test (Illumina Inc., USA) were used to sequence the most of SARS-CoV-2 samples. This kit combines ARTIC multiplex PCR protocol with Illumina sequencing technology [32]. Deep sequencing of all libraries was performed on the Illumina platforms (300-cycles and standard 150 bp paired-end reads).

2.4. GENPAT implementation

GENPAT implemented a next generation sequencing (NGS)-based workflow dedicated to SARS-CoV-2 from the assembly to the lineage identification and the variants analysis [33]. The first step of the workflow was the reads refinement using Trimmomatic (version 0.36, parameters: illuminaclip:2:30:10, leading:25 trailing:25 sliding windows:20:25, minlen: 36 [34] followed by an assembly step using Snippy (version 4.5.1, default parameters). The reference genome Wuhan-Hu-1/2019 NC_045512) was used for read mapping and variant calling analysis. Consensus sequences were derived using iVar (version 1.3, parameters: minimum length of read to retain after trimming m = 1, minimum quality threshold for sliding window to pass q = 20) [35]. The lineage assignment was implemented using the algorithm pangoLEARN from the workflow PANGOLIN 2.0 [36]. The results coming from the pipeline were imported in the GENPAT database through an automatic procedure.

2.5. Connecting SILAB and GENPAT: a Dashboard for real-time surveillance

An Oracle nightly scheduled procedure filled in a table in the GENPAT database with geo-referenced data provided with latitude and longitude; original information was read from a SILAB database view containing always-current data, including patient information, sampling reason, administrative unit, municipality and domicile coordinates and sampling date. In data transfer to the table, all samples from the same municipality were aggregated in the same row and received a conventional couple of coordinates (SDO_GEOMETRY type field) falling into the municipality centroid for privacy reasons.

The obtained spatial table was used to feed a Representational State Transfer (ReST) geo web service deployed with the ArcGIS Enterprise platform, developed by Esri, which made available the database information (in GeoJSON format) for a dashboard web application (Fig. 2).

Fig. 2.

Fig. 2

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Dashboard architecture and data flow.

The data originated from the described procedure were structured to integrate information coming from the sampling activities and the results of the analysis carried out on each sample with location information.

The dashboard front-end (Fig. 3) used for data filtering and investigation was developed using JavaScript open source libraries and consumes the ReST geo web service shared by the back-end infrastructure through asynchronous JavaScript and XML (AJAX) calls. The map and functionalities of the geographical information system (GIS) were built using Open Layers, while interactive charts and table views were realised with Chart.js and Tabulator respectively. The general look and feel of the whole application was defined through the bootstrap cascading style sheets (CSS) framework. Finally, Parceljs bundler was used to build a production version of the application with minified and bundled code.

Fig. 3.

Fig. 3

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Dashboard user interface. The figure shows the main interface used for data filtering and investigation.

3. Results

SILAB was adapted to offer different data entry methods functional to different situations: massive acquisition from file, prior acceptance, and automatic acceptance through application cooperation with ATTRA via Web Service. The operations in SILAB, i.e. data entry and selection of the tests to be carried out on the samples delivered, were simplified and reduced to a minimum, thus allowing training quickly new staff necessary to cover, in some periods, even 3 laboratory shifts a day, and to reduce the time for sample registration, reaching over 4000 daily admissions.

Overall, the entire diagnostic process was automatized. Consequently, the possibility of manual error was reduced at minimum, and a higher quality and homogeneity of the data were obtained.

During 2020 and 2021, under COVID-19 emergency a total of 322,950 and 277,554 nasopharyngeal swabs were tested respectively. The largest number of nasopharyngeal swabs was delivered in the last months of both years, reflecting the actual trend of the epidemic. Of the total samples tested in 2020, the 2.2% (seven thousand seventy-seven samples) came from Bergamo.

Over 88.0% of the test results were reported within 24 h from the delivery and registration of the samples in SILAB.

During 2021, the IZS-Teramo also provided molecular sequencing analysis, to quickly intercept the variants circulating in the area. All the sampling data archived in the database were geographically aggregated at municipality level, and made available for the dashboard (Fig. 4). The built graphical user interface was composed of different functional views focusing on several specific aspects of the information (Fig. 4).

Fig. 4.

Fig. 4

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Content of each tab in the lower right tabbed area of the dashboard user interface. The top left panel shows the distribution of samples per lineages, the top left panel shows the spread of the lineages per months, the bottom left panel shows the number of sample per day, the bottom right panel shows the metadata of the samples.

The map view in the left half of the user interface showed circular clusters for each municipality under surveillance (Fig. 4). The radius of each cluster is directly proportional to the number of collected samples in the corresponding municipality, through the considered time window, and for all the SARS-CoV-2 lineages (Fig. 4). The map view provides also useful tools to interact with the geographic component of the information (e.g. map navigation, zoom and data selection). From top to bottom of the right half of the dashboard, summarised information is accessible inside cards reporting the number of samples, lineages and municipalities, as well as the last update of the underlying database (Fig. 4). A series of filters located under the summary cards, allow selection of single or multiple lineages, provinces and ranges of sampling dates in order to display specific combined data on the dashboard (Fig. 4). For instance, the pie chart on the right of the filter panel, showed the percentage of the samples collected in each province of the considered geographic area. The bar charts in the lower tabbed area of the graphical user interface showed the number of samples per lineage, the number of lineages per month and the number of samples per day.

The last tab contains a table view of data accessible for downloading as a comma-separated values (CSV) file (Fig. 4).

Using the selection tool available on the upper left corner of the map view (Fig. 3), an area of interest focusing on virus distribution can be drawn (Fig. 3). All the summary badges and charts (Fig. 3) reacts immediately to the drawn area by filtering the displayed information.

4. Discussion

From the start of COVID-19 surveillance, data on laboratory-confirmed SARS-CoV-2 infections are provided on a daily basis to the National Health Institute (ISS) by all Regions and Autonomous Provinces. The ISS processes and analyses the data making them available to ensure monitoring of the epidemic across the country [37].

The outputs of the One Health system described in this paper provided timely information to public health authorities and to the general population on the evolution of the epidemic at regional level, thus contributing to the continuous re-assessment of risk related to transmission and impact of the epidemic, and to the surveillance of COVID-19 in Italy [38].

The dashboard made it possible to observe the geographical spread of the infection for each lineage, integrating spatial and metagenomics information stored in the database and combining them to create an easy to read web application (Fig. 3). Indeed, the spatial component of the information we collected is displayed on the map as circular clusters aggregating the number of samples at the municipality level (Fig. 3). This approach is very effective because of its easy and fast data acquisition during the sampling activity, allowing quick reactions in cases of new rapidly spreading lineages.

The One Health module implemented in SILAB was used by other IZSs (i.e. IZS of Sicila and IZS of Puglia and Basilicata), further harmonising the data collection and reporting of COVID-19 cases to the regions of their territorial competence (Sicilia, Puglia and Basilicata - south Italy) and, thus, to the ISS, contributing to the whole surveillance system in the view of a One Health approach.Moreover, a similar One Health module was funded by FAO for African countries, and installed in the 12 countries which already used SILAB for diagnostic activities in animal health and food safety [39], helping harmonise data collection in these countries.

It is worth emphasising that the flow developed for data collection and for the storage and use is independent of the disease of interest. The One Health module is based on SILAB web application which is already strongly parametric and is easy to add and to configure new fields related with specific requirements of the new disease. As described in the methods, the One Health module offers a dedicated “test report model” which also includes these additional fields. Thus, the module can be applied to other human diseases in restricted time. As for the test report, operative procedures managing patient data uploading and the test results (i.e. prior acceptance, massive acceptance from files) can be easily reused and adapted to new needs.

One of the limits of the dashboard is the aggregation on the municipal centroid of the spatial component of the data, necessary for reasons of privacy protection. This did not allow an extremely precise representation of the distribution of the infection on the target territory in space and over time. It should be emphasized, however, that given the emergency context, our approach remains very useful because of its easy and fast data acquisition during the sampling activity allowing quick reactions in cases of new rapidly spreading lineages. The use of non-aggregated spatial data would in any case represent an improvement capable of adding further capacity for analysis and extrapolation of useful information, such as the identification of space-time clusters. Tracking the changes in the distribution of infections would help epidemiologists and authorities to predict where the next hotspot will appear, and thus attempt to prevent it by ordering lockdowns before the rate of infection increases [40].

5. Conclusions

One Health Surveillance focuses on activities across multiple sectors including human, animal and environmental health to promote health for all [10,41]. This paper highlights how a greater spectrum of experts, comprising front-line healthcare workers (veterinarians or clinicians), epidemiologists, information technology specialists, and laboratory personnel, are needed for health surveillance and preparation for disease control and treatment, in particular in the view of future pandemic. The key to fight potential pandemic threats, such as zoonotic disease, is having well-integrated surveillance systems, capable of adapting to new challenges, like has been our system for COVID-19 surveillance during the main months of the epidemic. It is essential that information be timely exchanged between systems collecting data and information related to different sectors, in order to alert authorities to unusual disease outbreaks so that they can take appropriate action. At the global level, countries around the world intensified their efforts in the establishment of advanced systems for genomic surveillance such as the Belgian genomic surveillance consortium [42], the The Coronavirus Disease 2019 (COVID-19) Genomics UK Consortium (COG-UK) [43], the FAO EMPRES-i [44] and the GLEWS [45], and serve as early Warning One Health systems able to deal with pandemic threats.

SILAB implementation with the One Health module devoted for managing data of human origin, with automatic registration of several information, avoiding errors and improving the quality of the data, contributed substantially to the management of the epidemic providing the public health authorities with timely reports of the laboratory results. The developed dashboard, thanks to the connection to SILAB, has proven to be a very useful tool for daily surveillance of emerging space-time clusters of the SARS-CoV-2 variants at the municipality level.

In the light of the experience reported in this paper, this approach has proven to be the most effective in responding to the pandemic, and perhaps the only feasible one to effectively detect, respond and prevent future zoonoses or other public health risks.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

CRediT authorship contribution statement

Alessio Di Lorenzo: Conceptualization, Writing – original draft, Data curation, Formal analysis, Visualization, Methodology, Software. Iolanda Mangone: Conceptualization, Writing – original draft, Formal analysis, Methodology, Software. Patrizia Colangeli: Conceptualization, Investigation, Data curation, Supervision. Daniela Cioci: Conceptualization, Writing – original draft, Data curation, Formal analysis, Methodology, Software. Valentina Curini: Formal analysis, Writing – review & editing. Giacomo Vincifori: Writing – review & editing. Maria Teresa Mercante: Writing – review & editing. Adriano Di Pasquale: Conceptualization, Investigation, Supervision. Simona Iannetti: Conceptualization, Writing – original draft, Data curation, Methodology.

Declaration of Competing Interest

The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers' bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

Data availability

No data was used for the research described in the article.

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