Android and ODK based data collection framework to aid in epidemiological analysis
1
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
Android and ODK based data collection framework to aid in
epidemiological analysis
A. Raja1, A. Tridane2, A. Gaffar1, T. Lindquist 1and K. Pribadi3
1. Department of Engineering, Arizona State University, Mesa, Arizona, USA,
2. Department of Mathematical Science, United Arab Emirates University, Al Ain, UAE
3. Renaissance Sciences Corporation, Chandler, Arizona, USA.
Abstract
Periodic collection of field data, analysis and interpretation of data are key to a good healthcare
service. This data is used by the subsequent decision makers to recognize preventive measures,
provide timely support to the affected and to help measure the effects of their interventions. While
the resources required for good disease surveillance and proactive healthcare are available more
readily in developed countries, the lack of these in developing countries may compromise the quality
of service provided. This combined with the critical nature of some diseases makes this an essential
issue to be addressed. Taking advantage of the rapid growth of cell phone usage and related
infrastructure in developed as well as developing countries, several systems have been established to
address the gaps in data collection. Android, being an open sourced platform, has gained considerable
popularity in this aspect. Open data kit is one such tool developed to aid in data collection. The aim of
this paper is to present a prototype framework built using few such existing tools and technologies to
address data collection for seasonal influenza, commonly referred to as the flu.
Keywords: Android; Open data kit; Influenza; Data collection and surveillance
Correspondence: a-tridane@uaeu.ac.ae
Copyright ©2014 the author(s)
This is an Open Access article. Authors own copyright of their articles appearing in the Online Journal of Public Health Informatics.
Readers may copy articles without permission of the copyright owner(s), as long as the author and OJPHI are acknowledged in the copy
and the copy is used for educational, not-for-profit purposes.
Introduction
Public healthcare services rely on accurate and efficient surveillance of public’s health for
providing proactive and timely measures to prevent and control a disease. This information is not
only used to target interventions and start investigations of a disease, but also for alerting the
public on possible outbreaks and guide them through essential preventive measures. The bi-
directional communication allows for higher chances to control a disease [1].
The means of data collection and analysis have undergone several changes in the last few years.
Paper based modes of information gathering are slowly being replaced with the use of emerging
technologies for better, faster and more error-free processes. Health care business analytics are
also growing towards the use of cloud computing due to the lower financial risks involved and
the flexibility which it offers. Using traditional means such as paper forms or personal digital
assistants for information gathering is not only time consuming but also adds an additional cost
Android and ODK based data collection framework to aid in epidemiological analysis
2
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
to the organization collecting the data. The widespread nature of these tasks makes them
economically taxing as well. The distribution, maintenance, ease of use etc. are other factors
which need to be handled.
Many health departments recognize the need for adapting the emerging technology for
improving notifiable condition reporting (also known as case reporting – case reporting from
healthcare providers to public health agencies) and public health alerting. In recent years, the
Center for disease control and prevention (CDC) in collaboration with the Council for state and
territorial epidemiologists (CSTE) have proposed a model for the exchange, sharing and retrieval
of notifiable condition reporting from an electronic health record [1]. This suggests that
electronic notifiable condition reporting may soon be feasible at larger scales.
The use of mobile phones for data collection has also seen a considerable growth. Adapting the
mobile data collection to work with standard used in health care domain for data reporting could
very well be a futuristic way of case reporting.
Mobile phones, including smartphones, are becoming very popular in most parts of the world.
Quoting an IHS iSuppli Wireless Communications Market Tracker Report from information and
analytics provider IHS (NYSE: IHS), ‘The Smartphone shipments in 2013 are forecast to
account for 54 percent of the total cellphone market, up from 46 percent in 2012 and 35 percent
in 2011 [2]. By 2016, smartphones are expected to represent 67.4 percent of the total cellphone
market’, as shown in the fig 1 below.
Figure 1 Smartphone market share forecast by iSuppli [2]
With the rapid growth in the mobile industry, the capability of phones has also tremendously
increased. Mobile phones now have built in features to capture media, GPS, share information
seamlessly and have enhanced display. Technologies such as Bluetooth, SMS, Wi-Fi and web
are well integrated together. There has also been significant increase in the data holding capacity
in these devices and their processing powers. Another significant advantage is that competition
and the huge market for smart mobiles has rendered these devices more affordable than laptops,
computers and tablets.
Android, being an open source project has inspired various developers to use the API for
designing need based applications. In the field of data surveillance and analysis, various
applications such as Pendragon forms [3], Open data kit (ODK) [4], Epicollect [5], eCAALYX
Android and ODK based data collection framework to aid in epidemiological analysis
3
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
[6] have been talked about and explored. Open data kit in particular has exposed a few open
source generic tools which can be used either individually or together [7]. This is mentioned
more in detail in the framework section. The advantage of open data kit is that the tools are open
source and are based on open standard interfaces, which allows us to leverage them for our
needs.
In this report we talk about REACap. REACap is a part of a framework designed to use the
computational capability of smart phones for the process of information collection, modeling and
analysis for infectious diseases. Providing means for easier form design, form retrieval and
storage, analysis and modeling, all using the capability and features of smartphones is
demonstrated. In this report we focus on REACap – the surveillance application.
In order to build a prototype application showing the advantages of this framework we chose
Influenza or seasonal flu as the disease to demonstrate the application. Influenza, also known as
flu is a contagious respiratory illness spread through the air. The severity of the disease usually
varies with one season to the other depending on the type of flu virus, availability of the vaccines
and how well the flu vaccine is matched to the virus. It is often confused with common cold due
to the similarity between their symptoms. The disease affects thousands of individuals every
year. In Arizona USA alone, where the project was developed, the number of lab reported cases
of influenza for the 2012-2013 season was 10304 [8].
The first section in this report covers the framework technologies used in the development of this
project. The second section is the application description. It talks about the design of the
application and the various processes followed in its implementation. The third section talks
about the advantages and disadvantages seen in this approach. Section 4 presents the summary
and conclusion.
Framework technologies
This section describes the core technologies used in the development of REACap. REACap as
mentioned before is the data collection interface for a framework comprising of surveillance,
analysis, forecast and collaboration, developed for monitoring and analysis of seasonal Influenza.
The technologies describe here provide the foundations on which REACap was built. They are
described in detail below.
Figure 2: Transformation Code
XML is an established standard that has been around for several years, and has reached a level of
maturity that greatly enhances software applications compatibility. If fact, the power of XML is
Android and ODK based data collection framework to aid in epidemiological analysis
4
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
that it can enhance interoperability among disparate software applications even without XML
being an end goal to any of them; it can work as a middle ground [9]. Without XML, any
application can still communicate with another application by writing a special “transformation
code” that reads the output format of the source application, and generate the equivalent input
format of the “destination” application. For bi-directional communication, another set of
transformation code will be needed to support the reverse direction of communication (from
destination to source). With a large number of software application standards around, the number
of transformation classes needed would be prohibitively large. Assuming that we have N
software applications written with different standards, the number of transformation classes
needed for bi-directional communication would be 2 *(N (N-1)) as per figure 2 [10].
XML is the only standard format that is used as a middle ground between other software
standards (See Figure 3). If any software application is capable of using XML as a communication
middle ground (that would still require one transformation code to XML and one from XML
back to the application), we can reduce the number of transformation codes to 2N. This can be a
significant reduction in complexity looking at the large number of software standards (N) we
have today. Furthermore, any new software standard will only need to use XML as a common
ground rather than having to write transformation code to all existing standards.
Figure 3: XML as a Common Ground
For this reason, we focus on using XML as a base for our approach. With the widespread
versatility in mobile standards, XML will allow us to be compatible with any one of them. On
the back end, additional savings exist since the data collected will be written as XML data, which
is platform independent.
The XML Space
XML itself is text based, allowing it to be human readable, without the need of any specialized
software. We often refer to it as “Document Oriented XML”. However, greater advantages can
be attained beyond that. Being fully structured, XML could also be processed by software tools
as a “formal document” following well-defined models [11], [12]. Unlike HTML, XML is written
using a formal structure using several document descriptions (like the XML Schema, the Data
Object Model) to describe a strict document structure needed for software tools. The XML
document itself is validated against these rules to ensure that the document can be parsed by
software tools to correctly extract the semantics [13]. This allows developers to write several
Android and ODK based data collection framework to aid in epidemiological analysis
5
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
XML-based tools to automatically parse and process an XML document for different needs.
Therefore, in the realm of XML, numerous applications have been written to transform XML
document in many different ways, making it suitable to compile and run xml-based application
in the same way we do any other software.
XML document can work as a base, carring all necessary information and structure. It can then
be transformed by parsing it using XSL transformation, and then presented in any of the known
formats (Presentation) on the right. Marshalling and unmarshalling allows for structural
compatibility between linear (serial) formats (like HTML, PDF, and XML itself), and parallel
formats of high-level programming languages (like Java, C/C++).Sub-Heading 1
XFORMS
Xforms is a model view controller based XML format. It was developed by the World Wide
Web consortium (W3C) to overcome the limitations of older HTML forms [14]. Traditional
HTML web forms do not distinguish between the content and the presentation of a form. Xforms
on the other hand, is comprised of two parts, the Xforms Model (describing the form's purpose,
logic and initial data) and the Xforms User Interface (describing the forms presentation).
The connection between the Xforms model and the Xforms user interface is called the binding,
and it uses a common W3C technology called XPath. XPath uses path expressions to identify
nodes in an XML document. In Xforms this is done by using the 'ref' or 'bind' attribute. Fig 4
below describes the main components of an Xform.
Figure 4 Components of Xform
Xforms allow for flexible presentation options. The Xforms model is capable of working with
variety of standard or proprietary user interfaces in order to render the form. The presentation of
an Xform can be interpreted differently by different interfaces. For instance, based on the style
sheet used by a browser, the interpretation of xforms by a mobile browser and web browser
would be different. Controls in a cell phone are easier when described by lists and menus as
opposed to traditional pop-up choice boxes. Xforms also allow for form validation and data
restraints. A particular field could be defined read only or the restraints in data entered would
Android and ODK based data collection framework to aid in epidemiological analysis
6
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
prevent erroneous inputs. This acts a light validation mechanism before the data is sent to the
data manager/database [14].
Xforms have been demonstrated for usage in many different settings. Their use has been
explored in web services, xforms processors and even for linking data models to forms in
insurance industry. In the healthcare domain, the specification is still underutilized but is starting
to be recognized for its advantages [1]. Researchers in Australia used Xforms for developments
of decision system support. In Germany, developers implemented an information system to
maintain details of prescription drug formulary [1]. OpenMRS is an example of the usage of
Xforms in clinical and public health systems.
To read the Xforms and present the form document to the user, Xform clients are used. In this
project, the xform client we are using is Javarosa [15]. JavaRosa is a mobile based Xform client.
Due to the limited capability of cell phones as compared to desktops/laptops, Javarosa only
supports a subset of Xforms. Additional customizations, specific to mobile use, have also been
introduced. This will be covered in more detail in the section below.
JAVAROSA
JavaRosa is an Xforms client developed for mobile phones. It is written in J2ME. It was
developed as a product of the OpenRosa consortium. It is basically a mobile application platform
which can be tailored by developers to suit their needs. It contains an Xform engine at its core.
The Xform engine is responsible for reading the form elements, use the binding specified in
Xform to interpret the elements and present the element to the user [15]. The logic behind the
nature of the element and how it is presented to the user is determined by the Xform engine.
Mobile devices differ greatly from conventional desktops in their computational power and their
user interface. They are limited in power and have enhanced UI. This prompted the adaptation of
JavaRosa to tailor their Xform support to the mobile market. They support only a subset of
Xform specification and in some cases support a feature only in a particular way. JavaRosa has
also introduced some additional form features which enhances the Xform experience on mobiles.
This includes additional features as well as redefining preexisting Xform features.
One of the core components on which REACap was built, Open Data Kit Collect, utilizes
Javarosa for form Logic and form processing. Designing a form for REACap requires a good
understanding of the underlying Javarosa specifications.
Forms can be developed by writing raw XML or by using a form designer such as ODK Build
[7], PurcForms [11], XLS2XForm [16]. In the current implementation of REACap, forms were
developed using XLS2XFORM. These will be talked about in more detail below
Open data kit (ODK)
Open data kit is an open source suite of tools which was designed to help users build information
services for developing nations. ODK started as a google.org sponsored sabbatical project and
was continued back at the University of Washington Seattle. It currently supports various tools,
most notable of which are ODK build, ODK collect and ODK aggregate. The tools are designed
Android and ODK based data collection framework to aid in epidemiological analysis
7
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
to be used independently or together. Being built on existing open standards, they enable users to
build services to collect and distribute information in places where user limitations or limitations
on infrastructure has long posed problems [4].
For the design of REACap, we evaluated the a few of the ODK tools for compatibility. ODK
build [4] is a drag and drop web based form designer. Even though ODK build is a developing
application, It was best suited for designing simple forms. In order to allow flexibility in our
Influenza survey form, we decided to use ODK build only as a starting point in the design of our
form and allow for design of more complex forms.
ODK collect is an android based mobile client which acts as the interface between the user and
the underlying form. Collect takes the Xform logic of the form and displays it to the user in a one
prompt at a time format. Javarosa provides the form processing and form logic which ODK uses.
In disconnected mode, ODK Collect stores the application logic and the form data on the phone
in a xml format and as binary files for media. The user can choose to synchronize with a server
as required. Files are sent using standard HTTP POST to any Open Rosa compatible server [4].
Since REACap is a prototype application developed to aid in data collection for Influenza, using
ODK collect to design the android client seemed appropriate. Simple, ease of navigate, ease of
comprehension and thoroughness were some of the characteristics which we were looking at for
REACap. ODK collect was a good match. The form processing logic, its display and offline
storage were some of the features of ODK collect which was used in REACap. ODK is also
supported by an open source community that has contributed training documents, localization
support as well as additional tools. These advantages made ODK collect a suitable choice for this
project.
EpiCollect
Epicollect is a free, open source, data collection tool developed by researchers in the Imperial
college at London and the University of Bath in UK. It allows an mobile user to submit
geotagged forms with or without images to central server located within
www.spatialepidemiology.net. The server, allows the mapping and visualization (to Google
maps or earth) and analysis of the data. The data can also be downloaded or viewed on the phone
using Google maps [5]. Fig 5 below captures one of the use cases of epicollect. Data collected by
registered users is sent to a central database. Data can then be viewed on Google maps/earth or
on phones. Epicollect also allows the user to filter the data as shown in the figure.
Android and ODK based data collection framework to aid in epidemiological analysis
8
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
Figure 5 Data collection, collation and visualisation framework using EpiCollect and
www.spatialepidemiology.net [5]
Epicollect provides a generic framework for point data collection and analysis, both for Android
and iOS. The analysis is sorely done on the server/visualization platform and has to be
communicated back to the users. While this application can be widely adapted for simple data
collection and viewing, it lacks the engine to process complex forms which was seen in ODK.
The form interface presented to the users is made to suit the needs of informed data collection
agents. ODK presents a more intuitive form suitable for easier use. These factors led to the
preference of ODK over epicollect.
Dropbox
A central server or database as shown in Fig 6, having the ability to store information securely
and reliably is of prime importance in a multi-client application. This allows the clients to
synchronize information more easily and provide a storage space which is limited in mobiles.
Availability of data from all clients in one place also aids in better analysis and decision making.
The restriction of access based on privilege is also a good feature to have.
One of the disadvantages of having a standalone private server is the cost of maintaining the
server. This usually plays a huge role in the operational costs. The question of reliability and
security also exist. The need to keep with emerging technologies, constant updates, security
checks, maintaining synchronization between users are other factors due to which the popularity
of cloud computing is on the rise [17]. Cloud computing allows the enabling of convenient access
to a shared pool of computing resources, available on demand.
Android and ODK based data collection framework to aid in epidemiological analysis
9
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
Figure 6 Depiction of a centralized global server for a variety of devices
They require minimum management effort or service provider information. In huge
organizations, the use of cloud computing leads to much lower financial risks as well. The
following Table I illustrates some of the pros and cons of cloud computing.
TABLE I. Pros and cons of cloud computing
Pros Cons
Scalabiliy
and Cost Lock-In
Encapsulated
change
management Reliability
Next
generation
architectures
Lack of
control
Choice and
Agility Security
Dropbox is a cloud storage service provided to store and share your data among many
applications. It provides inbuilt encryption security and access restrictions. The API allows the
user to build the features of Dropbox directly into a mobile application or a desktop application.
It is compatible with Windows, Mac, Linux, iPhone, iPad, BlackBerry, and Android devices. The
API provides methods to read and write from Dropbox securely, any changes made can be
synchronized back to shared devices. Other notable features include simple sharing, search, and
restoring files to past revisions [18].
In the scope of this project, Dropbox serves two purposes. It acts as the storage medium for the
information collected by users using REACap. All the information is stored in a format which
would make the retrieval and analysis of data more efficient. It is also used to store information
which is the output of the REAView engine, to enable viewing the data gathered from REACap
across multiple users. The synchronization feature of dropbox ensures that with the availability
of network, all users will be notified of the availability of new information.
Android and ODK based data collection framework to aid in epidemiological analysis
10
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
EpiMl
EpiML is a new XML based document interchange protocol which was developed on the AREA
project. EpiML is being designed to allow data exchange between AREA apps and mobile
devices. EpiML is also the protocol to upload/download data to REAcloud. Data gateways can
be developed to translate EpiML data to Health Level 7 (HL7) protocol allowing data
interchange with MHS such as the Navy Marine Corps EpiData Center and other health
databases. EpiML is the core enabler of the AREA systems to interchange input data,
configurations, and output data allowing for full collaborations between AREA users and
data/analysis reporting to AREA cloud servers.
Application description
REACap, an information capture and survey application, is a part of overall project named
AREA which is aimed to research the use of mobile applications for health surveillance, data
analysis and forecasting. Applications for Rapid Epidemiological analysis, also know an AREA,
were targeted to study the feasibility of the using mobile devices for this purpose. Mobile
devices, smartphones in particular, have gained huge popularity in the last few years. They have
become more user programmable, allowing users to mould mobile devices to their taste. They
are now capable of capturing media, have inbuilt GPS capabilities, and provide numerous means
of sharing information. Communication modes amongst devices include Wi-Fi, Bluetooth, sms,
3G, touch and so on. These make smartphones a sought after choice for use as field instruments.
AREA apps consist of 5 main components. REACap, the focus of this paper, is the surveillance
app for collecting field information of the patients. It is built for Android and uses Open data
kit's Collect as its base. REACap allows the user to download pre built forms from the server and
display them to the user. Users can collect geospatial coordinates, photographs, video, audio, and
any number of structured data types as their inputs. The forms can be saved at any stage and on
completion, allow the user to submit the form to the server (REACloud).
The other components of AREA include REAView, REAModel, REACould and REAConfig.
REACap was developed to work in collaboration with these components. REAView is the
platform to view the analysis and forecast done on the information collected. REAModel is the
forecast engine available as an application on the smartphone. REAConfig is the mobile and web
based configuration tool which allows the user to design forms which will then be used in
REACap. The final component of AREA, REACloud, is the cloud based server which acts as the
core to all the other applications. EpiMl is the data interchange format designed to talk between
all these components.
The output from REACap is in the EpiML format. This data exchange format has the ability to
contain all information required for the forms and also the modeling and viewing of the
information collected. This information is stored in REACloud. REACloud also contains the
forms developed by the users which are sent to REACap on request. As of the current
implementation, the other components of AREA use the information collected from REACap
through the REACloud. Fig 7 shows the current peripherals of REACap.
Android and ODK based data collection framework to aid in epidemiological analysis
11
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
Figure 7 Current peripherals of REACap
The intent of REACap is to enable the collection of information for any canonical disease. A
prototype using Influenza (Seasonal flu) was built to demonstrate this. The scope of this project
involved studying influenza to determine the information to be collected. This information was
then translated into a format which was suitable to be read by REACap. JavaRosa complaint
Xforms were used for this purpose. The forms were stored in the server (Dropbox). On request
by a REACap user, the forms were downloaded to REACap. The user could then fill the forms
offline and send the forms back in EpiML format to the server. The other AREA components use
this information for their functions. Fig 8 depicts the process followed. The sections below
describe each of the components required for this project in detail.
Figure 8 Processes involved in the development of REACap
Gathering Information for form definition
Influenza was chosen to demonstrate the need for simple data surveillance and analysis in our
case. Influenza or seasonal flu is an airborne disease whose most common symptoms consist of
chills, fever, sore throat, muscle pains, severe headache, coughing and fatigue [19]. It is often
confused with other influenza like diseases such as common cold but Influenza is more severe
and is caused by a different kind of virus. The numbers of people affected vary depending on
season and the severity depends upon the circulating influenza types and subtypes and existing
immunity in the community. The survey below from the Arizona department of health services
show the number of influenza cases by season and age group in Arizona alone.
Android and ODK based data collection framework to aid in epidemiological analysis
12
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
TABLE II. Age Group of Reported Influenza Cases, 2010-2011 through 2012-2013 Seasons [8]
In order to design a form to gather information about influenza, the following parameters were
considered.
• What are the most common symptoms?
• How does the disease spread? What kind of environmental conditional facilitate the
survival of the virus?
• What makes a person more susceptible for influenza related complications?
• What kind of information do we need in order for us to do preventive analysis i.e.
analyze the information available and use it for precautionary measures. E.g. The
influenza has an average of 2 days incubation period and virus shedding,
depending on the age of person, vary from one day before symptoms through 6-11
days after the symptoms for an adult to several days before symptoms for children
and can be infectious up to two weeks.
• What clinical conditions are available for necessary tests in the vicinity?
Two types of users were considered. First - A health care professional. These people are trained
medical professionals well versed with the disease and are capable of making note of additional
information which can help provide a better analysis (Fig 9). Information such as the requirement
for the patient to seek medical assistance or the clinical tests which would aid the diagnosis can
be suggested by them. The second use case is for anyone to use the form for everyday analysis
(Fig 8). An example of the two use cases are shown below
Figure 9 Use case - Individual
Android and ODK based data collection framework to aid in epidemiological analysis
13
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
Figure 10 Use case - Health care professional
Table III below depicts the form contents which were determined based on our analysis. It
includes information on how the form is presented to the user as well as information on
conditional forwarding. Depending on the type of user and the previous inputs the next set of
information to be displayed is determined.
TABLE III. Influenza form flow logic information
Screen
number
Screen
information
summary
Screen contents Next screen
Patient Professional
1 Information to
determine who
is using the
REACap
application
3 2
2 Login screen
for the health
care
professional
NA If login was
successful –
10
Android and ODK based data collection framework to aid in epidemiological analysis
14
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
3 Basic details
about the
patient. Note:
Location can
be recorded
using GPS. In
case of no
network, it
will have to be
entered
manually
4
(A patient
ID is
generated
at this
point
)
NA
4 To capture
fever
If
Yes - 6
No - 5
I don't
know - 7
NA
5 Cough/Sore
throat
If
Yes - 8
No - 9
NA
6 Cough/Sore
throat
8 NA
7 Sweating,
shiver, chills
If
Yes - 6
No - 9
NA
Android and ODK based data collection framework to aid in epidemiological analysis
15
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
8 To make a
note of how
long the
patient has
been having
the symptoms
9 NA
9 Final Screen
NA NA
10 Locality
information
NA 11
11 Patient
information
NA 12
Android and ODK based data collection framework to aid in epidemiological analysis
16
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
12 Patient
symptoms
NA If fever is
yes and
any of the
rest are yes
- 13
If fever is a
'no' - 9
13 Epidemiologic
al inputs
NA 14
Android and ODK based data collection framework to aid in epidemiological analysis
17
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
14 Determine
tests
NA 9
(If from
screen 14,
we
determine
that the
patient
requires
diagnostic
tests, refer
to the
nearest
health care
facility for
the
required/
appropriate
influenza
diagnostic
tests)
The next section describes the conversion of this information into a suitable format which can be
read by REACap.
Defining the Influenza form using Xforms
Xforms as mentioned earlier is a model to represent form data using xml. But the Xforms
specification is long and complex. To have an engine running to read and translate Xforms
would require a lot of memory and CPU resources. This is usually not available and not sought
after when designing mobile applications. Javarosa is the mobile client used in this project for
Xforms. It is tailored to run on devices with limited computational capability. It performs the
task of rendering the form to the user.
There are numerous tools such as PurcForms designer, ODK build, Vellum, Kobo etc which are
available which help create Xforms that work on Javarosa platform. In this project, we have used
XLS2Xform to help create our Xform. XLS2Xform is a tool which simplifies the creation of
Xforms by letting us design the form with Microsoft excel and then converting this to a Javarosa
compatible Xform on their web based tool [16].
The working of XLS2Xform tool is as follows - The excel workbook contains two worksheets -
survey and choices. The survey describes the contents and the structure of the Xform. Control
structures such as groups or loops are specifies in this sheet as well. The questions which need to
be posed to the user, related media, and how it is resented is defined in the survey sheet.
Android and ODK based data collection framework to aid in epidemiological analysis
18
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
The choices worksheet is used to define the choices for the multiple choice questions. A row
represents an entry for a multiple choice. Choices are grouped be ‘list name’ column in Table VII
and the corresponding entries in the ‘Name’ column are displayed to the user. This design allows
the user to reuse the same set of multiple choice options (specified by list name). The elements
(columns) of the worksheets need to be maintained in order for the validity of the Xform. Certain
columns are mandatory. In addition, the worksheet contains optional columns which allow the
user to specify constraints for each row. For example, type, name and label entries are mandatory
columns in the worksheet. But other columns entries such as image, constraint etc need not be
specified at all times. The order of these columns is irrelevant. Optional columns could be left
out if not required. Blank rows are not processed. Another useful feature is that the xls
formatting is ignored while processing the sheet. User could highlight the entries to make it more
readable but this would not affect the creation of Xforms.
Some examples from the designing of Influenza form are shown below
Metadata - At the beginning of the form, we collect information in the background. This
metadata includes the Start time of the survey, the device id and the day of the survey (Table IV).
TABLE IV. Example of metadata
Type Name Label
Start Start
Today Day
Deviceid ID
Branching - The user type selected by the mobile user is stored in the <profession> tag as either
• patient
• professional
Based on the value in the tag, the locality information is displayed. This is done by using the
relevant column in the XLS form as shown below. Note the highlighted section. The locality
information is grouped and the constraint is applied on the entire group.
Repeating a particular set of questions based on previous answer - At many instances, the need to
repeat a particular set of questions arises. If a person has taken many prior tests and we need to
record details of all of them, we can use the repeat feature of XLSForm. This is shown in Table V.
TABLE V. Example of branching based on condition
Type Name Label Hint
constra
int
constraint_m
essage Relevant
begin
group
locality_i
nfo
Locality
Information
${professi
on}='profe
ssional'
Android and ODK based data collection framework to aid in epidemiological analysis
19
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
select one
from
yes_no Detected
Has infection
been
detected?
begin
group
infection
_info Infection data
${detected
}='yes'
decimal Rate Infection rate
Enter
percentage of
people infected
select one
from
inf_types Type Type detected
Choose the
influenza type
select one
from
infection_t
ype Infection
Prevelent
Infections
end group
begin
group env_info
Environment
information
Text Food Food
Enter
comments on
the food
(dietary habits
-
meat?/vegetari
an?, Livestock
condition)
Text Water Water
Enter
comments on
the water in the
locality
(hygiene,
supply source
etc)
end group
end group
Android and ODK based data collection framework to aid in epidemiological analysis
20
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
TABLE VI. Example of repetition based on condition
type Name label Hint constraint
constraint_
message Relevant
begin group prior_tests Prior tests
${fever}
='yes'
select one from
yes_no test_taken
Any prior
influenza tests
taken in the last
3-4 days?
begin repeat Test Test
${test_ta
ken}='ye
s'
begin group test_details Test details
Text test_name Name of the test
Text test_result Results
end group
end repeat
end group
Image names can be provided in the excel to specify which images to associate with a particular
screen. These Images must be stored in the corresponding <form name>-media folder in the
forms folder of ReaCap (ODK Collect)
The below table (Table VII) shows images associated with choices in the choices sheet of our
excel.
TABLE VII. Association of images with screens
list name Name label image
user_type patient Patient
user_type
professio
nal
Professio
nal
gender Male Male
gender female Female
Android and ODK based data collection framework to aid in epidemiological analysis
21
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
fever_opts Yes Yes
fever_opts No No
fever_opts Idk
I don’t
know
fever.pn
g
yes_no Yes Yes
yes_no No No
cough_yes
_no Yes Yes
cough_yes
_no No No
cough.pn
g
shiver_yes
_no Yes Yes
shiver_yes
_no No No
sweating
.png
Once the form has been created in the excel format, we can convert it to Xforms by uploading it
Formhub.com and publishing it. The web based tool lets you download the Xform when done.
All the media mentioned in the form needs to be in a folder named <Form name>-media, located
at the same level as the Xform. This is necessary for the reading of the form in the current
implementation. The next section describes the main features of the REACap application.
REACap
REACap is a data surveillance application designed for Android. As mentioned earlier, REACap
is built on top of Open data kit’s Collect application. It uses Javarosa engine to process its form
logic and display it to the user.
REACap allows the user to download the forms from a centralized server. The forms are stored
in the server along with the form media. Since these forms are to be used in mobile applications,
they need to be Javarosa compatible. Availability of network is necessary for downloading of the
forms. Once downloaded, the user can fill the form in an offline mode. The following fig 11
displays the capabilities of REACap.
Android and ODK based data collection framework to aid in epidemiological analysis
22
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
Figure 11 Screen depicting the capabilities of REACap
Connect to server establishes an authorization check for the user. Each healthcare worker will
have a separate login. This will allow us to track the locations which have been surveyed and
organize surveillance teams better. Health care workers will also have certain privileged access
to the server contents. General users will be provided a common logging to gain access to the
storage server.
Get blank form will allow the user to download forms from the server once they are authorized
to access it (i.e. once they are able to login to the server). The forms are stored as xml documents
and their corresponding media need to be stored in a folder with the same name with a '-media'
appended to the end. This enables the application to download the corresponding media along
with the form.
Fill blank form and Edit saved forms allow the user to fill the downloaded forms and edit forms
respectively. User can save the form at completion or in the middle. This will be stored on the
device till the user decides to send it to the server. Send finalized form button allows the user to
do that. There is a slight difference between saved forms and finalized forms. Finalized forms are
indicated by the user while filling the form. They tell the application that the forms are ready to
be sent. Saving forms while filling lets the user get back to them when they want but these forms
cannot be sent to the server unless the user specifically marks them finalized. Finalized forms
can be edited as well. This distinction is necessary to avoid confusion while dealing with
multiple forms and avoid flooding the servers with incomplete forms by error.
The last button, delete saved forms, helps clear the devices memory of the unnecessary forms
and media.
The javarosa engine and ODK collect displays the prompts to the user in a one prompt at a time
format. Users can navigate from one prompt by a simple swipe motion. The form logic
determines the sequence of prompts shown and the input variables. The Fig 12 below illustrate a
few screens.
Android and ODK based data collection framework to aid in epidemiological analysis
23
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
Figure 12: Few screens from the Influenza form on REACap
The form is saved in an XML format on the device. The output of REACap is a simple tag based
xml output. A sample output is shown in Fig 13.
As seen in the below figure, the value filled in the prompts are stored within their corresponding
name tags. This is enough to enable us to parse the information and analyze it. But in order to
enable the reuse of forms, we want to package the form structure as well as the form output. For
this purpose, we used EpiMl to convey the information to the server. This is covered in the
section below.
Android and ODK based data collection framework to aid in epidemiological analysis
24
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
Figure 13 XML output from ODK collect/REACap
Uploading the form to the server
EpiML is the proposed data interchange format between AREA devices. Access to other health,
environmental, and geographic information sources by the AREA systems will be accomplished
through a centralized data server (REAcloud) for uniform and timely data distribution to AREA
equipped mobile devices. More information on EpiMl is out of scope of this report.
Network connection is required to download or send forms to the server. Once uploaded to the
server, the information can be analyzed and stored back in the EpiMl format. This information
can then be used to view the finished form, analyze the information in the form of charts and
graphs, model the information to predict the spread etc.
Analysis
Many factors played a part in the design of this approach. First and foremost was the need for
efficient surveillance and analysis in the public health domain. Lack of awareness and adequate
medical response has a huge impact on the spread of the illness. This can be controlled to an
extent by taking proactive measures such as creating awareness and using analytics to model the
illness. With the growing popularity of smartphones, their role in this aspect has seen
considerable growth. It has been observed that the use of smartphones as opposed to traditional
modes of surveillance and analysis is not only quicker but also less expensive [20,21].
The decision for use of Xforms for data transfer was based on the flexibility and portability that
the specification provides. In addition to this, though it is not a defined standard used in
healthcare right now, there is an increasing adaptation of Xforms in the industry [1].
The application in itself has two audiences - health care organizations and the general public.
While the intent of the application is to enable faster data collection and analysis, it also serves
an a awareness tool among the general population. When working with bigger groups, the design
of the application allows the ability to track and coordinate information. Information can be
shared using EpiMl and REACloud.
Android and ODK based data collection framework to aid in epidemiological analysis
25
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
The simple layout of the forms ensures that the application is usable by a wider audience.
Another advantage is that the network connection is only required while downloading or
uploading the forms. Android and Xforms allow metadata collection as well. This eliminates the
need for the user to enter information such as device identity, time etc. Taking advantages of
Android features, we can use the gps to record the location coordinates instead of manually
entering such information. Using Xforms as the format to read the data also gives more freedom
to the users. Since XML is used as the mode of storage, the application does not impose memory
issues on the device.
At this stage, the form is created using the XLSTool. With the availability of REAConfig, this
process will also be simplified. This design in itself imposes certain restrictions as well.
Although REACap can be used as a standalone tool, the complete potential can only be realized
when used in conjunction with other AREA apps. The use of EpiMl makes it simpler to design
the interface targeting epidemiological data and hence makes it more efficient when parsing and
analyzing the data. Cloud storage allows the ability to store large quantities of data which can
then be used for analysis. Using mobile devices to interact this information, allows updated
information to be available faster to the users.
SUMMARY AND CONCLUSIONS
The AREA framework on the whole tries to use the capability of mobile applications to create a
framework for efficient data collection and analysis. Existing data collection frameworks tend be
of a very generic nature. We want to add to this and create a framework suitable for the
surveillance, analysis and modeling of infectious diseases. We have implemented a prototype
capable of handling complex form logic, displaying data and provide analysis. We have tried to
be generic and at the same time not compromise on the final product.
In this project, we have created the prototype for one of the AREA apps – REACap. REACap as
mentioned before is an android based data collection application. It allows the user to gather
information based on the form presented and uploads the data to the cloud based server once
submitted. In this project, we have depicted the use of REACap for monitoring influenza. As
mentioned before, the application has its strengths in its simplicity in the way the data is
presented and shown to the user. It caches the information on the device when there is no data
connectivity, thereby removing the complete dependency on the network. Another key advantage
is the use of Xforms and Javarosa engine for rendering complex form logic.
The computing power of smartphones is constantly increasing. Using this to our advantage, the
project is designed to allow to interaction between each of the AREA components. In the
absence of data connectivity, partial analysis can still be done using the mobile phone’s
processing capability. This can be very advantageous in regions with little or no network. In
conclusion we have tried to show that mobile devices can be used to a large extent in the
healthcare industry. They are not only already prevalent but have untapped potential which can
help make the process faster and more effective.
Android and ODK based data collection framework to aid in epidemiological analysis
26
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
References
1. Hills RA, Baseman JG, Revere D, Boge CL, Oberle WM, et al. 2011. Applying the XForms Standard to
Public Health Case Reporting and Alerting. Online J Public Health Inform. 3(2).
doi:http://dx.doi.org/10.5210/ojphi.v3i2.3656. PubMed
2. Lam W, "iSuppli press release - Smartphones See Accelerated Rise to Dominance," August.
2012.
3. Pendragon Software Corporation, "Pendragon forms," .
4. Hartung C, Lerer A, Anokwa Y, Tseng C, Brunette W, et al. 2010, Open data kit: Tools to build
information services for developing regions, Proceedings of the 4th ACM/IEEE
International Conference on Information and Communication Technologies and
Development, pp. 18.
5. Aanensen DM, Huntley DM, Feil EJ, Spratt BG. 2009. EpiCollect: linking smartphones to web applications
for epidemiology, ecology and community data collection. PLoS ONE. 4, e6968. PubMed
http://dx.doi.org/10.1371/journal.pone.0006968
6. Boulos MN, Wheeler S, Tavares C, Jones R. 2011. How smartphones are changing the face of mobile and
participatory healthcare: an overview, with example from eCAALYX. Biomed Eng Online. 10, 24. PubMed
http://dx.doi.org/10.1186/1475-925X-10-24
7. Anokwa Y, Hartung C, Brunette W, Borriello G, Lerer A. 2009. Open source data collection in the
developing world. Computer. 42, 97-99. http://dx.doi.org/10.1109/MC.2009.328
8. Arizona department of health services, Infectious disease epidemiology program influenza
& RSV surveillance. http://www.azdhs.gov/phs/oids/epi/flu/
9. Gaffar A, Seffah A. An XML multi-Tier Pattern Dissemination System, 2005. Encyclopedia of
Database Technologies and Applications. 740-744. Hershey, PA: Information Science
Reference. doi:10.4018/978-1-59140-560-3.ch121
10. Gaffar A, Moha A, Seffah A. 2005, User- Centered Design Practices Management and
Communication, Proceedings of HCII 2005, Human Computer Interaction International, Las
Vegas, Nevada, USA.
11. Gaffar A, Sinnig D, Seffah A, Forbrig P. 2004, Modeling patterns for task models. In proceedings
of TAMODIA. In 3rd International Workshop on Task Models and DIAgrams for user
interface design, pp. 99-104.
12. Sinnig, D., Gaffar, A., Seffah, A., and Forbrig, P. 2004, Patterns, Tools and Models for Interaction
Design. MBUI 103.
13. Metzker E, Seffah A, Gaffar A. 2003, Towards a Systematic and Empirical Validation of HCI
Knowledge Captured as Patterns. Proceedings of HCI International, the 10th International
Conference on Human-Computer Interaction 2003, Crete, Greece, appeared in Julie Jacko
http://dx.doi.org/10.5210/ojphi.v3i2.3656
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=23569609&dopt=Abstract
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=19756138&dopt=Abstract
http://dx.doi.org/10.1371/journal.pone.0006968
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=21466669&dopt=Abstract
http://dx.doi.org/10.1186/1475-925X-10-24
http://dx.doi.org/10.1109/MC.2009.328
Android and ODK based data collection framework to aid in epidemiological analysis
27
Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(3):e228, 2014
OJPHI
and Constantine Stephanidis (Eds.), Human Computer Interaction: Theory and Practice,
Lawrence Erlbaum Associates LEA Publishing, 2003, ISBN 0-805-84930-0, vol. 1, pp. 168-
172.
14. World Wide Web Consortium's Xform wiki. Available:
http://www.w3.org/MarkUp/Forms/wiki/Main_Page.
15. JavaRosa Wiki. Available: https://bitbucket.org/javarosa/javarosa/wiki/Home.
16. Modi Research Group at Columbia University. Formhub, Available: http://formhub.org/
17. Bhattacharya I. Healthcare Data Analytics on the Cloud, 2012, Online Journal of Health and
Allied Sciences, vol. 11.
18. Dropbox, "Rest API," .
19. Department of Health and Human Services. Center for disease control and prevention (CDC).
Available: http://www.cdc.gov/flu/about/disease/index.htm.
20. Schmoldt D, Rösch A, Hasenkamp U, Mondorf W, Pollmann H. 2012, Improved surveillance of
haemophilia home treatment using mobile phones, in ETELEMED 2012, the Fourth
International Conference on eHealth, Telemedicine, and Social Medicine, pp. 143-146.
21. Bredican J, Mills AJ and Plangger K,, 2013, iMedical: Integrating Smartphones into medical
practice design, Journal of Medical Marketing: Device, Diagnostic and Pharmaceutical
Marketing, 2013.
22. PurcForms. Broswer based Xforms form designer and runtime engine, Available:
https://code.google.com/p/purcforms/