The incubation period of coronavirus disease (COVID‐19): A tremendous public health threat—Forecasting from publicly available case data in India

The World Health Organization (WHO) declared the Coronavirus Disease (COVID‐19) a pandemic due to the huge upsurge in the number of reported cases worldwide. The COVID‐19 pandemic in India has become a public health threat, and if we go by the number of confirmed cases then the situation seems to be a matter of grave concern. According to real‐time data, the numbers of confirmed cases are growing exponentially. No doubt, substantial public health interventions both at the national and state levels are implemented immediately by the Government of India; there is a need for improved preparedness plans and mitigation strategies along with accurate forecasting. The present study aims to forecast the COVID‐19 outbreak infected cases in India. The data have been obtained from https://www.covid19india.org, https://www.worldometers.info/coronavirus, and ICMR reported publicly available information about COVID‐19 confirmation cases. We have used the double exponential smoothing method for forecasting the trends in terms of confirmed, active, recovered and death cases from COVID‐19 for emergency preparedness and future predictions. Findings reveal that the estimated value of point forecast is just 8.22% of the total number of confirmed cases reported on a daily basis across the country. It was observed that the deaths were lower for the states and union territories with a higher detection rate. It is suggested that by keeping in view the limited healthcare resources in the country, accurate forecasting, early detection, and avoidance of acute care for the majority of infected cases is indispensable.

The World Health Organization (WHO) declared the Coronavirus Disease (COVID-19) a pandemic due to the huge upsurge in the number of reported cases worldwide.
The COVID-19 pandemic in India has become a public health threat, and if we go by the number of confirmed cases then the situation seems to be a matter of grave concern. According to real-time data, the numbers of confirmed cases are growing exponentially. No doubt, substantial public health interventions both at the national and state levels are implemented immediately by the Government of India; there is a need for improved preparedness plans and mitigation strategies along with accurate forecasting. The present study aims to forecast the COVID-19 outbreak infected cases in India. The data have been obtained from https://www.covid19india.org, https://www.worldometers.info/coronavirus, and ICMR reported publicly available information about COVID-19 confirmation cases. We have used the double exponential smoothing method for forecasting the trends in terms of confirmed, active, recovered and death cases from COVID-19 for emergency preparedness and future predictions. Findings reveal that the estimated value of point forecast is just 8.22% of the total number of confirmed cases reported on a daily basis across the country.
It was observed that the deaths were lower for the states and union territories with a higher detection rate. It is suggested that by keeping in view the limited healthcare resources in the country, accurate forecasting, early detection, and avoidance of acute care for the majority of infected cases is indispensable.  (Banik et al., 2015;Hilgenfeld & Peiris, 2013;Paraskevis et al., 2020). However, significant genetic dissimilarities have been documented between pathogens of these three epidemics, particularly amid MERS and COVID-19 (Dhama et al., 2020;Kolifarhood et al., 2020;WHO, 2020;Yang et al., 2020). Initially, the hotspots for these epidemics were Middle East, Saudi Arabia, and China (Sutton & Subbarao, 2015;Wu et al., 2017), as they are transmitted from animal to human, and later transmissions of pathogens were reported from human to human in other countries as well. Epidemiological evidence of COVID-19 outbreak began in Wuhan, China from December 12, 2019 (Wu & McGoogan, 2020). The WHO declared COVID-19 a pandemic on March 11, 2020 due to a huge upsurge in the number of reported cases for COVID-19 as compared to SARS and MERS.
The index case of the COVID-19 pandemic in India was reported from Thrissur, Kerala on January 30, 2020. Being one of the largest populated countries, and having resource constraints, the case detection for COVID-19 was initially low (Guo et al., 2020;Singhal, 2020).
There were also notable variations in the predicted estimates of infection cases. India has observed a 14 h voluntary public curfew on March 22, 2020 followed by a lockdown in 75 districts where con- According to real-time data, COVID-19 cases are growing exponentially. Healthcare system in India is embracing itself for may be its biggest battle to treat the infected cases. It is, therefore, important to forecast the precise increase in the expected number of cases to comprehend what is required, and to arrest the perturbing trends.
Accurate forecasting dissemination can play a critical role in apprising the governments and healthcare professionals what to expect and which measures to impose to motivate the public to adhere with them. In this paper, we have estimated the possible forecasted cases across the country from the publicly available case data by using the double exponential smoothing (DES) method. We assume that the publically available daily case data are legitimate subject to certain reporting biases if any. The objective is not to attempt for meticulous precision but to merely provide useful insights on COVID-19 in India.

| Data and methods
The data were collected from the publically available secondary sources (https://www.covid19india.org; https://www.worldometers. info/coronavirus/; Ministry of Health and Family Welfare (MoHFW), and Indian Council of Medical Research (ICMR, n.d.) reports) on COVID-19 (2020) confirmed, active, recovered, and death cases. We assume on the basis of available literature that the mean duration between the onsets of symptoms and in case of no recovery, resultant death may occur in 14 days (Cascella et al., 2020). We collected and analysed the data from inception to April 14, 2020 (First phase of lockdown), by using a DES method, a family of forecasting models.
DES technique, also known as averaging, is used for the detection of notable variations in the data by considering the most recent data.
This method uses the historical data for forecasted values. This is one of the most efficient short-term forecasting methods that apply to univariate time series data as a sequence of observations. The present study has used Holt's two-parameter DES model, which allows to separately smooth the trends by using the second smoothing constant. In this method, we use weighted averages of past observations to forecast the new values. The main idea here is to give more priority to the recent values in the series. As observation gets older (in terms of time), the importance of these values get exponentially smaller.
The DES method combines error, trend, and seasonal components in a smoothing calculation. Each term can be combined either additively, multiplicatively, or be left out of the model (Hox & Boeije, 2005;Verity et al., 2020). The DES method is a revised smoothing method than simple exponential smoothing method, which is applicable to the time series data with trend and no seasonality. Mainly this method is a linear combination of previous value of series for generating and predicting the future values (Bajric et al., 2013;Brown-Smoothing, 1963;Newbold & Granger, 1974). The application of the DES method requires three equations, Forecast = estimated level + trend at most recent time point where F t + K represents the forecast for m-steps ahead period and K represents the number of period forecasts.
Forecasting in R software is done with both simple and DES methods using a forecast package, which is available in base R (Hyndman & Khandakar, 2007;Montgomery et al., 2015). The advantages associated with this model are its better precision power as compared to other methods. It takes into account the most recent observations and weights them accordingly for reasonable future predictions. As such this method is best suited for short-term forecasting, as it assumes trends and patterns that will be similar to the current patterns and trends. However, major limitations associated with the DES methods are its inflexibility in terms of using few statistical data, especially for long-term accurate predictions. It can be used only when the time series data have minimal variations, with no seasonality.

| RESULTS
The study estimates suggested that the total number of COVID rate has increased at 7.01% (of 716.58 estimated deaths) (Tables 1 and   2). The point forecast values for confirmed cases from 15 to 24 April, 2020 were 12, 612.22, 13,741.99, 14,871.76, 16,001.53, 17,131.30, 18,261.07, 19,390.84, 20,520.61, 21,650.38, and 22,780.15. These values indicate the point forecast for the confirmed cases, which will be increasing 1,129.77 per day. and blue colours. The red colour curve indicates the number of confirmed and active cases from 11 March to April 14, 2020 based on the actual data. This data were the base for the predicted values of confirmed and active cases in India, which was portrayed via blue colour trend line, that is, predicted region (Hyndman & Khandakar, 2007). It is predicting the number of confirmed and active cases in India from 15 to April 24, 2020. As per our predictions from 15 to April 24, 2020, the count of confirmed and active cases in India will fall in this region. The number of confirmed cases is presented at Y axis, and X axis represents day from March 11, 2020 to April 24, 2020.
Similarly, recovered and death cases are depicted in Figure 2a,b.

| DISCUSSION
The COVID-19 pandemic in India has become a public health threat, and if we go as per the forecasts then the situation seems to be a matter of grave concern. This new virus represents tremendous concerns what we are going to encounter in the coming time, the accurate forecasting methods can significantly contribute in designing a more unified approach to fight with the pandemic. It will also help the policymakers to introduce better regulatory mechanisms such as fiscal measures to reduce the spread of viruses. However, forecasting alone should never be the only solution to this pandemic until there is a desired change in knowledge, attitude, and practices of the population towards this disease. The unified decision-making frameworks may help in reducing the distress, over optimism, and make us mentally prepared to address this biggest public health threat worldwide, especially for the highly populated country such as India. We hope that forecasting COVID-19 will be more helpful for adapting better intervention policies.

DATA AVAILABILITY STATEMENT
The data that support the findings of this study are openly available in The data used for the present study are available in the public domain.
The supplementary material is not available but references to the repository is already provided in the article.