Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis
Abstract
:1. Introduction
2. Materials and Methods
- The possible correlations among the trends of confirmed cases in different case studies were investigated, and then a binary classification model was constructed to predict and classify using the group method of data handling (GMDH) algorithm based upon some critical factors; maximum, minimum, and average temperature, the density of a city, relative humidity, and wind speed were considered as the input dataset and the number of confirmed cases was selected as the output dataset for 30 days.
- Regression analysis was used, and a trend of the confirmed cases of COVID-19 analyzed in the five provinces with the highest confirmed cases, including Hubei, Guangdong, Henan, Zhejiang, and Hunan, and the daily fluctuations of confirmed cases were compared with fluctuations of weather parameters.
- The environmental and urban parameters in the analysis included density, sex ratio, average age, elevation, maximum, minimum, and average temperature, relative humidity, and wind.
- For daily analysis of the possible trend between confirmed cases of COVID-19 and environmental factors, the data of Hubei province was used.
- The climate data is based on the stations situated in the capital of the provinces or regions because the population is generally higher in these areas.
- The analysis period was from 28 January 2020 to 26 February 2020 (30 days).
- The analysis of the possible correlations about trends of confirmed cases in different case studies was based on the average values in one month.
2.1. Case Study
2.2. Group Method of Data Handling (GMDH)
3. Results
3.1. Binary Classification Modelling Using GMDH
3.2. Regression Analysis
3.3. The Correlations among the Trends of Confirmed Cases and Weather Parameters
4. Discussions
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
Country | Province | Properties | February, 2020 | COVID-19 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Population | Density, Population/km2 | Gender Ratio | Average Age | Elevation, m | Max T °C | Min T °C | Average Temperature °C | Humidity % | Wind km/h | Confirmed Cases | Deaths | ||
China | Hubei | 59,170,000 | 318 | 1.06 | 38.4 | 37 | 15.4 | 1.4 | 8.3 | 77.9 | 5.4 | 64786 | 2563 |
Guangdong | 113,460,000 | 630 | 1.06 | 38.4 | 21 | 21.0 | 10.3 | 15.1 | 76.8 | 8.2 | 1347 | 7 | |
Henan | 96,050,000 | 575 | 1.06 | 38.4 | 104 | 13.7 | −0.1 | 6.3 | 61.9 | 6.8 | 1271 | 19 | |
Zhejiang | 57,370,000 | 562 | 1.06 | 38.4 | 19 | 15.1 | 4.5 | 9.3 | 70.1 | 7.6 | 1205 | 1 | |
Hunan | 68,990,000 | 329 | 1.06 | 38.4 | 63 | 16.2 | 4.4 | 9.6 | 75.2 | 8.4 | 1016 | 4 | |
Anhui | 63,240,000 | 454 | 1.06 | 38.4 | 37 | 14.4 | 0.1 | 7.1 | 76.9 | 9.6 | 989 | 6 | |
Jiangxi | 46,480,000 | 278 | 1.06 | 38.4 | 37 | 16.6 | 5.9 | 10.5 | 73.4 | 5.3 | 934 | 1 | |
Shandong | 100,470,000 | 653 | 1.06 | 38.4 | 23 | 11.6 | 0.2 | 5.4 | 56.0 | 8.3 | 755 | 6 | |
Jiangsu | 80,510,000 | 785 | 1.06 | 38.4 | 15 | 14.2 | 2.4 | 7.8 | 73.0 | 9.0 | 631 | 0 | |
Chongqing | 31,020,000 | 377 | 1.06 | 38.4 | 244 | 14.3 | 8.0 | 10.7 | 78.3 | 3.4 | 576 | 6 | |
Sichuan | 83,410,000 | 172 | 1.06 | 38.4 | 500 | 14.9 | 5.4 | 9.75 | 65.50 | 6.10 | 529 | 3 | |
Heilongjiang | 37,730,000 | 83 | 1.06 | 38.4 | 126 | −5.8 | −20.6 | −12.7 | 69.9 | 9.9 | 480 | 12 | |
Beijing | 21,540,000 | 1313 | 1.06 | 38.4 | 43.5 | 7.8 | −4.8 | 1.0 | 55.7 | 7.2 | 400 | 4 | |
Shanghai | 24,240,000 | 3823 | 1.06 | 38.4 | 4 | 14.1 | 2.4 | 8.1 | 72.8 | 9.1 | 335 | 3 | |
Hebei | 75,560,000 | 403 | 1.06 | 38.4 | 83 | 10.6 | −1.5 | 4.1 | 54.6 | 8.5 | 311 | 6 | |
Fujian | 39,410,000 | 324 | 1.06 | 38.4 | 14 | 18.4 | 8.3 | 12.7 | 70.6 | 7.4 | 294 | 1 | |
Guangxi | 49,260,000 | 209 | 1.06 | 38.4 | 499 | 20.4 | 11.5 | 15.5 | 74.4 | 9.6 | 252 | 2 | |
Shaanxi | 38,640,000 | 247 | 1.06 | 38.4 | 405 | 14.5 | 2.3 | 7.8 | 62.4 | 3.9 | 245 | 1 | |
Yunnan | 48,300,000 | 123 | 1.06 | 38.4 | 1892 | 18.0 | 3.4 | 10.6 | 64.1 | 9.0 | 174 | 2 | |
Hainan | 9,340,000 | 275 | 1.06 | 38.4 | 222 | 24.5 | 16.8 | 19.9 | 81.1 | 11.3 | 168 | 5 | |
Guizhou | 36,000,000 | 205 | 1.06 | 38.4 | 1275 | 12.5 | 3.9 | 7.6 | 82.0 | 8.5 | 146 | 2 | |
Tianjin | 15,600,000 | 1380 | 1.06 | 38.4 | 1078 | 8.6 | −4.0 | 1.8 | 61.8 | 9.3 | 135 | 3 | |
Shanxi | 37,180,000 | 181 | 1.06 | 38.4 | 800 | 10.0 | −7.1 | 0.6 | 52.6 | 7.0 | 133 | 0 | |
Liaoning | 43,590,000 | 299 | 1.06 | 38.4 | 55 | 2.0 | −12.6 | −5.36 | 64.22 | 8.26 | 121 | 1 | |
Jilin | 27,040,000 | 2704 | 1.06 | 38.4 | 202 | −2.4 | −15.7 | −8.96 | 66.52 | 9.60 | 93 | 1 | |
South Korea | Seoul | 10,010,983 | 16541 | 1 | 43.2 | 38 | 7.2 | −1.1 | 2.6 | 56.1 | 9.1 | 4 | 0 |
Daejeon | 1,493,979 | 2767 | 1 | 43.2 | 94 | 9.2 | −0.8 | 3.6 | 67.3 | 5.1 | 49 | 0 | |
Gyeonggi | 13,653,984 | 1341 | 1 | 43.2 | 87 | 7.4 | −1.8 | 2.5 | 79.3 | 7.1 | 2 | 0 | |
South Gyeongsang | 3,438,676 | 326 | 1 | 43.2 | 2 | 12.0 | 4.5 | 8.0 | 67.0 | 10.3 | 922 | 10 | |
Italy | Lazio | 5,879,082 | 341 | 0.93 | 44.6 | 13 | 15.9 | 6.5 | 11 | 71 | 9 | 3 | 0 |
Veneto | 4,905,854 | 272 | 0.96 | 45.1 | 1 | 11.3 | 3.1 | 7 | 83 | 7 | 42 | 1 | |
Emilia−Romagna | 4,459,477 | 199 | 0.95 | 45.7 | 54 | 14.5 | 3.1 | 8 | 72 | 7 | 23 | 0 | |
Lombardy | 10,060,574 | 422 | 0.94 | 44.8 | 120 | 15.2 | 0.1 | 7 | 69 | 8 | 240 | 9 | |
Japan | Tokyo | 13,929,286 | 6349 | 0.95 | 48.6 | 40 | 13.5 | 4.3 | 8 | 58 | 10 | 14 | 0 |
Kanagawa Prefecture | 9,058,094 | 3770 | 0.95 | 48.6 | 500 | 13.3 | 5.5 | 9.1 | 56.3 | 13.9 | 1 | 0 | |
Aichi Prefecture | 7,552,873 | 1500 | 0.95 | 48.6 | 56 | 12.1 | 3.6 | 7 | 65 | 12 | 2 | 0 | |
Nara Prefecture | 1,348,930 | 365 | 0.95 | 48.6 | 56.4 | 11.6 | 2.9 | 6.6 | 71.5 | 7.7 | 1 | 0 | |
Kansai region | 22,757,897 | 690 | 0.95 | 48.6 | 50 | 11.8 | 3.8 | 7.2 | 68.3 | 7.5 | 1 | 0 | |
Tokushima Prefecture | 728,633 | 180 | 0.95 | 48.6 | 11 | 12.6 | 5.2 | 8.59 | 64.02 | 11.94 | 831 | 5 |
Appendix B
Hubei (Wuhan) | Date | Max T °C | Min T °C | T Avg °C | Hr Avg (%) | Wind km/h | Confirmed Cases |
---|---|---|---|---|---|---|---|
Jan 28 | 28 | 8.3 | −1.6 | 3.5 | 80.3 | 3.2 | 1291 |
Jan 29 | 29 | 12.2 | −2.7 | 4.1 | 78.2 | 5.4 | 840 |
Jan 30 | 30 | 14 | −2.7 | 5 | 75.9 | 3.6 | 1032 |
Ian 31 | 31 | 14 | −2.2 | 4.7 | 71.2 | 4.1 | 1220 |
Feb 1 | 1 | 14.1 | −2.2 | 7.1 | 66.6 | 5 | 1347 |
Feb 2 | 2 | 14.1 | 0.1 | 9.3 | 74.1 | 2.7 | 1921 |
Feb 3 | 3 | 14 | 1.8 | 7.9 | 78.1 | 4.5 | 2103 |
Feb 4 | 4 | 16.2 | −0.6 | 6.4 | 78.2 | 3.2 | 2345 |
Feb 5 | 5 | 16.2 | −0.6 | 6.8 | 76.4 | 5.4 | 3156 |
Feb 6 | 6 | 15.4 | 0.6 | 6.2 | 90.6 | 11.7 | 2977 |
Feb 7 | 7 | 8.2 | 3.3 | 4.3 | 85.6 | 8.1 | 2457 |
Feb 8 | 8 | 9.4 | 3.3 | 6 | 80.6 | 3.6 | 2841 |
Feb 9 | 9 | 14.7 | −1.2 | 6 | 80.1 | 2.7 | 2147 |
Feb 10 | 10 | 14.7 | −1.2 | 7.6 | 84.5 | 3.6 | 2531 |
Feb 11 | 11 | 11.8 | 2.8 | 9.8 | 86.9 | 4.5 | 2097 |
Feb 12 | 12 | 14.1 | 7.4 | 11 | 87.1 | 2.3 | 1638 |
Feb 13 | 13 | 18.7 | 4.1 | 10.8 | 89.3 | 4.1 | 1508 |
Feb 14 | 14 | 18.7 | 4.1 | 14.2 | 89.9 | 7.7 | 1728 |
Feb 15 | 15 | 16.8 | −0.2 | 4.6 | 91.3 | 17.6 | 2420 |
Feb 16 | 16 | 8.4 | −1.3 | 2.2 | 76 | 3.2 | 1843 |
Feb 17 | 17 | 12.7 | −2.7 | 3.7 | 70.9 | 3.2 | 1933 |
Feb 18 | 18 | 13 | −2.7 | 5.2 | 68.5 | 3.2 | 1807 |
Feb 19 | 19 | 15 | −2.5 | 10.1 | 64 | 4.1 | 1693 |
Feb 20 | 20 | 18 | 0.5 | 7.9 | 73 | 3.6 | 349 |
Feb 21 | 21 | 18 | 0.5 | 11.1 | 72.9 | 4.5 | 631 |
Feb 22 | 22 | 17.9 | 2.9 | 10.1 | 80.5 | 4.1 | 366 |
Feb 23 | 23 | 20.1 | 2.9 | 15.2 | 55.5 | 7.7 | 630 |
Feb 24 | 24 | 24.9 | 10.6 | 17.9 | 66.7 | 7.7 | 398 |
Feb 25 | 25 | 24.9 | 10.6 | 18.1 | 77.4 | 6.3 | 499 |
Feb 26 | 26 | 22.8 | 10.6 | 12.5 | 86.7 | 10.4 | 401 |
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Country | Province/ Region | Capital | Population [21,22,23] | Density, Population/km2 [22,24,25,26,27,28,29,30,31,32,33] | Gender Ratio [34,35] | Average Age, years [36,37] | Elevation, m [38] |
---|---|---|---|---|---|---|---|
China | Hubei | Wuhan | 59,170,000 | 318 | 1.06 | 38.4 | 37 |
Guangdong | Canton (Guangzhou) | 113,460,000 | 630 | 1.06 | 38.4 | 21 | |
Henan | Zhengzhou | 96,050,000 | 575 | 1.06 | 38.4 | 104 | |
Zhejiang | Hangzhou | 57,370,000 | 562 | 1.06 | 38.4 | 19 | |
Hunan | Changsha | 68,990,000 | 329 | 1.06 | 38.4 | 63 | |
Anhui | Hefei | 63,240,000 | 454 | 1.06 | 38.4 | 37 | |
Jiangxi | Nanchang | 46,480,000 | 278 | 1.06 | 38.4 | 37 | |
Shandong | Jinan | 100,470,000 | 653 | 1.06 | 38.4 | 23 | |
Jiangsu | Nanchino | 80,510,000 | 785 | 1.06 | 38.4 | 15 | |
Chongqing | Chongqing | 31,020,000 | 377 | 1.06 | 38.4 | 244 | |
Sichuan | Chengdu | 83,410,000 | 172 | 1.06 | 38.4 | 500 | |
Heilongjiang | Harbin | 37,730,000 | 83 | 1.06 | 38.4 | 126 | |
Beijing | Beijing | 21,540,000 | 1313 | 1.06 | 38.4 | 43.5 | |
Shanghai | Shanghai | 24,240,000 | 3823 | 1.06 | 38.4 | 4 | |
Hebei | Shijiazhuang | 75,560,000 | 403 | 1.06 | 38.4 | 83 | |
Fujian | Fuzhou | 39,410,000 | 324 | 1.06 | 38.4 | 14 | |
Guangxi | Nanning | 49,260,000 | 209 | 1.06 | 38.4 | 499 | |
Shaanxi | Xi’an | 38,640,000 | 247 | 1.06 | 38.4 | 405 | |
Yunnan | Kunming | 48,300,000 | 123 | 1.06 | 38.4 | 1892 | |
Hainan | Haikou | 9,340,000 | 275 | 1.06 | 38.4 | 222 | |
Guizhou | Guiyang | 36,000,000 | 205 | 1.06 | 38.4 | 1275 | |
Tianjin | Tianjin | 15,600,000 | 1380 | 1.06 | 38.4 | 1078 | |
Shanxi | Taiyuan | 37,180,000 | 181 | 1.06 | 38.4 | 800 | |
Liaoning | Shenyang | 43,590,000 | 299 | 1.06 | 38.4 | 55 | |
Jilin | Changchun | 27,040,000 | 2704 | 1.06 | 38.4 | 202 | |
South Korea | Seoul | Seoul | 10,010,983 | 16541 | 1 | 43.2 | 38 |
Daejeon | Daejeon | 1,493,979 | 2767 | 1 | 43.2 | 94 | |
Gyeonggi | Suwon | 13,653,984 | 1341 | 1 | 43.2 | 87 | |
South Gyeongsang | Changwon | 3,438,676 | 326 | 1 | 43.2 | 2 | |
Italy | Lazio | Rome | 5,879,082 | 341 | 0.93 | 44.6 | 13 |
Veneto | Venice | 4,905,854 | 272 | 0.96 | 45.1 | 1 | |
Emilia-Romagna | Bologna | 4,459,477 | 199 | 0.95 | 45.7 | 54 | |
Lombardy | Milan | 10,060,574 | 422 | 0.94 | 44.8 | 120 | |
Japan | Tokyo | Tokyo | 13,929,286 | 6349 | 0.95 | 48.6 | 40 |
Kanagawa Prefecture | Yokohama | 9,058,094 | 3770 | 0.95 | 48.6 | 500 | |
Aichi Prefecture | Nagoya | 7,552,873 | 1500 | 0.95 | 48.6 | 56 | |
Nara Prefecture | Nara | 1,348,930 | 365 | 0.95 | 48.6 | 56.4 | |
Kansai region | Kyoto | 22,757,897 | 690 | 0.95 | 48.6 | 50 | |
Tokushima Prefecture | Tokushima | 728,633 | 180 | 0.95 | 48.6 | 11 |
Environmental Factors | Maximum Temperature °C | Minimum Temperature °C | Average Temperature °C | Relative Humidity % | Wind Speed km/h |
---|---|---|---|---|---|
Maximum Temperature | 1 | 0.63 | 0.83 | −0.11 | 0.35 |
Minimum Temperature | 0.63 | 1 | 0.78 | 0.24 | 0.28 |
Average Temperature | 0.83 | 0.78 | 1 | −0.14 | 0.15 |
Relative Humidity | −0.11 | 0.24 | −0.14 | 1 | 0.33 |
Wind Speed | 0.35 | 0.28 | 0.15 | 0.33 | 1 |
Models No. | SP | Maximum Number of Layers | Maximum Number of Neurons in a Layer | Accuracy of Training (%) | Accuracy of Testing (%) |
---|---|---|---|---|---|
1 | 0.6 | 5 | 5 | 73.9 | 71.4 |
2 | 0.6 | 5 | 10 | 91.3 | 71.4 |
3 | 0.6 | 5 | 15 | 95.7 | 85.7 |
4 | 0.6 | 5 | 20 | 80.5 | 42.9 |
5 | 0.6 | 5 | 25 | 91.3 | 71.4 |
6 | 0.6 | 10 | 5 | 95.7 | 71.4 |
7 | 0.6 | 10 | 10 | 73.9 | 71.4 |
8 | 0.6 | 10 | 15 | 82.6 | 71.4 |
9 | 0.6 | 10 | 20 | 95.7 | 57.1 |
10 | 0.6 | 10 | 25 | 87 | 71.4 |
11 | 0.6 | 15 | 5 | 82.6 | 71.4 |
12 | 0.6 | 15 | 10 | 91.3 | 71.4 |
13 | 0.6 | 15 | 15 | 87 | 71.4 |
14 | 0.6 | 15 | 20 | 91.3 | 71.4 |
15 | 0.6 | 15 | 25 | 87 | 85.7 |
16 | 0.6 | 20 | 5 | 91.3 | 85.7 |
17 | 0.6 | 20 | 10 | 95.7 | 42.9 |
18 | 0.6 | 20 | 15 | 91.3 | 42.9 |
19 | 0.6 | 20 | 20 | 82.6 | 85.7 |
20 | 0.6 | 20 | 25 | 95.7 | 71.4 |
Models No. | SP | Maximum Number of Layers | Maximum Number of Neurons in a Layer | Ranking for Accuracy of Training | Ranking for Accuracy of Testing | Total Rank |
---|---|---|---|---|---|---|
1 | 0.6 | 5 | 5 | 15 | 19 | 34 |
2 | 0.6 | 5 | 10 | 19 | 19 | 38 |
3 | 0.6 | 5 | 15 | 20 | 20 | 40 |
4 | 0.6 | 5 | 20 | 16 | 18 | 33 |
5 | 0.6 | 5 | 25 | 19 | 19 | 38 |
6 | 0.6 | 10 | 5 | 20 | 19 | 39 |
7 | 0.6 | 10 | 10 | 15 | 19 | 34 |
8 | 0.6 | 10 | 15 | 17 | 19 | 36 |
9 | 0.6 | 10 | 20 | 20 | 18 | 38 |
10 | 0.6 | 10 | 25 | 18 | 19 | 37 |
11 | 0.6 | 15 | 5 | 17 | 19 | 36 |
12 | 0.6 | 15 | 10 | 19 | 19 | 38 |
13 | 0.6 | 15 | 15 | 18 | 19 | 37 |
14 | 0.6 | 15 | 20 | 19 | 19 | 38 |
15 | 0.6 | 15 | 25 | 18 | 20 | 38 |
16 | 0.6 | 20 | 5 | 19 | 20 | 39 |
17 | 0.6 | 20 | 10 | 20 | 17 | 37 |
18 | 0.6 | 20 | 15 | 19 | 17 | 36 |
19 | 0.6 | 20 | 20 | 17 | 20 | 37 |
20 | 0.6 | 20 | 25 | 20 | 19 | 39 |
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Pirouz, B.; Shaffiee Haghshenas, S.; Shaffiee Haghshenas, S.; Piro, P. Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis. Sustainability 2020, 12, 2427. https://doi.org/10.3390/su12062427
Pirouz B, Shaffiee Haghshenas S, Shaffiee Haghshenas S, Piro P. Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis. Sustainability. 2020; 12(6):2427. https://doi.org/10.3390/su12062427
Chicago/Turabian StylePirouz, Behrouz, Sina Shaffiee Haghshenas, Sami Shaffiee Haghshenas, and Patrizia Piro. 2020. "Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis" Sustainability 12, no. 6: 2427. https://doi.org/10.3390/su12062427
APA StylePirouz, B., Shaffiee Haghshenas, S., Shaffiee Haghshenas, S., & Piro, P. (2020). Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis. Sustainability, 12(6), 2427. https://doi.org/10.3390/su12062427