COVID-19 Pandemic: Power Law Spread and Flattening of the Curve

Verma, Mahendra K.; Ali, Asad; Chatterjee, Soumyadeep

doi:10.1007/s41403-020-00104-y

Cited by 40 publications

(63 citation statements)

References 29 publications

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“…The time series for both I(t) and D(t) follow exponential regimes during the early phases of the pandemic and subsequently transition to power-law regimes. This is in accordance with the earlier work of Verma et al [36] and Chatterjee et al [8]. The bestfit functions along with their relative errors are listed in Tables 1 and 2 for the infected and death cases respectively.…”

Section: Analysis and Resultssupporting

confidence: 91%

“…We calculate the derivative using Python's gradient function and take a 5-day moving average in order to smoothen theİ(t) andḊ(t) curves. We observe that in the exponential regimes, the daily counts are proportional to the cumulative number of infected and death cases i.e.İ ≈ β i I andḊ ≈ β d D. Verma et al [36] show that power-law regime can be approximated as I(t) ∼ At n , and hence,İ ∼ I 1−1/n . Similarly, it can be shown that for power-lawsḊ ∼ D 1−1/n .…”

Section: Analysis and Resultsmentioning

confidence: 99%

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Evolution of COVID-19 Pandemic in India

Ali

Srivastava

Verma

2020

Preprint

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A mathematical analysis of patterns for the evolution of COVID-19 cases is key to the development of reliable and robust predictive models potentially leading to efficient and effective governance against COVID-19. Towards this objective, we study and analyze the temporal growth pattern of COVID-19 infection and death counts in various states of India. Our analysis up to June 16, 2020 shows that several states (namely Maharashtra, Tamil Nadu, Delhi, Uttar Pradesh) have reached t-squared power-law growth, while some others like Gujarat, Rajasthan, and Madhya Pradesh have reached linear growth. Karnataka and Kerala are exhibiting a second wave. In addition, we report that the death counts exhibit similar behavior as the infection counts. These observations indicate that Indian states are far from flattening their epidemic curves.

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Section: Analysis and Resultssupporting

confidence: 91%

Section: Analysis and Resultsmentioning

confidence: 99%

Evolution of COVID-19 Pandemic in India

Ali

Srivastava

Verma

2020

Preprint

Self Cite

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“…In this paper, we have proposed three regression models for the prediction of death cases by COVID-19 in Pakistan and selected quadratic modelling based on the model selection criterion. There are four stages of the epidemic, S1: exponential, S2: power law, S3: linear and S4: flat ( VermaAli, 2020 ). The death cases in Pakistan have entered the phase of balanced and quadratic regression in this term is giving an excellent fit to the data.…”

Section: Discussionmentioning

confidence: 99%

Predictive modeling of COVID-19 death cases in Pakistan

Daniyal¹,

Ogundokun²,

Abid³

et al. 2020

Infectious Disease Modelling

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Background The world is presently facing the challenges posed by COVID-19 (2019-nCoV), especially in the public health sector, and these challenges are dangerous to both health and life. The disease results in an acute respiratory infection that may result in pain and death. In Pakistan, the disease curve shows a vertical trend by almost 256K established cases of the diseases and 6035 documented death cases till August 5, 2020. Objective The primary purpose of this study is to provide the statistical model to predict the trend of COVID-19 death cases in Pakistan. The age and gender of COVID-19 victims were represented using a descriptive study. Method ology: Three regression models, which include Linear, logarithmic, and quadratic, were employed in this study for the modelling of COVID-19 death cases in Pakistan. These three models were compared based on R 2 , Adjusted R 2 , AIC, and BIC criterions. The data utilized for the modelling was obtained from the National Institute of Health of Pakistan from February 26, 2020 to August 5, 2020. Conclusion The finding deduced after the prediction modelling is that the rate of mortality would decrease by the end of October. The total number of deaths will reach its maximum point; then, it will gradually decrease. This indicates that the curve of total deaths will continue to be flat, i.e., it will shift to be constant, which is also the upper bound of the underlying function of absolute death.

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“…In the red circle, some of the countries that have been most infected. As several authors have already discussed [ 10 , 11 , 26 ], we think that the spatial diffusion of Covid-19 follows a power law. In fact, the plots in Fig 1(f) is calculated on a logarithmic scale, representing the number of infected people for the 217 countries and a ship, while the red curve of Fig 1(g) shows how a straight line intercepts this logarithmic distribution.…”

Section: Geo-spatial Diffusion Of Epidemicmentioning

confidence: 57%

“…Some authors have already investigated the infectious disease ecology, from the first mathematical modelling of infectious diseases of Bernoulli [ 4 ], to the Nobel Laureate Ross [ 5 ], to Kermack and McKendrick [ 6 , 7 ], just to cite a few examples. Some of them finding interesting correlations with power law expression [ 8 , 9 ], also for the Covid-19 disease [ 10 , 11 ]. Although we have models of the major currently known infectious diseases [ 12 ], such as HIV [ 13 ], malaria [ 14 ], SARS-coronavirus [ 15 ], rabies [ 16 ], and influenza [ 17 ], the unpredictable behaviour of SARS-CoV-2 has been unexpected.…”

Section: Introductionmentioning

confidence: 99%

On the temporal spreading of the SARS-CoV-2

2020

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The behaviour of SARS-CoV-2 virus is certainly one of the most challenging in contemporary world. Although the mathematical modelling of the virus has made relevant contributions, the unpredictable behaviour of the virus is still not fully understood. To identify some aspects of the virus elusive behaviour, we focused on the temporal characteristics of its course. We have analysed the latency trends the virus has realized worldwide, the outbreak of the hot spots, and the decreasing trends of the pandemic. We found that the spatio-temporal pandemic dynamics shows a complex behaviour. As with physical systems, these changes in the pandemic’s course, which we have called transitional stages of contagion , highlight shared characteristics in many countries. The main results of this work is that the pandemic progression rhythms have been clearly identified for each country, providing the processes and the stages at which the virus develops, thus giving important information on the activation of containment and control measures.

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COVID-19 Pandemic: Power Law Spread and Flattening of the Curve

Abstract: In this paper, we analyze the real-time infection data of COVID-19 epidemic for nine nations. Our analysis is up to May 04,

Cited by 40 publications

References 29 publications

Evolution of COVID-19 Pandemic in India

Evolution of COVID-19 Pandemic in India

Predictive modeling of COVID-19 death cases in Pakistan

On the temporal spreading of the SARS-CoV-2

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