Multi-resistant Bacterial Infection Surveillance using a Graph Database

with Spatio-temporal Information

Lorena Pujante

1 a

, Manuel Campos

1,2,3 b

, Jose M. Juarez

1,2 c

, Bernardo Canovas-Segura

1 d

and

Antonio Morales

1 e

AIKE, Research Group, Faculty of Computer Science, University of Murcia, Spain

INTICO Research Institute, Spain

Murcian Bio-Health Institute (IMIB-Arrixaca), Spain

Keywords:

Graph Analytics, Graph Database, Epidemiology, Infection Surveillance.

Abstract:

Some of epidemiologists’ efforts in dealing with multi-resistant bacterial infections acquired in healthcare

settings focus on tracing patient’s activities, carrying out a contact analysis, and identifying the main risk

factors that lead the appearance of these infections. Most contact analysis studies assume information is stored

in conventional relational databases. To date, little attention has been paid to other storage paradigms. This

paper explores the potential of graph databases to establish the complex relations required to compute contact

analysis. We discuss the advances in modelling of the temporal and the spatial information of the Electronic

Health Record that has to be introduced in the graph database. In this position paper we propose three points

for discussion: advances in formal modelling, speciﬁc algorithms for graph analysis, and visualisation tools.

1 INTRODUCTION

We are living in a global pandemic situation this year.

We are facing a context where capacity restrictions in

any establishment are imperative, especially in hospi-

tals and health care centers, since they may became

important sources of contagion. However, the spread

of diseases among inpatients is not new, and the ap-

pearance of viral or bacterial infection outbreaks is

usual in hospitals. These are the nosocomial infec-

tions that have become a major health problem today.

They are a widespread scourge, which appears in both

developed and in-development countries. And its

clinical and economic impact are increasingly signiﬁ-

cant due to increasingly crowded conditions in hospi-

tals, the emergence of new organisms, and increasing

bacterial resistance to antibiotics (World Health Or-

ganization, 2003).

Infections acquired or associated to healthcare are

among the leading causes of death and increased mor-

bidity in hospitalized patients (de Leon, 1991). These

https://orcid.org/0000-0003-3884-3049

https://orcid.org/0000-0002-5233-3769

https://orcid.org/0000-0003-1776-1992

https://orcid.org/0000-0002-0777-0441

https://orcid.org/0000-0002-0872-5351

infections can be caused by a pathogen contracted

from another patient in the hospital or by the patient’s

own ﬂora; and transmitted both through the air, by

food or by contact with any inanimate object (World

Health Organization, 2003). Given the high level of

importance of these infections, together with the wide

range of causes they encompass, their detection and

prevention has become one of the current challenges

in epidemiology (Edwarson and Cairns, 2019).

One important bottleneck of the data analysis

of contacts is to extract a clear trace of the activ-

ity of patients from the Electronic Health Records

(EHRs) stored in the Health Information System

(HIS) databases. In general, commercial HIS are sup-

ported by relational databases (RDBs) making difﬁ-

cult to manage the complex relations between the dif-

ferent dimensions of study: clinical data and spatial-

temporal information.

Speciﬁcally, the objective of this preliminary re-

search is to evaluate the use of spatial and temporal

data modeling with the language and tools provided

by Neo4j

, a graph-oriented database to carry out

a contact analysis among hospitalized patients. This

analysis is situated in the context of the appearance of

bacterial outbreaks among hospitalized patients, and

Neo4j: https://neo4j.com/

Pujante, L., Campos, M., Juarez, J., Canovas-Segura, B. and Morales, A.

Multi-resistant Bacterial Infection Surveillance using a Graph Database with Spatio-temporal Information.

DOI: 10.5220/0010387207410746

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 741-746

ISBN: 978-989-758-490-9

741

its purpose is to provide support in the detection of

the focus of infection through the area of inﬂuence

of the outbreak. We also compare the results with

those of a previous work (Pujante et al., 2020) using

the OrientDB database, showing differences between

languages and modeling.

2 BACKGROUND

Non-relational databases, among which the graph-

oriented databases, GDB, arise in response to the lim-

itations that relational databases present to face the

new challenges and trends that are emerging in the

current world of computing and data management.

The GBDs specify their scope of action in scenarios

in which it is necessary to work with highly connected

data sets, being designed for optimal storage and nav-

igation through the relationships established between

them. Thus, in this type of databases, the data are

stored as the nodes of a graph, and their relationships

as the edges, allowing to apply the theory of graphs

to traverse the data stored in the database (Angles and

Gutierrez, 2008).

Neo4j is a native graph-oriented database that uses

a model of labeled and property graphs. Labeled

graphs consist of graphs in which labels can be as-

signed (generally an integer, although they can also

be of another type of data, such as text strings) both to

their nodes and to their edges to distinguish them from

the rest. As for property graphs, they are weighted

graphs (their edges have some kind of numerical eval-

uation) in which properties can be deﬁned in key-

value format on both nodes and edges. Neo4j uses

its own descriptive language, Cypher, with which

the queries are made by describing the nodes, edges

and properties to be obtained, having a very different

structure from that of SQL.

Regarding the study of infection diseases, re-

searchers of (Lose et al., 2019) focus on Tuberculosis

disease research from the biology perspective, repre-

senting biological entities in a labelled property graph

using Neo4J. From the theoretical point of view, sev-

eral models recently described the spread of disease

(Huang et al., 2016). However, little attention is paid

on the use of GDBs for infectious diseases from an

epidemiological perspective. There are several stud-

ies that explore the advantages that graph modeling

can offer for tracing the spread of epidemics. Exam-

ples of them are (Grande et al., 2015), where graphs

have been used to represent the spread of the hep-

atitis C virus, distinguishing different types of per-

son nodes according to certain characteristics, such

as sex and age; or (Chen et al., 2011), where the au-

thors used social network analysis with a contact net-

work. In (Y. Kai and Jun, 2018) weighted graphs has

been theoretically studied to simulate the spread of an

epidemic. In (M. Boman and Stenhem, 2006), three-

dimensional graphs are used to represent the analysis

of contacts between individuals in speciﬁc geographic

areas in which an outbreak has been detected. The

work of (Chen et al., 2007) used geographical infor-

mation to analyse outbreaks for SARS diseases. As

far as we know, there is not research on the litera-

ture of the use of GDBs for analysing infection spread

in hospitals considering spatial and temporal informa-

tion.

3 MODELING CLINICAL

EVENTS AND

SPATIO-TEMPORAL

RELATIONS

With this modeling we can represent a common case

like the following:

A patient arrives at the emergency room with se-

vere stomach pain and is treated by the digestive

medicine service, speciﬁcally by its emergency unit,

and remains in a box for 4 hours. She then goes to

the plant where another unit of the same service is re-

sponsible for her care for a day. The next day she un-

derwent an operation, which entailed a change of bed

for the operating room, although she was still treated

by the same unit in the same ﬂoor. At the end of the

surgery, she returns to the same room and bed where

she remains until he is discharged after three days.

It is necessary to model the clinical events, the

spatial conﬁguration of the hospital, and the temporal

relations between the events and the locations of the

patient during the care trajectory. This case is mod-

eled as a graph in Figure 1.

Regarding the locations, it is necessary to estab-

lish a physical organization of the hospital that is spe-

ciﬁc enough to track the care trajectory, but also gen-

eral enough to adapt to the different architectures that

hospital may present. It is also required to have a ﬂex-

ible structure that allows the grouping of Areas and

Zones of the same or different ﬂoor. This concept is

LogicalZone, which can also host other logical zones

under the sole condition that no cycles occur. It would

also be necessary to create another organization of

the space related to the functional distribution of the

hospital, with which the units and services that work

in it are represented. Thus, spatial modeling can be

subdivided into three distributions: physical, logical

and functional, which can be seen in a UML domain

HEALTHINF 2021 - 14th International Conference on Health Informatics

742

Figure 1: Graph for the case shown.

model in Figure 2.

For temporal modeling and clinical events, which

can be seen in Figure 3, we represent in a generic

way all the events of the patient’s medical history,

which have a temporal annotation. These events may

represent, for example, infections or diagnostic tests.

We distinguish events with duration in time (inter-

val) from those without duration (points). The patient

stays are special interval events since they are associ-

ated with a spatial location.

4 EPIDEMIOLOGY QUERIES

In order to advance from our previous work, we in-

clude two new queries of interest for epidemiologists:

• Query (Q1): Given a set of patients, determine

what events or locations they have had in common

during a time interval.

• Query (Q2): Given a set of patients, determine

what other patients have had in common over a

period of time.

4.1 Q1: Events or Locations in

Common for a Set of Patients

In this query, given a set of patients, we retrieve a sub-

graph formed by all the spatial elements or events that

they have had in common. This query is constrained

to a given period of time, that is considered a param-

eter of the query. The meaning of in common should

be as broad as possible, since the ﬁnal objective is that

with the resulting graph to be able to analyze which

are the facts with the greatest coincidences between

the given patients, and thus be able to detect a possi-

ble source of infection. Starting from the set of pa-

tients given, it will be veriﬁed which admissions and

other events they have registered in the same location

(device, room, corridor, ﬂoor, etc.), in which units or

department they have been, or if some of these pa-

tients the same bacteria have been detected in a mi-

crobiology test. To design and solve this query, the

admissions, microbiology tests and other events have

to be considered. The steps followed in query Q1 are:

1. The given Patients nodes are retrieved and, from

these, we extract all the Events associated in the

given Interval period of time (a parameter) and

that are not of the Admission or MicroTest sub-

class.

2. For each pair of Events belonging to different Pa-

tients, it is checked whether both have been car-

ried out in Locations on the same Floor.

3. If they share the Location, the edges that connect

each Patient nodes with their Event nodes, and the

edges for the Locations in which both Events took

place, are returned as a result.

Figure 4 depicts part of the Cypher code for the

query Q1. In Figure 5 we show the resulting sub-

graph using as parameters idPatient: [1, 2, 3, 11, 22],

dateStart: ”2019-03-13T00:00:00”, dateEnd:”2019-

03-23T23:59:59”.

4.2 Q2: Contact Analysis

The query Q2 tries to establish the relation among a

set of patients. Given an initial set and all those pa-

Multi-resistant Bacterial Infection Surveillance using a Graph Database with Spatio-temporal Information

743

Figure 2: UML domain for spatial model: physical, logical and functional elements.

Figure 3: UML domain for temporal model: clinical events and temporal relations.

Figure 4: Code for the query Q1.

tients who within a given time interval have had a spa-

tial relationship with the former will be retrieved, re-

gardless of whether or not they have been diagnosed

with a bacterial infection. Instead of helping to ﬁnd

the origin of the outbreak of a certain bacterial infec-

tion, the result is a a group of patients among whom

the probability of suffering the same infection is high

due to their proximity to others who already suffer

from it.

The following are the step for the resolution of the

query Q1:

1. Given a set of patients identiﬁers, their corre-

sponding Patient nodes are retrieved.

2. For each Patient, the Admissions and other Events

found in the given Interval of time are retrieved.

3. From these Admissions and other Events, the Lo-

cations (Devices or Rooms) in which they have

been carried out are retrieved.

4. For each Location, other Patients than those given

as an input parameter that have an Admission or

another type of Event that has been carried out in

the Device or Room located on the same Floor as

the given one.

5. The Patients identiﬁed in the step 4 will be the

outcome of the query.

Note in Figure 6 that query Q2 does not return a

complete subgraph, but a set of patients that should be

analyzed.

5 DICUSSION

Graph-oriented databases allow the storage of highly

connected data such as a patient’s medical history. In

the problem of surveillance of multidrug-resistant in-

fections, decision support for an epidemiologist can

be carried out with a 3-part modeling: clinical events,

spatial locations (physical, logical and functional),

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744

Figure 5: Subgraph retrieved for the query Q1.

Figure 6: Subgraph retrieved for the second query.

and temporal relationships between the above. We

have focused on the analysis of infections within a

hospital, not in the geography or with general popula-

tion, for which well-established models are known to

the scientiﬁc community.

Our preliminary evaluation of Neo4j for this pur-

pose has been positive and it presents advantages with

respect to OrientDB that we have presented in a pre-

vious work. It has been possible to develop all the

proposed queries, in a simpler and more readable way

than with OrientDB. Furthermore, the more ﬂexible

and descriptive structure of the queries has allowed

us to be organized the in a way that makes their exe-

cution more efﬁcient. There are facilities in function-

ality such as queries with grouping (OrientDB does

not have any grouping mechanism, neither explicit

nor implicit) or the passing of parameters. Cypher is

a much more powerful and expressive language than

OrientDB SQL. We consider its expressiveness one of

its main strengths, since a query can be implemented

in innumerable ways.

A further efﬁciency and scalability evaluation of

the queries with a simulated dataset or with a real

dataset is necessary. Other queries are also possible

(e.g. root case search or outbreak detection), but in

this preliminary work we have only shown two of the

needs of epidemiologists.

As points of discussion and future work after these

preliminary results, we propose the following:

- The modeling of graphs with properties and la-

bels is very ﬂexible, but it would be necessary to ad-

vance in representing fuzzy relations or probabilities

that adapt to the problem of epidemiology. Can we

make the relationships more formal and introduce in-

tegrity constraints such as those provided by tempo-

ral and spatial logics or by spatio-temporal reasoning

methods in general? For example, what would happen

if only a percentage of patients met a contact criterion

in consultation Q1?

- The scalability of GOBs in information storage

and retrieval is known, but the resolution of some of

the issues in epidemiology requires a more complex

graph analysis that is not normally implemented in

these GOBs. For example, it should be noted that the

analysis of the resulting subgraph to obtain clusters

of nodes highly connected to each other does not fall

within the objectives of this work. Is it worth aban-

doning GOBs to go to a strictly graph solution where

it is easier to program algorithms? How easy is it to

implement algorithms in these databases?

- Graph visualization tools are necessary to help

the clinical expert make a decision. The graphs shown

in this preliminary work are useful to the data analyst,

but the clinician will have serious difﬁculties in query-

ing and interpreting the resulting graphs. What vi-

sualization functionalities are necessary to turn them

into a tool for your daily work in decision making?

Multi-resistant Bacterial Infection Surveillance using a Graph Database with Spatio-temporal Information

745

ACKNOWLEDGEMENTS

This work was partially funded by the SITSUS project

(Ref: RTI2018-094832-B-I00), given by the Span-

ish Ministry of Science, Innovation and Universi-

ties (MCIU), the Spanish Agency for Research (AEI)

and by the European Fund for Regional Development

(FEDER).

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