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Research Article

Predicting COVID-19 outbreak in India using modified SIRD model

Sakshi Shringia Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

Harish Sharmab Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

Pushpa Narayan Rathiec Department of Statistics, University of Brasilia, Brasilia, Brazil

Jagdish Chand Bansald Department of Mathematics, South Asian University, New Delhi, India

Atulya Nagare School of Mathematics, Computer Science and Engineering, Liverpool Hope University, UK

Daya Lal Sutharf Department of Mathematics, Wollo University, Dessie, EthiopiaCorrespondence[email protected]

Figures & data

Figure 1. Compartments of the SIRD Model.

Figure 2. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ for Andaman & Nicobar Islands.

Figure 3. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Andhra Pradesh.

Figure 4. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Arunachal Pradesh.

Figure 5. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Assam.

Figure 6. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Bihar.

Figure 7. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Chandigarh.

Figure 8. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Chhattisgarh.

Figure 9. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Dadra & Nagar Haveli and Daman & Diu.

Figure 10. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Delhi.

Figure 11. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Goa.

Figure 12. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Gujarat.

Figure 13. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Haryana.

Figure 14. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Himachal Pradesh.

Figure 15. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Jammu and Kashmir.

Figure 16. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Jharkhand.

Figure 17. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Karnataka.

Figure 18. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Kerala.

Figure 19. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Ladakh.

Figure 20. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Madhya Pradesh.

Figure 21. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Maharashtra.

Figure 22. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Manipur.

Figure 23. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Meghalaya.

Figure 24. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Mizoram.

Figure 25. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Nagaland.

Figure 26. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Odisha.

Figure 27. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Puducherry.

Figure 28. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Punjab.

Figure 29. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Rajasthan.

Figure 30. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Sikkim.

Figure 31. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Tamil Nadu.

Figure 32. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Telangana.

Figure 33. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Tripura.

Figure 34. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Uttarakhand.

Figure 35. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of Uttar Pradesh.

Figure 36. Graphical analysis of $β_{i}$ , $R_{0}$ , $Δ D$ , $Δ I$ , and $Δ R$ of West Bengal.

Table 6. Comparative analysis of actual predicted values of confirmed, recovered, and deceased cases for all States/UT of India.

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Data availability statement

We used data from the link – https://www.covid19india.org/ to support this study.

Predicting COVID-19 outbreak in India using modified SIRD model

Table 1. The SIRD model parameters.

Table 2. Dataset (30 January 2020 to 31 July 2021).

Table 3. Comaprative analysis of $pred R^{2}$ for all States/UT of India.

Table 4. Peaks and recent trends.

Table 5. Effects of government measures using $R_{0}$ .

Table 6. Comparative analysis of actual predicted values of confirmed, recovered, and deceased cases for all States/UT of India.

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Predicting COVID-19 outbreak in India using modified SIRD model

Figures & data

Table 1. The SIRD model parameters.

Table 2. Dataset (30 January 2020 to 31 July 2021).

Table 3. Comaprative analysis of predR2 for all States/UT of India.

Table 4. Peaks and recent trends.

Table 5. Effects of government measures using R0.

Table 6. Comparative analysis of actual predicted values of confirmed, recovered, and deceased cases for all States/UT of India.

Data availability statement

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Table 3. Comaprative analysis of $pred R^{2}$ for all States/UT of India.

Table 5. Effects of government measures using $R_{0}$ .