Table 9.

The methods, properties, and features of SOM-COVID-19 mechanisms.

Authors	Main idea	Advantages	Research challenges	Security mechanism?	Dataset	Using TL?	Method	Usage?
Melin, Monica [44]	Utilizing an unsupervised SOM based on clustering can spatially group countries together.	-High scalability	-It is not considered to integrate both the spatial and temporal dimensions of the COVID-19 spread problem.	No	The Humanitarian Data Exchange was used to collect the dataset.	No	SOM	Analyze the global coronavirus pandemic's spatial evolution
		-High accuracy	- Low robustness
			-Low dependability
Galvan, Effting [45]	Using SOM's spatial clustering capability to spatially group related towns, states, and regions based on COVID-19 cases.	-Able to spatially group cities, states, and regions based on the prevalence of coronavirus	-Comorbidities, the number of hospital beds available, trained staff, the human development index, and the environment are not considered.	No	The Brazilian Ministry of Health dataset on May 31, 2020.	No	SOM	To determine COVID-19's geographic and temporal distribution in Brazil
		-Low complexity
Simsek and Kantarci [76]	Proposing a mobile epidemic assessment agent mobilization approach based on AI.	-The results show that a 5 km coverage restriction achieves 99.783% coverage	-Dataset is not mentioned.	No	Not mentioned	No	SOM	To monitor, model and forecast reported cases
		-High robustness	-Low dependability
			-High energy consumption
Triayudi [77]	Using SOM, visualize the relationship between socioeconomic factors and outbreaks.	-Low response time	-It is necessary to conduct additional tests to expand the data coverage	No	The Indonesian central statistics agency provided the data.	No	SOM	Depicts the connection between socioeconomic factors and the outbreak of the COVID-19
		-Low energy consumption	-High complexity
		-High accuracy