Activity based Traffic Indicator System for Monitoring 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, Data Science, Decision Support System, Pandemic Management.

Abstract: This study describes an activity based traffic indicator system to provide information for COVID-19

pandemic management. The activity based traffic indicator system does this by utilizing a social probability

model based on the birthday paradox to determine the exposure risk, the probability of meeting someone

infected (PoMSI). COVID-19 data, particularly the 7-day moving average of the daily growth rate of cases

(7-DMA of DGR) and cumulative confirmed cases of next week covering a period from April to September

2020, were then used to test PoMSI using Pearson correlation to verify whether it can be used as a factor for

the indicator. While there is no correlation for the 7-DMA of DGR, PoMSI is strongly correlated (0.671 to

0.996) with the cumulative confirmed cases and it can be said that as the cases continuously rise, the

probability of meeting someone COVID positive will also be higher. This shows that indicator not only

shows the current exposure risk of certain activities but it also has a predictive nature since it correlates to

cumulative confirmed cases of next week and can be used to anticipate the values of confirmed cumulative

cases. This information can then be used for pandemic management.

1 INTRODUCTION

One of the most recent viruses is the severe acute

respiratory syndrome coronavirus 2 or SARS-CoV-2.

Çelik et al. said that it is the zoonotic virus that causes

the disease called COVID-19 (Çelik et al., 2020).

Shereen et al. stated that COVID-19 is a highly

transmissible and pathogenic viral infection (Shereen

et al., 2020). As of November 2020, according to the

Philippines Department of Health (DOH), the country

has a total case of more than 416,000 infected while

more than 375,000 recovered and more than 8,000

Filipinos died (DOH, 2020c). Information technology

has a huge impact when it comes to handling

infectious disease pandemics because the success of a

nation’s health program depends on having rapid

access and exchange of information regarding the

disease (Fauci, 2001). For the Philippines, numerous

data visualizations regarding the local spread of the

virus have already been developed for 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, 2020b; FASSSTER,

2020). These websites are already an ideal example of

how a monitoring website for infectious disease looks

like and it already presents the relevant statistics when

it comes to monitoring a pandemic. These systems are

good for planning and pandemic response. However,

data is not in the lens of activities that people do in

real life. Not everyone knows how to act

appropriately nor interpret the data once it is shown to

them.

Which is why this study, aims to create an activity

based traffic indicator system for COVID-19. The

goal is to be able to know what certain activities (e.g.,

grocery shopping, sports, mall shopping, etc.) are

safe, uncertain, or dangerous to do on a per-region

basis in the Philippines through a traffic light’s

corresponding green, yellow, and red colors. Part of

this study is to compile research that contains

potential factors for determining infection risk and

choose which one to be used for the indicator. The

chosen factor would then be validated using existing

COVID-19 data. Since the scope of the study is only

within the Philippines, pandemic related case

information would be limited to the daily data drop of

the DOH whose content will be a component for the

calculations of the indicator and also for the

validation of the indicator’s factor (DOH, 2020a). The

activity based traffic indicator system would relate the

data to everyday life so that it becomes much easier to

comprehend and understand on an individual level.

This hopefully leads to better educated decisions on

how to act accordingly and ultimately culminates in

lower potential infections.

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

Activity based Trafﬁc Indicator System for Monitoring the COVID-19 Pandemic.

DOI: 10.5220/0010399201830191

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 1, pages 183-191

ISBN: 978-989-758-509-8

183

2 LITERATURE REVIEW

2.1 Discarded Factors

This subsection contains different studies regarding

the exploration of the safeness or riskiness of

various activities. Numerous online academic

databases were scoured for studies that showcase a

potential factor for determining the risk of activities

during a pandemic. These factors were considered as

candidates for the activity based traffic indicator

system but were discarded due to the factor selection

process of the study. The factors found from the

studies in this section may not be used for the indi-

cator, but it does not mean that they were irrelevant.

Choosing the right factor depends on the context and

surely the other factors listed could be useful if used

for a different type of risk evaluation tool.

The efficacy of social distance and ventilation

effectiveness in preventing COVID-19 transmission

was a study that used distance and ventilation as a key

component to determine the risk of COVID-19

indoors (Sun and Zhai, 2020). The study claimed that

social distancing interacts tightly with ventilation and

ventilation indoors is a key factor in the spread of

respiratory infectious diseases. Therefore, the goal of

the study was to investigate the relationship between

social distancing (physical distance), minimum

ventilation rate, and the probability. A modified

Wells-Riley Model was used to get the projected

infection probability in the study. A Wells-Riley

model is a popular model used for predicting infection

risk. For the study, two new indices, the social

distance index (Pd) and ventilation index (Ez), were

added for the WR (Wells-Riley) model. Based on the

study the exposure time or the length of stay in a

setting, distance and ventilation have significant

effects on infection risk. A limitation of the study is

the fact that it only uses the droplet route of infection

and not considering direct contact. The modified WR

model that the study proposed was considered as a

factor because it can be used for numerous activities

as long as the standards for minimum ventilation and

air distribution effectiveness are known.

The American Institute of Architects (AIA)

wrote a document called Re-occupancy Assessment

Tool V3.0 (AIA, 2020). The purpose of the

document was to provide stakeholders a guide to

make buildings safe when reopening during the

pandemic. The document has various mitigation

measures for use during the COVID-19 pandemic.

The appendix section of the document contains a

section called “occupancy evaluation” and it

discusses how the occupant load factor of various

establishments is problematic when taking into

consideration the safe distance required to be

implemented, which is 6 ft. The area of a 6-foot

radius circle is 113.097 square feet and if it was used

as the social distancing measure, then any occupant

load factor below that is unsafe. The occupancy

evaluation found in the document was considered as a

factor for the activity based traffic indicator system

since it can be used to assess the safety of a particular

activity by determining the occupant load factor of the

establishment where that activity occurs.

Leclerc et al performed a study that explored

various indoor and outdoor settings where

transmission of COVID-19 occurred and happened in

clusters (Leclerc et al., 2020). In identifying which

settings have the most clusters, people would know

which areas need to have close surveillance or to be

closed down as the pandemic progresses. For the

research, a cluster was defined as 1st generation cases

that got infected and also transmitted the disease in

the same single setting and specific time. To achieve

the goal of the study, a systematic review of literature

that was related to the COVID-19 clusters was

conducted. Based on the results, households have the

most number of clusters and most clusters are from

indoor settings. A limitation of the study would be its

bias due to the methodology used to gather data

(compilation of scientific literature and media

reports). With the compiled literature, some

epidemiological data were not included. Furthermore,

attack rates cannot be estimated using the data

gathered in the study. The study was considered

because the researchers of this study thought the

methodology used in determining the number of

clusters per setting may be used in terms of knowing a

particular activity's total number of cases across all

clusters (Leclerc et al., 2020) in the Philippines.

The World Health Organization (WHO) wrote a

guideline titled Water, sanitation, hygiene, and waste

management for SARS-CoV-2, the virus that causes

COVID-19 (WHO, 2020b). The document contains

information regarding proper disinfection, hygiene

and the management of wastewater. There is a section

in the document about WASH (water, sanitation,

hygiene) in a health care setting. To summarize this

section, workers should always engage in frequent

hand hygiene / do regular disinfection / discard waste

properly / managing health care waste / discarding

dead bodies properly. The document also discusses

general information on hand hygiene for the public

like the ideal hand hygiene material used by the

public. Sanitation requirement for the public was

considered as a factor for the activity based traffic

indicator since it seems to be an effective qualitative

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criterion to determine which activities are riskier than

others based on the number of precautions needed to

be done before participating. However, in the factor

selection process of the study, this factor was

discarded because it cannot be quantified.

2.2 Chosen Factor

This subsection contains the chosen factor for the

activity based traffic indicator system, the exposure

risk that dictates the probability of meeting someone

infected with COVID-19 in public. It was chosen as

a factor for the indicator since it passed the factor

selection process done in the study.

Sun performed a study titled COSRE: Community

Exposure Risk Estimator for the COVID-19

Pandemic and the goal of the study was to raise

awareness regarding the exposure risk of the activities

done in one’s daily life (Sun, 2020b). He developed a

probability model based on the birthday-paradox

model and it was implemented using a web-based

system called COSRE. Specifically, the risk it estima-

tes is the probability of people meeting potential

COVID-19 hosts in public places like grocery stores,

gyms, etc. The model utilizes 3 parameters: p, a, and

n. As stated, the output of the model is the probability

of meeting someone that has COVID-19. The variable

p is the total community population whether the

community defined is a country, region, city, etc. The

variable a would be the total number of potential

COVID-19 cases in the area and it excludes the ones

that already recovered or died. Variable n would be

the number of people in the businesses like gyms,

shopping centers, and restaurants. An experimentation

was done to get the county-level exposure risks of the

United States from April 1 to 15th of May (Sun,

2020b). The exposure risk was visualized using a map

of the United States with white to red markings

dictating the severity of the exposure risk in specific

communities. The model is ideal because the

parameters are all obtainable with the dataset on the

pandemic generally available. This study will aim to

adopt this model to use a more real time indicator that

is automatically generated. The researchers will then

compare the predicted exposure risk per activity per

area against actuals for verification.

3 METHODS

3.1 Activity Selection

To determine the activities to be indicated by the

monitoring system, the activities that impose a risk

to one’s health during the COVID-19 pandemic

must be identified first. The possible activities for

the indicator were based on the ranking of the safety

level of particular activities during this pandemic by

several sources (Mayo Clinic, 2020; The South

Dakota Department of Health, 2020; Doolittle,

2020). The activities to be used for the indicator

were then aligned with the categories of activities

defined in the Google Mobility Report (Google,

2020). For the activity based traffic indicator, it is

assumed that activities are to be done indoors

because outdoor scenarios do not have a substantial

amount of evidence to assess the risk of COVID-19

(Freeman and Eykebosh, 2020). Furthermore, the

activities chosen were based on how the activities

can also be applicable to other countries to make

them more nonexclusive. Overall, there are 10

activities chosen for the indicator. The activities

under retail and recreation are: exercise with

equipment, exercising without equipment, shopping

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

restaurant dining. For grocery and pharmacy:

grocery shopping was chosen. For transit: riding a

bus and a train was picked. And lastly, going to the

office is under the category of the workplace.

3.2 Factor Selection

What factors to include in the activity based traffic

indicator system were primarily based on qualitative

and quantitative related research about factors that

determine risk or safeness of carrying out certain

activities during a pandemic as seen in the Literature

Review section of the paper. All these factors were

then compiled into a spreadsheet. Once compiled, a

process of elimination was then conducted wherein

the ideal factor to be used for the indicator was

chosen. An ideal factor for the study has four

criteria. First, the factor chosen for the indicator can

represent the 10 activities selected for the study.

Second, the factor must be feasible in the sense that

updating the indicator should not be cumbersome or

in other words, it can be automated. Third, the factor

should be dynamic meaning that the value or the risk

that it indicates should change overtime. The fourth

and last criteria for the factor are that the risk it

dictates can be applicable for the 17 regions in the

Philippines. The methodology of the research paper,

where the factor was to be taken from, dictates the

process of data gathering and the various

components, and steps that make up the factor

candidate which was then used as a basis if the

particular factor met the criteria or not. If the factor

Activity based Trafﬁc Indicator System for Monitoring the COVID-19 Pandemic

185

candidate did not meet the criteria mentioned above,

it was discarded from the pool of factors compiled.

There are 6 potential candidates as a factor for

the indicator and the first one is the probability of

infection via an aerosol transmission (Sun and Zhai,

2020). It was discarded since it cannot be applied

regionally, and it was not dynamic if it was to be

used as an indicator. It could be argued that

exposure time and distance within an establishment

can be dynamic but this would only be possible if

risk evaluation done for the study was through a

calculator, where the user manually inputs the

components and the calculator computes for the risk.

Therefore, the probability of infection for the 10

activities would have the same values for all the

regions and the same values through time. The

second factor is the occupant load factor (AIA,

2020). It was also discarded since it cannot also be

represented regionally, and it was also static. The

occupant load factor, when used alone, cannot

dynamically change unless other variables are used

with it. The third factor was the total number of

cases across all clusters per activity (Leclerc et al.,

2020). The factor was discarded since the

methodology used in the study requires the

researchers of this study to do a manual meta-

analysis of news sites and articles to get the clusters

which are not feasible. The fourth factor is sanitation

requirement, and it was also discarded since this

factor was static for the activities and cannot be

applied regionally (WHO, 2020b). In addition, this

qualitative factor cannot be quantified; that was why

it was discarded from the selection process. The last

factor is the exposure risk derived from the COSRE

model (Sun, 2020b). This factor was selected for our

indicator since it can be applied to any activity as

long as the occupancy of its venue can be

determined. Moreover, data gathering is feasible

since the infected population, which is the only

factor that changes, can be found in the DOH data

drop. It is essentially dynamic since the COSRE

model depends on the number of infected people in a

given time (Sun, 2020b). The model can also be

applicable per region as long as the total population

of the region can be determined.

3.3 Calculating the Risk

𝑃𝑟

(,,)

=1−

(



)



!



, if p≠0 and n≠0 and a≠0

(1)

𝑃𝑟

(,,)

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

(2)

The factor for the activity-based traffic indicator is

the exposure risk from the COSRE social probability

model (Sun, 2020b). The exposure risk was derived

from a probability model based on the birthday-

paradox theory and the risk it estimates is the

probability of people meeting potential COVID

hosts in public places like grocery stores, gyms, etc.

For this study, the exposure risk associated with the

COSRE model is called the probability of meeting

someone infected (PoMSI). As seen on Equation 1

and Equation 2 (if 𝑃𝑟

(,,)

=0), the model utilizes

three parameters: 𝑝, 𝑎, and 𝑛. The idea is to first

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 patient in

that group of people (Sun, 2020a). This was done by

reusing the algorithm of the birthday paradox and

changing the option of a maximum number of days

(365) to the total population. The variable 𝑝 in the

COSRE model represents the total population and it

would then be subtracted to the total number of

potential COVID-19 cases in the area excluding

people that recovered and died, which is variable 𝑎,

to get the no-clash probability (Sun, 2020b).

Furthermore, in the original birthday paradox model,

n dictates the percentage at which at least two people

in the room have the same birthday (Geeks for

Geeks, 2020). For example, to get a probability of

50%, a room must have 23 people. The variable 𝑛 in

the COSRE model is the number of people in

businesses (Sun, 2020b). After all of that, PoMSI,

using the model, was determined when 1 is

subtracted from the probability to get the chance of

clash with COVID-19 hosts.

Table 1: Occupancy Per Activity.

Activity

100%

occupancy

50%

occupancy

Exercise w/

Equipment

19 10

Exercise w/o

Equipment

24 12

Sales (retail store)

90 45

Malls

2,334 1167

Restaurant Dining

139 70

Concert

20,000 10,000

Supermarket

694 347

Bus

45 23

Train

1,182 591

Office

122 61

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For this study, the value of 𝑝 was the total

population per region (I, II, III, IV-A, IV-B, V, VI,

VII, VIII, IX, X, XI, XII, NCR, CAR, BARMM, and

CARAGA) in the Philippines (DOH, 2019). The

model assumes that everyone in the population has

the same chance of showing up in one store.

Additionally, the variable a is equivalent to the

active cases and it is the cumulative confirmed cases

without the people who already recovered or died:

Total Confirmed Cases - (Recovered + Deaths)

(FASSSTER, 2020). All types of patient statuses

(asymptomatic, mild, severe, and critical) that did

not die or recover were included in active cases. The

value of n is the occupant load, and it dictates the

number of people in a building. As stated by the

International Code Council (ICC) and its formula is

the square footage of an area over the occupant load

factor (ICC, 2015). Previously a candidate factor for

the indicator, the occupant load factor used per type

of establishment are standards defined in the

International Building Code (ICC, 2015). Moreover,

if the venue has fixed seating, then the occupant load

is equivalent to the seating capacity. Given the

limitations of the study, actual square footage areas

of buildings cannot be determined that is why the

researchers relied on sample square footage areas

found on the internet to be used as a component of

the occupant load for each establishment. The square

footage area from the sample programs defined by

the National Institute of Building Sciences (NIBS)

was used for exercise with equipment (free weight

room), exercise without equipment (fitness

instruction room), office space, and restaurants

(NIBS, 2019). The Minnesota Department of Public

Safety State Fire Marshal Division (MNDP-SSF)

was used as a reference for the square footage area

of a retail store and the occupant load was also based

on their calculations (MNDP-SSF, 2020). Also, the

Food Industry Association (FMI) was used as a

reference for the square footage area of a

supermarket and this was based on the median total

store size in square feet (FMI, 2018). The occupant

load calculation for malls was taken from a sample

calculation for a covered mall building (Geren,

2016). For seating capacity, the Philippines’ Mall of

Asia Arena’s (MOA) full house capacity was the

basis for the value used for concerts (MOA, 2014).

For the seating capacity of trains, the Department of

Transportation (DOTr) was the reference for the

capacity of the trains in the Philippines’ MRT Line 3

(DOTr, 2020). Lastly, the seating capacity of buses

was based on a standard bus with 4 seats per row

(Kosokubus, n.d.). Table 1 contains the occupant

load for all the activities contained in the indicator at

100% and 50% occupancy. When computing for

50% occupancy, if the output was a decimal number,

it was rounded up to the next largest whole number.

This was done since it makes no sense to represent

people with decimal numbers when computing for

occupancy. As seen on Listing 1, the Python code

for the modified birthday paradox model used to

compute PoMSI was already provided in a different

article supplementary to COSRE (Sun, 2020a).

However, the variables were changed for the study

to match the variables in the probability model (p, n,

a).

def covid_clash(p, a, n)

x = 1

for i in range(n):

x = x * ((p - a - i) / (p - i))

clashp = 1 - x

return round(clashp * 100, 2)

Listing 1: Python Code for Modified Birthday Paradox

Model.

3.4 Risk Level Classification

Table 2: Modified Risk Level Classification (red, yellow,

green).

Color PoMSI What to do

Red

75% and

above

Do not partake in the activity

Yellow

(1)

50% to

75%

Physical distancing

Avoiding touching surfaces

Non-medical mask

Gloves

Eye protectors

Yellow

(2)

25% to

50%

Physical distancing

Non-medical mask

Gloves

Green

25% and

below

Physical distancing

Non-medical Mask

Since the indicator must be a traffic indicator

system, the risk it outputs or calculates for every

activity must be classified based on how risky the

activity is. A proposed risk level classification

example for the indicator that is divided into four

levels can be found in the COSRE paper (Sun,

2020b). The greater the exposure risk for an activity,

the more precautionary measures need to be done

like wearing gloves and face shields. For all the risk

levels in the proposed model except 75% and above,

the use of a mask is required. Wearing masks for all

the levels can be backed up by the guideline on

Activity based Trafﬁc Indicator System for Monitoring the COVID-19 Pandemic

187

mask use by WHO since the organization

recommended the use of a non-medical mask for all

the 10 activities selected for the indicator no matter

how safe or risky it is (WHO, 2020a). No protective

gear is needed for a risk level above 75% because

any activity at this range would be too risky since

the chances of meeting an infected person are high.

Therefore, people should not partake in a particular

activity with that kind of risk level. For the study,

since it is a traffic indicator like system, green,

yellow, and red would be used instead of a four-

level indicator. Essentially, the risk level of the

proposed example can still be retained which would

make the 2nd and 3rd levels become subcategories

of yellow. This would mean that the range of each

level, including the 1st and last one, would not

change and the precautionary measures for each

level would also be the same. Table 2 contains a

summary of the risk level classification used in the

study. To clarify, the original classification of risk

level proposed in the COSRE paper was only an

example and it is not verified using real exposure

data yet (Sun, 2020b). As mentioned in COSRE the

paper and up until now, real-world exposure data is

scarce due to the pandemic. These real-world

datasets are relatively sensitive and hard to retrieve

at present. Since there is access to Philippine case

data, this can be used to test the model in the

absence of actual exposure data.

3.5 Testing the Model

To check the validity and effectiveness of the chosen

factor, a correlation between the factor of the

indicator and COVID-19 data was done. To be

specific, the University of the West of England

(UWE) stated Pearson's correlation coefficient (r)

would be used to measure the strength of the

association between two variables (UWE, n.d.). The

correlation coefficient ranges from -1 to 1 and as r

goes towards 0, the relationship between the two

variables will be weaker. A perfect degree of

correlation has a value near ± 1 and as one variable

increases, the other variable also increases (if

positive) or decreases (if negative) (Statistics

Solutions, n.d.). Furthermore, a high degree

correlation has a coefficient value that lies between

± 0.50 and ± 1. A moderate degree of correlation has

a value that lies between ± 0.30 and ± 0.49.

Moreover, a low degree of correlation has a value

that lies below ± 0.29. The last degree of correlation

would be a coefficient value of 0 which does not

correlate. The data correlated to the computed

PoMSI per region are the 7-day moving average of

the daily growth rate of COVID-19 cases (7-DMA

of DGR) and the cumulative cases of COVID-19.

The formula for cumulative cases is just the sum of

all the cases for the specific region up to the specific

point in time indicated. Both types of data can be

derived from the dataset in the DOH data drop

(DOH, 2020a). The values for the 7-DMA of DGR

and cumulative cases are both the week after the

particular week chosen to compute PoMSI.

Moreover, since the 7-DMA of DGR is a single

value and only the cumulative cases of the 7th day

of the week were used which is also a single value,

PoMSI was computed using the 7-DMA of the

cumulative active cases of the week chosen. Python

(Google Colab) was used to extract data from the

DOH data drop CSV file and to compute the

necessary computations needed (active cases,

PoMSI, DGR, cumulative sum of cases, etc.). The

range of the data taken in the DOH dataset was from

April 1 to September 1. PoMSI was computed per

region based on April to August data from the DOH

data drop. The computations were done weekly and

August ended on the 6th day that was why the range

of the data used reached September 1. It is worth

noting that there are inconsistencies present in the

Data Drop like unstandardized region names,

nonuniform date formats, and missing recovery

dates. For the missing recovery dates, an

approximation of recovered cases was done. All

cases after 14 days that were not considered as dead

were tagged as recovered (DOH, 2020b). Rather

than using 100% occupancy, which is unlikely

during a pandemic, 50% occupancy was used to

better simulate physical distancing in an

establishment as seen in Table 1 and this type of

occupancy restriction is usually utilized during the

modified general community quarantine (MGCQ) in

which most businesses, that handles the activities

included in the indicator, can operate (Crismundo,

2020). The CSV file output of the Python code was

then imported to Google Sheets to do the correlation

attempts (vs. DGR and vs Cumulative Cases). To get

the Pearson's correlation coefficient (r), the Pearson

correlation formula was used in Google Sheets and

correlation was done per region and activity.

4 RESULTS AND DISCUSSION

As stated in the Methods section of the paper, the

correlation was done using the Pearson correlation

formula in Google Sheets. Based on the results of

the correlation process, the correlation coefficients

(r) of PoMSI (per region and activity) versus the

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Figure 1: Summarized Flowchart of the Methodology.

7-DMA of DGR of next week were mostly low

ranging from -0.304 to 0.329 (without Region VII).

The correlation coefficient of 0.329 only applies to

Region IV-A’s PoMSI for a concert. Furthermore,

the correlation coefficient value of -0.304 applies to

the PoMSI of 9 out of the 10 activities in Region XI

(except for concert). Other than the ones mentioned,

the coefficients of the other activities per region

generally have a low degree of correlation. This

implies that PoMSI and DGR next week do not have

any relationship with each other. To add, the only

outlier of the correlation result is Region VII,

ranging from -0.756 to -0.598. For the correlation

coefficients (r) of PoMSI (per region and activity)

versus next week’s cumulative confirmed cases, the

range of values are from 0.671 to 0.996 and the

coefficients are positive. Table 3 contains the r

values of the three out of 17 regions in the

Philippines as a visual example of the results.

Regarding Region VII, the correlation is lower than

the other regions because the number of active cases,

which is a factor for PoMSI, dropped since July and

is constantly decreasing. The correlation of POMSI

to cumulative sum of cases is 97% (estimated for all

activities) from April until July but from July

onwards was -63%. When looking at another region,

for example Region VIII’s PoMSI, which still had a

correlation coefficient of 90% compared to the

cumulative sum of cases, even though the number of

active cases for PoMSI dropped around July it began

to increase again around August and dropped again

onwards. In general, the r values of Region 8 and

NCR are aligned with the other regions, since

PoMSI and the cumulative confirmed cases of the

following week have a high degree of correlation, it

can be said that as the cases continuously rise, the

probability of meeting someone COVID positive

will also be higher. This also shows that the activity

based indicator not only shows the current exposure

risk of certain activities but also has a predictive

nature and can be used to anticipate the values of

confirmed cumulative cases. There is a correlation

between PoMSI and cumulative cases while no

correlation between DGR is likely due to DGR

being derived from the cumulative sum of cases (as

the rate of change of the total amount of cases per

day) and active cases (which is a component for

PoMSI) being highly correlated with the running

total or daily cumulative sum of cases. This makes it

an ideal indicator.

Table 3: PoMSI vs Next Week’s Cumsum Cases: Region

VII, Region VIII, and NCR only (r).

Activity

Region

VII VIII NCR

Exercise w/

Equipment

0.670 0.942 0.992

Exercise w/o

Equipment

0.670 0.942 0.992

Shopping

(retail store)

0.671 0.942 0.992

Malls

0.688 0.945 0.970

Restaurant

Dining

0.671 0.942 0.992

Concert

0.680 0.935 0.756

Supermarket

0.676 0.943 0.992

Bus

0.671 0.942 0.992

Train

0.673 0.943 0.993

Office

0.671 0.942 0.992

5 CONCLUSIONS

The aim of this study was to create an activity based

traffic indicator system for COVID-19. An indicator

was tested that utilizes the COSRE social probability

model to derive PoMSI (Sun, 2020b). The exposure

Activity based Trafﬁc Indicator System for Monitoring the COVID-19 Pandemic

189

Figure 2: Example of an activity based indicator that shows the PoMSI values for all ten activities in a region for one day.

risk, which is PoMSI, is the probability of meeting a

COVID-19 host in public and as its value increases,

the chances of meeting an infected person also

increases. As seen in the Activity Selection section

of the paper, the exposure risk was computed for 10

activities: 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. In addition, the computations

were also done for all the regions in the Philippines,

Before, PoMSI was chosen, several factor

candidates were considered but were discarded

through the factor selection process. The chosen

factor, PoMSI, was verified through correlating it to

the cumulative confirmed cases in the Philippines

(from April to August) using Pearson correlation.

Based on the results, PoMSI is strongly correlated

(0.671 to 0.996) with the cumulative confirmed

cases. It can be said that as the cases continuously

rise, the probability of meeting someone COVID

positive will also be higher. Since there is a strong

correlation of PoMSI to the cumulative confirmed

cases of the next week, the indicator may also have a

predictive nature and may be used to anticipate the

values of confirmed cumulative cases per activity.

Since the indicator caters to COVID-19, the

usability of the indicator for other infectious diseases

will depend on their similarities with COVID-19. In

addition, existing COVID-19 data was only limited

to the Philippines for this study. For improvement,

the Google Mobility Report was used to define the

categories of the activities that were chosen for the

indicator, but mobility data was not used in the study

(Google, 2020). Therefore, the use of it might be an

extension for the study since mobility data can be

used to track generalized people movement. Overall,

the aim of the study was achieved with the viability

of PoMSI as a factor for the activity based traffic

indicator being validated.

ACKNOWLEDGEMENTS

We would like to thank Dr. William Yu for being

our research advisor and for directing the flow of the

study. We would also like to give thanks to the

FASSSTER team and the Ateneo Center for

Computing Competency and Research (ACCRe) for

creating and giving access to the FASSSTER

website. We would also like to thank the DOH’s

Epidemiology Bureau for creating and maintaining

the data drop used for the activity based traffic

indicator in the study. Lastly, we would also like to

give thanks to Kyle Bigcas for evaluating the paper

for mechanical and grammatical errors.

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