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e ISSN: 2645-9248 Journal homepage: www.jidhealth.com Open Access
Why pandemic coronavirus (SARS-CoV-2) hit different age groups of
people in Southeast Asia? a case study in Bangladesh
Tasnim Abdary Anonna1, Md Moniruzzaman2*, Abdul Hadi Al Nafi Khan2, Ashis Kumar Sarker3,
Palas Samanta4, Mohammad Iqbal Naser5, Shamim Ahmed6, Hafiz Al Asad3
Abstract
The new catastrophe of a novel coronavirus (COVID-19s) with unstable symptoms has rapidly pulled danger to all age
groups worldwide. We investigate possible causes of the different nature and demography of COVID-19. We collected
and used secondary data from the IEDCR website and “Worldometer” from 1st April to 24th June for the statistical
analyses, including multi-criteria decision-making method (MCDM), topsis, advanced topsis, simple additive weighting
(SAW) and weighting product method (WPM) and PCA. The total number of known COVID-19 patients in Bangladesh
was 122,709 as of 24th June. Radical growth will be found with 4912 cases in one day on 16th July as per the time-
series forecasting. The infection rate among the young (<30) was highest, i.e., 37.8%, while the elderly (>60) had the
maximum death rate (≈39%). Both of India and Bangladesh, approximately one-third of total COVID-19 cases belong
to the under 30 age group. Preliminary observation finds India and Bangladesh have a high risk for young people and
the working class. PCA indicates the highest positive association among the youths and the highest negative
association among the older. In this study, older age (>60) individuals are in danger with the fifth rank, and the young
and working-age people are at comparatively lower risk with a third to the fourth rank in terms of infection rate as
indicated by MCDM. 41-50 age group remains at lower risk with the first rank in all cases. The nature of activities of
younger people and the poor immunity system of older people are the reason for the non-homogenous attitude toward
the coronavirus among different age groups. In Bangladesh, drug addiction, gambling habits, uncontrolled lifestyle, and
social obliquity have led the youth through danger, threatening the older age of family and society.
Keywords: COVID-19, Age-group, Transmission, Youngsters, Older-age, Immunity, Risk analysis, Bangladesh
Background
The present-day coronavirus pandemic of 2019 (COVID-19)
has become a global concern. Since December 2019, in Wuhan,
Hubei Province, China, coronavirus ailment (COVID-19), a
recently developing irresistible pneumonia with unknown
causes, was reported [1,2]. The COVID-19 pandemic has
created a terrible crisis that led the world's health system and
medical science to question [3, 4, 5, 6, 7]. The new coronavirus
termed SARS-CoV-2 is the germ to spread this disease [8, 9,
10, 11, 12] and has extended its claw up to 213 Countries and
Territories [13]. As of June 24, 2020, statistics from Johns
Hopkins University showed that nearly 9.07 million people had
been affected by this virus, while nearly half a million lives
were taken [14]. After its earliest exposure in China at the end
of 2019 [11], COVID-19 patients started being detected in other
parts of the world. Thailand, Japan, the USA, and South Korea
reported their respective first COVID-19 patient was mid-
January [15, 16]. In Europe, France was the first country to
report the emergence of Coronavirus on January 24, 2020. After
that, it took only six weeks to spread its claw to the whole
continent [17, 18]. The earlier transmission of Coronavirus in
South Asian countries started from late January to early March
2020. Within the Indian sub-continent, the first reported case of
COVID-19 was found in Nepal on January 23, 2020 [19]. In
India, COVID-19 was first revealed on January 30, 2020 [20],
while the number of patients did not see any lift up to February
2020. Despite having fewer patients, India could not manage to
limit the spread. As a result, the transmission pace got
momentum from the start of April [21]. The most delayed
coronavirus transmission among South Asian countries
occurred in Bangladesh, its first appearance on 7th March 2020
___________________________________________________
monir1.gm@gmail.com
2Isotope Hydrology Division, Institute of Nuclear Science and Technology,
AERE, Bangladesh Atomic Energy Commission, Dhaka, Bangladesh
Full list of author information is available at the end of the article
https://doi.org/10.47108/jidhealth.Vol5.Iss2.207
http://www.jidhealth.com/
Anonna TA, et al., Journal of Ideas in Health (2022); 5(2):655-663 656
[22]. Nonetheless, this country also failed to have the situation
under control. As of August 16, 2021, the number of infected
persons in India and Bangladesh is 1,418,902 and 32,225,513,
respectively [13]. Outside of South Asia, maximum patients
(>2.4 M) and deaths (nearly 0.13 M) have been reported in the
USA. The European countries, Italy, Spain, the United
Kingdom, Germany, and France have seen large death tolls and
a huge number of growing patients. Except for Germany, each
country has experienced 28-43 thousand deaths. The number of
affected patients diverges from approximately 0.16-0.28M in
most affected European countries. Recently, Brazil has been
devastated by an intensified attack of COVID-19, having more
than 50 thousand death and approximately half a million
affected [13]. Demographic science is always important in
conceptualizing the dynamics of a pandemic [23]. Several
studies have been published where the role of age structure in
death rates and transmission of the different viral diseases like
Hepatitis B [24], Influenza [23], La Crosse Virus [25], etc.,
have been investigated. Similar age-dependent studies are also
available for COVID-19 [26,27]. Unlike Europe and the USA,
the subcontinent holds a relatively younger population.
In European countries and also in the USA, people with age
more than 65 hold a large share of the total population, for
example, Italy (23.1%), Spain (19.38%), France (20.5%),
United Kingdom (18.4%), Germany (17.88%), USA (15.81%).
In India (6.18%) and Bangladesh (5.16%), the portion of 65+
aged people is much lower in comparison with the developed
countries [28]. As of 18th June 2020, the number of affected
patients and the death toll are still comparatively higher in the
western countries (although transmission has been heavy
recently) and the USA, where the major portion of death and
transmission belong to the elderly people. In countries like
China, Italy, France, the United Kingdom, and Spain, less than
30% of patients are below 40 [21]. In the USA, 42% of patients
have an age limit of ≤45 [29], while in Germany, it is
presumably less than 50% [21]. However, it becomes a great
concern for the subcontinent since young people are highly
affected. The latest report from IEDCR shows that 65% of
COVID-19 patients in Bangladesh are from the 0-39 years
group [30], while in India, the share becomes 58.25%, as of a
Statistical report [21]. Newspapers and mass media have
reported this crisis where the working group 21-50 has been
identified as the most vulnerable class in India [31] and
Bangladesh [32]. Owing to the mobility and unwillingness to
maintain a disciplined life, young people may have played a
vital role in spreading the coronavirus worldwide [33]. Since all
age groups should be equally susceptible to the pandemic in the
ideal case [34], studying the reason and mode of infection
among young individuals in South Asian countries is necessary.
Studies show that young ones can be asymptomatic and
transmit the disease to children and the most vulnerable elderly
people with greater ease [35-37]. In countries like Bangladesh
and India, youngster infections have shown a dimension in the
international community [36]. Statistical methods are always
important to find out the risk groups of the society when any
threat is posed to them. Several research studies have
successfully demonstrated the risk groups and the associated
factors in the recent and historical pandemics, including the
recent COVID-19 [38-43].
Very little research on COVID-19 in Bangladesh has been
published, and those works mostly focused on medical,
biomedical, and mental health issues. The demography is
mostly absent in those researches except in Hossain et al. [44]
and Paul et al. [20]. Most of the work failed to address any
notable research explaining the nature and reason for the high
infection rate among young groups. The authors aim to present
the scenario of youngsters' infection by the coronavirus and
provide statistical analysis to find the associated factors with the
aid of statistical and demographic analysis. Studying the age
distribution will help understand the transmission mode of this
viral disease among the youths and help policymakers save the
whole community from being affected.
Methods
Data Collection
A retrospective study recruiting secondary data was conducted
from 1st April to 24th June of 2020. The source of data was the
Institute of Epidemiology, Disease Control and Research
(IEDCR, https://dghs-dashboard.com/pages/covid19.php, 24th June
2020), Worldometer (https://www.worldometers.info/coronavirus/,
24th June 2020), and Statista
(https://www.statista.com/topics/5994/the-coronavirus-disease-covid-
19-outbreak/#dossierContents__outerWrapper, 25th June 2020).
Some ideas on young people's psychological and behavioral
issues were taken from a short pilot survey on different blogs on
the social networking site (Facebook) among young aged
people. These were observed before June 2020.
Data analysis
Statistical Analysis
Time Series forecasting models were calculated using the Built-
in program named "Forecast sheet" in Excel 19. Principle
Component Analysis (PCA) was carried out with Excel 19
using the XLSTAT statistical Software as Add-in.
Multi-criteria Decision-Making Method (MCDM)
COVID-19 infection prevalence in various countries has
differed according to different age groups. The multi-criteria
decision-making method provided a ranking solution for
assessing overall risk analysis among five countries in different
age groups. This method makes detecting specific findings
simple and allows one to make more accurate decisions.
Entropy weight:
C=
[
𝐶11 𝐶12 ⋯ 𝐶1𝑛
𝐶21 𝐶22 ⋯ 𝐶2𝑛
𝐶31 𝐶32 ⋯ 𝐶3𝑛
⋮ ⋮ ⋮ ⋮
𝐶𝑚1 𝐶𝑚2 ⋯ 𝐶𝑚𝑛]
Here, Cij is the matrix component.
Step-1: The normalize matrix of C is,
R=
[
𝑅11 𝑅12 ⋯ 𝑅1𝑛
𝑅21 𝑅22 ⋯ 𝑅2𝑛
𝑅31 𝑅32 ⋯ 𝑅3𝑛
⋮ ⋮ ⋮ ⋮
𝑅𝑚1 𝑅𝑚2 ⋯ 𝑅𝑚𝑛]
, Where, Rij=
𝑪𝒊𝒋
∑ 𝑪𝒊𝒋
𝒎
𝒊=𝟏
Anonna TA, et al., Journal of Ideas in Health (2022); 5(2):655-663 657
Step-2: The output entropy of the jth factor is calculated as,
𝑒𝑗 = −𝑘∑ 𝑅𝑖𝑗
𝑚
𝑖=1 𝑙𝑛𝑅𝑖𝑗, where k= 1/ln(m)
Step-3: Then the entropy weight can be calculated as follows,
𝑤𝑗 =
1−𝑒𝑗
∑ 1−𝑒𝑗
𝑛
𝑗−1
[45, 46]
Topsis Method:
It's a compensatory aggregation method that compares a set of
alternatives by determining weights for each criterion,
normalizing scores for each criterion, and calculating the
geometric distance between each alternative and the ideal
alternative, which is the one with the best score in each
criterion.
Step-1: Here, the standard normalized matrix is,
R=
[
𝑅11 𝑅12 ⋯ 𝑅1𝑛
𝑅21 𝑅22 ⋯ 𝑅2𝑛
𝑅31 𝑅32 ⋯ 𝑅3𝑛
⋮ ⋮ ⋮ ⋮
𝑅𝑚1 𝑅𝑚2 ⋯ 𝑅𝑚𝑛]
[47]
Where, Rij = 𝐶𝑖𝑗/[∑ 𝐶𝑖𝑗
2𝑚
𝑖=1 ]
Step-2: Weighted normalized decision matrix can be calculated
as 𝑉𝑖𝑗 = 𝑅𝑖𝑗 ∗ 𝑤𝑗
Where wj is the entropy weight.
Step-3: Positive and Negative Ideal solution can be determined
as follows,
{
𝑣+ = 𝑚𝑎𝑥{𝑣1𝑗,𝑣2𝑗 ⋯𝑣𝑚𝑗}
𝑣− = 𝑚𝑖𝑛{𝑣1𝑗,𝑣2𝑗 ⋯𝑣𝑚𝑗}
(𝑗 = 1,2,….𝑛)
Step-4: Euclidian distance between the positive-ideal and the
negative-ideal reference points can be calculated as
{
𝑑+ = √∑ (𝑣𝑖𝑗 − 𝑣
+)
2𝑛
𝑗=1
𝑑− = √∑ (𝑣𝑖𝑗 − 𝑣
−)
2 𝑛
𝑗=1
Step-5: The final step of the Topsis Method is to determine the
Closeness Coefficient, and the formula is 𝐶𝐶 =
𝑑−
𝑑++𝑑−
The higher value of CC is considered the better alternative [45-
47].
Advance Topsis Method:
TOPSIS is a valuable strategy for dealing with multi-attribute or
multi-criteria decision-making situations in the real world. It
aids decision-makers in organizing issues to be addressed and
conducting analyses, comparisons, and rankings of options.
In the Advance Topsis method, the Euclidian distances are
calculated as follows.
{
𝑑+ = √∑𝑤𝑗(𝑣𝑖𝑗 − 𝑣
+)
2
𝑛
𝑗=1
𝑑− = √∑𝑤𝑗(𝑣𝑖𝑗 − 𝑣
−)
2
𝑛
𝑗=1
Then, the relative Closeness coefficient of a particular
alternative can be calculated by the following formula, 𝐶𝐶 =
𝑑−
𝑑++𝑑−
[30].
Simple additive weighting (SAW) and weighting product
method (WPM):
One of the strategies for solving multi-attribute choice issues is
simple additive weighting (SAW). The SAW method's core
principle of determining the number of weighted performance
ratings for each option on all qualities is quite valuable. A
weighted product model (WPM) is a straightforward and widely
used method for resolving multi-criteria decision analysis
(MCDA) issues. To achieve a score, just multiply all of the
characteristics' values. The greater the number, the better. A
normalized decision matrix again needs to be created in this
method. The equations are as follows:
𝑟𝑖𝑗 =
𝐶𝑖𝑗
𝑀𝑎𝑥(𝐶𝑖𝑗)
(Benefit)
𝑟𝑖𝑗 =
𝑀𝑖𝑛(𝐶𝑖𝑗)
𝐶𝑖𝑗
(Cost)
In SAW, the Preference value for each variable can be
calculated as,
𝑣𝑖 = ∑ 𝑤𝑗𝑟𝑖𝑗
𝑛
𝑗=1 [48, 46]
In WPM, preference values can be calculated as,
𝑣𝑖 = ∏ (𝑟𝑖𝑗
𝑤𝑗
)𝑛𝑗=1 [46]
Results and Discussion
COVID-19 infections in Bangladesh
In Bangladesh, the first 3 cases of COVID-19 were reported on
the 8th of March and increased gently over time. Nevertheless,
the number of cases increased significantly over time from the
first week of April. The total number of COVID-19 cases
identified during April and May are 7716 and 39,486,
respectively, while 51 cases were found between 8th March to
31st March (IEDCR, https://dghs-
dashboard.com/pages/covid19.php, 5th May 2020). Another
75,507 cases were found until 24th June (IEDCR, https://dghs-
dashboard.com/pages/covid19.php, 24th June 2020).
Figure 1: Number of new Covid-19 cases per day; Cumulative percentage curve
shows the regular increment rate of patients.
Anonna TA, et al., Journal of Ideas in Health (2022); 5(2):655-663 658
This enormous increase of COVID-19 patients may have
evolved as negligence of people about COVID-19, and the
different stakeholders have made some paradoxical decisions.
For example, the light coming from Europe arrives at the
airport, though the country restricts all the national and
international flights [49]. The government authority announced
public shouldn't move their station during lockdown to avoid
community transmission. However, the people did not restrict
their movement immediately, which led them outside of the
capital. Therefore, people spread the COVID-19 to every part of
the country. Furthermore, BGMEA decided to open the
garments factories on 4th April 2020. Garment workers started
moving toward their workplace. Latterly, the same institution
changed its decision to close the factories to consider on behalf
of the health risk to the workers. Community transmission
mostly occurred at that time through their arrival and
subsequent departure. The number of infected patients increases
day by day, and the cumulative number of patients also
accelerates (Figure 1 and Figure 2).
Figure 2: Ogive curve showing cumulative growth of Covid-19 cases.
Trend and Forecast of COVID-19 infections in Bangladesh
Although the 1st COVID-19 case was identified in early March,
the rapid growth of infected people started in April. Here, data
for the time series forecasting and trend analysis were shown
from 1st April to 24th June (12 weeks). Additional four weeks of
infection prediction also be added in the time series. The line
chart showed the forecasting line, including the 16th week. The
total number of affected persons was approximately 4912 as per
time series forecasting in a single day with an R-Squared value
of 0.92 (Figure 3).
Figure 3: Time series plot of Covid-19 cases within 16th July; the forecast line
moves upward, as the number of patients will increased day by day.
From this trend analysis, the upper confidence bound showed a
speedy increase of patients in the upcoming days. The lower
confidence bound indicated that the number of cases would
remain similar (Figure 3). Some factors can fluctuate the
number of cases depending on the day. Because Bangladesh is
cladding insufficiency of the testing kit, for this reason, some
COVID-19 patients cannot be adequately detected. These
circumstances can affect the actual number of patients as the
management reopened well through the offices, marketplaces,
and transport. However, Peoples are still frequently moving
from place to place without taking safety measures. Social
distancing is not appropriately maintained by most of the people
in Bangladesh. These types of negligence may cause a
significant increase in COVID-19 patients.
Infected Rate vs. Death rate in Bangladesh
In Bangladesh, about 37.8% of people owing to below 30 years
were diagnosed with Novel Coronavirus, according to IEDCR
data (24th June 2020). However, recent statistics of IEDCR
showed that the death rate of old people was higher, about 39%.
Hence, the infection rate and age death rate showed an inverse
relationship (Figure 4). The key reasons for people of young
age (<30) being more infected because of disobeying and don't
care about the authority decisions even though declared
lockdown remains. Usually, this younger age group gets rid of
this virus after a few days, having a high immune system. On
the other hand, the elderly (>60) has a weakened immune
system and suffer from numerous senile diseases such as
fatigue, body ache, rheumatic pain, dementia, sinus infection,
trouble breathing, asthma, palpitation, high blood pressure,
incompetence micturition, etc. [50]. The patients had already
suffered from one or more of these diseases are likely to be in a
riskier situation. These days, older people are rare without
multiple health issues. Therefore, the death toll is high in the
60+ age group in Bangladesh, even though being in the
comparatively less affected class.
Figure 4: Infected vs death rate graph; death rate is high (39%) at >60 age group
and higher infected rate (37.8%) is under <30 age group.
Comparison of COVID-19 Infection among Bangladesh and
other countries by age group
Neighboring country India showed nearly the same curve as
Bangladesh. It had the highest 32.78% of patients in the <30
age group (37.8% in Bangladesh) and the lowest 13.07% of
patients in the>60 age group against 7% in Bangladesh for the
same age group. The comparative information has been
presented in a line chart and box-whisker plot (Figure 5A, 5B).
Anonna TA, et al., Journal of Ideas in Health (2022); 5(2):655-663 659
Other countries like the USA, Spain, and China had the highest
percentage of patients (32.33%, 31.2%, and 47%) in the>60 age
group, respectively. The lowest share of COVID-19 cases
(14.96%) belongs to the 41-50 age group in the USA.
Moreover, 10.2% and 8% cases were found in the <30 age
group in China and Spain, respectively.
Figure 5: Age Distribution of infected people; (A) Line chart shows the different
patterns of infected patients by age among different countries; (B) Box-whisker
plot shows the distribution of mean, median, mode, 1st quartile, 3rd quartile and
outliners at different age group.
Demography is certainly a significant cause of this type of
difference, as we guessed primarily. According to the
Bangladesh population census (2011), about 61% of people
belong to the < 30 age group. Similarly, per the Indian
population pyramid (2016), about 57.2% of people belong to
the <30 age group. The number of older people is growing
faster than the younger generation in the USA. According to the
US census bureau, the growth rate is around 31.5% for the
generation group of 45-64 and 15.1% for the age group of >65.
In Spain, about 24.94 % of the population belongs to the 0-24
age group, about 30.59 % in the>55 age group, and the
remainder (about 55.53 %) belongs to the 25-54 age group
(population pyramid, 2017). About 22.62% of the population in
China belongs to the>60 age group (China Pyramid of
Population, 2018). Therefore, the overall number of young and
working-age people is comparatively higher in India and
Bangladesh.
The socio-cultural infrastructure and lifestyle of the Young
and working-age population pose many similarities. The most
common phenomenon between these two countries is
fanaticism and superstition. These two characteristics led a
significant portion of society to lead a stubborn and unhealthy
life. The infected prevalence showed the same trend in different
age groups. As the number of older people is higher in the
USA, the level of the infected rate for older age groups in the
United States is higher. In Spain, the percentage of the>55 age
group is comparatively higher than in others, so the incidence of
infection within this group is high. China also displayed the
same trend as Spain and the USA.
Figure 6 (A): Scree plot of the PCA.
Around 31% of those affected were over 60 years of age. All of
this information has been summarized in Figure 6 (A). As the
health status started to worsen with age due to several senile
diseases [51], COVID-19 affects the people of >60 age groups
more. Except for demography, others factors are also
responsible for the variation of COVID-19 cases among
different age groups in these countries. One of these issues, i.e.,
socio-cultural thoughts, are sometimes difficult to present with
some lacking authentic data sources. However, a few issues can
be discussed based on social media and different social
networking sites. For instance, in developing countries like the
USA, Spain, and China, young people may have updated
recreational facilities, which is very much needed during the
lockdown. This sort of facility is more helpful in keeping the
young people at home and making them safe. On the contrary,
those countries' authorities can convince the people about the
COVID-19 pandemic situation. Though India is now being
developed to some extent, most of its young people may not be
able to have these sorts of facilities like the developed world.
Bangladesh is still a developing country, and most people live
underneath the neediness line. So, the young people can't get
those kinds of facilities. Also, the authorities and defense forces
are unable to control people.
Principal Component Analysis (PCA)
PCA analysis was applied to determine the association between
the parameters and principal components. Eigenvalues greater
than one were considered to demarcate the principal
components. The scree plot of the PCA is shown in Figure 6
(B). Therefore, two principal components were derived from the
analysis. These two components explained 97.91% of the
variation in the data. Amongst different age groups, PC1
explains the highest 72.79% of the variation, whereas PC2
explains 25.12% of the total variation. The age group of <30
and 31-40 had the highest positive relation with PC1, while >60
and 51-60 had the highest negative association with PC1.
Besides, the 41-50 age group had the highest positive relation
with PC2, whereas a strong negative association couldn't find in
PC2 (Table 1).
Anonna TA, et al., Journal of Ideas in Health (2022); 5(2):655-663 660
Figure 6 (B): Biplot showing the relation of components in rotated space.
Table 1: Component matrix for PCA analysis.
Variables (age group) PC1 PC2
<30 0.986 -0.149
31-40 0.985 0.162
>60 -0.981 -0.148
51-60 -0.805 0.545
41-50 0.296 0.943
Eigenvalue 3.64 1.26
Variability (%) 72.79 25.12
Cumulative % 72.79 97.91
Multi-criteria Decision Making (MCDM)
Entropy Weight:
Here the weight of entropy for each parameter, i.e., different
age groups and five countries, is determined. The weighted
entropy values for each parameter are shown below (Table 2).
These weighted entropy values are used to determine the
ranking solutions of MCDM methods, including topsis, advance
topsis, SAW, and WPM.
Table 2: Entropy weight for calculating the ranks of topsis,
advance topsis, SAW and WPM methods of MCDM.
Age Distribution (%)
>60 51-60 41-50 31-40 <30
0.43 0.05 0.013 0.09 0.41
Country
USA China Spain India Bangladesh
0.07 0.09 0.41 0.12 0.29
Topsis and Advance Topsis Method:
Topsis and Advance Topsis methods showed that the>60 age
group was at higher risk of infection in the five countries with
the rank of five (Table-3). This applied method suggested that
between 51-60 and <30 age groups rank varied between 3rd to
4th with moderate risk and 41-50 age group always showed the
first rank with lower risk thread in terms of infection rate of
COVID-19.
Table 3: Values and Rankings of Topsis, Advance Topsis, SAW, and WPM methods
Topsis Advance Topsis SAW WPM
Age Group Closeness Coefficient Rank Closeness Coefficient Rank V Rank V Rank
>60 0.38 5 0.50 5 1.09 5 1.71E-05 5
51-60 0.44 4 0.57 3 1.55 2 4.28E-04 2
41-50 0.49 1 0.66 1 1.77 1 7.35E-04 1
31-40 0.45 2 0.61 2 1.44 3 1.38E-04 3
<30 0.44 3 0.55 4 1.17 4 2.57E-05 4
Simple additive weighting (SAW) and weight product
method (WPM):
Simple additives weighting and weight product method showed
the same rating as Topsis and Advanced Topsis. According to
these criteria, >60 age group people are at higher risk of
infection rate for COVID-19 with the fifth rank. The age group
below <30 has a comparatively low-risk infection compared to
group >60 with fourth rank (Table 3). People under 41-50 age
groups are safe worldwide with the first rank const
Causes to affect young and working-age people in
Bangladesh by COVID-19
During the COVID-19 pandemic, young and working-age
individuals are affected most, as seen from the study's observed
data and subsequent analysis. The high infected rate occurred
mainly due to the lacking knowledge and awareness. Working-
age individuals are predominantly involved in work and
business activities. These guys are always threatening older
people, especially in a joint family. According to the pilot
survey among young people, most youngsters prefer not to stay
at home due to dysfunctional relationships with parents and
other family members, freedom-seeking inclination, boredom,
gang activities, etc. Gang activities lead to underage smoking,
gambling habits, and drug abuse, which in turn causes societal
demoralization. Nearly 25 lakhs are substance addicts. Around
80 % of drug users in Bangladesh are teenagers and young
people between the ages of 15 and 30 [52]. After opioid abuse,
about 80% of drug users lose control in their everyday lives and
continue to lose morals and judgments [44]. This is one of the
significant factors to get them alienated from the family, which
also drives them to have negative health consequences. Like
most members of civil society, young individuals are likely to
move out of the house too. This form of inclination has
increased because of societal practices and family issues. To get
this sort of anxiety instantly released, people start going outside.
This could also be because those affected by COVID-19 are
young and working-age. Wide exposure to the outer
environment coupled with uncontrolled as well as unhealthy
lifestyles has made the young ones more vulnerable to the
disease. Ignoring the rapid pace of COVID-19 transmission,
Anonna TA, et al., Journal of Ideas in Health (2022); 5(2):655-663 661
people in Bangladesh are still adamant about visiting the
markets and other crowded places. Sometimes, they visit those
places without any significant reasons. These types of activities
have proven to be life-threatening and risky in this country. In
the context of Bangladesh, young people from all
classes/sectors can pose a threat to other family members since
they can work as a bearer of the disease. The threat is more
severe for the elderly ones in a pandemic like COVID-19. The
resultant scenario would be more satisfactory if the analysis was
conducted on a wide-angle dataset.
Limitations of this Study
The Covid-19-infected were only studied for 12 weeks over the
summer from April 1st to June 24th. Participants in this
research range from under 30 to over 60 years old. The
prevalence of infection in children and pregnant women must
be considered while analyzing the data since these groups are
more vulnerable to covid-19's effects. In this study, only the
summer impacts were examined; therefore, winter effects might
likely differ.
Conclusion
In this study, the time series forecasting method, PCA, and
MCDM were utilized to foresee the eventual fate of the
COVID-19 pandemic in Bangladesh. Analysis has also been
made to assess and disseminate risk for various age groups and
discover the potential reasons for variety among the age groups
in different countries. Within 108 days of the COVID-19
pandemic, 122,709 patients were found, and as indicated by
time-series forecasting, the number of patients will be roughly
211843. If this sort of progression stays at its genuine rate, at
that point, the nearest future will be an excess of trying for
Bangladesh through the health sector, which is not prepared to
carry the load. Preliminary statistical analysis showed
differences in COVID-19 cases in certain age groups in
Bangladesh and India compared to Europe and the USA. Unlike
the developed countries, Bangladesh has got much younger
patients, while the death toll is higher among the old people as
expected. The PCA analysis specifically determines the highest
positive association among the youths and demonstrates the
highest negative association among the older in PC1. On the
other hand, the 41-50 age group had the highest positive
relation with PC2, whereas a strong negative association
couldn't be found in PC2. Again, the MCDM ranking solutions
demonstrated the general risk investigation for various age
groups among various nations. As per the MCDM result, the
fifth position was constantly saved of infection rate for >60 age
group, which was in peril, and the age bunch <30 switches its
position between 3rd to the fourth rank showing the nearly
lower chance of getting infected. Dissimilarity among the
various age group in various nations happened because India
and Bangladesh hold a relatively higher number of young and
working-age people. In contrast, the USA, Spain, and China
hold many old individuals. Moreover, the infection among
adolescents was involved in employment and business
activities. Various sorts of addiction and gambling activities,
social demoralization, dysfunctional relationship with guardians
and relatives, freedom looking for intentions, and so on also
lead them towards the danger due to COVID-19. These make a
huge threat to the old guardians and other family members and
the community too. Immunity and discipline in lifestyle are
most significant for the COVID-19 pandemic and its control.
Youngsters have nearly dynamic immunity but less control over
their life. Therefore, it makes them highly likely to be affected,
while they get rid of easily through gifted immunity. However,
the risk is carried through the veteran part of the community in
the meantime.
Abbreviation
COVID-19: Coronavirus Disease-19; WHO: World Health
Organization; MCDM: Multi-Criteria Decision Making; SAW: Simple
Additive Weighting; WPM: Weighting Product Method; PCA: Principal
Component Analysis; IEDCR: Institute of Epidemiology, Disease
Control and Research
Declaration
Acknowledgment
The authors are grateful to the authority of Bangladesh Atomic Energy
Commission, Institute of Epidemiology, Disease Control and Research
(IEDCR), Worldometer and Statista for providing data facilities and
others logistic support during the research period.
Funding
The author received no financial support for the research, authorship,
and/or publication of this article.
Availability of data and materials
Data will be available by emailing monir1.gm@gmail.com;
monir@korea.ac.kr
Authors’ contributions
MM designed, planned, conceptualized, MM, TAA and AHANK
drafted the original manuscript. TAA, AKS, PS, MIN, SA and HAA
was involved in statistical analysis and interpretation; MIN, SA and
HAA contributed in data analysis, and validation; MM, TAA, AHANK,
AKS and PS contributed to editing the manuscript, literature review,
and proofreading; TAA, HAA and MM, were involved in software,
mapping, and proofreading during the manuscript drafting stage.
Ethics approval and consent to participate
We conducted the research following the Declaration of
Helsinki, and data is open for use from the original sites that is why
authors no need to any approval and consent to participation.
Consent for publication
Not applicable
Competing interest
The authors declare that they have no competing interests.
Open Access
This article is distributed under the terms of the Creative Commons
Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if
changes were made. The Creative Commons Public Domain Dedication
waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to
the data made available in this article, unless otherwise stated.
Anonna TA, et al., Journal of Ideas in Health (2022); 5(2):655-663 662
Author details
1Department of Geography and Environment, School of Physical
Science, Shahjalal University of Science and Technology, Sylhet,
Bangladesh.2Isotope Hydrology Division, Institute of Nuclear Science
and Technology, AERE, Bangladesh Atomic Energy Commission,
Dhaka, Bangladesh. 3Department of Chemistry, Mawlana Bhashani
Science & Technology University, Santosh, Tangail-1902, Bangladesh.
4Deptartment of Environmental Science, Sukanta Mahavidyalaya,
University of North Bengal, Dhupguri, Jalpaiguri-735210, West Bengal,
India. 5Department of International Relations, Dhaka University,
Dhaka, Bangladesh. 6Department of Geology and Mining, University of
Rajshahi, Rajshahi, Bangladesh.
Article Info
Received: 25 February 2022
Accepted: 27 April 2022
Published: 13 May 2022
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