Exploring the COVID-19 Research Landscape in Malaysia: A Bibliometric Case Study

.title[
# Exploring the COVID-19 Research Landscape in Malaysia: A Bibliometric Case Study
]
.subtitle[
## R confeRence 2024
]
.author[
### Tengku Muhammad Hanis Bin Tengku Mokhtar, PhD
]
.institute[
### Founder and Academic Trainer, <br>Jom Research
]
.date[
### October 27, 2024
]

---

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## About me

<p style="font-size:20pt; font-weight:bold; font-family:lato">Tengku Muhammad Hanis Bin Tengku Mokhtar</p>

**Founder and Academic Trainer, Jom Research**

**MBBCh, MSc (Medical Statistics), <br>PhD (Public Health Epidemiology)**

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---

## COVID-19

- Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus
- Previously, we had also been affected by the severe acute respiratory syndrome (SARS) and middle east respiratory syndrome (MERS)
- The virus has affected countries globally, including Malaysia 
- Since the outbreak, extensive research has been conducted to understand and manage the disease
- Thus, we can use a bibliometric analysis to explore the landscape of this research area

---

## Bibliometric analysis

- Bibliometric analysis is an umbrella term for a set of analyses 
- Basically, it refers to an analysis of bibliographic information
- Bibliometric analysis is explorative in nature
- In this talk, we going to use both R (bibliometrix) and Python (pyBibX)

---

## Data and search strategy

| Parameters    | Details             |
| ------------- |:-------------------:|
| Database      | Scopus              |
| Date searched | September30, 2024   |
| Search terms  | TITLE ( covid-19 ) OR TITLE ( covid ) OR TITLE ( 2019-ncov ) OR TITLE ( coronavirus ) AND ( LIMIT-TO ( AFFILCOUNTRY , "Malaysia" ) ) AND ( LIMIT-TO ( DOCTYPE , "ar" ) OR LIMIT-TO ( DOCTYPE , "cp" ) OR LIMIT-TO ( DOCTYPE , "re" ) OR LIMIT-TO ( DOCTYPE , "ch" ) OR LIMIT-TO ( DOCTYPE , "ed" ) OR LIMIT-TO ( DOCTYPE , "bk" ) )     | 
| Results       | 5,995               |

---

## Analysis in R

Packages:

``` r
library(dplyr)
library(ggplot2)
library(bibliometrix)
```

Descriptive:

``` r
res <- biblioAnalysis(covid_data)
summary(res, k = 10)
```

```
## 
## 
## MAIN INFORMATION ABOUT DATA
## 
##  Timespan                              2008 : 2025 
##  Sources (Journals, Books, etc)        2238 
##  Documents                             5932 
##  Annual Growth Rate %                  8.5 
##  Document Average Age                  1.97 
##  Average citations per doc             14.37 
##  Average citations per year per doc    3.967
```

---

## Analysis in R (cont.)

Basic plots:

``` r
allplots <- plot(res) # we get 5 plots here
```

``` r
allplots$AnnualScientProd
```

---

## Analysis in R (cont.)

``` r
allplots$AverTotCitperYear
```

---

## Analysis in R (cont.)

Top authors in the research area:

``` r
authorProdOverTime(covid_data, k=10)
```

---

## Analysis in R (cont.)

thematic map:

``` r
thematicMap(covid_data, field = 'DE', n.labels = 5)
```

---

## Analysis in Python

Install libraries:

- Make sure to install reticulate package first (if using RStudio)
- Some of the functions are not well executed in RStudio

``` python
reticulate::py_install('pyBibX')
```

Load libraries

``` python
import numpy as np
import pandas as pd
import textwrap

from pyBibX.base import pbx_probe #read data
```

---

## Analysis in Python (cont.)

Most common n-grams from author's keywords (entry = 'kwa')

``` python
rm = ['2019', 'disease', '19', 'pandemic'] #remove few words
cov_data.get_top_ngrams(view = 'notebook', entry = 'kwa', ngrams = 2, stop_words = ['en'], rmv_custom_words = rm, wordsn = 15)
```

---
## Analysis in Python (cont.)

Top keywords evolution for the last 10 years:

``` python
rm2 = ['pandemic', 'covid', 'covid-19', 'sars-cov-2', 'coronavirus', 'malaysia', 'covid-19 pandemic']
cov_data.plot_evolution_year(view = 'notebook', stop_words = ['en'], rmv_custom_words = rm2, key = 'kwa', topn = 5, txt_font_size = 12, start = 2015, end = 2024)
```

---

## Analysis in Python (cont.)

Let's focus on the first first 5 years (2015-2019)

``` python
cov_data.plot_evolution_year(view = 'notebook', stop_words = ['en'], rmv_custom_words = rm2, key = 'kwa', topn = 5, txt_font_size = 12, start = 2015, end = 2019)
```

---

## Analysis in Python (cont.)

Next, let's see the next 5 years (2020-2024)

``` python
cov_data.plot_evolution_year(view = 'notebook', stop_words = ['en'], rmv_custom_words = rm2, key = 'kwa', topn = 5, txt_font_size = 12, start = 2019, end = 2024)
```

---

## Analysis in Python (cont.)

Tree map of countries:

``` python
cov_data.tree_map(entry = 'ctr', topn = 20, size_x = 30, size_y = 15, txt_font_size = 22)
```

---

## Analysis in Python (cont.)

Further details on countries collaboration:

``` python
cov_data.network_adj_map(view = 'notebook', connections = False, country_lst = [])
```

---

## Analysis in Python (cont.)

Alternatively, we can use bibliometrix package from R:

``` r
# Extract authors' countries
author_country <- metaTagExtraction(dat, Field = "AU_CO")

# Country collab network
country_collab <- biblioNetwork(author_country, analysis = "collaboration",  network = "countries")

# Plot the network
set.seed(123)
networkPlot(country_collab, n = 20, cluster = "none", Title = "Collaboration between top 20 countries using bibliometrix (R)", type = "auto", size.cex = T)
```

---

## Conclusion

- We have a good research landscape especially in terms of availability of the experts and collaboration between countries
- We see a few dominant research area such as COVID-19 research related online learning and mental health
- COVID-19 research in Malaysia before 2019 were almost non-existent
- COVID-19 research related to AI especially deep learning in Malaysia have yet to be extensively studied

---

## Some remarks

- Both bibliometrix (R) and pyBibX (python) have their own advantages
- bibliometrix vs pyBibX :
  - pyBibX is a little bit heavier to run (biased opinion)
  - pyBibX takes a bit more effort to be able to run smoothly in RStudio
  - pyBibX is a larger package (and probably more powerful) than bibliometrix
- Both packages have several similar overlapped functions
- Both packages have limited documentations

---

## Suggested readings

- About bibliometric analysis:
  - [bibliometrix : An R-tool for comprehensive science mapping analysis](https://doi.org/10.1016/j.joi.2017.08.007)
  - [How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research](https://doi.org/10.1016/j.jbusres.2021.04.070)
  - [Preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO): a minimum requirements](https://doi.org/10.1186/s13643-023-02410-2) 
- pyBibX: https://github.com/Valdecy/pyBibX
- bibliometrix: https://github.com/massimoaria/bibliometrix

---

# Thank you!

.center[<svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M579.8 267.7c56.5-56.5 56.5-148 0-204.5c-50-50-128.8-56.5-186.3-15.4l-1.6 1.1c-14.4 10.3-17.7 30.3-7.4 44.6s30.3 17.7 44.6 7.4l1.6-1.1c32.1-22.9 76-19.3 103.8 8.6c31.5 31.5 31.5 82.5 0 114L422.3 334.8c-31.5 31.5-82.5 31.5-114 0c-27.9-27.9-31.5-71.8-8.6-103.8l1.1-1.6c10.3-14.4 6.9-34.4-7.4-44.6s-34.4-6.9-44.6 7.4l-1.1 1.6C206.5 251.2 213 330 263 380c56.5 56.5 148 56.5 204.5 0L579.8 267.7zM60.2 244.3c-56.5 56.5-56.5 148 0 204.5c50 50 128.8 56.5 186.3 15.4l1.6-1.1c14.4-10.3 17.7-30.3 7.4-44.6s-30.3-17.7-44.6-7.4l-1.6 1.1c-32.1 22.9-76 19.3-103.8-8.6C74 372 74 321 105.5 289.5L217.7 177.2c31.5-31.5 82.5-31.5 114 0c27.9 27.9 31.5 71.8 8.6 103.9l-1.1 1.6c-10.3 14.4-6.9 34.4 7.4 44.6s34.4 6.9 44.6-7.4l1.1-1.6C433.5 260.8 427 182 377 132c-56.5-56.5-148-56.5-204.5 0L60.2 244.3z"/></svg> Slides: https://tengku-hanis.github.io/bibliocovidmalaysia/]