A Website with an Activity based Traffic Indicator System as a

Warning Tool for the COVID-19 Pandemic

Justin Junsay, Aaron Joaquin Lebumfacil, Ivan George Tarun and William Emmanuel Yu

School of Science and Engineering, Ateneo de Manila University, Katipunan Avenue, Loyola Heights, Quezon City,

Philippines

Keywords: Big Data and the Web, Web Programming, Pandemic Management.

Abstract: This study describes an activity based traffic indicator system to provide information for the management of

the COVID-19 pandemic. The purpose of the indicator is to be able to discern what activities (e.g., grocery

shopping and sports) are dangerous, uncertain, or safe to do in the Philippines on a per-region basis through

a traffic light’s color of red, yellow, and green. The activity based traffic indicator system does this by utilizing

a social probability model based on the birthday paradox to determine the exposure risk which is the

probability of meeting someone infected (PoMSI). Additionally, a website called SANITTISE was created to

host the indicator system and also to display other pandemic related graphs. Furthermore, a user interface/user

experience (UI/UX) test was conducted through a survey to measure the effectiveness of the website created.

Regarding the results of the test, it was positive since all of the sections were well received in the survey. This

meant that the work done on the website appears substantial as the respondents were able to understand the

contents and purpose of the website and also effectively traverse the website and create deductions from the

information available on the website.

1 INTRODUCTION

In 2020, Shereen et al. stated that the world

experienced a pandemic due to a highly transmissible

and pathogenic viral infection, called COVID-19

(Shereen et al., 2020). Additionally, Çelik et al. said

that the zoonotic virus that caused the disease is called

SARS-CoV-2 (Çelik et al., 2020). As of April 2021,

according to the Philippines Department of Health

(DOH), the Philippines has a total case of more than

812,000 infected while more than 646,000 recovered

and more than 13,000 Filipinos died (DOH, 2021b).

Regarding information technology, there are already

data visualizations developed in the Philippines to

keep track of the local spread of COVID-19. An

example of which is the DOH COVID-19 tracker and

the Feasibility Analysis of Syndromic Surveillance

using Spatio-Temporal Epidemiological Modeler

(FASSSTER) website (DOH, 2021b; FASSSTER,

2020). These websites already present the relevant

information like the total cases and they are vital for

devising a response for the COVID-19 pandemic.

However, the problem here is that not everyone is

able to make sense of the current situation given the

information found in these websites. To be specific,

not everyone knows how to act appropriately nor

interpret the data once it is shown to them. Therefore,

this study aims to create an activity based traffic

indicator system for COVID-19. The purpose of the

indicator is to be able to discern what activities (e.g.,

grocery shopping and sports) are dangerous,

uncertain, or safe to do in the Philippines on a per-

region basis through a traffic light’s corresponding

red, yellow, and green colors. In the study titled

Activity based Traffic Indicator System for

Monitoring the COVID-19 Pandemic, the team

already established the calculations needed to be done

to indicate the risk of certain activities (Junsay et al.,

2021a). In the previous study, the probability model

used for the calculations was based on the birthday

paradox theory and the risk it estimates is the

exposure risk or the probability of meeting potential

COVID hosts in public places. Moreover, part of this

study is also to create a website to host the indicator

system. After that, a user interface/user experience

(UI/UX) test must be conducted to measure the

effectiveness of the indicator and other

statistics/graphs to be implemented on the website.

Since the scope of the study is only within the

Philippines, any pandemic related case information

used for the calculations of the indicator is limited to

the daily data drop of the DOH (DOH, 2021a). In

250

Junsay, J., Lebumfacil, A., Tarun, I. and Yu, W.

A Website with an Activity based Trafﬁc Indicator System as a Warning Tool for the COVID-19 Pandemic.

DOI: 10.5220/0010648700003058

In Proceedings of the 17th International Conference on Web Information Systems and Technologies (WEBIST 2021), pages 250-256

ISBN: 978-989-758-536-4; ISSN: 2184-3252

visualizing the risk of activities through a traffic

light’s color, the activity based traffic indicator

system would make it much easier for people to

understand the situation during the COVID-19

pandemic. Hopefully, the indicator should also help

in lowering potential infections.

2 METHODS

2.1 Components of the Traffic

Indicator

As mentioned in the team’s previous study, there are

a total of 10 activities for the indicator: exercise with

equipment, exercising without equipment, shopping

in a store, mall strolling, going to a concert, restaurant

dining, grocery shopping, riding a bus, riding a train,

and going to the office (Junsay et al., 2021a). In

addition, the exposure risk used for the activity based

traffic indicator was based on the modified birthday

paradox model established in the study titled COSRE:

Community Exposure Risk Estimator for the

COVID-19 Pandemic (Sun, 2020). Furthermore, the

team relabeled the exposure risk that the COSRE

model calculates into PoMSI or the probability of

meeting someone infected (Junsay et al., 2021a).

𝑃𝑟

(,,)

=1−

(



)







, if p≠0 and n≠0 and a≠0

(1)

𝑃𝑟

(,,)

=0, if p = 0 or n = 0 or a = 0

(2)

As seen on Equation 1 and Equation 2, the model

utilizes three parameters: [p, a, and n]. To summarize

the model, the first thing to do is to calculate the odds

of not meeting any infected person and subtract that

odds from 1 to get the probability of meeting at least

one infected person (Junsay et al., 2021a). In the

team’s previous study, it was already established that

p is equivalent to the total population of each of the

17 regions in the Philippines and a is equivalent to the

active cases per region which is the infected

population. The value of n is the occupant load, and

it is the number of people in a building or

establishment. Its formula is the square footage of an

area over the occupant load factor. If the venue has

fixed seating, then the occupant load is equivalent to

the seating capacity. Moreover, the team relied on

sample square footage areas and seating capacities

obtained on the Internet to be used as a component of

the occupant load for each activity.

Table 1: Activities’ Capacity Percentage and its Occupant

Load per Quarantine Type.

Activity ECQ MECQ GCQ MGCQ

Quarantine

Exercise w/

Equipment

B B

30%

50%

100%

Exercise w/o

Equipment

B B

30%

50%

100%

Sales (Retail

Stores)

50%

100%

Malls B

50%

1167

100%

2334

100%

2334

100%

2334

Restaurant

Dining

B B

30%

50%

100%

139

Concert B B B

50%

10000

100%

20000

Supermarket

50%

347

100%

694

100%

694

100%

694

100%

694

Bus B B

50%

100%

Train B B

50%

591

50%

591

100%

1182

Office B

50%

100%

122

100%

122

100%

112

Legend: B = Banned

However, it is worth noting that the team in their

previous study did not consider the different

quarantine types established in the Philippines

(Junsay et al., 2021a). Essentially, the Philippines’

Inter-Agency Task Force for the Management of

Emerging Infectious Diseases (IATF) stated that

there are four community quarantine types: enhanced

community quarantine (ECQ), modified enhanced

community quarantine (MECQ), general community

quarantine (GCQ), and modified general community

quarantine (MGCQ) (IATF, 2020). In addition, ECQ

would be the most restrictive while MGCQ would be

the most lenient. Basically, not all activities can

operate in certain quarantine types since some

activities are riskier than the others. If an activity can

function, capacity must also be reduced depending on

the quarantine type. Therefore, the capacity

percentage of an activity per quarantine type

authorized by the Philippines’ Department of Trade

and Industry (DTI) and Department of Transportation

(DOTr) was applied to the initial calculations of n or

the occupant load (100% occupancy) (DTI, 2020;

DOTr, 2020a, 2020b). However, 100% occupancy

would still be included as an option for the indicator

so that the people would still have an idea what the

A Website with an Activity based Trafﬁc Indicator System as a Warning Tool for the COVID-19 Pandemic

251

Figure 1: Activity Based Traffic Indicator (MECQ, NCR).

value of PoMSI would be if no quarantine were

imposed. It is worth noting that local government

units (LGUs) in the Philippines implement different

protocols and requirements for domestic travelers to

follow (Aguilar, 2021). For consistency, the

authorized statements of the departments mentioned

above were used as the basis for the activity

restrictions per quarantine type. Table 1 contains the

capacity percentage of an activity and its

corresponding occupant load per quarantine type. In

the table, the activity is marked as “banned” if it is not

allowed to operate. When applying the different

capacity percentage to the occupant loads, if the

output was a decimal number, it was rounded up to

the next largest whole number. This was done since it

is illogical to represent people with decimal numbers

when computing for n. It is also important that the risk

that the indicator system calculates must be classified

based on the riskiness of the activity. In the team’s

previous study, a risk level classification was already

devised (Junsay et al., 2021a). Basically, there are 3

risk levels: green (below 25%), yellow (25% to 75%),

and red (above 75%). For yellow, it has two sub levels

which is why the range of values is greater compared

to the other two colors. As the risk level increases,

more precautionary measures are needed to be done

when the exposure risk of an activity is greater.

2.2 SANITTISE Showcase

The created website has adopted the name

SANITTISE (Systematic Analysis of nCoV

Information Through Traffic Indicator Structured

Evaluations) out of preference for having a

convenient name that is easy to remember for the

public (Junsay et al., 2021b). The website includes a

Landing Screen that visitors would first see upon

viewing the website. The Landing Screen contains the

logo of the website, a quick description, and the top

header incorporating the primary navigation menu.

The section below this contains the Region Selection

portion of the website. This is where users can select

what region the activity based traffic indicator will

display further down the website. The website also

shows the legend for the traffic indicator and it is

displayed right after the Region Selection Screen. The

legend reflects the finalized risk level classification

which shows the necessary precautions that are

needed to be taken depending on the output of the

indicator. Next is the actual implementation of the

activity based traffic indicator. This comes after the

legend and it shows the ten activities along with their

respective PoMSI values and corresponding color

based on the legend. Each activity in the indicator is

accompanied by a short description which can be

toggled through a dropdown menu. The top portion of

the indicator contains buttons where users can select

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252

Figure 2: Sections of the Survey.

what quarantine type should be displayed, choosing

one will subsequently change the PoMSI values along

with which activities are allowed or banned. The

values shown in Figure 1 are the PoMSI values for

NCR when the quarantine type is set to MECQ. The

black bordered activities signify that an activity is

prohibited when that particular quarantine type is

being enforced. Below the indicator would be the

accompanying regional statistics graphs. These

graphs show the cases, deaths, and recoveries over

time for the selected region. They are fully interactive

with the ability to do panning, box zoom, mouse

wheel zoom, x-axis zoom, crosshairs, and hover

tooltips. There are two types of ranges that can be

displayed. One would be the entire duration of the

pandemic so far while the other range only depicts the

last seven days. Their corresponding headings exhibit

the total number of cases, deaths, or recoveries that

have occurred thus far. In general, these are the main

features of the SANITTISE website. As for the

mobile version, the website is responsive and can

handle varying screen sizes. The layout of the

elements change depending on the screen dimensions,

but the content remains the same overall. Lastly, the

website checks for new updates every three hours

since the DOH Data Drop is not updated at a regular

time each day (DOH, 2021a).

3 UI/UX TESTING

To gauge the usability and effectiveness of the

indicator, testing was done on the website. Hence, the

website must be tested by multiple people to find out

the improvements needed to be done and also to

identify what features should be retained.

Unmoderated testing was carried out since it entails

the participants to complete the test requirements at

their own convenience. Candidate participants were

contacted through online messaging sites (e.g.,

Facebook Messenger) to answer the survey. Since the

indicator is for the use of the public, healthy adults

(including university students) are the perfect

candidate participants since this categorization is still

broad enough to represent the public and they are the

only ones allowed to leave their houses during the

pandemic. Even though it is unmoderated, answering

the survey would take at least 15 minutes for it to be

completed. Additionally, the target number of

respondents for the survey is 20. After the participant

has finished answering the survey, the findings can be

recorded, and the personal data of the participant will

be stored for six months before deletion. Moreover,

the survey used to test the website was created

through Google Forms and it was split into four

sections (Junsay et al., 2021c). For the first section,

its purpose is to introduce the goal of the study and to

obtain the consent of the participants. The second

section of the survey contains questions that gauge

the first impression of the respondents regarding the

website. Basically, it was made to check whether they

find the interface of the website understandable and

appealing. In addition, this section and the fourth

section of the survey were inspired by some questions

found on the article titled 15 Website Survey

Questions to Customers in 2021 (Dossetto, 2021). For

the third section of the survey, it was created to test

the ease of using the website when the respondents

are given a certain scenario or a certain thing to look

at to measure the accessibility of the features of the

website. The fourth section of the survey was created

to ask the respondents regarding their thoughts about

the website after going through and using it. It also

asks them how likely they will be using the website

in the future and how likely they will be

recommending it to others. Lastly, the last part of the

survey asks the participants if they have comments

and suggestions that can help improve the website.

Responses for most of the questions in the survey are

limited to a 5-point scale except for the questions that

require typewritten responses from the respondents

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253

(e.g., third section of the survey). The survey was

opened on February 10, 2021 and the final survey was

answered on March 5, 2021. The respondents

consisted of people with ages ranging from 18 to 50

years old. The study included 6 non-university

students.

4 RESULTS

Table 2: Survey Results for Section 2 (Basic Ratings and

Questions).

Question 1 2 3 4 5

How well do you understand what

SANITTISE does from its description?

0 0 3 11 8

How readable is the font used on the

website?

0 0 0 3 17

What would you rate the color scheme

of the website?

0 0 5 9 6

How clear is the distinction between the

colors of the traffic indicator system

(

red,

ellow, and

reen

)

0 0 1 6 13

How well are you able to understand the

information given on the website?

0 0 3 9 8

Without being told what to do, how well

are you able to use the website in

determining the risk of each activity in

each region?

0 0 3 7 10

Judging by appearances only, how

inclined are you to use the website again

in the future?

0 0 1 9 10

These are the results for the UI/UX testing done for

the study which is carried out through

asynchronous/unmoderated testing. For the first

impressions, which is the second section of the

survey, they looked promising with four out of seven

of the questions having a majority of a rating of 5

which was excellent. These questions had to do with

the font, distinction between the colors, and their

inclination to use the website again. Three out of the

seven questions had a rating of four out of five which

meant that the respondents felt more than unmoved

(3) but less than excellent (5). These questions had to

do with the understanding of the SANITTISE

website, the color scheme, the information shared on

the website, and the inclination to reuse the website

in the future. For the remaining questions, which

tackled the understanding of SANITTISE from its

description, the color scheme of the website, and the

ability to understand the information given on the

website, 4 and 5 were the majority chosen instead of

3 which leads the researchers to believe that the

website was taken in good favor.

Table 3: Survey Results for Section 2 (Short Tasks on the

Website).

Short Task Correct Wrong

(Easy) How many total recoveries from

COVID-19 are there in Region VII?

16 4

(Medium) What is the risk of meeting

someone with COVID-19 in the office

in NCR under no

uarantine?

19 1

(Hard) What activity is banned in

ARMM if the quarantine type is set to

“GCQ”?

20 0

For the results of the short tasks, which is the third

section of the survey, it showed different results on

the easy, medium, and hard tasks. In order to get these

results, the researchers had to check the date the

survey was answered and compared the answers of

the respondents to an SQL file where all the data of

the COVID cases were recorded. The task wherein

people blundered the most was the easy task (How

many Total Recoveries from COVID-19 are there in

Region VII?) only having 80% of the participants

getting the right answer. This most likely had to do

with the fact that the total recoveries on the website

was labeled as “200,000 Recoveries” for example and

does not include the word “Total” which the

respondents were probably trying to find specifically.

Therefore, the researchers changed the label of

confirmed cases/deaths/recoveries to daily cases/total

deaths/total recoveries and added a total count

amount right below the “daily” titles for clarity. For

the medium difficulty task (What is the risk of

meeting someone with COVID-19 in the office in

NCR under no quarantine?), 95% of the respondents

got the answer correct. Lastly, for the hard difficulty

task (What activity is banned in ARMM if the

quarantine type is set to "GCQ") 100% of the

respondents got it correct.

For the user experience results, which is the fourth

section of the survey, they were collected after the

short tasks were carried out. This meant that at the

time of taking these responses, the respondents have

had ample time to go through the website and

experience it firsthand. For seven out of the eight

questions on the user experience part of the survey, it

showed that the majority of the participants thought

that overall, using the website was a pleasant

experience. These questions had to do with the

confidence in using the website, the difficulty of

finding recovered cases, the difficulty of finding the

risk level of each activity, the difficulty of finding the

banned activities, the confidence in repeating the 3

tasks from the previous section, the likeliness to visit

the website again, and lastly, the likeliness of

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Table 4: Survey Results for Section 2 (User Experience).

Question 1 2 3 4 5

After completing the 3 tasks asked in

the previous section, how confident

are you now with using the website

to determine each risk of an activity

in each re

ion?

0 0 1 6 13

What was the difficulty of finding

the number of recovered cases for

each re

ion?

1 0 2 6 11

What was the difficulty of finding

the risk level of each activity for

each re

ion?

1 0 3 5 11

What was the difficulty of finding

banned activities, given a quarantine

type, for each region?

0 1 1 6 12

How confident are you in completing

the 3 previous tasks again if asked?

0 0 1 1 18

How likely are you to visit the

website again?

0 0 3 8 9

How likely are you to consult this

website in planning what activities to

do?

0 0 2 9 9

How likely are you to recommend

this website to other people?

0 0 0 5 15

recommending the website to other people. Although

it is believed that the website has been taken very

well, in a few of the questions there is one outlier who

believed that it was difficult to do some tasks on the

website. Upon further analysis, when compared to

their other answers on the questionnaire, it appears as

though the outlier misunderstood the question. This is

because when it came to answering the questions on

whether they would go back to using the website

again and when they were asked about their

confidence on completing the tasks again, the outlier

gave those questions high marks. Also, when it came

to the short task results, the outlier got every answer

correct (from easy to hard) which meant that it was

not impossible for them to use the website. This can

be seen as one of the disadvantages of unmoderated

and asynchronous testing as the participants are not

able to clear up misunderstandings with the

surveyors. For the last question, which tackled the

likeliness of the participants to consult the website on

what planning for activities, rather than having a

majority of responses on the highest rated option (5),

this question had a split tie between the two highest

rated options (4 and 5). This would mean that

although the respondents would not guarantee using

the website, most of the respondents believe that they

would return to the website to use it for planning their

activities.

5 CONCLUSIONS

The aim of this study was to create an activity based

traffic indicator system for managing the COVID-19

pandemic. The indicator created uses the COSRE

social probability model which calculates the

exposure risk or the probability of meeting someone

infected (PoMSI) (Sun, 2020). Basically, when the

value of PoMSI is high, the chances of meeting

someone infected with COVID-19 is also high.

Furthermore, part of this study was also to create a

website to host the indicator system and also test its

usability by conducting a UI/UX test through a

survey. With this, the created website was called

SANITTISE and it contains the activity based traffic

indicator which calculates PoMSI for all of the 17

regions in the Philippines. To accompany the

indicator, the website also contains regional statistics

and graphs which can normally be found in tracker

websites. The results of the UI/UX testing showed

positive results as all of the sections were well

received in the survey. This meant that the work done

on the website appears substantial as the respondents

were able to understand the purpose of the website

and also effectively traverse the website and create

deductions from the information available on the

website. Although it appears as though the website is

already in its final form, the survey showed the

researchers that there were some improvements that

could be done on the website. This was manifested

through changing some of the labels on the website as

well as adding support for the visually impaired on

the website. Overall, the aim of the study was

achieved with the creation of the activity based traffic

indicator being successful along with it being deemed

usable by the public.

6 LIMITATIONS AND

IMPROVEMENTS

Regarding the focus of the indicator, it is restricted to

an activity based traffic indicator system for COVID-

19. Since the indicator caters to one disease, the

indicator may only apply to the COVID-19 pandemic.

If used for other infectious disease pandemics, the

usability and effectiveness of the core functions of the

indicator depends on the similarities of potential

diseases to COVID-19. Next, the indicator is not

diagnostic nor medical, it is more of a visualization

tool. This means that the indicator is meant to aid in

public awareness to support pandemic management.

As the data is in aggregates, it is not meant to be the

A Website with an Activity based Trafﬁc Indicator System as a Warning Tool for the COVID-19 Pandemic

255

only tool for pandemic-related decision making.

Hence, it cannot reflect individual cases. Third, it’s

also worth mentioning that this indicator is not meant

to replace existing visualizations of pandemic data

such as the DOH COVID-19 tracker (DOH, 2021b).

Tracker websites already present relevant data about

a specific infectious disease and the indicator of this

study gives insights or interpret these established data

instead so that it can be more readable or easily

understood supplemented by research on the safety of

particular activities. Lastly, the geographic scope of

the indicator encapsulates every region of the

Philippines. There is an indicator per region that

shows the selected region’s risks associated with

carrying out certain tasks while in the midst of a

pandemic.

As for potential improvements, existing COVID-

19 data used in the study was only limited to the

Philippines. With this, a possible improvement is to

create an activity based indicator system which can

be applied for more than one country. Facebook also

published a per municipality public dataset about how

people are responding to physical distancing

measures (Facebook, n.d.). The dataset has two

different metrics: the Change in Movement metric

(how much people are moving around) and the Stay

Put metric (the fraction of the population that appears

to stay within a small area surrounding their home for

an entire day). This may further be incorporated into

the calculations of the indicator system since it is

related to general movement of a population.

ACKNOWLEDGEMENTS

We would like to show our gratitude to Dr. William

Yu, our research advisor, for directing the flow of the

study. To the FASSSTER team and the Ateneo Center

for Computing Competency and Research (ACCRe),

thank you for creating and giving access to the

FASSSTER website. We would also like to give

thanks to the DOH’s Epidemiology Bureau for

creating and maintaining the COVID-19 data drop

used in the study.

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