Skip to content
Research Article Open access CC BY 4.0

Doubling Time and Its Interpretation for COVID-19 Cases: A Comparative Study in Pimpri Chinchwad Municipal Corporation, Pune, Maharashtra, India

Parth Aphale, Aditya Dilipkumar Patil, Dharmendra B. Sharma

Journal of Pharmaceutical Research International · pp. 75–83 · Published 29 Jul 2021

10.9734/jpri/2021/v33i39A32144

Abstract

Background: COVID-19, also known as Novel Corona Virus, causes respiratory disorder in humans and has been declared as a global pandemic in the first quarter of the year 2020 by the World Health Organization. As this pandemic persists the second, third and fourth quarters had shown variation in COVID-19 cases. In the first quarter of the year 2021 again there seems to be a rise in cases of COVID-19. So, a comparative analysis from March 2020 – April 2021 of COVID-19 cases has been studied. Methodology: An exponential statistical model was used to calculate the predicted value of COVID-19 using doubling time for a short duration. The cases of COVID-19 were predicted from 10th April 2021 to 10th June 2021 using doubling time and exponential regression method. Results: The distribution of cases showed a curve-linear trend over the last one year. We explored various models like exponential, logarithmic (lo-linear), linear, quadratic, and generalized linear model (GLM) to fit into the observed distribution of cases. Since the distribution of cases was perfectly following a linear trend in each of the four segments, we applied a linear regression model to observed distribution and then predicted the anticipated no. of cases by extending this linear trend to a future period (120 day). With the help of the exponential statistical model, doubling time/rate based on prior 9 days, the predictions of up to 2.61 lakhs cases have been done by the end of June 2021. Conclusion: This study will be useful for the Government of India and Maharashtra state-specific to Pimpri- Chinchwad Municipal Corporation (PCMC), Administrative Units, Frontline health workforce, researchers, and scientists. From this study it has been evident that during the pre-lockdown period, in the initial stages there seems to be rise in cases. While, during lockdown period it was observed that there was relatively decrease in the number of cases. After the government authorities imposed the unlock strategies the cases began to rise as compared to the lockdown period. Thus, it appears that only essential services should be open for the citizens of India and the state lockdown should be carried on for the next 3 months (April 2021 - June 2021).

COVID-19 doubling time generalized linear model

Cited by 10

Modeling and Global Sensitivity Analysis of Strategies to Mitigate Covid-19 Transmission on a Structured College Campus

Lihong Zhao, Fabian Santiago, Erica M. Rutter · Bulletin of Mathematical Biology · 2022

Mathematical Modelling and Control of COVID-19 Transmission in the Presence of Exposed Immigrants

R. Gweryina, C. E. Madubueze, Martins Afam Nwaokolo · Communication in Biomathematical Sciences · 2021

Estimation of the doubling time and reproduction number for COVID-19

Shamim Ahmed, M. Shemanto, H. Azhari · Computer Methods in Biomechanics and Biomedical Engineering · 2021

Stochastic analysis of the relationship between atmospheric variables and coronavirus disease (COVID‐19) in a hot, arid climate

M. Yassin, Hassan A. Aldashti · Integrated Environmental Assessment and Management · 2021

Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis

Kamal Kishore, Vidushi Jaswal, Madhur Verma · JMIR Public Health and Surveillance · 2021

The Impact of Health Policies and Sociodemographic Factors on Doubling Time of the COVID-19 Pandemic in Mexico

L. Díaz-Castro, H. Cabello-Rangel, K. Hoffman · International Journal of Environmental Research and Public Health · 2021

Association between the dynamics of the COVID-19 epidemic and ABO blood type distribution

Yue-Fei Liu, Lisa Häussinger, J. Steinacker · Epidemiology and Infection · 2021

Article metrics

Real usage data collected on this platform.

29

Page views

0

PDF downloads

10

Outbound clicks

10

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.