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Projections for COVID-19 pandemic in India and effect of temperature and humidity
Abstract
Background and aims
As, the COVID-19 has been deemed a pandemic by World Health Organization (WHO), and since it spreads everywhere throughout the world, investigation in relation to this disease is very much essential. Investigation of pattern in the occurrence of COVID-19, to check the influence of different meteorological factors on the incidence of COVID-19 and prediction of incidence of COVID-19 are the objectives of this paper.
Methods
For trend analysis, Sen’s Slope and Man-Kendall test have been used, Generalized Additive Model (GAM) of regression has been used to check the influence of different meteorological factors on the incidence and to predict the frequency of COVID-19, and Verhulst (Logistic) Population Model has been used.
Results
Statistically significant linear trend found for the daily-confirmed cases of COVID-19. The regression analysis indicates that there is some influence of the interaction of average temperature (AT) and average relative humidity (ARH) on the incidence of COVID-19. However, this result is not consistent throughout the study area. The projections have been made up to 21st May, 2020.
Conclusions
Trend and regression analysis give an idea of the incidence of COVID-19 in India while projection made by Verhulst (Logistic) Population Model for the confirmed cases of the study area are encouraging as the sample prediction is as same as the actual number of confirmed COVID-19 cases.
Highlights
• Daily confirmed COVID-19 cases follow linear trend in India.
• Average temperature and average relative humidity have some influence in the incidence of COVID-19.
• Verhulst (Logistic) Population Model gives promising prediction for daily confirmed cases of COVID-19.
1 Introduction
The COVID-19 pandemic (Coronavirus disease 2019), caused by SARS-CoV-2 (severe acute respiratory coronavirus syndrome 2), has created chaos in human society. According to World Health Organization reports, the disease is spread by respiratory droplets and communication pathways. Fever, cough, shortness of breath to pneumonia, kidney failure, and even death are some of the symptoms of this disease, which can take 2–14 days to appear in human body [1]. The pandemic began in the city of Wuhan (Hubei District) in China and affected an overwhelming majority of the countries. Since then, it has been a persistent march of new cases and deaths. This infectious COVID-19 disease has reported thousands of deaths worldwide due to the rapid pandemic risk and the lack of antiviral drugs and vaccinations [2].
Now, the pandemic COVID-19 has become a major threat to India. Several nations, like India, have gone into a lockdown situation to keep this deadly virus from spreading. Throughout India, since January 30, 2020, COVID-19 cases have been gradually growing. As reported on May 10, 2020, the Ministry of Health and Family Welfare has confirmed 62,939 cases with 2,109 deaths [3] and accordingly, all districts of India are classified as red, orange and green zones on the basis of the incidence of COVID-19 cases. India is the world’s second most populated nation after China. Uncontrolled pandemic in India has the potential to affect about 1/6th of the world’s population. Study of this epidemic, allows the Government to take the requisite measures to reduce the effects of this global pandemic. A range of factors may influence the transmission of coronaviruses including climatic conditions (such as temperature and humidity), population density, and standard of the medical facility and so forth [4,5]. But realizing the relationship between environment and COVID-19 propagation is the secret to predicting this pandemic’s severity and end-time [6]. Using data from reported cases of India, we examined the associations between meteorological causes and the frequent occurrences of COVID-19 and also the trend of the growth of the disease. The aim is to give statistical evidence on the potential evolution of COVID-19 under changing climate conditions. The current situation of India can be witnessed from Fig. 1 .
Fig. 1 a) Confirmed cases of COVID-19 (Map not for scale) and b) Classified zones on the basis of COVID-19 cases in India as of 10th of May, 2020 [©Wikipedia].
2 Materials and methods
2.1 Study area and data
The Government of India is offering a number of websites and applications to track COVID-19 events. Daily counts of those states and union territories in India having more than 1000 laboratory-confirmed cases were obtained from https://www.covid19india.org/, the official reports of the Ministry of Health and Family Welfare of India from 1st of April, 2020 to 10th of May, 2020. Accordingly, 10 states and 1 union territory have been selected namely Maharashtra, Gujarat, Tamil Nadu, Delhi, Rajasthan, Madhya Pradesh, Uttar Pradesh, Andhra Pradesh, West Bengal, Punjab and Telengana [3]. The period of analysis was selected taking into account the lockdown declared by Govt. of India and also the total daily counts of other states are less than 1000.
The meteorological data, daily minimum temperature (MinT) and maximum temperature (MaxT), daily average temperature (AT) and daily average relative humidity (ARH) of each state and union territory have been retrieved from https://en.tutiempo.net/that provides a web base platform for the researcher to examine the climate data. The website provides a generous amount of the world weather data.
2.2 Statistical analysis
The Sen’s Slope [7] and Mann-Kendall [8,9] method were used to verify the existence or absence of linear trend in daily laboratory-confirmed cases of COVID-19. Significant + ve value of Sen’s Slope indicates a significant linear increase in daily confirmed cases where, as on the other hand, Sen’s Slope’s significant -ve value implies a significant linear decrease in daily confirmed cases.
Generalized additive models (GAM) have been applied during the study periods to quantify the states-specific associations between meteorological factors and daily cases of COVID-19 events, accounting for short-term temporal patterns [10]. GAM is a generalized regression model in which the linear predictor is linearly dependent on undefined smooth functions of certain predictor variables, and the subject of concern is on inferences regarding such smooth functions. The model relates a univariate response variable Y, to some predictor variables x i and an exponential family of distribution is specified for Y such as normal, binomial, poisson distributions and so on.
As the variances of the daily counts were larger than their means, and hence the distribution of COVID-19 cases was assumed to be a negative binomial. According to WHO, coronavirus carriers are infectious 2 days before the onset of the symptoms. And hence, three-day average temperature and relative humidity have been considered for the model [6]. The model is given by:Yt=β0+β1X1+β2X2+β3X3+β4X4+β5X5+etwhere Yt is the daily cases of confirmed COVID-19 count, β0 is the intercept term, β1 denotes the effect of moving average of AT, β2 denotes the effect of moving average of ARH, β3 denotes the effect of MaxT, β4 denotes the effect of MinT, β5 denotes the effect of the interaction of AT and ARH and et is the disturbance term.
For prediction of the COVID-19 cases we have used Verhulst (Logistic) Population Model. To incorporate exponential growth time series we use this prediction model for forecasting [11].
3 Results
The total counts of confirmed cases from 1st of April, 2020 to 10th of May, 2020 have been presented in Table 1 . Table 2 shows that all the states other than Telangana exhibit a significant upward liner trend for the confirmed cases. In case of Telangana, the trend is negative. These results are statistically significant as the p-value of Mann-Kendall is less than 0.05. These results of increase and decrease are also witnessed in Fig. 2 .
Table 1 Total count of confirmed cases.
State Total Count of Confirmed Cases
Andhra Pradesh 1936
Delhi 6803
Gujrat 8121
Madhya Pradesh 3548
Maharashtra 21869
Punjab 1781
Rajasthan 3721
Tamil Nadu 7080
Telangana 1099
Uttar Pradesh 3363
West Bengal 1902
Table 2 Sen’s Slope estimates for trend detection.
State Sen’s Slope Mann-Kendall
Andhra Pradesh 1.000000 <0.001
Delhi 6.600000 <0.001
Gujrat 11.28571 <0.001
Madhya Pradesh 2.175192 <0.001
Maharashtra 29.07500 <0.001
Punjab 1.444444 <0.001
Rajasthan 2.266667 <0.001
Tamil Nadu 6.098462 <0.001
Telangana −0.79285 <0.001
Uttar Pradesh 2.826050 <0.001
West Bengal 2.361413 <0.001
Fig. 2 Day wise Confirmed cases of COVID-19 upto 10th of May, 2020.
The estimates of regression coefficients of the GAM for the states are listed in Table 3 . Statistically significant effect of AT were found for Madhya Pradesh (1.42575), Maharashtra (2.75604), Punjab (1.48788) and Tamil Nadu (−15.89823), effect of ARH were found for Madhya Pradesh (1.21126), Punjab (0.58497) and Tamil Nadu (−6.79347), effect of MaxT were found for Maharashtra (−0.31561) and Tamil Nadu (0.43246), effect of MinT were found for Gujrat (0.20924) and Uttar Pradesh (0.189119) and effect of interaction between AT and ARH were found for Madhya Pradesh (0.03761), Punjab (0.02753) and Tamil Nadu (0.22832). However, these effects of meteorological variables vary from state to state [Table 4 ].
Table 3 Results of GAM regression.
States Parameter Estimates p-value
Andhra Pradesh Intercept, β0 −28.10783 0.2132
AT, β1 0.88819 0.1817
ARH, β2 0.49752 0.2597
MaxT, β3 −0.05697 0.5006
MinT, β4 0.13458 0.0712
ATxARH, β5 0.01415 0.2932
Delhi Intercept, β0 9.999257 0.216
AT, β1 −0.036883 0.902
ARH, β2 −0.207407 0.237
MaxT, β3 −0.103124 0.126
MinT, β4 −0.084796 0.313
ATxARH, β5 0.008437 0.148
Gujrat Intercept, β0 −3.37997 0.8149
AT, β1 −0.01978 0.9621
ARH, β2 −0.38041 0.3559
MaxT, β3 0.0485 0.6484
MinT, β4 0.20924 0.0209a
ATxARH, β5 0.01318 0.2785
Madhya Pradesh Intercept, β0 −43.84455 0.0161a
AT, β1 1.42575 0.0171a
ARH, β2 1.21126 0.0341a
MaxT, β3 0.05988 0.6292
MinT, β4 0.01476 0.8649
ATxARH, β5 0.03761 0.0336a
Maharashtra Intercept, β0 −73.01116 0.08333
AT, β1 2.75604 0.04875a
ARH, β2 0.85921 0.14815
MaxT, β3 −0.31561 0.00205a
MinT, β4 0.10324 0.32944
ATxARH, β5 0.02675 0.16511
Punjab Intercept, β0 −32.17071 0.01551a
AT, β1 1.48788 0.00664a
ARH, β2 0.58497 0.00540a
MaxT, β3 0.01837 0.86233
MinT, β4 0.16984 0.18284
ATxARH, β5 0.02753 0.00058a
Rajasthan Intercept, β0 −2.751481 0.587
AT, β1 0.255311 0.23
ARH, β2 0.149194 0.394
MaxT, β3 −0.071146 0.413
MinT, β4 0.053367 0.366
ATxARH, β5 −0.004053 0.462
Tamil Nadu Intercept, β0 464.62175 0.0167a
AT, β1 −15.89823 0.0127a
ARH, β2 −6.79347 0.0115a
MaxT, β3 0.43246 0.0158a
MinT, β4 −0.09937 0.4576
ATxARH, β5 0.22832 0.0102a
Telangana Intercept, β0 1.562944 0.965
AT, β1 −0.100075 0.925
ARH, β2 0.217862 0.756
MaxT, β3 0.142809 0.292
MinT, β4 0.117128 0.322
ATxARH, β5 −0.009049 0.673
Uttar Pradesh Intercept, β0 4.354975 0.586
AT, β1 −0.066434 0.851
ARH, β2 0.020638 0.899
MaxT, β3 −0.064778 0.606
MinT, β4 0.189119 0.040a
ATxARH, β5 −0.00067 0.908
West Bengal Intercept, β0 18.65133 0.4096
AT, β1 −0.666464 0.3498
ARH, β2 −0.06807 0.7822
MaxT, β3 −0.008686 0.9179
MinT, β4 0.159345 0.0991
ATxARH, β5 0.002448 0.7522
a Significant with 95% confidence.
Table 4 Effect of at, ARH, MaxT and MinT on COVID-19 incidence.
State Effect on COVID-19 incidence
ATa ARHb MaxTc MinTd
Andhra Pradesh + ve + ve -ve + ve
Delhi -ve -ve -ve -ve
Gujrat -ve -ve + ve + ve
Madhya Pradesh + ve + ve + ve + ve
Maharashtra + ve + ve -ve + ve
Punjab + ve + ve + ve + ve
Rajasthan + ve + ve -ve + ve
Tamil Nadu -ve -ve + ve -ve
Telangana -ve + ve + ve + ve
Uttar Pradesh -ve + ve -ve + ve
West Bengal -ve -ve -ve + ve
a Average Temperature,
b Average Relative Humidity,
c Maximum Temperature,
d Minimum Temperature.
Results of Verhulst (Logistic) Population Model are listed in Table 5 . Unlike the trend analysis and GEM analysis, here we have accounted the confirmed cases from 2nd May to 13th of May, 2020 as the incidence of confirmed COVID-19 cases rises significantly after April, 2020. Prediction of for three group up to May 5, 2020, up to May 9, 2020 and May 13, 2020 are same as that of actual figure. Two group of prediction have been listed up to May 17, 2020 and up to May 21, 2020.
Table 5 Predicting results of Verhulst Population Model.
States Total Confirmed Cases (from 02/05/2020) Predicted Confirmed Cases (from 02/05/2020)
Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 17/5/20 Upto 21/5/20
AndhraPradesh 254 467 674 254 467 674 804.62 866.09
Delhi 1366 2804 4260 1366 2804 4260 5123.81 5484.91
Gujrat 1524 3076 4547 1524 3076 4547 5361.51 5685.01
Madhya Pradesh 334 742 1458 334 742 1458 2382.07 3196.53
Maharashtra 4019 8722 14416 4019 8722 14416 18490.40 20440.34
Punjab 866 1177 1339 866 1177 1339 1404.11 1427.49
Rajasthan 492 1042 1662 492 1042 1662 2073.76 2260.66
Tamil Nadu 1532 4009 6701 1532 4009 6701 8042.90 8464.04
Telangana 52 119 323 52 119 323 2022.67 2280.67
Uttar Pradesh 552 1045 1430 552 1045 1430 1608.66 1671.62
West Bengal 549 991 1495 549 991 1495 1899.52 2142.24
4 Discussion
The linear upward (increasing) trend that has been found in the study area except Telangana is a worrisome sign for India. Additionally, from the beginning of May the incidence of COVID-19 rises in more recurrent way. The study argued that both daily temperature and relative humidity had an effect on the incidence of COVID-19 in most of the study region. Nevertheless, the relationship between COVID-19 and AT and ARH has not been consistent across the nations. Incidence of meteorological variables varies due to vast geographical heterogeneity across India. The cumulative incidence of COVID-19 cases was higher in North and South India, as more business, agricultural, industrial and other associated activities are happening in this region of India than in the rest of the country. In addition, owing to the lockout declared by the Government on March 2020, the staffs from other areas of India are compelled to stay there. WHO finds coronavirus carriers to be contagious 2 days before the start of symptoms [12]. We, therefore, used three-day moving average of daily AT and ARH for the analysis of GAM. As India announced its lockdown at a stage when total, confirmed cases were less than 600, so in this research data are used after 7 days of lockdown. Another significant finding of this study is the significant interaction between ARH and AT, and COVID-19 incidence. Such results are compatible with the findings of China [10]. According to them, improved AT (ARH) culminated in a decreased influence of ARH (AT) on the incidence of COVID-19 in Hubei Province. The precise method of contact, however, is uncertain. They suggest one probable reason might be that a combination of low AT and humidity make the nasal mucosa prone to small ruptures, creating opportunities for virus invasion [10]. In addition, it is recommended that associations between different meteorological variables be included in the estimation process of the environment effect on the likelihood of COVID-19 transmission. Research findings of meteorological variables will be incorporated into the anticipation and regulation of COVID-19. With the help of Verhulst (Logistic) Population Model, projection of confirmed cases have been given up to 21st May. The predicted findings are quite promising as the predicting behaviour of the model as same as the already confirmed cases from 2nd May 2020 to 13th of May, 2020. In addition, due to this predicting nature, it is found quite useful than the time series forecasting methods like exponential smoothing, ARIMA for forecasting purposes in terms of COVID-19 pandemic. Because, ARIMA need a stationary time series and exponential smoothing cannot corporate with a dynamic change in time series.
5 Conclusion
In accordance to this analysis, the incidence of COVID-19 has a significant linear trend. Moreover, meteorological factors influence COVID-19 particularly the interactive effect between daily temperature and relative humidity on COVID-19 incidence. However, due to the inconsistency of results between various states, further studies are needed which include other meteorological variables as well. Keeping in mind the forecasting behaviour of Verhulst Population Model, it can be said that this research will definitely help the researchers as well as the policy makers in this field.
Funding
None.
Author’s contribution
J. Hazarika supervised the work. K. Goswami and S. Bharali conceived the idea presented, discussed the methodology, S. Bharali organized the theoretical discussion and K. Goswami collected, analyzed the data and describe the results. All authors discussed the results and contributed to the final version of the manuscript.
Declaration of competing interest
The authors declare that there is no known competing interest, which could have influence in this paper.
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Document structure show
| article-title | Projections for COVID-19 pandemic in India and effect of temperature and humidity |
| abstract | Background and aims As, the COVID-19 has been deemed a pandemic by World Health Organization (WHO), and since it spreads everywhere throughout the world, investigation in relation to this disease is very much essential. Investigation of pattern in the occurrence of COVID-19, to check the influence of different meteorological factors on the incidence of COVID-19 and prediction of incidence of COVID-19 are the objectives of this paper. Methods For trend analysis, Sen’s Slope and Man-Kendall test have been used, Generalized Additive Model (GAM) of regression has been used to check the influence of different meteorological factors on the incidence and to predict the frequency of COVID-19, and Verhulst (Logistic) Population Model has been used. Results Statistically significant linear trend found for the daily-confirmed cases of COVID-19. The regression analysis indicates that there is some influence of the interaction of average temperature (AT) and average relative humidity (ARH) on the incidence of COVID-19. However, this result is not consistent throughout the study area. The projections have been made up to 21st May, 2020. Conclusions Trend and regression analysis give an idea of the incidence of COVID-19 in India while projection made by Verhulst (Logistic) Population Model for the confirmed cases of the study area are encouraging as the sample prediction is as same as the actual number of confirmed COVID-19 cases. |
| sec | Background and aims As, the COVID-19 has been deemed a pandemic by World Health Organization (WHO), and since it spreads everywhere throughout the world, investigation in relation to this disease is very much essential. Investigation of pattern in the occurrence of COVID-19, to check the influence of different meteorological factors on the incidence of COVID-19 and prediction of incidence of COVID-19 are the objectives of this paper. |
| title | Background and aims |
| p | As, the COVID-19 has been deemed a pandemic by World Health Organization (WHO), and since it spreads everywhere throughout the world, investigation in relation to this disease is very much essential. Investigation of pattern in the occurrence of COVID-19, to check the influence of different meteorological factors on the incidence of COVID-19 and prediction of incidence of COVID-19 are the objectives of this paper. |
| sec | Methods For trend analysis, Sen’s Slope and Man-Kendall test have been used, Generalized Additive Model (GAM) of regression has been used to check the influence of different meteorological factors on the incidence and to predict the frequency of COVID-19, and Verhulst (Logistic) Population Model has been used. |
| title | Methods |
| p | For trend analysis, Sen’s Slope and Man-Kendall test have been used, Generalized Additive Model (GAM) of regression has been used to check the influence of different meteorological factors on the incidence and to predict the frequency of COVID-19, and Verhulst (Logistic) Population Model has been used. |
| sec | Results Statistically significant linear trend found for the daily-confirmed cases of COVID-19. The regression analysis indicates that there is some influence of the interaction of average temperature (AT) and average relative humidity (ARH) on the incidence of COVID-19. However, this result is not consistent throughout the study area. The projections have been made up to 21st May, 2020. |
| title | Results |
| p | Statistically significant linear trend found for the daily-confirmed cases of COVID-19. The regression analysis indicates that there is some influence of the interaction of average temperature (AT) and average relative humidity (ARH) on the incidence of COVID-19. However, this result is not consistent throughout the study area. The projections have been made up to 21st May, 2020. |
| sec | Conclusions Trend and regression analysis give an idea of the incidence of COVID-19 in India while projection made by Verhulst (Logistic) Population Model for the confirmed cases of the study area are encouraging as the sample prediction is as same as the actual number of confirmed COVID-19 cases. |
| title | Conclusions |
| p | Trend and regression analysis give an idea of the incidence of COVID-19 in India while projection made by Verhulst (Logistic) Population Model for the confirmed cases of the study area are encouraging as the sample prediction is as same as the actual number of confirmed COVID-19 cases. |
| abstract | Highlights • Daily confirmed COVID-19 cases follow linear trend in India. • Average temperature and average relative humidity have some influence in the incidence of COVID-19. • Verhulst (Logistic) Population Model gives promising prediction for daily confirmed cases of COVID-19. |
| title | Highlights |
| p | • Daily confirmed COVID-19 cases follow linear trend in India. • Average temperature and average relative humidity have some influence in the incidence of COVID-19. • Verhulst (Logistic) Population Model gives promising prediction for daily confirmed cases of COVID-19. |
| label | • |
| p | Daily confirmed COVID-19 cases follow linear trend in India. |
| label | • |
| p | Average temperature and average relative humidity have some influence in the incidence of COVID-19. |
| label | • |
| p | Verhulst (Logistic) Population Model gives promising prediction for daily confirmed cases of COVID-19. |
| body | 1 Introduction The COVID-19 pandemic (Coronavirus disease 2019), caused by SARS-CoV-2 (severe acute respiratory coronavirus syndrome 2), has created chaos in human society. According to World Health Organization reports, the disease is spread by respiratory droplets and communication pathways. Fever, cough, shortness of breath to pneumonia, kidney failure, and even death are some of the symptoms of this disease, which can take 2–14 days to appear in human body [1]. The pandemic began in the city of Wuhan (Hubei District) in China and affected an overwhelming majority of the countries. Since then, it has been a persistent march of new cases and deaths. This infectious COVID-19 disease has reported thousands of deaths worldwide due to the rapid pandemic risk and the lack of antiviral drugs and vaccinations [2]. Now, the pandemic COVID-19 has become a major threat to India. Several nations, like India, have gone into a lockdown situation to keep this deadly virus from spreading. Throughout India, since January 30, 2020, COVID-19 cases have been gradually growing. As reported on May 10, 2020, the Ministry of Health and Family Welfare has confirmed 62,939 cases with 2,109 deaths [3] and accordingly, all districts of India are classified as red, orange and green zones on the basis of the incidence of COVID-19 cases. India is the world’s second most populated nation after China. Uncontrolled pandemic in India has the potential to affect about 1/6th of the world’s population. Study of this epidemic, allows the Government to take the requisite measures to reduce the effects of this global pandemic. A range of factors may influence the transmission of coronaviruses including climatic conditions (such as temperature and humidity), population density, and standard of the medical facility and so forth [4,5]. But realizing the relationship between environment and COVID-19 propagation is the secret to predicting this pandemic’s severity and end-time [6]. Using data from reported cases of India, we examined the associations between meteorological causes and the frequent occurrences of COVID-19 and also the trend of the growth of the disease. The aim is to give statistical evidence on the potential evolution of COVID-19 under changing climate conditions. The current situation of India can be witnessed from Fig. 1 . Fig. 1 a) Confirmed cases of COVID-19 (Map not for scale) and b) Classified zones on the basis of COVID-19 cases in India as of 10th of May, 2020 [©Wikipedia]. 2 Materials and methods 2.1 Study area and data The Government of India is offering a number of websites and applications to track COVID-19 events. Daily counts of those states and union territories in India having more than 1000 laboratory-confirmed cases were obtained from https://www.covid19india.org/, the official reports of the Ministry of Health and Family Welfare of India from 1st of April, 2020 to 10th of May, 2020. Accordingly, 10 states and 1 union territory have been selected namely Maharashtra, Gujarat, Tamil Nadu, Delhi, Rajasthan, Madhya Pradesh, Uttar Pradesh, Andhra Pradesh, West Bengal, Punjab and Telengana [3]. The period of analysis was selected taking into account the lockdown declared by Govt. of India and also the total daily counts of other states are less than 1000. The meteorological data, daily minimum temperature (MinT) and maximum temperature (MaxT), daily average temperature (AT) and daily average relative humidity (ARH) of each state and union territory have been retrieved from https://en.tutiempo.net/that provides a web base platform for the researcher to examine the climate data. The website provides a generous amount of the world weather data. 2.2 Statistical analysis The Sen’s Slope [7] and Mann-Kendall [8,9] method were used to verify the existence or absence of linear trend in daily laboratory-confirmed cases of COVID-19. Significant + ve value of Sen’s Slope indicates a significant linear increase in daily confirmed cases where, as on the other hand, Sen’s Slope’s significant -ve value implies a significant linear decrease in daily confirmed cases. Generalized additive models (GAM) have been applied during the study periods to quantify the states-specific associations between meteorological factors and daily cases of COVID-19 events, accounting for short-term temporal patterns [10]. GAM is a generalized regression model in which the linear predictor is linearly dependent on undefined smooth functions of certain predictor variables, and the subject of concern is on inferences regarding such smooth functions. The model relates a univariate response variable Y, to some predictor variables x i and an exponential family of distribution is specified for Y such as normal, binomial, poisson distributions and so on. As the variances of the daily counts were larger than their means, and hence the distribution of COVID-19 cases was assumed to be a negative binomial. According to WHO, coronavirus carriers are infectious 2 days before the onset of the symptoms. And hence, three-day average temperature and relative humidity have been considered for the model [6]. The model is given by:Yt=β0+β1X1+β2X2+β3X3+β4X4+β5X5+etwhere Yt is the daily cases of confirmed COVID-19 count, β0 is the intercept term, β1 denotes the effect of moving average of AT, β2 denotes the effect of moving average of ARH, β3 denotes the effect of MaxT, β4 denotes the effect of MinT, β5 denotes the effect of the interaction of AT and ARH and et is the disturbance term. For prediction of the COVID-19 cases we have used Verhulst (Logistic) Population Model. To incorporate exponential growth time series we use this prediction model for forecasting [11]. 3 Results The total counts of confirmed cases from 1st of April, 2020 to 10th of May, 2020 have been presented in Table 1 . Table 2 shows that all the states other than Telangana exhibit a significant upward liner trend for the confirmed cases. In case of Telangana, the trend is negative. These results are statistically significant as the p-value of Mann-Kendall is less than 0.05. These results of increase and decrease are also witnessed in Fig. 2 . Table 1 Total count of confirmed cases. State Total Count of Confirmed Cases Andhra Pradesh 1936 Delhi 6803 Gujrat 8121 Madhya Pradesh 3548 Maharashtra 21869 Punjab 1781 Rajasthan 3721 Tamil Nadu 7080 Telangana 1099 Uttar Pradesh 3363 West Bengal 1902 Table 2 Sen’s Slope estimates for trend detection. State Sen’s Slope Mann-Kendall Andhra Pradesh 1.000000 <0.001 Delhi 6.600000 <0.001 Gujrat 11.28571 <0.001 Madhya Pradesh 2.175192 <0.001 Maharashtra 29.07500 <0.001 Punjab 1.444444 <0.001 Rajasthan 2.266667 <0.001 Tamil Nadu 6.098462 <0.001 Telangana −0.79285 <0.001 Uttar Pradesh 2.826050 <0.001 West Bengal 2.361413 <0.001 Fig. 2 Day wise Confirmed cases of COVID-19 upto 10th of May, 2020. The estimates of regression coefficients of the GAM for the states are listed in Table 3 . Statistically significant effect of AT were found for Madhya Pradesh (1.42575), Maharashtra (2.75604), Punjab (1.48788) and Tamil Nadu (−15.89823), effect of ARH were found for Madhya Pradesh (1.21126), Punjab (0.58497) and Tamil Nadu (−6.79347), effect of MaxT were found for Maharashtra (−0.31561) and Tamil Nadu (0.43246), effect of MinT were found for Gujrat (0.20924) and Uttar Pradesh (0.189119) and effect of interaction between AT and ARH were found for Madhya Pradesh (0.03761), Punjab (0.02753) and Tamil Nadu (0.22832). However, these effects of meteorological variables vary from state to state [Table 4 ]. Table 3 Results of GAM regression. States Parameter Estimates p-value Andhra Pradesh Intercept, β0 −28.10783 0.2132 AT, β1 0.88819 0.1817 ARH, β2 0.49752 0.2597 MaxT, β3 −0.05697 0.5006 MinT, β4 0.13458 0.0712 ATxARH, β5 0.01415 0.2932 Delhi Intercept, β0 9.999257 0.216 AT, β1 −0.036883 0.902 ARH, β2 −0.207407 0.237 MaxT, β3 −0.103124 0.126 MinT, β4 −0.084796 0.313 ATxARH, β5 0.008437 0.148 Gujrat Intercept, β0 −3.37997 0.8149 AT, β1 −0.01978 0.9621 ARH, β2 −0.38041 0.3559 MaxT, β3 0.0485 0.6484 MinT, β4 0.20924 0.0209a ATxARH, β5 0.01318 0.2785 Madhya Pradesh Intercept, β0 −43.84455 0.0161a AT, β1 1.42575 0.0171a ARH, β2 1.21126 0.0341a MaxT, β3 0.05988 0.6292 MinT, β4 0.01476 0.8649 ATxARH, β5 0.03761 0.0336a Maharashtra Intercept, β0 −73.01116 0.08333 AT, β1 2.75604 0.04875a ARH, β2 0.85921 0.14815 MaxT, β3 −0.31561 0.00205a MinT, β4 0.10324 0.32944 ATxARH, β5 0.02675 0.16511 Punjab Intercept, β0 −32.17071 0.01551a AT, β1 1.48788 0.00664a ARH, β2 0.58497 0.00540a MaxT, β3 0.01837 0.86233 MinT, β4 0.16984 0.18284 ATxARH, β5 0.02753 0.00058a Rajasthan Intercept, β0 −2.751481 0.587 AT, β1 0.255311 0.23 ARH, β2 0.149194 0.394 MaxT, β3 −0.071146 0.413 MinT, β4 0.053367 0.366 ATxARH, β5 −0.004053 0.462 Tamil Nadu Intercept, β0 464.62175 0.0167a AT, β1 −15.89823 0.0127a ARH, β2 −6.79347 0.0115a MaxT, β3 0.43246 0.0158a MinT, β4 −0.09937 0.4576 ATxARH, β5 0.22832 0.0102a Telangana Intercept, β0 1.562944 0.965 AT, β1 −0.100075 0.925 ARH, β2 0.217862 0.756 MaxT, β3 0.142809 0.292 MinT, β4 0.117128 0.322 ATxARH, β5 −0.009049 0.673 Uttar Pradesh Intercept, β0 4.354975 0.586 AT, β1 −0.066434 0.851 ARH, β2 0.020638 0.899 MaxT, β3 −0.064778 0.606 MinT, β4 0.189119 0.040a ATxARH, β5 −0.00067 0.908 West Bengal Intercept, β0 18.65133 0.4096 AT, β1 −0.666464 0.3498 ARH, β2 −0.06807 0.7822 MaxT, β3 −0.008686 0.9179 MinT, β4 0.159345 0.0991 ATxARH, β5 0.002448 0.7522 a Significant with 95% confidence. Table 4 Effect of at, ARH, MaxT and MinT on COVID-19 incidence. State Effect on COVID-19 incidence ATa ARHb MaxTc MinTd Andhra Pradesh + ve + ve -ve + ve Delhi -ve -ve -ve -ve Gujrat -ve -ve + ve + ve Madhya Pradesh + ve + ve + ve + ve Maharashtra + ve + ve -ve + ve Punjab + ve + ve + ve + ve Rajasthan + ve + ve -ve + ve Tamil Nadu -ve -ve + ve -ve Telangana -ve + ve + ve + ve Uttar Pradesh -ve + ve -ve + ve West Bengal -ve -ve -ve + ve a Average Temperature, b Average Relative Humidity, c Maximum Temperature, d Minimum Temperature. Results of Verhulst (Logistic) Population Model are listed in Table 5 . Unlike the trend analysis and GEM analysis, here we have accounted the confirmed cases from 2nd May to 13th of May, 2020 as the incidence of confirmed COVID-19 cases rises significantly after April, 2020. Prediction of for three group up to May 5, 2020, up to May 9, 2020 and May 13, 2020 are same as that of actual figure. Two group of prediction have been listed up to May 17, 2020 and up to May 21, 2020. Table 5 Predicting results of Verhulst Population Model. States Total Confirmed Cases (from 02/05/2020) Predicted Confirmed Cases (from 02/05/2020) Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 17/5/20 Upto 21/5/20 AndhraPradesh 254 467 674 254 467 674 804.62 866.09 Delhi 1366 2804 4260 1366 2804 4260 5123.81 5484.91 Gujrat 1524 3076 4547 1524 3076 4547 5361.51 5685.01 Madhya Pradesh 334 742 1458 334 742 1458 2382.07 3196.53 Maharashtra 4019 8722 14416 4019 8722 14416 18490.40 20440.34 Punjab 866 1177 1339 866 1177 1339 1404.11 1427.49 Rajasthan 492 1042 1662 492 1042 1662 2073.76 2260.66 Tamil Nadu 1532 4009 6701 1532 4009 6701 8042.90 8464.04 Telangana 52 119 323 52 119 323 2022.67 2280.67 Uttar Pradesh 552 1045 1430 552 1045 1430 1608.66 1671.62 West Bengal 549 991 1495 549 991 1495 1899.52 2142.24 4 Discussion The linear upward (increasing) trend that has been found in the study area except Telangana is a worrisome sign for India. Additionally, from the beginning of May the incidence of COVID-19 rises in more recurrent way. The study argued that both daily temperature and relative humidity had an effect on the incidence of COVID-19 in most of the study region. Nevertheless, the relationship between COVID-19 and AT and ARH has not been consistent across the nations. Incidence of meteorological variables varies due to vast geographical heterogeneity across India. The cumulative incidence of COVID-19 cases was higher in North and South India, as more business, agricultural, industrial and other associated activities are happening in this region of India than in the rest of the country. In addition, owing to the lockout declared by the Government on March 2020, the staffs from other areas of India are compelled to stay there. WHO finds coronavirus carriers to be contagious 2 days before the start of symptoms [12]. We, therefore, used three-day moving average of daily AT and ARH for the analysis of GAM. As India announced its lockdown at a stage when total, confirmed cases were less than 600, so in this research data are used after 7 days of lockdown. Another significant finding of this study is the significant interaction between ARH and AT, and COVID-19 incidence. Such results are compatible with the findings of China [10]. According to them, improved AT (ARH) culminated in a decreased influence of ARH (AT) on the incidence of COVID-19 in Hubei Province. The precise method of contact, however, is uncertain. They suggest one probable reason might be that a combination of low AT and humidity make the nasal mucosa prone to small ruptures, creating opportunities for virus invasion [10]. In addition, it is recommended that associations between different meteorological variables be included in the estimation process of the environment effect on the likelihood of COVID-19 transmission. Research findings of meteorological variables will be incorporated into the anticipation and regulation of COVID-19. With the help of Verhulst (Logistic) Population Model, projection of confirmed cases have been given up to 21st May. The predicted findings are quite promising as the predicting behaviour of the model as same as the already confirmed cases from 2nd May 2020 to 13th of May, 2020. In addition, due to this predicting nature, it is found quite useful than the time series forecasting methods like exponential smoothing, ARIMA for forecasting purposes in terms of COVID-19 pandemic. Because, ARIMA need a stationary time series and exponential smoothing cannot corporate with a dynamic change in time series. 5 Conclusion In accordance to this analysis, the incidence of COVID-19 has a significant linear trend. Moreover, meteorological factors influence COVID-19 particularly the interactive effect between daily temperature and relative humidity on COVID-19 incidence. However, due to the inconsistency of results between various states, further studies are needed which include other meteorological variables as well. Keeping in mind the forecasting behaviour of Verhulst Population Model, it can be said that this research will definitely help the researchers as well as the policy makers in this field. Funding None. Author’s contribution J. Hazarika supervised the work. K. Goswami and S. Bharali conceived the idea presented, discussed the methodology, S. Bharali organized the theoretical discussion and K. Goswami collected, analyzed the data and describe the results. All authors discussed the results and contributed to the final version of the manuscript. Declaration of competing interest The authors declare that there is no known competing interest, which could have influence in this paper. |
| sec | 1 Introduction The COVID-19 pandemic (Coronavirus disease 2019), caused by SARS-CoV-2 (severe acute respiratory coronavirus syndrome 2), has created chaos in human society. According to World Health Organization reports, the disease is spread by respiratory droplets and communication pathways. Fever, cough, shortness of breath to pneumonia, kidney failure, and even death are some of the symptoms of this disease, which can take 2–14 days to appear in human body [1]. The pandemic began in the city of Wuhan (Hubei District) in China and affected an overwhelming majority of the countries. Since then, it has been a persistent march of new cases and deaths. This infectious COVID-19 disease has reported thousands of deaths worldwide due to the rapid pandemic risk and the lack of antiviral drugs and vaccinations [2]. Now, the pandemic COVID-19 has become a major threat to India. Several nations, like India, have gone into a lockdown situation to keep this deadly virus from spreading. Throughout India, since January 30, 2020, COVID-19 cases have been gradually growing. As reported on May 10, 2020, the Ministry of Health and Family Welfare has confirmed 62,939 cases with 2,109 deaths [3] and accordingly, all districts of India are classified as red, orange and green zones on the basis of the incidence of COVID-19 cases. India is the world’s second most populated nation after China. Uncontrolled pandemic in India has the potential to affect about 1/6th of the world’s population. Study of this epidemic, allows the Government to take the requisite measures to reduce the effects of this global pandemic. A range of factors may influence the transmission of coronaviruses including climatic conditions (such as temperature and humidity), population density, and standard of the medical facility and so forth [4,5]. But realizing the relationship between environment and COVID-19 propagation is the secret to predicting this pandemic’s severity and end-time [6]. Using data from reported cases of India, we examined the associations between meteorological causes and the frequent occurrences of COVID-19 and also the trend of the growth of the disease. The aim is to give statistical evidence on the potential evolution of COVID-19 under changing climate conditions. The current situation of India can be witnessed from Fig. 1 . Fig. 1 a) Confirmed cases of COVID-19 (Map not for scale) and b) Classified zones on the basis of COVID-19 cases in India as of 10th of May, 2020 [©Wikipedia]. |
| label | 1 |
| title | Introduction |
| p | The COVID-19 pandemic (Coronavirus disease 2019), caused by SARS-CoV-2 (severe acute respiratory coronavirus syndrome 2), has created chaos in human society. According to World Health Organization reports, the disease is spread by respiratory droplets and communication pathways. Fever, cough, shortness of breath to pneumonia, kidney failure, and even death are some of the symptoms of this disease, which can take 2–14 days to appear in human body [1]. The pandemic began in the city of Wuhan (Hubei District) in China and affected an overwhelming majority of the countries. Since then, it has been a persistent march of new cases and deaths. This infectious COVID-19 disease has reported thousands of deaths worldwide due to the rapid pandemic risk and the lack of antiviral drugs and vaccinations [2]. |
| p | Now, the pandemic COVID-19 has become a major threat to India. Several nations, like India, have gone into a lockdown situation to keep this deadly virus from spreading. Throughout India, since January 30, 2020, COVID-19 cases have been gradually growing. As reported on May 10, 2020, the Ministry of Health and Family Welfare has confirmed 62,939 cases with 2,109 deaths [3] and accordingly, all districts of India are classified as red, orange and green zones on the basis of the incidence of COVID-19 cases. India is the world’s second most populated nation after China. Uncontrolled pandemic in India has the potential to affect about 1/6th of the world’s population. Study of this epidemic, allows the Government to take the requisite measures to reduce the effects of this global pandemic. A range of factors may influence the transmission of coronaviruses including climatic conditions (such as temperature and humidity), population density, and standard of the medical facility and so forth [4,5]. But realizing the relationship between environment and COVID-19 propagation is the secret to predicting this pandemic’s severity and end-time [6]. Using data from reported cases of India, we examined the associations between meteorological causes and the frequent occurrences of COVID-19 and also the trend of the growth of the disease. The aim is to give statistical evidence on the potential evolution of COVID-19 under changing climate conditions. The current situation of India can be witnessed from Fig. 1 . Fig. 1 a) Confirmed cases of COVID-19 (Map not for scale) and b) Classified zones on the basis of COVID-19 cases in India as of 10th of May, 2020 [©Wikipedia]. |
| figure | Fig. 1 a) Confirmed cases of COVID-19 (Map not for scale) and b) Classified zones on the basis of COVID-19 cases in India as of 10th of May, 2020 [©Wikipedia]. |
| label | Fig. 1 |
| caption | a) Confirmed cases of COVID-19 (Map not for scale) and b) Classified zones on the basis of COVID-19 cases in India as of 10th of May, 2020 [©Wikipedia]. |
| p | a) Confirmed cases of COVID-19 (Map not for scale) and b) Classified zones on the basis of COVID-19 cases in India as of 10th of May, 2020 [©Wikipedia]. |
| sec | 2 Materials and methods 2.1 Study area and data The Government of India is offering a number of websites and applications to track COVID-19 events. Daily counts of those states and union territories in India having more than 1000 laboratory-confirmed cases were obtained from https://www.covid19india.org/, the official reports of the Ministry of Health and Family Welfare of India from 1st of April, 2020 to 10th of May, 2020. Accordingly, 10 states and 1 union territory have been selected namely Maharashtra, Gujarat, Tamil Nadu, Delhi, Rajasthan, Madhya Pradesh, Uttar Pradesh, Andhra Pradesh, West Bengal, Punjab and Telengana [3]. The period of analysis was selected taking into account the lockdown declared by Govt. of India and also the total daily counts of other states are less than 1000. The meteorological data, daily minimum temperature (MinT) and maximum temperature (MaxT), daily average temperature (AT) and daily average relative humidity (ARH) of each state and union territory have been retrieved from https://en.tutiempo.net/that provides a web base platform for the researcher to examine the climate data. The website provides a generous amount of the world weather data. 2.2 Statistical analysis The Sen’s Slope [7] and Mann-Kendall [8,9] method were used to verify the existence or absence of linear trend in daily laboratory-confirmed cases of COVID-19. Significant + ve value of Sen’s Slope indicates a significant linear increase in daily confirmed cases where, as on the other hand, Sen’s Slope’s significant -ve value implies a significant linear decrease in daily confirmed cases. Generalized additive models (GAM) have been applied during the study periods to quantify the states-specific associations between meteorological factors and daily cases of COVID-19 events, accounting for short-term temporal patterns [10]. GAM is a generalized regression model in which the linear predictor is linearly dependent on undefined smooth functions of certain predictor variables, and the subject of concern is on inferences regarding such smooth functions. The model relates a univariate response variable Y, to some predictor variables x i and an exponential family of distribution is specified for Y such as normal, binomial, poisson distributions and so on. As the variances of the daily counts were larger than their means, and hence the distribution of COVID-19 cases was assumed to be a negative binomial. According to WHO, coronavirus carriers are infectious 2 days before the onset of the symptoms. And hence, three-day average temperature and relative humidity have been considered for the model [6]. The model is given by:Yt=β0+β1X1+β2X2+β3X3+β4X4+β5X5+etwhere Yt is the daily cases of confirmed COVID-19 count, β0 is the intercept term, β1 denotes the effect of moving average of AT, β2 denotes the effect of moving average of ARH, β3 denotes the effect of MaxT, β4 denotes the effect of MinT, β5 denotes the effect of the interaction of AT and ARH and et is the disturbance term. For prediction of the COVID-19 cases we have used Verhulst (Logistic) Population Model. To incorporate exponential growth time series we use this prediction model for forecasting [11]. |
| label | 2 |
| title | Materials and methods |
| sec | 2.1 Study area and data The Government of India is offering a number of websites and applications to track COVID-19 events. Daily counts of those states and union territories in India having more than 1000 laboratory-confirmed cases were obtained from https://www.covid19india.org/, the official reports of the Ministry of Health and Family Welfare of India from 1st of April, 2020 to 10th of May, 2020. Accordingly, 10 states and 1 union territory have been selected namely Maharashtra, Gujarat, Tamil Nadu, Delhi, Rajasthan, Madhya Pradesh, Uttar Pradesh, Andhra Pradesh, West Bengal, Punjab and Telengana [3]. The period of analysis was selected taking into account the lockdown declared by Govt. of India and also the total daily counts of other states are less than 1000. The meteorological data, daily minimum temperature (MinT) and maximum temperature (MaxT), daily average temperature (AT) and daily average relative humidity (ARH) of each state and union territory have been retrieved from https://en.tutiempo.net/that provides a web base platform for the researcher to examine the climate data. The website provides a generous amount of the world weather data. |
| label | 2.1 |
| title | Study area and data |
| p | The Government of India is offering a number of websites and applications to track COVID-19 events. Daily counts of those states and union territories in India having more than 1000 laboratory-confirmed cases were obtained from https://www.covid19india.org/, the official reports of the Ministry of Health and Family Welfare of India from 1st of April, 2020 to 10th of May, 2020. Accordingly, 10 states and 1 union territory have been selected namely Maharashtra, Gujarat, Tamil Nadu, Delhi, Rajasthan, Madhya Pradesh, Uttar Pradesh, Andhra Pradesh, West Bengal, Punjab and Telengana [3]. The period of analysis was selected taking into account the lockdown declared by Govt. of India and also the total daily counts of other states are less than 1000. |
| p | The meteorological data, daily minimum temperature (MinT) and maximum temperature (MaxT), daily average temperature (AT) and daily average relative humidity (ARH) of each state and union territory have been retrieved from https://en.tutiempo.net/that provides a web base platform for the researcher to examine the climate data. The website provides a generous amount of the world weather data. |
| sec | 2.2 Statistical analysis The Sen’s Slope [7] and Mann-Kendall [8,9] method were used to verify the existence or absence of linear trend in daily laboratory-confirmed cases of COVID-19. Significant + ve value of Sen’s Slope indicates a significant linear increase in daily confirmed cases where, as on the other hand, Sen’s Slope’s significant -ve value implies a significant linear decrease in daily confirmed cases. Generalized additive models (GAM) have been applied during the study periods to quantify the states-specific associations between meteorological factors and daily cases of COVID-19 events, accounting for short-term temporal patterns [10]. GAM is a generalized regression model in which the linear predictor is linearly dependent on undefined smooth functions of certain predictor variables, and the subject of concern is on inferences regarding such smooth functions. The model relates a univariate response variable Y, to some predictor variables x i and an exponential family of distribution is specified for Y such as normal, binomial, poisson distributions and so on. As the variances of the daily counts were larger than their means, and hence the distribution of COVID-19 cases was assumed to be a negative binomial. According to WHO, coronavirus carriers are infectious 2 days before the onset of the symptoms. And hence, three-day average temperature and relative humidity have been considered for the model [6]. The model is given by:Yt=β0+β1X1+β2X2+β3X3+β4X4+β5X5+etwhere Yt is the daily cases of confirmed COVID-19 count, β0 is the intercept term, β1 denotes the effect of moving average of AT, β2 denotes the effect of moving average of ARH, β3 denotes the effect of MaxT, β4 denotes the effect of MinT, β5 denotes the effect of the interaction of AT and ARH and et is the disturbance term. For prediction of the COVID-19 cases we have used Verhulst (Logistic) Population Model. To incorporate exponential growth time series we use this prediction model for forecasting [11]. |
| label | 2.2 |
| title | Statistical analysis |
| p | The Sen’s Slope [7] and Mann-Kendall [8,9] method were used to verify the existence or absence of linear trend in daily laboratory-confirmed cases of COVID-19. Significant + ve value of Sen’s Slope indicates a significant linear increase in daily confirmed cases where, as on the other hand, Sen’s Slope’s significant -ve value implies a significant linear decrease in daily confirmed cases. |
| p | Generalized additive models (GAM) have been applied during the study periods to quantify the states-specific associations between meteorological factors and daily cases of COVID-19 events, accounting for short-term temporal patterns [10]. GAM is a generalized regression model in which the linear predictor is linearly dependent on undefined smooth functions of certain predictor variables, and the subject of concern is on inferences regarding such smooth functions. The model relates a univariate response variable Y, to some predictor variables x i and an exponential family of distribution is specified for Y such as normal, binomial, poisson distributions and so on. |
| p | As the variances of the daily counts were larger than their means, and hence the distribution of COVID-19 cases was assumed to be a negative binomial. According to WHO, coronavirus carriers are infectious 2 days before the onset of the symptoms. And hence, three-day average temperature and relative humidity have been considered for the model [6]. The model is given by:Yt=β0+β1X1+β2X2+β3X3+β4X4+β5X5+etwhere Yt is the daily cases of confirmed COVID-19 count, β0 is the intercept term, β1 denotes the effect of moving average of AT, β2 denotes the effect of moving average of ARH, β3 denotes the effect of MaxT, β4 denotes the effect of MinT, β5 denotes the effect of the interaction of AT and ARH and et is the disturbance term. |
| p | For prediction of the COVID-19 cases we have used Verhulst (Logistic) Population Model. To incorporate exponential growth time series we use this prediction model for forecasting [11]. |
| sec | 3 Results The total counts of confirmed cases from 1st of April, 2020 to 10th of May, 2020 have been presented in Table 1 . Table 2 shows that all the states other than Telangana exhibit a significant upward liner trend for the confirmed cases. In case of Telangana, the trend is negative. These results are statistically significant as the p-value of Mann-Kendall is less than 0.05. These results of increase and decrease are also witnessed in Fig. 2 . Table 1 Total count of confirmed cases. State Total Count of Confirmed Cases Andhra Pradesh 1936 Delhi 6803 Gujrat 8121 Madhya Pradesh 3548 Maharashtra 21869 Punjab 1781 Rajasthan 3721 Tamil Nadu 7080 Telangana 1099 Uttar Pradesh 3363 West Bengal 1902 Table 2 Sen’s Slope estimates for trend detection. State Sen’s Slope Mann-Kendall Andhra Pradesh 1.000000 <0.001 Delhi 6.600000 <0.001 Gujrat 11.28571 <0.001 Madhya Pradesh 2.175192 <0.001 Maharashtra 29.07500 <0.001 Punjab 1.444444 <0.001 Rajasthan 2.266667 <0.001 Tamil Nadu 6.098462 <0.001 Telangana −0.79285 <0.001 Uttar Pradesh 2.826050 <0.001 West Bengal 2.361413 <0.001 Fig. 2 Day wise Confirmed cases of COVID-19 upto 10th of May, 2020. The estimates of regression coefficients of the GAM for the states are listed in Table 3 . Statistically significant effect of AT were found for Madhya Pradesh (1.42575), Maharashtra (2.75604), Punjab (1.48788) and Tamil Nadu (−15.89823), effect of ARH were found for Madhya Pradesh (1.21126), Punjab (0.58497) and Tamil Nadu (−6.79347), effect of MaxT were found for Maharashtra (−0.31561) and Tamil Nadu (0.43246), effect of MinT were found for Gujrat (0.20924) and Uttar Pradesh (0.189119) and effect of interaction between AT and ARH were found for Madhya Pradesh (0.03761), Punjab (0.02753) and Tamil Nadu (0.22832). However, these effects of meteorological variables vary from state to state [Table 4 ]. Table 3 Results of GAM regression. States Parameter Estimates p-value Andhra Pradesh Intercept, β0 −28.10783 0.2132 AT, β1 0.88819 0.1817 ARH, β2 0.49752 0.2597 MaxT, β3 −0.05697 0.5006 MinT, β4 0.13458 0.0712 ATxARH, β5 0.01415 0.2932 Delhi Intercept, β0 9.999257 0.216 AT, β1 −0.036883 0.902 ARH, β2 −0.207407 0.237 MaxT, β3 −0.103124 0.126 MinT, β4 −0.084796 0.313 ATxARH, β5 0.008437 0.148 Gujrat Intercept, β0 −3.37997 0.8149 AT, β1 −0.01978 0.9621 ARH, β2 −0.38041 0.3559 MaxT, β3 0.0485 0.6484 MinT, β4 0.20924 0.0209a ATxARH, β5 0.01318 0.2785 Madhya Pradesh Intercept, β0 −43.84455 0.0161a AT, β1 1.42575 0.0171a ARH, β2 1.21126 0.0341a MaxT, β3 0.05988 0.6292 MinT, β4 0.01476 0.8649 ATxARH, β5 0.03761 0.0336a Maharashtra Intercept, β0 −73.01116 0.08333 AT, β1 2.75604 0.04875a ARH, β2 0.85921 0.14815 MaxT, β3 −0.31561 0.00205a MinT, β4 0.10324 0.32944 ATxARH, β5 0.02675 0.16511 Punjab Intercept, β0 −32.17071 0.01551a AT, β1 1.48788 0.00664a ARH, β2 0.58497 0.00540a MaxT, β3 0.01837 0.86233 MinT, β4 0.16984 0.18284 ATxARH, β5 0.02753 0.00058a Rajasthan Intercept, β0 −2.751481 0.587 AT, β1 0.255311 0.23 ARH, β2 0.149194 0.394 MaxT, β3 −0.071146 0.413 MinT, β4 0.053367 0.366 ATxARH, β5 −0.004053 0.462 Tamil Nadu Intercept, β0 464.62175 0.0167a AT, β1 −15.89823 0.0127a ARH, β2 −6.79347 0.0115a MaxT, β3 0.43246 0.0158a MinT, β4 −0.09937 0.4576 ATxARH, β5 0.22832 0.0102a Telangana Intercept, β0 1.562944 0.965 AT, β1 −0.100075 0.925 ARH, β2 0.217862 0.756 MaxT, β3 0.142809 0.292 MinT, β4 0.117128 0.322 ATxARH, β5 −0.009049 0.673 Uttar Pradesh Intercept, β0 4.354975 0.586 AT, β1 −0.066434 0.851 ARH, β2 0.020638 0.899 MaxT, β3 −0.064778 0.606 MinT, β4 0.189119 0.040a ATxARH, β5 −0.00067 0.908 West Bengal Intercept, β0 18.65133 0.4096 AT, β1 −0.666464 0.3498 ARH, β2 −0.06807 0.7822 MaxT, β3 −0.008686 0.9179 MinT, β4 0.159345 0.0991 ATxARH, β5 0.002448 0.7522 a Significant with 95% confidence. Table 4 Effect of at, ARH, MaxT and MinT on COVID-19 incidence. State Effect on COVID-19 incidence ATa ARHb MaxTc MinTd Andhra Pradesh + ve + ve -ve + ve Delhi -ve -ve -ve -ve Gujrat -ve -ve + ve + ve Madhya Pradesh + ve + ve + ve + ve Maharashtra + ve + ve -ve + ve Punjab + ve + ve + ve + ve Rajasthan + ve + ve -ve + ve Tamil Nadu -ve -ve + ve -ve Telangana -ve + ve + ve + ve Uttar Pradesh -ve + ve -ve + ve West Bengal -ve -ve -ve + ve a Average Temperature, b Average Relative Humidity, c Maximum Temperature, d Minimum Temperature. Results of Verhulst (Logistic) Population Model are listed in Table 5 . Unlike the trend analysis and GEM analysis, here we have accounted the confirmed cases from 2nd May to 13th of May, 2020 as the incidence of confirmed COVID-19 cases rises significantly after April, 2020. Prediction of for three group up to May 5, 2020, up to May 9, 2020 and May 13, 2020 are same as that of actual figure. Two group of prediction have been listed up to May 17, 2020 and up to May 21, 2020. Table 5 Predicting results of Verhulst Population Model. States Total Confirmed Cases (from 02/05/2020) Predicted Confirmed Cases (from 02/05/2020) Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 17/5/20 Upto 21/5/20 AndhraPradesh 254 467 674 254 467 674 804.62 866.09 Delhi 1366 2804 4260 1366 2804 4260 5123.81 5484.91 Gujrat 1524 3076 4547 1524 3076 4547 5361.51 5685.01 Madhya Pradesh 334 742 1458 334 742 1458 2382.07 3196.53 Maharashtra 4019 8722 14416 4019 8722 14416 18490.40 20440.34 Punjab 866 1177 1339 866 1177 1339 1404.11 1427.49 Rajasthan 492 1042 1662 492 1042 1662 2073.76 2260.66 Tamil Nadu 1532 4009 6701 1532 4009 6701 8042.90 8464.04 Telangana 52 119 323 52 119 323 2022.67 2280.67 Uttar Pradesh 552 1045 1430 552 1045 1430 1608.66 1671.62 West Bengal 549 991 1495 549 991 1495 1899.52 2142.24 |
| label | 3 |
| title | Results |
| p | The total counts of confirmed cases from 1st of April, 2020 to 10th of May, 2020 have been presented in Table 1 . Table 2 shows that all the states other than Telangana exhibit a significant upward liner trend for the confirmed cases. In case of Telangana, the trend is negative. These results are statistically significant as the p-value of Mann-Kendall is less than 0.05. These results of increase and decrease are also witnessed in Fig. 2 . Table 1 Total count of confirmed cases. State Total Count of Confirmed Cases Andhra Pradesh 1936 Delhi 6803 Gujrat 8121 Madhya Pradesh 3548 Maharashtra 21869 Punjab 1781 Rajasthan 3721 Tamil Nadu 7080 Telangana 1099 Uttar Pradesh 3363 West Bengal 1902 Table 2 Sen’s Slope estimates for trend detection. State Sen’s Slope Mann-Kendall Andhra Pradesh 1.000000 <0.001 Delhi 6.600000 <0.001 Gujrat 11.28571 <0.001 Madhya Pradesh 2.175192 <0.001 Maharashtra 29.07500 <0.001 Punjab 1.444444 <0.001 Rajasthan 2.266667 <0.001 Tamil Nadu 6.098462 <0.001 Telangana −0.79285 <0.001 Uttar Pradesh 2.826050 <0.001 West Bengal 2.361413 <0.001 Fig. 2 Day wise Confirmed cases of COVID-19 upto 10th of May, 2020. |
| table-wrap | Table 1 Total count of confirmed cases. State Total Count of Confirmed Cases Andhra Pradesh 1936 Delhi 6803 Gujrat 8121 Madhya Pradesh 3548 Maharashtra 21869 Punjab 1781 Rajasthan 3721 Tamil Nadu 7080 Telangana 1099 Uttar Pradesh 3363 West Bengal 1902 |
| label | Table 1 |
| caption | Total count of confirmed cases. |
| p | Total count of confirmed cases. |
| table | State Total Count of Confirmed Cases Andhra Pradesh 1936 Delhi 6803 Gujrat 8121 Madhya Pradesh 3548 Maharashtra 21869 Punjab 1781 Rajasthan 3721 Tamil Nadu 7080 Telangana 1099 Uttar Pradesh 3363 West Bengal 1902 |
| tr | State Total Count of Confirmed Cases |
| th | State |
| th | Total Count of Confirmed Cases |
| tr | Andhra Pradesh 1936 |
| td | Andhra Pradesh |
| td | 1936 |
| tr | Delhi 6803 |
| td | Delhi |
| td | 6803 |
| tr | Gujrat 8121 |
| td | Gujrat |
| td | 8121 |
| tr | Madhya Pradesh 3548 |
| td | Madhya Pradesh |
| td | 3548 |
| tr | Maharashtra 21869 |
| td | Maharashtra |
| td | 21869 |
| tr | Punjab 1781 |
| td | Punjab |
| td | 1781 |
| tr | Rajasthan 3721 |
| td | Rajasthan |
| td | 3721 |
| tr | Tamil Nadu 7080 |
| td | Tamil Nadu |
| td | 7080 |
| tr | Telangana 1099 |
| td | Telangana |
| td | 1099 |
| tr | Uttar Pradesh 3363 |
| td | Uttar Pradesh |
| td | 3363 |
| tr | West Bengal 1902 |
| td | West Bengal |
| td | 1902 |
| table-wrap | Table 2 Sen’s Slope estimates for trend detection. State Sen’s Slope Mann-Kendall Andhra Pradesh 1.000000 <0.001 Delhi 6.600000 <0.001 Gujrat 11.28571 <0.001 Madhya Pradesh 2.175192 <0.001 Maharashtra 29.07500 <0.001 Punjab 1.444444 <0.001 Rajasthan 2.266667 <0.001 Tamil Nadu 6.098462 <0.001 Telangana −0.79285 <0.001 Uttar Pradesh 2.826050 <0.001 West Bengal 2.361413 <0.001 |
| label | Table 2 |
| caption | Sen’s Slope estimates for trend detection. |
| p | Sen’s Slope estimates for trend detection. |
| table | State Sen’s Slope Mann-Kendall Andhra Pradesh 1.000000 <0.001 Delhi 6.600000 <0.001 Gujrat 11.28571 <0.001 Madhya Pradesh 2.175192 <0.001 Maharashtra 29.07500 <0.001 Punjab 1.444444 <0.001 Rajasthan 2.266667 <0.001 Tamil Nadu 6.098462 <0.001 Telangana −0.79285 <0.001 Uttar Pradesh 2.826050 <0.001 West Bengal 2.361413 <0.001 |
| tr | State Sen’s Slope Mann-Kendall |
| th | State |
| th | Sen’s Slope |
| th | Mann-Kendall |
| tr | Andhra Pradesh 1.000000 <0.001 |
| td | Andhra Pradesh |
| td | 1.000000 |
| td | <0.001 |
| tr | Delhi 6.600000 <0.001 |
| td | Delhi |
| td | 6.600000 |
| td | <0.001 |
| tr | Gujrat 11.28571 <0.001 |
| td | Gujrat |
| td | 11.28571 |
| td | <0.001 |
| tr | Madhya Pradesh 2.175192 <0.001 |
| td | Madhya Pradesh |
| td | 2.175192 |
| td | <0.001 |
| tr | Maharashtra 29.07500 <0.001 |
| td | Maharashtra |
| td | 29.07500 |
| td | <0.001 |
| tr | Punjab 1.444444 <0.001 |
| td | Punjab |
| td | 1.444444 |
| td | <0.001 |
| tr | Rajasthan 2.266667 <0.001 |
| td | Rajasthan |
| td | 2.266667 |
| td | <0.001 |
| tr | Tamil Nadu 6.098462 <0.001 |
| td | Tamil Nadu |
| td | 6.098462 |
| td | <0.001 |
| tr | Telangana −0.79285 <0.001 |
| td | Telangana |
| td | −0.79285 |
| td | <0.001 |
| tr | Uttar Pradesh 2.826050 <0.001 |
| td | Uttar Pradesh |
| td | 2.826050 |
| td | <0.001 |
| tr | West Bengal 2.361413 <0.001 |
| td | West Bengal |
| td | 2.361413 |
| td | <0.001 |
| figure | Fig. 2 Day wise Confirmed cases of COVID-19 upto 10th of May, 2020. |
| label | Fig. 2 |
| caption | Day wise Confirmed cases of COVID-19 upto 10th of May, 2020. |
| p | Day wise Confirmed cases of COVID-19 upto 10th of May, 2020. |
| p | The estimates of regression coefficients of the GAM for the states are listed in Table 3 . Statistically significant effect of AT were found for Madhya Pradesh (1.42575), Maharashtra (2.75604), Punjab (1.48788) and Tamil Nadu (−15.89823), effect of ARH were found for Madhya Pradesh (1.21126), Punjab (0.58497) and Tamil Nadu (−6.79347), effect of MaxT were found for Maharashtra (−0.31561) and Tamil Nadu (0.43246), effect of MinT were found for Gujrat (0.20924) and Uttar Pradesh (0.189119) and effect of interaction between AT and ARH were found for Madhya Pradesh (0.03761), Punjab (0.02753) and Tamil Nadu (0.22832). However, these effects of meteorological variables vary from state to state [Table 4 ]. Table 3 Results of GAM regression. States Parameter Estimates p-value Andhra Pradesh Intercept, β0 −28.10783 0.2132 AT, β1 0.88819 0.1817 ARH, β2 0.49752 0.2597 MaxT, β3 −0.05697 0.5006 MinT, β4 0.13458 0.0712 ATxARH, β5 0.01415 0.2932 Delhi Intercept, β0 9.999257 0.216 AT, β1 −0.036883 0.902 ARH, β2 −0.207407 0.237 MaxT, β3 −0.103124 0.126 MinT, β4 −0.084796 0.313 ATxARH, β5 0.008437 0.148 Gujrat Intercept, β0 −3.37997 0.8149 AT, β1 −0.01978 0.9621 ARH, β2 −0.38041 0.3559 MaxT, β3 0.0485 0.6484 MinT, β4 0.20924 0.0209a ATxARH, β5 0.01318 0.2785 Madhya Pradesh Intercept, β0 −43.84455 0.0161a AT, β1 1.42575 0.0171a ARH, β2 1.21126 0.0341a MaxT, β3 0.05988 0.6292 MinT, β4 0.01476 0.8649 ATxARH, β5 0.03761 0.0336a Maharashtra Intercept, β0 −73.01116 0.08333 AT, β1 2.75604 0.04875a ARH, β2 0.85921 0.14815 MaxT, β3 −0.31561 0.00205a MinT, β4 0.10324 0.32944 ATxARH, β5 0.02675 0.16511 Punjab Intercept, β0 −32.17071 0.01551a AT, β1 1.48788 0.00664a ARH, β2 0.58497 0.00540a MaxT, β3 0.01837 0.86233 MinT, β4 0.16984 0.18284 ATxARH, β5 0.02753 0.00058a Rajasthan Intercept, β0 −2.751481 0.587 AT, β1 0.255311 0.23 ARH, β2 0.149194 0.394 MaxT, β3 −0.071146 0.413 MinT, β4 0.053367 0.366 ATxARH, β5 −0.004053 0.462 Tamil Nadu Intercept, β0 464.62175 0.0167a AT, β1 −15.89823 0.0127a ARH, β2 −6.79347 0.0115a MaxT, β3 0.43246 0.0158a MinT, β4 −0.09937 0.4576 ATxARH, β5 0.22832 0.0102a Telangana Intercept, β0 1.562944 0.965 AT, β1 −0.100075 0.925 ARH, β2 0.217862 0.756 MaxT, β3 0.142809 0.292 MinT, β4 0.117128 0.322 ATxARH, β5 −0.009049 0.673 Uttar Pradesh Intercept, β0 4.354975 0.586 AT, β1 −0.066434 0.851 ARH, β2 0.020638 0.899 MaxT, β3 −0.064778 0.606 MinT, β4 0.189119 0.040a ATxARH, β5 −0.00067 0.908 West Bengal Intercept, β0 18.65133 0.4096 AT, β1 −0.666464 0.3498 ARH, β2 −0.06807 0.7822 MaxT, β3 −0.008686 0.9179 MinT, β4 0.159345 0.0991 ATxARH, β5 0.002448 0.7522 a Significant with 95% confidence. Table 4 Effect of at, ARH, MaxT and MinT on COVID-19 incidence. State Effect on COVID-19 incidence ATa ARHb MaxTc MinTd Andhra Pradesh + ve + ve -ve + ve Delhi -ve -ve -ve -ve Gujrat -ve -ve + ve + ve Madhya Pradesh + ve + ve + ve + ve Maharashtra + ve + ve -ve + ve Punjab + ve + ve + ve + ve Rajasthan + ve + ve -ve + ve Tamil Nadu -ve -ve + ve -ve Telangana -ve + ve + ve + ve Uttar Pradesh -ve + ve -ve + ve West Bengal -ve -ve -ve + ve a Average Temperature, b Average Relative Humidity, c Maximum Temperature, d Minimum Temperature. |
| table-wrap | Table 3 Results of GAM regression. States Parameter Estimates p-value Andhra Pradesh Intercept, β0 −28.10783 0.2132 AT, β1 0.88819 0.1817 ARH, β2 0.49752 0.2597 MaxT, β3 −0.05697 0.5006 MinT, β4 0.13458 0.0712 ATxARH, β5 0.01415 0.2932 Delhi Intercept, β0 9.999257 0.216 AT, β1 −0.036883 0.902 ARH, β2 −0.207407 0.237 MaxT, β3 −0.103124 0.126 MinT, β4 −0.084796 0.313 ATxARH, β5 0.008437 0.148 Gujrat Intercept, β0 −3.37997 0.8149 AT, β1 −0.01978 0.9621 ARH, β2 −0.38041 0.3559 MaxT, β3 0.0485 0.6484 MinT, β4 0.20924 0.0209a ATxARH, β5 0.01318 0.2785 Madhya Pradesh Intercept, β0 −43.84455 0.0161a AT, β1 1.42575 0.0171a ARH, β2 1.21126 0.0341a MaxT, β3 0.05988 0.6292 MinT, β4 0.01476 0.8649 ATxARH, β5 0.03761 0.0336a Maharashtra Intercept, β0 −73.01116 0.08333 AT, β1 2.75604 0.04875a ARH, β2 0.85921 0.14815 MaxT, β3 −0.31561 0.00205a MinT, β4 0.10324 0.32944 ATxARH, β5 0.02675 0.16511 Punjab Intercept, β0 −32.17071 0.01551a AT, β1 1.48788 0.00664a ARH, β2 0.58497 0.00540a MaxT, β3 0.01837 0.86233 MinT, β4 0.16984 0.18284 ATxARH, β5 0.02753 0.00058a Rajasthan Intercept, β0 −2.751481 0.587 AT, β1 0.255311 0.23 ARH, β2 0.149194 0.394 MaxT, β3 −0.071146 0.413 MinT, β4 0.053367 0.366 ATxARH, β5 −0.004053 0.462 Tamil Nadu Intercept, β0 464.62175 0.0167a AT, β1 −15.89823 0.0127a ARH, β2 −6.79347 0.0115a MaxT, β3 0.43246 0.0158a MinT, β4 −0.09937 0.4576 ATxARH, β5 0.22832 0.0102a Telangana Intercept, β0 1.562944 0.965 AT, β1 −0.100075 0.925 ARH, β2 0.217862 0.756 MaxT, β3 0.142809 0.292 MinT, β4 0.117128 0.322 ATxARH, β5 −0.009049 0.673 Uttar Pradesh Intercept, β0 4.354975 0.586 AT, β1 −0.066434 0.851 ARH, β2 0.020638 0.899 MaxT, β3 −0.064778 0.606 MinT, β4 0.189119 0.040a ATxARH, β5 −0.00067 0.908 West Bengal Intercept, β0 18.65133 0.4096 AT, β1 −0.666464 0.3498 ARH, β2 −0.06807 0.7822 MaxT, β3 −0.008686 0.9179 MinT, β4 0.159345 0.0991 ATxARH, β5 0.002448 0.7522 a Significant with 95% confidence. |
| label | Table 3 |
| caption | Results of GAM regression. |
| p | Results of GAM regression. |
| table | States Parameter Estimates p-value Andhra Pradesh Intercept, β0 −28.10783 0.2132 AT, β1 0.88819 0.1817 ARH, β2 0.49752 0.2597 MaxT, β3 −0.05697 0.5006 MinT, β4 0.13458 0.0712 ATxARH, β5 0.01415 0.2932 Delhi Intercept, β0 9.999257 0.216 AT, β1 −0.036883 0.902 ARH, β2 −0.207407 0.237 MaxT, β3 −0.103124 0.126 MinT, β4 −0.084796 0.313 ATxARH, β5 0.008437 0.148 Gujrat Intercept, β0 −3.37997 0.8149 AT, β1 −0.01978 0.9621 ARH, β2 −0.38041 0.3559 MaxT, β3 0.0485 0.6484 MinT, β4 0.20924 0.0209a ATxARH, β5 0.01318 0.2785 Madhya Pradesh Intercept, β0 −43.84455 0.0161a AT, β1 1.42575 0.0171a ARH, β2 1.21126 0.0341a MaxT, β3 0.05988 0.6292 MinT, β4 0.01476 0.8649 ATxARH, β5 0.03761 0.0336a Maharashtra Intercept, β0 −73.01116 0.08333 AT, β1 2.75604 0.04875a ARH, β2 0.85921 0.14815 MaxT, β3 −0.31561 0.00205a MinT, β4 0.10324 0.32944 ATxARH, β5 0.02675 0.16511 Punjab Intercept, β0 −32.17071 0.01551a AT, β1 1.48788 0.00664a ARH, β2 0.58497 0.00540a MaxT, β3 0.01837 0.86233 MinT, β4 0.16984 0.18284 ATxARH, β5 0.02753 0.00058a Rajasthan Intercept, β0 −2.751481 0.587 AT, β1 0.255311 0.23 ARH, β2 0.149194 0.394 MaxT, β3 −0.071146 0.413 MinT, β4 0.053367 0.366 ATxARH, β5 −0.004053 0.462 Tamil Nadu Intercept, β0 464.62175 0.0167a AT, β1 −15.89823 0.0127a ARH, β2 −6.79347 0.0115a MaxT, β3 0.43246 0.0158a MinT, β4 −0.09937 0.4576 ATxARH, β5 0.22832 0.0102a Telangana Intercept, β0 1.562944 0.965 AT, β1 −0.100075 0.925 ARH, β2 0.217862 0.756 MaxT, β3 0.142809 0.292 MinT, β4 0.117128 0.322 ATxARH, β5 −0.009049 0.673 Uttar Pradesh Intercept, β0 4.354975 0.586 AT, β1 −0.066434 0.851 ARH, β2 0.020638 0.899 MaxT, β3 −0.064778 0.606 MinT, β4 0.189119 0.040a ATxARH, β5 −0.00067 0.908 West Bengal Intercept, β0 18.65133 0.4096 AT, β1 −0.666464 0.3498 ARH, β2 −0.06807 0.7822 MaxT, β3 −0.008686 0.9179 MinT, β4 0.159345 0.0991 ATxARH, β5 0.002448 0.7522 |
| tr | States Parameter Estimates p-value |
| th | States |
| th | Parameter |
| th | Estimates |
| th | p-value |
| tr | Andhra Pradesh Intercept, β0 −28.10783 0.2132 |
| td | Andhra Pradesh |
| td | Intercept, β0 |
| td | −28.10783 |
| td | 0.2132 |
| tr | AT, β1 0.88819 0.1817 |
| td | AT, β1 |
| td | 0.88819 |
| td | 0.1817 |
| tr | ARH, β2 0.49752 0.2597 |
| td | ARH, β2 |
| td | 0.49752 |
| td | 0.2597 |
| tr | MaxT, β3 −0.05697 0.5006 |
| td | MaxT, β3 |
| td | −0.05697 |
| td | 0.5006 |
| tr | MinT, β4 0.13458 0.0712 |
| td | MinT, β4 |
| td | 0.13458 |
| td | 0.0712 |
| tr | ATxARH, β5 0.01415 0.2932 |
| td | ATxARH, β5 |
| td | 0.01415 |
| td | 0.2932 |
| tr | Delhi Intercept, β0 9.999257 0.216 |
| td | Delhi |
| td | Intercept, β0 |
| td | 9.999257 |
| td | 0.216 |
| tr | AT, β1 −0.036883 0.902 |
| td | AT, β1 |
| td | −0.036883 |
| td | 0.902 |
| tr | ARH, β2 −0.207407 0.237 |
| td | ARH, β2 |
| td | −0.207407 |
| td | 0.237 |
| tr | MaxT, β3 −0.103124 0.126 |
| td | MaxT, β3 |
| td | −0.103124 |
| td | 0.126 |
| tr | MinT, β4 −0.084796 0.313 |
| td | MinT, β4 |
| td | −0.084796 |
| td | 0.313 |
| tr | ATxARH, β5 0.008437 0.148 |
| td | ATxARH, β5 |
| td | 0.008437 |
| td | 0.148 |
| tr | Gujrat Intercept, β0 −3.37997 0.8149 |
| td | Gujrat |
| td | Intercept, β0 |
| td | −3.37997 |
| td | 0.8149 |
| tr | AT, β1 −0.01978 0.9621 |
| td | AT, β1 |
| td | −0.01978 |
| td | 0.9621 |
| tr | ARH, β2 −0.38041 0.3559 |
| td | ARH, β2 |
| td | −0.38041 |
| td | 0.3559 |
| tr | MaxT, β3 0.0485 0.6484 |
| td | MaxT, β3 |
| td | 0.0485 |
| td | 0.6484 |
| tr | MinT, β4 0.20924 0.0209a |
| td | MinT, β4 |
| td | 0.20924 |
| td | 0.0209a |
| tr | ATxARH, β5 0.01318 0.2785 |
| td | ATxARH, β5 |
| td | 0.01318 |
| td | 0.2785 |
| tr | Madhya Pradesh Intercept, β0 −43.84455 0.0161a |
| td | Madhya Pradesh |
| td | Intercept, β0 |
| td | −43.84455 |
| td | 0.0161a |
| tr | AT, β1 1.42575 0.0171a |
| td | AT, β1 |
| td | 1.42575 |
| td | 0.0171a |
| tr | ARH, β2 1.21126 0.0341a |
| td | ARH, β2 |
| td | 1.21126 |
| td | 0.0341a |
| tr | MaxT, β3 0.05988 0.6292 |
| td | MaxT, β3 |
| td | 0.05988 |
| td | 0.6292 |
| tr | MinT, β4 0.01476 0.8649 |
| td | MinT, β4 |
| td | 0.01476 |
| td | 0.8649 |
| tr | ATxARH, β5 0.03761 0.0336a |
| td | ATxARH, β5 |
| td | 0.03761 |
| td | 0.0336a |
| tr | Maharashtra Intercept, β0 −73.01116 0.08333 |
| td | Maharashtra |
| td | Intercept, β0 |
| td | −73.01116 |
| td | 0.08333 |
| tr | AT, β1 2.75604 0.04875a |
| td | AT, β1 |
| td | 2.75604 |
| td | 0.04875a |
| tr | ARH, β2 0.85921 0.14815 |
| td | ARH, β2 |
| td | 0.85921 |
| td | 0.14815 |
| tr | MaxT, β3 −0.31561 0.00205a |
| td | MaxT, β3 |
| td | −0.31561 |
| td | 0.00205a |
| tr | MinT, β4 0.10324 0.32944 |
| td | MinT, β4 |
| td | 0.10324 |
| td | 0.32944 |
| tr | ATxARH, β5 0.02675 0.16511 |
| td | ATxARH, β5 |
| td | 0.02675 |
| td | 0.16511 |
| tr | Punjab Intercept, β0 −32.17071 0.01551a |
| td | Punjab |
| td | Intercept, β0 |
| td | −32.17071 |
| td | 0.01551a |
| tr | AT, β1 1.48788 0.00664a |
| td | AT, β1 |
| td | 1.48788 |
| td | 0.00664a |
| tr | ARH, β2 0.58497 0.00540a |
| td | ARH, β2 |
| td | 0.58497 |
| td | 0.00540a |
| tr | MaxT, β3 0.01837 0.86233 |
| td | MaxT, β3 |
| td | 0.01837 |
| td | 0.86233 |
| tr | MinT, β4 0.16984 0.18284 |
| td | MinT, β4 |
| td | 0.16984 |
| td | 0.18284 |
| tr | ATxARH, β5 0.02753 0.00058a |
| td | ATxARH, β5 |
| td | 0.02753 |
| td | 0.00058a |
| tr | Rajasthan Intercept, β0 −2.751481 0.587 |
| td | Rajasthan |
| td | Intercept, β0 |
| td | −2.751481 |
| td | 0.587 |
| tr | AT, β1 0.255311 0.23 |
| td | AT, β1 |
| td | 0.255311 |
| td | 0.23 |
| tr | ARH, β2 0.149194 0.394 |
| td | ARH, β2 |
| td | 0.149194 |
| td | 0.394 |
| tr | MaxT, β3 −0.071146 0.413 |
| td | MaxT, β3 |
| td | −0.071146 |
| td | 0.413 |
| tr | MinT, β4 0.053367 0.366 |
| td | MinT, β4 |
| td | 0.053367 |
| td | 0.366 |
| tr | ATxARH, β5 −0.004053 0.462 |
| td | ATxARH, β5 |
| td | −0.004053 |
| td | 0.462 |
| tr | Tamil Nadu Intercept, β0 464.62175 0.0167a |
| td | Tamil Nadu |
| td | Intercept, β0 |
| td | 464.62175 |
| td | 0.0167a |
| tr | AT, β1 −15.89823 0.0127a |
| td | AT, β1 |
| td | −15.89823 |
| td | 0.0127a |
| tr | ARH, β2 −6.79347 0.0115a |
| td | ARH, β2 |
| td | −6.79347 |
| td | 0.0115a |
| tr | MaxT, β3 0.43246 0.0158a |
| td | MaxT, β3 |
| td | 0.43246 |
| td | 0.0158a |
| tr | MinT, β4 −0.09937 0.4576 |
| td | MinT, β4 |
| td | −0.09937 |
| td | 0.4576 |
| tr | ATxARH, β5 0.22832 0.0102a |
| td | ATxARH, β5 |
| td | 0.22832 |
| td | 0.0102a |
| tr | Telangana Intercept, β0 1.562944 0.965 |
| td | Telangana |
| td | Intercept, β0 |
| td | 1.562944 |
| td | 0.965 |
| tr | AT, β1 −0.100075 0.925 |
| td | AT, β1 |
| td | −0.100075 |
| td | 0.925 |
| tr | ARH, β2 0.217862 0.756 |
| td | ARH, β2 |
| td | 0.217862 |
| td | 0.756 |
| tr | MaxT, β3 0.142809 0.292 |
| td | MaxT, β3 |
| td | 0.142809 |
| td | 0.292 |
| tr | MinT, β4 0.117128 0.322 |
| td | MinT, β4 |
| td | 0.117128 |
| td | 0.322 |
| tr | ATxARH, β5 −0.009049 0.673 |
| td | ATxARH, β5 |
| td | −0.009049 |
| td | 0.673 |
| tr | Uttar Pradesh Intercept, β0 4.354975 0.586 |
| td | Uttar Pradesh |
| td | Intercept, β0 |
| td | 4.354975 |
| td | 0.586 |
| tr | AT, β1 −0.066434 0.851 |
| td | AT, β1 |
| td | −0.066434 |
| td | 0.851 |
| tr | ARH, β2 0.020638 0.899 |
| td | ARH, β2 |
| td | 0.020638 |
| td | 0.899 |
| tr | MaxT, β3 −0.064778 0.606 |
| td | MaxT, β3 |
| td | −0.064778 |
| td | 0.606 |
| tr | MinT, β4 0.189119 0.040a |
| td | MinT, β4 |
| td | 0.189119 |
| td | 0.040a |
| tr | ATxARH, β5 −0.00067 0.908 |
| td | ATxARH, β5 |
| td | −0.00067 |
| td | 0.908 |
| tr | West Bengal Intercept, β0 18.65133 0.4096 |
| td | West Bengal |
| td | Intercept, β0 |
| td | 18.65133 |
| td | 0.4096 |
| tr | AT, β1 −0.666464 0.3498 |
| td | AT, β1 |
| td | −0.666464 |
| td | 0.3498 |
| tr | ARH, β2 −0.06807 0.7822 |
| td | ARH, β2 |
| td | −0.06807 |
| td | 0.7822 |
| tr | MaxT, β3 −0.008686 0.9179 |
| td | MaxT, β3 |
| td | −0.008686 |
| td | 0.9179 |
| tr | MinT, β4 0.159345 0.0991 |
| td | MinT, β4 |
| td | 0.159345 |
| td | 0.0991 |
| tr | ATxARH, β5 0.002448 0.7522 |
| td | ATxARH, β5 |
| td | 0.002448 |
| td | 0.7522 |
| table-wrap-foot | a Significant with 95% confidence. |
| footnote | a Significant with 95% confidence. |
| label | a |
| p | Significant with 95% confidence. |
| table-wrap | Table 4 Effect of at, ARH, MaxT and MinT on COVID-19 incidence. State Effect on COVID-19 incidence ATa ARHb MaxTc MinTd Andhra Pradesh + ve + ve -ve + ve Delhi -ve -ve -ve -ve Gujrat -ve -ve + ve + ve Madhya Pradesh + ve + ve + ve + ve Maharashtra + ve + ve -ve + ve Punjab + ve + ve + ve + ve Rajasthan + ve + ve -ve + ve Tamil Nadu -ve -ve + ve -ve Telangana -ve + ve + ve + ve Uttar Pradesh -ve + ve -ve + ve West Bengal -ve -ve -ve + ve a Average Temperature, b Average Relative Humidity, c Maximum Temperature, d Minimum Temperature. |
| label | Table 4 |
| caption | Effect of at, ARH, MaxT and MinT on COVID-19 incidence. |
| p | Effect of at, ARH, MaxT and MinT on COVID-19 incidence. |
| table | State Effect on COVID-19 incidence ATa ARHb MaxTc MinTd Andhra Pradesh + ve + ve -ve + ve Delhi -ve -ve -ve -ve Gujrat -ve -ve + ve + ve Madhya Pradesh + ve + ve + ve + ve Maharashtra + ve + ve -ve + ve Punjab + ve + ve + ve + ve Rajasthan + ve + ve -ve + ve Tamil Nadu -ve -ve + ve -ve Telangana -ve + ve + ve + ve Uttar Pradesh -ve + ve -ve + ve West Bengal -ve -ve -ve + ve |
| tr | State Effect on COVID-19 incidence |
| th | State |
| th | Effect on COVID-19 incidence |
| tr | ATa ARHb MaxTc MinTd |
| th | ATa |
| th | ARHb |
| th | MaxTc |
| th | MinTd |
| tr | Andhra Pradesh + ve + ve -ve + ve |
| td | Andhra Pradesh |
| td | + ve |
| td | + ve |
| td | -ve |
| td | + ve |
| tr | Delhi -ve -ve -ve -ve |
| td | Delhi |
| td | -ve |
| td | -ve |
| td | -ve |
| td | -ve |
| tr | Gujrat -ve -ve + ve + ve |
| td | Gujrat |
| td | -ve |
| td | -ve |
| td | + ve |
| td | + ve |
| tr | Madhya Pradesh + ve + ve + ve + ve |
| td | Madhya Pradesh |
| td | + ve |
| td | + ve |
| td | + ve |
| td | + ve |
| tr | Maharashtra + ve + ve -ve + ve |
| td | Maharashtra |
| td | + ve |
| td | + ve |
| td | -ve |
| td | + ve |
| tr | Punjab + ve + ve + ve + ve |
| td | Punjab |
| td | + ve |
| td | + ve |
| td | + ve |
| td | + ve |
| tr | Rajasthan + ve + ve -ve + ve |
| td | Rajasthan |
| td | + ve |
| td | + ve |
| td | -ve |
| td | + ve |
| tr | Tamil Nadu -ve -ve + ve -ve |
| td | Tamil Nadu |
| td | -ve |
| td | -ve |
| td | + ve |
| td | -ve |
| tr | Telangana -ve + ve + ve + ve |
| td | Telangana |
| td | -ve |
| td | + ve |
| td | + ve |
| td | + ve |
| tr | Uttar Pradesh -ve + ve -ve + ve |
| td | Uttar Pradesh |
| td | -ve |
| td | + ve |
| td | -ve |
| td | + ve |
| tr | West Bengal -ve -ve -ve + ve |
| td | West Bengal |
| td | -ve |
| td | -ve |
| td | -ve |
| td | + ve |
| table-wrap-foot | a Average Temperature, |
| footnote | a Average Temperature, |
| label | a |
| p | Average Temperature, |
| table-wrap-foot | b Average Relative Humidity, |
| footnote | b Average Relative Humidity, |
| label | b |
| p | Average Relative Humidity, |
| table-wrap-foot | c Maximum Temperature, |
| footnote | c Maximum Temperature, |
| label | c |
| p | Maximum Temperature, |
| table-wrap-foot | d Minimum Temperature. |
| footnote | d Minimum Temperature. |
| label | d |
| p | Minimum Temperature. |
| p | Results of Verhulst (Logistic) Population Model are listed in Table 5 . Unlike the trend analysis and GEM analysis, here we have accounted the confirmed cases from 2nd May to 13th of May, 2020 as the incidence of confirmed COVID-19 cases rises significantly after April, 2020. Prediction of for three group up to May 5, 2020, up to May 9, 2020 and May 13, 2020 are same as that of actual figure. Two group of prediction have been listed up to May 17, 2020 and up to May 21, 2020. Table 5 Predicting results of Verhulst Population Model. States Total Confirmed Cases (from 02/05/2020) Predicted Confirmed Cases (from 02/05/2020) Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 17/5/20 Upto 21/5/20 AndhraPradesh 254 467 674 254 467 674 804.62 866.09 Delhi 1366 2804 4260 1366 2804 4260 5123.81 5484.91 Gujrat 1524 3076 4547 1524 3076 4547 5361.51 5685.01 Madhya Pradesh 334 742 1458 334 742 1458 2382.07 3196.53 Maharashtra 4019 8722 14416 4019 8722 14416 18490.40 20440.34 Punjab 866 1177 1339 866 1177 1339 1404.11 1427.49 Rajasthan 492 1042 1662 492 1042 1662 2073.76 2260.66 Tamil Nadu 1532 4009 6701 1532 4009 6701 8042.90 8464.04 Telangana 52 119 323 52 119 323 2022.67 2280.67 Uttar Pradesh 552 1045 1430 552 1045 1430 1608.66 1671.62 West Bengal 549 991 1495 549 991 1495 1899.52 2142.24 |
| table-wrap | Table 5 Predicting results of Verhulst Population Model. States Total Confirmed Cases (from 02/05/2020) Predicted Confirmed Cases (from 02/05/2020) Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 17/5/20 Upto 21/5/20 AndhraPradesh 254 467 674 254 467 674 804.62 866.09 Delhi 1366 2804 4260 1366 2804 4260 5123.81 5484.91 Gujrat 1524 3076 4547 1524 3076 4547 5361.51 5685.01 Madhya Pradesh 334 742 1458 334 742 1458 2382.07 3196.53 Maharashtra 4019 8722 14416 4019 8722 14416 18490.40 20440.34 Punjab 866 1177 1339 866 1177 1339 1404.11 1427.49 Rajasthan 492 1042 1662 492 1042 1662 2073.76 2260.66 Tamil Nadu 1532 4009 6701 1532 4009 6701 8042.90 8464.04 Telangana 52 119 323 52 119 323 2022.67 2280.67 Uttar Pradesh 552 1045 1430 552 1045 1430 1608.66 1671.62 West Bengal 549 991 1495 549 991 1495 1899.52 2142.24 |
| label | Table 5 |
| caption | Predicting results of Verhulst Population Model. |
| p | Predicting results of Verhulst Population Model. |
| table | States Total Confirmed Cases (from 02/05/2020) Predicted Confirmed Cases (from 02/05/2020) Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 17/5/20 Upto 21/5/20 AndhraPradesh 254 467 674 254 467 674 804.62 866.09 Delhi 1366 2804 4260 1366 2804 4260 5123.81 5484.91 Gujrat 1524 3076 4547 1524 3076 4547 5361.51 5685.01 Madhya Pradesh 334 742 1458 334 742 1458 2382.07 3196.53 Maharashtra 4019 8722 14416 4019 8722 14416 18490.40 20440.34 Punjab 866 1177 1339 866 1177 1339 1404.11 1427.49 Rajasthan 492 1042 1662 492 1042 1662 2073.76 2260.66 Tamil Nadu 1532 4009 6701 1532 4009 6701 8042.90 8464.04 Telangana 52 119 323 52 119 323 2022.67 2280.67 Uttar Pradesh 552 1045 1430 552 1045 1430 1608.66 1671.62 West Bengal 549 991 1495 549 991 1495 1899.52 2142.24 |
| tr | States Total Confirmed Cases (from 02/05/2020) Predicted Confirmed Cases (from 02/05/2020) |
| th | States |
| th | Total Confirmed Cases (from 02/05/2020) |
| th | Predicted Confirmed Cases (from 02/05/2020) |
| tr | Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 5/5/20 Upto 9/5/20 Upto 13/5/20 Upto 17/5/20 Upto 21/5/20 |
| th | Upto 5/5/20 |
| th | Upto 9/5/20 |
| th | Upto 13/5/20 |
| th | Upto 5/5/20 |
| th | Upto 9/5/20 |
| th | Upto 13/5/20 |
| th | Upto 17/5/20 |
| th | Upto 21/5/20 |
| tr | AndhraPradesh 254 467 674 254 467 674 804.62 866.09 |
| td | AndhraPradesh |
| td | 254 |
| td | 467 |
| td | 674 |
| td | 254 |
| td | 467 |
| td | 674 |
| td | 804.62 |
| td | 866.09 |
| tr | Delhi 1366 2804 4260 1366 2804 4260 5123.81 5484.91 |
| td | Delhi |
| td | 1366 |
| td | 2804 |
| td | 4260 |
| td | 1366 |
| td | 2804 |
| td | 4260 |
| td | 5123.81 |
| td | 5484.91 |
| tr | Gujrat 1524 3076 4547 1524 3076 4547 5361.51 5685.01 |
| td | Gujrat |
| td | 1524 |
| td | 3076 |
| td | 4547 |
| td | 1524 |
| td | 3076 |
| td | 4547 |
| td | 5361.51 |
| td | 5685.01 |
| tr | Madhya Pradesh 334 742 1458 334 742 1458 2382.07 3196.53 |
| td | Madhya Pradesh |
| td | 334 |
| td | 742 |
| td | 1458 |
| td | 334 |
| td | 742 |
| td | 1458 |
| td | 2382.07 |
| td | 3196.53 |
| tr | Maharashtra 4019 8722 14416 4019 8722 14416 18490.40 20440.34 |
| td | Maharashtra |
| td | 4019 |
| td | 8722 |
| td | 14416 |
| td | 4019 |
| td | 8722 |
| td | 14416 |
| td | 18490.40 |
| td | 20440.34 |
| tr | Punjab 866 1177 1339 866 1177 1339 1404.11 1427.49 |
| td | Punjab |
| td | 866 |
| td | 1177 |
| td | 1339 |
| td | 866 |
| td | 1177 |
| td | 1339 |
| td | 1404.11 |
| td | 1427.49 |
| tr | Rajasthan 492 1042 1662 492 1042 1662 2073.76 2260.66 |
| td | Rajasthan |
| td | 492 |
| td | 1042 |
| td | 1662 |
| td | 492 |
| td | 1042 |
| td | 1662 |
| td | 2073.76 |
| td | 2260.66 |
| tr | Tamil Nadu 1532 4009 6701 1532 4009 6701 8042.90 8464.04 |
| td | Tamil Nadu |
| td | 1532 |
| td | 4009 |
| td | 6701 |
| td | 1532 |
| td | 4009 |
| td | 6701 |
| td | 8042.90 |
| td | 8464.04 |
| tr | Telangana 52 119 323 52 119 323 2022.67 2280.67 |
| td | Telangana |
| td | 52 |
| td | 119 |
| td | 323 |
| td | 52 |
| td | 119 |
| td | 323 |
| td | 2022.67 |
| td | 2280.67 |
| tr | Uttar Pradesh 552 1045 1430 552 1045 1430 1608.66 1671.62 |
| td | Uttar Pradesh |
| td | 552 |
| td | 1045 |
| td | 1430 |
| td | 552 |
| td | 1045 |
| td | 1430 |
| td | 1608.66 |
| td | 1671.62 |
| tr | West Bengal 549 991 1495 549 991 1495 1899.52 2142.24 |
| td | West Bengal |
| td | 549 |
| td | 991 |
| td | 1495 |
| td | 549 |
| td | 991 |
| td | 1495 |
| td | 1899.52 |
| td | 2142.24 |
| sec | 4 Discussion The linear upward (increasing) trend that has been found in the study area except Telangana is a worrisome sign for India. Additionally, from the beginning of May the incidence of COVID-19 rises in more recurrent way. The study argued that both daily temperature and relative humidity had an effect on the incidence of COVID-19 in most of the study region. Nevertheless, the relationship between COVID-19 and AT and ARH has not been consistent across the nations. Incidence of meteorological variables varies due to vast geographical heterogeneity across India. The cumulative incidence of COVID-19 cases was higher in North and South India, as more business, agricultural, industrial and other associated activities are happening in this region of India than in the rest of the country. In addition, owing to the lockout declared by the Government on March 2020, the staffs from other areas of India are compelled to stay there. WHO finds coronavirus carriers to be contagious 2 days before the start of symptoms [12]. We, therefore, used three-day moving average of daily AT and ARH for the analysis of GAM. As India announced its lockdown at a stage when total, confirmed cases were less than 600, so in this research data are used after 7 days of lockdown. Another significant finding of this study is the significant interaction between ARH and AT, and COVID-19 incidence. Such results are compatible with the findings of China [10]. According to them, improved AT (ARH) culminated in a decreased influence of ARH (AT) on the incidence of COVID-19 in Hubei Province. The precise method of contact, however, is uncertain. They suggest one probable reason might be that a combination of low AT and humidity make the nasal mucosa prone to small ruptures, creating opportunities for virus invasion [10]. In addition, it is recommended that associations between different meteorological variables be included in the estimation process of the environment effect on the likelihood of COVID-19 transmission. Research findings of meteorological variables will be incorporated into the anticipation and regulation of COVID-19. With the help of Verhulst (Logistic) Population Model, projection of confirmed cases have been given up to 21st May. The predicted findings are quite promising as the predicting behaviour of the model as same as the already confirmed cases from 2nd May 2020 to 13th of May, 2020. In addition, due to this predicting nature, it is found quite useful than the time series forecasting methods like exponential smoothing, ARIMA for forecasting purposes in terms of COVID-19 pandemic. Because, ARIMA need a stationary time series and exponential smoothing cannot corporate with a dynamic change in time series. |
| label | 4 |
| title | Discussion |
| p | The linear upward (increasing) trend that has been found in the study area except Telangana is a worrisome sign for India. Additionally, from the beginning of May the incidence of COVID-19 rises in more recurrent way. The study argued that both daily temperature and relative humidity had an effect on the incidence of COVID-19 in most of the study region. Nevertheless, the relationship between COVID-19 and AT and ARH has not been consistent across the nations. Incidence of meteorological variables varies due to vast geographical heterogeneity across India. The cumulative incidence of COVID-19 cases was higher in North and South India, as more business, agricultural, industrial and other associated activities are happening in this region of India than in the rest of the country. In addition, owing to the lockout declared by the Government on March 2020, the staffs from other areas of India are compelled to stay there. WHO finds coronavirus carriers to be contagious 2 days before the start of symptoms [12]. We, therefore, used three-day moving average of daily AT and ARH for the analysis of GAM. As India announced its lockdown at a stage when total, confirmed cases were less than 600, so in this research data are used after 7 days of lockdown. Another significant finding of this study is the significant interaction between ARH and AT, and COVID-19 incidence. Such results are compatible with the findings of China [10]. According to them, improved AT (ARH) culminated in a decreased influence of ARH (AT) on the incidence of COVID-19 in Hubei Province. The precise method of contact, however, is uncertain. They suggest one probable reason might be that a combination of low AT and humidity make the nasal mucosa prone to small ruptures, creating opportunities for virus invasion [10]. In addition, it is recommended that associations between different meteorological variables be included in the estimation process of the environment effect on the likelihood of COVID-19 transmission. Research findings of meteorological variables will be incorporated into the anticipation and regulation of COVID-19. With the help of Verhulst (Logistic) Population Model, projection of confirmed cases have been given up to 21st May. The predicted findings are quite promising as the predicting behaviour of the model as same as the already confirmed cases from 2nd May 2020 to 13th of May, 2020. In addition, due to this predicting nature, it is found quite useful than the time series forecasting methods like exponential smoothing, ARIMA for forecasting purposes in terms of COVID-19 pandemic. Because, ARIMA need a stationary time series and exponential smoothing cannot corporate with a dynamic change in time series. |
| sec | 5 Conclusion In accordance to this analysis, the incidence of COVID-19 has a significant linear trend. Moreover, meteorological factors influence COVID-19 particularly the interactive effect between daily temperature and relative humidity on COVID-19 incidence. However, due to the inconsistency of results between various states, further studies are needed which include other meteorological variables as well. Keeping in mind the forecasting behaviour of Verhulst Population Model, it can be said that this research will definitely help the researchers as well as the policy makers in this field. |
| label | 5 |
| title | Conclusion |
| p | In accordance to this analysis, the incidence of COVID-19 has a significant linear trend. Moreover, meteorological factors influence COVID-19 particularly the interactive effect between daily temperature and relative humidity on COVID-19 incidence. However, due to the inconsistency of results between various states, further studies are needed which include other meteorological variables as well. Keeping in mind the forecasting behaviour of Verhulst Population Model, it can be said that this research will definitely help the researchers as well as the policy makers in this field. |
| sec | Funding None. |
| title | Funding |
| p | None. |
| sec | Author’s contribution J. Hazarika supervised the work. K. Goswami and S. Bharali conceived the idea presented, discussed the methodology, S. Bharali organized the theoretical discussion and K. Goswami collected, analyzed the data and describe the results. All authors discussed the results and contributed to the final version of the manuscript. |
| title | Author’s contribution |
| p | J. Hazarika supervised the work. K. Goswami and S. Bharali conceived the idea presented, discussed the methodology, S. Bharali organized the theoretical discussion and K. Goswami collected, analyzed the data and describe the results. All authors discussed the results and contributed to the final version of the manuscript. |
| sec | Declaration of competing interest The authors declare that there is no known competing interest, which could have influence in this paper. |
| title | Declaration of competing interest |
| p | The authors declare that there is no known competing interest, which could have influence in this paper. |
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