Could Lockdown Really Lockdown Covid-19 Spread: A Data-based Analysis in India

Abstract

Objective: To compare the impact of lockdown in Maharashtra, Kerala and United States. Methodology: We conducted a prospective observational study in order to assess and compare the impact of lockdown between Kerala, Maharashtra and United States (US). The study involved the collection of data from two phases. The first phase is the time from the index case reported in each country till the 14 th day after the declaration of lockdown in that country. The second phase involved the data collected from the 15 th day to the 28 th day of lockdown. Data storage and analysis were performed using Microsoft excel and SPSS version 24. Result: As per 21 st April, 426 cases were reported in Kerala among which 307 (70.07%) recovered, active cases were 117 and 2 deaths (0.4694%). Whereas in Maharashtra 7628 cases were reported and among which 1076 had recovered and there was 323 deaths. The total number of cases reported before lockdown in Maharashtra was 97 and in Kerala was 95. After the 29 days of lockdown the increase in the number of cases in Maharashtra was significantly higher when compared with Kerala. Also, the recovery rate before and after lockdown in Kerala was 4.10% and 72.53% respectively. In Maharashtra recovery rate before and after was 70.93% and 14.00% respectively. Conclusion: The implementation of lockdown had a great impact on reducing the transmission of infection across the country. The number of cases also reduced due to this lockdown, but its effectiveness varied from place to place.

Does COVID-19 affect metro use in Taipei?

“…In response to this serious threat, many cities or countries have implemented border controls, lockdowns, social distancing, and quarantines to contain the spread of COVID-19 ( Cheng et al, 2020a , Cheng et al, 2020b ). 1 While these public health measures are effective in reducing the prevalence of COVID-19 ( Jarvis et al, 2020 ; Qiu et al, 2020 ; Saijan et al, 2020 ), they have changed many aspects of our lives. Public transportation has been especially impacted, with drastic drops in ridership across many cities worldwide ( Comfort, 2020 ).…”

What If…? Pandemic policy-decision-support to guide a cost-benefit-optimised, country-specific response

“…Nevertheless, mining this data remains complex, and only few studies have attempted to provide models for policy decision support. Furthermore, they are mostly limited in their scope, often only focus on single countries [12][13][14], single policies [15,16], single impact measures [17] or a finite time span [18]. Many do not make use of machine learning for updatable insights compatible with real time data streams and most only provide retrospective analyses, associating policies to proxies of the assumed impact.…”