Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning.

Aman Khakharia, Vruddhi Shah, Sankalp Jain, Jash Shah, Amanshu Tiwari, Prathamesh Daphal, Mahesh Warang, Ninad Mehendale
Author Information
  1. Aman Khakharia: K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, 400077 India.
  2. Vruddhi Shah: K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, 400077 India.
  3. Sankalp Jain: K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, 400077 India.
  4. Jash Shah: K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, 400077 India.
  5. Amanshu Tiwari: K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, 400077 India.
  6. Prathamesh Daphal: K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, 400077 India.
  7. Mahesh Warang: K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, 400077 India.
  8. Ninad Mehendale: K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, 400077 India.

Abstract

The Coronavirus Disease-2019 (COVID-19) pandemic persists to have a mortifying impact on the health and well-being of the global population. A continued rise in the number of patients testing positive for COVID-19 has created a lot of stress on governing bodies across the globe and they are finding it difficult to tackle the situation. We have developed an outbreak prediction system for COVID-19 for the top 10 highly and densely populated countries. The proposed prediction models forecast the count of new cases likely to arise for successive 5 days using 9 different machine learning algorithms. A set of models for predicting the rise in new cases, having an average accuracy of 87.9%  �� 3.9% was developed for 10 high population and high density countries. The highest accuracy of 99.93% was achieved for Ethiopia using Auto-Regressive Moving Average (ARMA) averaged over the next 5 days. The proposed prediction models used by us can help stakeholders to be prepared in advance for any sudden rise in outbreak to ensure optimal management of available resources.

Keywords

References

  1. Science. 2020 May 15;368(6492):742-746 [PMID: 32269067]
  2. J Thorac Dis. 2020 Mar;12(3):165-174 [PMID: 32274081]
  3. Chaos Solitons Fractals. 2020 Jun;135:109829 [PMID: 32313405]
  4. Lancet. 2020 Feb 29;395(10225):689-697 [PMID: 32014114]
  5. Infect Dis Model. 2020;5:282-292 [PMID: 32292868]
  6. Internet Things (Amst). 2020 Sep;11:100222 [PMID: 38620477]
  7. Sci Total Environ. 2020 Aug 1;728:138762 [PMID: 32334157]
  8. EClinicalMedicine. 2020 Apr 18;22:100354 [PMID: 32313879]
  9. Innovation (Camb). 2020 Aug 28;1(2):100023 [PMID: 32914139]
  10. J Microbiol Immunol Infect. 2020 Jun;53(3):396-403 [PMID: 32305271]
  11. PLoS One. 2020 Mar 31;15(3):e0231236 [PMID: 32231392]
  12. Ann Data Sci. 2020;7(3):417-425 [PMID: 38624317]

Word Cloud

Created with Highcharts 10.0.0COVID-19predictionriseoutbreakmodelspopulationdeveloped10countriesproposednewcases5daysusinglearningaccuracyhighMachineCoronavirusDisease-2019pandemicpersistsmortifyingimpacthealthwell-beingglobalcontinuednumberpatientstestingpositivecreatedlotstressgoverningbodiesacrossglobefindingdifficulttacklesituationsystemtophighlydenselypopulatedforecastcountlikelyarisesuccessive9differentmachinealgorithmssetpredictingaverage879%  �� 39%densityhighest9993%achievedEthiopiaAuto-RegressiveMovingAverage ARMAaveragednextuseduscanhelpstakeholderspreparedadvancesuddenensureoptimalmanagementavailableresourcesOutbreakPredictionDensePopulatedCountriesUsingLearning

Similar Articles

Cited By