LSTM and RNN to Predict COVID Cases: Lethality’s and Tests in GCC Nations and India

doi:10.23940/ijpe.21.03.p5.299306

Abstract

Abstract:

The spread of COVID across world countries is better handled by applying learning algorithms. Machine learning and deep learning algorithms can be applied to analyze the effects of COVID in multidimensional ways. This paper brings a detailed study of the COVID cases, deaths and tests across five of the GCC countries and India. The proposed method analyzes the COVID count against the population density of each of the countries. An analysis of the raw count would only give a false impression, whereas a population-based comparison gives the exact measure of the effect of COVID. As India is a densely populated country, the number of precautionary steps taken by the country against the population count needs to be measured for accurate prediction. Recurrent Neural Network and Long Short-term memory are used to predict the future cases, deaths and tests of India. A time span of 20 days is used in the prediction. In the sense that i^th day to (i+20)^th day values are taken to predict the (i+21)^thday values. The accuracy of the LSTM model designed with multiple hidden layers is notable and the prediction error is minimal.

Key words: long short-term memory, recurrent neural network, COVID, Gulf corporation, India

Razia Sulthana A., Arokiaraj Jovith, and Jaithunbi A. K.. LSTM and RNN to Predict COVID Cases: Lethality’s and Tests in GCC Nations and India [J]. Int J Performability Eng, 2021, 17(3): 299-306.

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Ith Day	UAE	Bahrain	India	Kuwait	Qatar	Saudi Arabia
Day 1	1	1	1	3	1	1
Day 20	8	211	3	123	501	767
Day 40	29	643	44	556	2979	4934
Day 60	333	2217	1071	2892	14872	27011
Day 80	4749	5816	15712	12860	40481	70161
Day 100	14730	12311	59662	29921	75071	119942
Day 120	29485	22407	165799	41879	97003	197608
Day 140	41990	32941	366946	56174	107430	258156
Day 160	50141	41190	742417	68299	did not reach	did not reach
Day 180	57988	did not reach	1483156	did not reach	did not reach	did not reach

Ith Day	UAE	Bahrain	India	Kuwait	Qatar	Saudi Arabia
Day 1	0	0	0	0	0	0
Day 20	0	0	0	0	0	1
Day 40	0	4	0	1	7	65
Day 60	2	8	29	19	12	184
Day 80	28	10	507	96	19	379
Day 100	137	19	1981	236	69	893
Day 120	245	65	4706	337	115	1752
Day 140	288	109	12237	396	160	2601
Day 160	318	147	20642	461	did not reach	did not reach
Day 180	342	did not reach	33425	did not reach	did not reach	did not reach

Ith Day	Country
1^st day of COVID	.
2^nd day of COVID	.
3^rd day of COVID	.
4^th day of COVID	.
	.
	.
	.
Nth day of COVID	.

Adam Classifier: RNN using LSTM Country: India 4 hidden layer, each with 10 units. A dropout of 0.2 in each layer. Output layer with 1 unit Calculated: mean_squared_error
epoch 1	Total Cases	Total Deaths	Total tests
epoch 2	0.0463	0.0562	0.0334
epoch 3	0.0378	0.0498	0.0310
epoch 4	0.0361	0.0455	0.0298
epoch 5	0.0339	0.0344	0.0251
epoch 6	0.0325	0.0321	0.0201
epoch 7	0.0263	0.0314	0.0098
epoch 8	0.0224	0.0214	0.0077
epoch 9	0.0131	0.0121	0.0052
epoch 10	0.0057	0.0088	0.0001