The Uncertain COVID-19 Spread Pattern in India: A Statistical Analysis of the Current Situation

Baruah, Hemanta K.

doi:10.1101/2020.08.30.20184598

Cited by 7 publications

(9 citation statements)

References 12 publications

(14 reference statements)

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“…It was seen that [8] during June 1 to June 9 it became 0.04063. When the matter was studied with data for 60 consecutive days [9], from June 23 to July 12, the average value of Δ z ( t ) was 0.034576, from July 13 to August 1 it was 0.034458, and from August 2 to August 21 it was 0.026449. So we have seen that as time progressed, the values of Δ z ( t ) were reducing.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…It was observed [5, 6, 7, 8, 9] that from Δ z ( t ) we can extract an important information. When the pandemic continues to grow exponentially, Δ z ( t ) would continue to decrease linearly in time.…”

Section: Methodsmentioning

confidence: 99%

“…We shall use a simple numerical procedure of our own for this study using data from Worldometers.info [4]. Regarding our method of forecasting without using the standard epidemiological methods [5, 6, 7, 8, 9], it was observed that our numerical method does work very well in forecasting of the spread pattern during the nearly exponential phase. In this article, we shall put forward a way to compare the rates of growth of the natural logarithm of the cumulative total of the cases of the pandemic in India, the USA and the World as a whole.…”

Section: Introductionmentioning

confidence: 99%

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A Numerical Study of the Current COVID-19 Spread Patterns in India, the USA and the World

Baruah

2020

Preprint

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In this article, we are going to study the current COVID-19 spread patterns in India and the United States. We are interested to show how the daily increase in the total number of cases in these two countries is affecting the COVID-19 spread pattern in the World. For the study, we have considered the cumulative total numbers of cases in India, the United States and the World. We have found that the situation in the United States is already on the threshold of a change towards retardation. In the World as a whole also we have observed that a similar conclusion can be made. In India, the situation can be expected to move towards betterment soon, and once that happens the situation in the World as a whole would start improving. We shall demonstrate that as long as the rate of change of the logarithm of the cumulative total number of cases with respect to time in a pandemic continues to reduce, the pattern of growth would continue to remain nearly exponential, and as soon as it is seen that the rate of change starts to become nearly constant the growth can be expected to start to change towards a nearly logarithmic pattern.

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Section: Analysis and Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Numerical Study of the Current COVID-19 Spread Patterns in India, the USA and the World

Baruah

2020

Preprint

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“…As mentioned earlier, during the sub-exponential stage of growth of COVID-19, Δ z ( t ) was observed to have followed a decreasing trend. It was shown in [11] that from June 23 to August 21, in India Δ z ( t ) had followed the following three linear equations established using the method of least squares. The study was made for 60 days, separated into three equal parts of 20 days each.…”

Section: Methodsmentioning

confidence: 99%

“…It was statistically concluded that during the first 20 days the coefficient of regression of Δ z ( t ) on t was negative, during the next 20 days it was insignificant and in the last 20 days the regression coefficient was negative again. Using the equation fitted from data from August 2 to August 21, a forecast was made in [11] that starting from August 2, within no more than 78 days retardation would start in India. That forecast could be seen to be true.…”

Section: Methodsmentioning

confidence: 99%

Hyper-Exponential Growth of COVID-19 during Resurgence of the Disease in Russia

Baruah¹

2020

Preprint

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In Russia, COVID-19 has currently been growing hyper-exponentially. This type of a spread pattern was not seen during the first wave of the pandemic the world over. Indeed when the disease had first appeared, in the accelerating stage the spread pattern was observed to have followed a highly nonlinear pattern that could be said to be approximately exponential or sub-exponential. As to why in the resurgence the growth has become hyper-exponential is another matter. But this has been happening in Europe and how long this would continue cannot be predicted. It may so happen that in the countries in which retardation has already been taking place, there may be resurgence of the disease. It was observed that in the World as a whole, retardation was on the threshold during the second half of September. But if the resurgence happens to follow the hyper-exponential growth pattern in different countries, there may be resurgence in the World as a whole.

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A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis

Khan

Ali

Mohsin

et al. 2022

Environ Dev Sustain

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COVID-19 has had an impact on the entire humankind and has been proved to spread in deadly waves. As a result, preparedness and planning are required to better deal with the epidemic’s upcoming waves. Effective planning, on the other hand, necessitates detailed vulnerability assessments at all levels, from the national to the state or regional. There are several issues at the regional level, and each region has its own features. As a result, each region needs its own COVID-19 vulnerability assessment. In terms of climate, terrain and demographics, the state of Uttarakhand differs significantly from the rest of India. As a result, a vulnerability assessment of the next COVID-19 variation (Omicron BA.2) is required for district-level planning to meet regional concerns. A total of 17 variables were chosen for this study, including demographic, socio-economic, infrastructure, epidemiological and tourism-related factors. AHP was used to compute their weights. After applying min–max normalisation to the data, a district-level quantitative SWOT is created to compare the performance of 13 Uttarakhand districts. A COVID-19 vulnerability index (normalised R i ) ranging between 0 and 1 was produced, and district-level vulnerabilities were mapped. Quantitative SWOT results depict that Dehradun is a best performing district followed by Haridwar, while Bageshwar, Rudra Prayag, Champawat and Pithoragarh are on the weaker side and the normalised Ri proves Dehradun, Nainital, Champawat, Bageshwar and Chamoli to be least vulnerable to COVID-19 (normalised R i ≤ 0.25) and Pithoragarh to be the most vulnerable district (normalised R i > 0.90). Pauri Garwal and Uttarkashi are moderately vulnerable (normalised R i 0.50 to 0.75). Electronic supplementary material The online version of this article (10.1007/s10668-022-02727-3) contains supplementary material, which is available to authorised users.

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The Uncertain COVID-19 Spread Pattern in India: A Statistical Analysis of the Current Situation

Cited by 7 publications

References 12 publications

A Numerical Study of the Current COVID-19 Spread Patterns in India, the USA and the World

A Numerical Study of the Current COVID-19 Spread Patterns in India, the USA and the World

Hyper-Exponential Growth of COVID-19 during Resurgence of the Disease in Russia

A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis

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