Abstract
The demands of different regions can be predicted and supplies may be dispatched by the central agencies based on certain predictions. Region-wise growth factors of Covid-19, diabetic patients, cardiovascular patients and other important factors are taken to generate a priority metric based on the correlation matrix, which is calculated from the different covariance matrix against different influencing factors including growth factor and doubling period. All the factors are normalized on a scale of 1 to 10 to adjust different quantities from all the factors. A dynamic priority queue is used to store the priority scores of each region, which is calculated from all the correlation values of correlated factors with respect to growth factor. Priority for each region is calculated and stored in the priority queue and sorted it in decreasing order, based on which, the supply of food and emergency supplies are dispatched according to the priority of different regions.Article Preview
TopIi. Data Used In This Paper
Initially we used some of the data, and with this data we calculated other datasets.
The initial dataset is-
Table 1.
Showing different initial data
| State |
No of beds |
Density (/km2) |
Population |
Area(km2) |
No of Active Cases |
| Chandigarh |
3756 |
555 |
1055450 |
114 |
15 |
| Maharashtra |
68998 |
350 |
112374333 |
307713 |
240 |
| Tamil Nadu |
72616 |
2598 |
72147030 |
130058 |
280 |
| Madhya Pradesh |
38140 |
123 |
72626809 |
308245 |
60 |
| Gujarat |
41129 |
312 |
60439692 |
196024 |
75 |