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Smart Computing and Systems Engineering, 2021
Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka
113
. Paper No: SC-18 Smart Computing
Estimation of the incubation period of COVID-19
using boosted random forest algorithm
P. P. P. M. T. D. Rathnayake*
Department of Industrial Management
University of Kelaniya, Sri Lanka
thidasala.demintha@gmail.com
Janaka Senanayake
Department of Industrial Management
University of Kelaniya, Sri Lanka
janakas@kln.ac.lk
Dilani Wickramaarachchi
Department of Industrial Management
University of Kelaniya, Sri Lanka
dilani@kln.ac.lk
Abstract - Coronavirus disease was first discovered in
December 2019. As of July 2021, within nineteen months since
this infectious disease started, more than one hundred and
eighty million cases have been reported. The incubation
period of the virus, severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2), can be defined as the period
between exposure to the virus and symptom onset. Most of the
affected cases are asymptomatic during this period, but they
can transmit the virus to others. The incubation period is an
important factor in deciding quarantine or isolation periods.
According to current studies, the incubation period of SARS-
CoV-2 ranges from2 to 14 days. Since there is a range, it is
difficult to identify a specific incubation period for suspected
cases. Therefore, all suspected cases should undergo an
isolation period of 14 days, and it may lead to unnecessarily
allocation of resources. The main objective of this research is
to develop a classification model to classify the incubation
period using machine learning techniques after identifying
the factors affecting the incubation period. Patient records
within the age group 5-80 years were used in this study. The
dataset consists of 500 patient records from various countries
such as China, Japan, South Korea and the USA. This study
identified that the patients' age, immunocompetent state,
gender, direct/indirect contact with the affected patients and
the residing location affect the incubation period. Several
supervised learning classification algorithms were compared
in this study to find the best performing algorithm to classify
the incubation classes. The weighted average of each
incubation class was used to evaluate the overall model
performance. The random forest algorithm outperformed
other algorithms achieving 0.78 precision, 0.84 recall, and
0.80 F1-score in classifying the incubation classes. To fine-
tune the model AdaBoost algorithm was used.
Keywords - AdaBoost, boosted Random Forest, COVID-19,
incubation period
I. INTRODUCTION
The Coronavirus disease 2019 (COVID-19) is one of
the disastrous infectious diseases identified in late 2019
from a seafood wholesale market in China. Some of the
common symptoms of COVID-19 include fever, dry
cough, difficulty in breathing, muscle pain, sputum
production, diarrhea, and sore throat [1]. While the
majority of cases display mild symptoms, some progress to
pneumonia and multi-organ failures. As for current
findings, the death rate per diagnosed case is 4.4 percent;
however, it could range between 0.2%-15% based on the
age group and other health problems [2]. The virus
typically spreads from one person to another via respiratory
droplets released mostly during coughing and sneezing. As
of July 2021, the virus has spread over 222 countries and
territories resulting in 188,404,542 cases and 4,059,223
deaths [16]. Due to the high rate of diagnosed cases and
deaths, the World Health Organization (WHO) has
declared the COVID-19 disease as a pandemic on 11th
March 2020.
Incubation period of COVID-19 can be defined as the
time range a person spends between exposure to the virus
and symptom onset. During the incubation period, most of
the patients do not show any symptoms of being infected,
but they are capable of transmitting the virus to others [17].
It is very important to isolate the suspected cases during
this period to avoid virus transmission. Since the
incubation period greatly varies among individuals, it is
very important to identify the incubation period accurately
in order to decide quarantine periods and to allocate limited
resources effectively towards controlling the pandemic.
WHO has declared a time range of 2 to 14 days as the
incubation period of COVID-19 patients [19]. Since there
is a range to the incubation period, every suspected case
should undergo a quarantine period of 14 days. During the
quarantine period, active monitoring and resource
allocation for the suspected cases are mandatory. Although
all the suspected cases are quarantined for 14 days, some
may have lesser incubation periods than others, because
incubation period greatly varies depending on patients’
gender, age, chronic disease history, direct/indirect contact
with the affected persons, and the residing country. If there
is a mechanism to identify the incubation period of each
individual based on their characteristics, it will help
prevent unnecessary resource allocation for
quarantine/active monitoring, and effectively use the
limited resources towards controlling the pandemic. The
main purpose of this study is to develop a predictive model
to classify the incubation period of the COVID-19
suspected cases based on their characteristics.
Section-wise organization of the paper is as follows.
Section - II discusses related work. Section – III describes
the methodology of the system. Results are discussed in
detail in Section -IV. Finally, section – V presents the
conclusion and future work directions.
II. RELATED WORK
A. Findings on incubation period
There are a number of studies to calculate the mean
incubation period for the selected populations. One study
has calculated the incubation period using 181 cases. This
study has referred to patients' residing country, exposure
date and time, dates of symptom onset, fever onset and
hospitalization and calculated the median incubation
period as 5.1 days [3]. The study states that 97.5% of the
cases develop symptoms around 11.5 days. Another early
analysis has referred to 158 cases outside the Chinese
regions and estimated the median incubation period as 5
days which ranges from 2 to 14 days [4]. Authors have
estimated the incubation period using lognormal
Smart Computing and Systems Engineering, 2021
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distribution. This study specifies that the median time from
illness onset to hospital admission was 3-4 days and the
median delay between illness onset to death is 17 days.
Another analysis based on 10 confirmed cases in China
estimates the mean incubation period as 5.2 days (ranges
from 4 to 7 days) [2]. This study specifies that children are
less likely to be infected and may show milder symptoms.
They have identified that age is one of the crucial factors
that decide the incubation period. Their studies specify that
27% of the patients are hospitalized after two days of
symptom onset which implies that time available to seek
medical attention is generally short. Another analysis on 88
affected cases in Chinese regions outside Wuhan, specifies
a mean incubation period of 6.4 days which ranges between
2.1 to 11.1 days [5]. They have obtained the possible values
for the incubation period by considering the number of
days the person has stayed in Wuhan and the date of
symptom onset and fitted three parametric forms for the
incubation period: The Weibull distribution, the gamma
distribution, and the lognormal distribution.
B. Factors affecting to the incubation period
Studies about factors affecting the incubation period of
COVID-19 patients are limited. One study has identified
that age is directly related to the incubation period. This
study was based on 136 patients who had travelled to
Hubei, China, and identified the median incubation period
as 8.3 days for all patients, 7.6 days for younger adults, and
11.2 days for older adults. This study specifies that elderly
patients have a longer incubation period [6]. A study
conducted by referring to r Chinese COVID-19 patients
specify that men's cases tend to be more serious than
women's cases [7]. Using a public dataset of 37 cases,
Authors have identified that the number of male deaths
from COVID-19 is 2.4 times the number of female deaths.
Further, they have identified that the percentage of males
were higher in the deceased group than in the survived
group. There is strong evidence which suggests that men
may have a larger concentration of ACE2 (angiotensin-
converting enzyme 2) receptors in their body, which helps
coronavirus to latch on and spread inside the body. This is
one of the primary reasons why COVID-19 seems to affect
men seriously, when compared to women [8]. Centre of
disease control and prevention in the United States has
identified that the people who have cancer, chronic kidney
disease, COPD immunocompromised state (weakened
immune system) due to solid organ transplant, obesity,
BMI of 30 or higher), serious heart conditions such as heart
failure, coronary artery disease or cardiomyopathies, sickle
cell disease, type 2 diabetes mellitus have a higher risk of
getting severely ill from COVID-19 [9]. Since chronic
diseases directly affect the immune system of patients, the
incubation period can differ from the immunocompetent
people. Studies regarding the factors affecting the
incubation period of COVID-19 patients are limited. Out
of those studies one study has identified that the age is
directly related to the incubation period. Authors have
identified that the median incubation period for aset of
COVID-19 patients who had traveled to Hubei, China was
8.3 days, and for the younger adults the incubation period
was 7.6 days, and for older adults, 11.2 days. This study
specifies that elderly patients have a longer incubation
period than the younger adults [6]. A study conducted on
two populations of COVID-19 patients from two
geographic locations to identify the deviation of incubation
period across residing location, has proved that there is a
deviation of incubation period across two regions. Out of
the 181 patients used for the study, 108 patients were
diagnosed outside of mainland China with a median
incubation period of 5.5 days and 73 patients diagnosed
inside China with a median incubation period of 4.8 days
[3]. The above literature specifies that the patients Age,
Gender, Chronic disease history, and residing country
directly affect the incubation period of the COVID-19
patients.
C. Supervised learning classification algorithms used in
COVID-19 domain
One study has identified factors such as patients' age,
residing country, if from Wuhan, if theyy have visited
Wuhan and gender directly affect the death/recovery of
COVID-19 patients using 100 confirmed laboratory cases
in China [10]. This study has used the Naïve Bayes
approach to classify the death/ recovery of COVID-19
patients and achieved 93% accuracy. Another study has
used the Logistic Regression approach to detect COVID-
19 using clinical text data. Authors have labeled 212
clinical records into four categories named COVID, SARS,
ARDS, and both (COVID, ARDS). Various text features
such as TF/IDF, a bag of words has been extracted from
these clinical reports to classify them. This study has
reached 94% precision, 96% recall, and 95% f1 score using
Logistic regression approach [11]. Support Vector
Machine (SVM) has been used in the COVID-19 domain
to classify the X-ray images of COVID-19 suspected cases.
The study in [12] has used this method to identify the X-
ray images of COVID-19 patients by comparing normal X-
ray images with X-ray images showing pneumonia. [12]
This study has reached an accuracy of 97% by classifying
the X-ray images into classes using SVM approach.
Another study has used the decision-tree classifier to
identify COVID-19 patients by referring to their Chest x-
ray (CXR) images [13]. They have used three binary trees
to identify the abnormality of the CXR images, identify the
symptoms of tuberculosis and to identify COVID-19
symptoms. They have achieved an accuracy of 98% and
80% for the first two decision trees respectively, whereas
the average accuracy of the third decision tree has been
95%. One of the studies have used the Random Forest
algorithm to identify if a person is infected with the SARS-
Cov2 virus and the type of hospitalization (regular ward,
semi-ICU, or ICU) needed, based on the hematological
parameters such as red blood cells, hemoglobin,
neutrophils, lymphocytes, etc. collected from blood tests..
Authors have achieved 92.8% accuracy in identifying the
type of hospitalization patients needed based on the
hematological parameters from blood tests [14].
III. METHODOLOGY
The key purpose of the study is to identify the factors
affecting the incubation period and to design a model that
can classify the incubation period of the suspected cases
based on patients’ characteristics. Machine learning
techniques were used to build the classification models.
Next, the modelling techniques were compared on
validation and model accuracy, to select the best technique.
At last, the best classification technique was fine-tuned
using a boosting algorithm to achieve higher accuracy.
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Fig. 1. The methodology of the proposed solution
Publicly available patient data and clinical records
were used for this study. The following information about
patients was gathered by analyzing the records manually.
i. Age
ii. Gender
iii. Residing Country
iv. Chronic disease history
v. Direct/ indirect contact with the affected cases
vi. Symptom onset date
vii. Exposure date/Travel dates
viii. Hospitalized date
Most of the data were collected from social media
posts and status related to the COVID-19 patients. Chinese
social media WeChat accounts are one of the major data
sources which release daily information on the list of
COVID‐19 cases. Other than social media and WeChat
accounts, following sources were used to collect data.
● Kyodo News
● Weibo.com
● Kaggle
In some of the cases, precise information was not
recorded to identify the type (direct/indirect) of contact
with the affected persons. If the patients travelled together
with affected ones or if they got the virus from a family
member, those scenarios are considered direct contact with
the affected cases. Otherwise, an assumption was made -
that they had indirect contact with the affected persons.
The incubation period was calculated using the date
difference between symptom onset date and the exposure
date. Since the incubation period of the selected population
ranges from 5 to 24 days, it was divided into four classes
as below, for classification.
● Class A: 20 - 24 days
● Class B: 15 – 19 days
● Class C: 10 -14 days
● Class D: 5 – 9 days
The incubation class was added to the dataset by
creating a new column named ‘Incubation Class’. The
median age of each incubation class was used to fill the
missing values of the age column. Finally, label encoding
was performed on the dataset. For analyzing the data,
descriptive statistics were used. Bar charts were used to
identify the distribution of the incubation period across
patients’ age, gender, residing country, direct/indirect
contact with the affected cases and chronic disease history.
Next, Pearson’s Correlation Coefficient (PCC) was used to
identify the variables which have the strongest relationship
with the incubation period.
A number of supervised learning classification models
were compared in this study to identify the best model for
this particular problem. Models were implemented using
Google Collab platform which provides a Jupyter notebook
environment that requires no setup and runs entirely in the
cloud with the accessibility of powerful computing
resources from the browser. Classification algorithms such
as multiple regression, support vector machine, random
forest, K- nearest neighbor algorithm, naive bayes, and
decision tree were compared to find the best model with
highest accuracy, to classify the incubation period class
based on patients’ demographics and other characteristics.
In order to validate the classification models, percentage
split technique was used. The dataset was divided into two
categories randomly, mainly 20% for testing and 80% for
training. Furthermore, performance metrics such as
Precision, Recall and F1 Score were used to compare
model performance.
Boosting algorithms were used in this study to achieve
higher accuracy in machine learning algorithms. Boosting
algorithms are very useful to create high accuracy models
by combining low accuracy models. AdaBoost algorithm
was used in this study to improve the accuracy of the best
performing classification model.[18] The main concept of
AdaBoost is that it assigns weights to classifiers and
training the data samples in each iteration such that it
ensures the accurate predictions of unusual observations.
IV. RESULTS AND DISCUSSION
This section mainly describes the details related to the
results obtained from the implementation process and the
discussion of the results.
The gathered dataset for the study consists of 500
patient records with the age ranging from 5-80 years. Out
of those records, 285 were male and 215 were female. The
dataset includes patients’ information from most of the
countries around the world with the majority of cases from
China Singapore, France, Germany, Taiwan, Japan,
Malaysia, United States, and South Korea. Following is the
incubation period distribution for the dataset.
Smart Computing and Systems Engineering, 2021
Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka
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Fig. 2. Incubation period distribution for the dataset
The incubation period of the selected population
ranges from 5-24 days with a median value of 13.86 days.
The highest number of patients (51) have their incubation
period as 14 days. Out of the 500 patient records, 31 of
them (7.3% of the overall population) have their incubation
period more than or equal to 20 days. 79 patients (15.8% of
the overall population) have their incubation period less
than or equal to 9 days. Majority of the patients have their
incubation period between 10-19 days which is 76.8% of
the overall population.
Correlation analysis was used in this study to identify
the variables which have the strongest relationship with the
incubation period. Based on the results of the correlation
analysis, patients' age and the incubation class have a very
strong positive relationship which is 0.819. When it comes
to the direct contact with the affected cases, it also has a
moderate positive relationship with the incubation class
which is 0.360. Having a history of chronic diseases such
as cardiac, respiratory and metabolic diseases also have a
strong positive relationship with the incubation class.
Patients’ residing country also has a weak relationship with
the incubation class which is 029.
Results based on descriptive statistics and the
correlation analysis suggest that men’s COVID-19 cases
tend to decrease as the incubation period increases. This
implies that men’s COVID-19 cases tend to show
symptoms quickly than women’s cases do. Patients with
chronic disease history such as Serious heart conditions,
heart failures, coronary artery disease, cardiomyopathies,
sickle cell disease, type 2 diabetes mellitus tend to show
symptoms quicker than others. The different incubation
periods can be the result of different types of inflammation
and immune responses. When it comes to the method of
exposure to the virus, results specify that patients who got
direct exposure to the virus have a shorter incubation
period than others. This implies that, if the patients had
close contact with someone who has COVID-19 and got
exposed to the virus directly, they tend to show symptoms
very quickly than others who have got indirect exposure to
the virus.
Number of supervised learning classification
algorithms were compared in this study to identify the best
model to classify the incubation class based on patients
age, gender, chronic disease history, direct/indirect
exposure to the virus and the residing country. The
following figure explains the accuracy of each model in
classifying the incubation class.
Fig. 3. Comparison of model performance without boosting algorithms
The above figure specifies that the Random forest
algorithm performed better in classifying the incubation
class by achieving higher precision, recall, and F1 score.
Since the F1 score provides the harmonic mean between
precision and recall, it was considered the best performance
metric to evaluate the models. Following is the model
performances in tabular format.
TABLE I. COMPARISON OF MODEL PERFORMANCE IN TABULAR FORMAT
Classifier
Precision
Recall
F1 Score
Naïve Bayes
0.764
0.750
0.741
SVM
0.735
0.750
0.741
Logistic Regression
0.780
0.782
0.777
Random Forest
0.788
0.840
0.809
Decision Tree
0.772
0.780
0.775
AdaBoost algorithm was used in this study to improve
the accuracy of the classification algorithms. Since the
AdaBoost algorithm needs a base classifier, random forest
was used as the base classifier since it outperforms other
classification algorithms.
Fig. 4. Comparison of model performance with AdaBoost algorithm
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Fig. 4 displays the model performance after
implementing the AdaBoost algorithm with the random
forest algorithm as the base classifier
From Fig. 4, we can identify that the performance of
the random forest algorithm increased after the application
of the AdaBoost algorithm. Before applying the AdaBoost
algorithm Random Forest algorithm outperformed other
algorithms achieving a 0.78 Precision, 0.84 Recall, and a
0.80 F1 score. After applying the AdaBoost algorithm the
performance metrics of the Random Forest algorithm
increases up to 0.87 Precision score, 0.86 Recall Score, and
a 0.86 F1 score.
V. CONCLUSION
This study implies that patients' age, gender, residing
country, the method of exposure to the virus (direct/indirect
exposure), and the history of chronic diseases such as
cancer, chronic kidney disease, COPD, serious heart
conditions, type 2 diabetes directly affect the incubation
period of the SARS-CoV-2 virus. When it comes to age,
older people tend to show symptoms quicker than younger
people and they have a shorter incubation period compared
to others. Gender wise, male cases tend to show symptoms
quicker than others. Patients who have chronic diseases and
immunocompromised states have a shorter incubation
period than others and show symptoms quicker. The people
who got direct exposure to the virus and who had a closer
relationship with the affected cases tend to show symptoms
quicker than people who got indirect exposure to the virus.
In this study, several supervised learning classification
algorithms such as SVM, naïve nayes, logistic regression,
random forest, and decision tree were compared to find the
best model with the highest accuracy to classify the
incubation period. Random forest algorithm outperformed
in classifying the incubation period achieving higher
precision, recall, and F1 score. Finally, boosting algorithms
such as the AdaBoost algorithm was integrated with the
random forest algorithm to achieve 0.87 Precision, 0.86
Recall, and a 0.86 F1 score in classifying the incubation
period.
This study mainly focused on the symptomatic
transmission of COVID-19. Symptomatic transmission
refers to transmission from a person while they are
experiencing symptoms such as fever, cough, tiredness,
etc. In a symptomatic case, we are able to track the
incubation period by the date difference, between exposure
to symptom onset. There are some cases showing
asymptomatic transmission of COVID-19. Asymptomatic
transmission can be defined as the transmission of virus
from person to person, without showing symptoms of being
infected. Very few asymptomatic transmission cases have
been reported as a result of contact tracing efforts in some
countries. Since asymptomatic patients do not show
symptoms, it is relatively difficult to identify the incubation
period. This study was conducted using only 500 patient
records from several countries around the world. If there is
larger number of patient records representing all the
countries around the world with patients’ clinical
information, a comprehensive study can be carried out.
Further, unsupervised machine learning algorithms such as
artificial neural networks can be implemented with a larger
dataset in order to achieve higher accuracy.
As future work, chest X-ray images of COVID-19
affected persons can be combined with geographic and
healthcare data processing models which will then be
integrated into applications that will support the decision-
making process for the authorities and for the growth of the
healthcare systems. This will finally lead to the
development of semi-autonomous classification systems
that can provide the facility to detect the incubation period
of COVID-19 patients accurately and prepare us for future
outbreaks.
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