Systematic Assessment of COVID-19 Pandemic in Bangladesh: Effectiveness of Preparedness in the First Wave

Background: To develop an effective countermeasure and determine our susceptibilities to the outbreak of COVID-19 is challenging for a densely populated developing country like Bangladesh and a systematic review of the disease on a continuous basis is necessary.Methods: Publicly available and globally acclaimed datasets (4 March 2020–30 September 2020) from IEDCR, Bangladesh, JHU, and ECDC database are used for this study. Visual exploratory data analysis is used and we fitted a polynomial model for the number of deaths. A comparison of Bangladesh scenario over different time points as well as with global perspectives is made.Results: In Bangladesh, the number of active cases had decreased, after reaching a peak, with a constant pattern of death rate at from July to the end of September, 2020. Seventy-one percent of the cases and 77% of the deceased were males. People aged between 21 and 40 years were most vulnerable to the coronavirus and most of the fatalities (51.49%) were in the 60+ population. A strong positive correlation (0.93) between the number of tests and confirmed cases and a constant incidence rate (around 21%) from June 1 to August 31, 2020 was observed. The case fatality ratio was between 1 and 2. The number of cases and the number of deaths in Bangladesh were much lower compared to other countries.Conclusions: This study will help to understand the patterns of spread and transition in Bangladesh, possible measures, effectiveness of the preparedness, implementation gaps, and their consequences to gather vital information and prevent future pandemics.

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Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

10.2196/preprints.24226 ◽

2020 ◽

Cited By ~ 2

Author(s):

Jayesh S

Keyword(s):

Data Analysis ◽

Exploratory Data Analysis ◽

Case Fatality ◽

Public Health Emergency ◽

Virus Spread ◽

Analysis Model ◽

Case Fatality Ratio ◽

First Case ◽

Exploratory Data ◽

The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

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Covid-19 Cases in India: A Visual Exploratory Data Analysis Model

10.1101/2020.09.11.20193029 ◽

2020 ◽

Author(s):

Jayesh S ◽

Shilpa Sreedharan

Keyword(s):

Data Analysis ◽

Exploratory Data Analysis ◽

Case Fatality ◽

Public Health Emergency ◽

Virus Spread ◽

Analysis Model ◽

Case Fatality Ratio ◽

First Case ◽

Exploratory Data ◽

The Government

Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

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COVID-19: Finally on wane, with reduced lethality

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210811130046 ◽

2021 ◽

Vol 24 ◽

Author(s):

Deepak Sharma ◽

Shalimar

Keyword(s):

Death Rate ◽

Case Fatality ◽

Healthy People ◽

Third Wave ◽

Vaccination Programs ◽

Case Fatality Ratio ◽

Pathogenic Variants

: As most of the countries have already passed the peaks of the second or third wave of infections, COVID-19 is (finally) on the wane and will soon touch the baseline persistence with a reduced death rate. The case fatality ratio (CFR) or lethality is presently only ~2.0%, which is relatively low in comparison to the high of 14% in April 2020. More importantly, the lethality in healthy people is 20 times lower. In addition, successful vaccination programs are currently underway in several countries. Thus, although new pathogenic variants, as well as a few isolated cases of resurgence, will continue to emerge, the worst seems to be over, and we will surely win this battle soon!

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Towards Understanding the COVID-19 Case Fatality Rate

10.1101/2021.03.01.21252698 ◽

2021 ◽

Author(s):

Donghui Yan ◽

Aiyou Chen ◽

Buqing Yang

Keyword(s):

Public Health ◽

Case Fatality Rate ◽

Exploratory Data Analysis ◽

Age Groups ◽

Case Fatality ◽

Age Effect ◽

Fatality Rate ◽

Apparent Discrepancy ◽

Exploratory Data ◽

Second Wave

AbstractAn important parameter for COVID-19 is the case fatality rate (CFR). It has been applied to wide applications, including the measure of the severity of the infection, the estimation of the number of infected cases, risk assessment etc. However, there remains a lack of understanding on several aspects of CFR, including population factors that are important to CFR, the apparent discrepancy of CFRs in different countries, and how the age effect comes into play. We analyze the CFRs at two different time snapshots, July 6 and Dec 28, with one during the first wave and the other a second wave of the COVID-19 pandemic. We consider two important population covariates, age and GDP as a proxy for the quality and abundance of public health. Extensive exploratory data analysis leads to some interesting findings. First, there is a clear exponential age effect among different age groups, and, more importantly, the exponential index is almost invariant across countries and time in the pandemic. Second, the roles played by the age and GDP are a little surprising: during the first wave, age is a more significant factor than GDP, while their roles have switched during the second wave of the pandemic, which may be partially explained by the delay in time for the quality and abundance of public health and medical research to factor in.

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Evaluating alternative hypotheses to explain the downward trend in the indices of the COVID-19 pandemic death rate

PeerJ ◽

10.7717/peerj.11150 ◽

2021 ◽

Vol 9 ◽

pp. e11150

Author(s):

Sonali Shinde ◽

Pratima Ranade ◽

Milind Watve

Keyword(s):

Death Rate ◽

Time Lag ◽

Herd Immunity ◽

Case Fatality ◽

Downward Trend ◽

Treatment Protocols ◽

Systematic Analysis ◽

Case Fatality Ratio ◽

Alternative Hypotheses ◽

Global Data

Background In the ongoing Covid-19 pandemic, in the global data on the case fatality ratio (CFR) and other indices reflecting death rate, there is a consistent downward trend from mid-April to mid-November. The downward trend can be an illusion caused by biases and limitations of data or it could faithfully reflect a declining death rate. A variety of explanations for this trend are possible, but a systematic analysis of the testable predictions of the alternative hypotheses has not yet been attempted. Methodology We state six testable alternative hypotheses, analyze their testable predictions using public domain data and evaluate their relative contributions to the downward trend. Results We show that a decline in the death rate is real; changing age structure of the infected population and evolution of the virus towards reduced virulence are the most supported hypotheses and together contribute to major part of the trend. The testable predictions from other explanations including altered testing efficiency, time lag, improved treatment protocols and herd immunity are not consistently supported, or do not appear to make a major contribution to this trend although they may influence some other patterns of the epidemic. Conclusion The fatality of the infection showed a robust declining time trend between mid April to mid November. Changing age class of the infected and decreasing virulence of the pathogen were found to be the strongest contributors to the trend.

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Downward Trend in the Indices of Death Rate in the COVID-19 Pandemic: Evaluating Alternative Hypotheses

10.20944/preprints202008.0648.v1 ◽

2020 ◽

Author(s):

Milind Watve ◽

Sonali Shinde ◽

Pratima Ranade

Keyword(s):

Death Rate ◽

Time Lag ◽

Herd Immunity ◽

Case Fatality ◽

Downward Trend ◽

Treatment Protocols ◽

Systematic Analysis ◽

Case Fatality Ratio ◽

Alternative Hypotheses ◽

Global Data

In the ongoing Covid-19 pandemic, in the global data on the case fatality ratio and other indices reflecting death rate, there is a consistent downward trend from mid-April to mid-August. The downward trend can be an illusion caused by biases and limitations of data or it could faithfully reflect a declining death rate. A variety of explanations for this trend are possible, but a systematic analysis of the testable predictions of the alternative hypotheses has not yet been attempted. We state six testable alternative hypotheses, analyse their testable predictions using public domain data and evaluate their relative contributions to the downward trend. We show that a decline in the death rate is real; changing age structure of the infected population and evolution of the virus towards reduced virulence are the most supported hypotheses and together contribute to major part of the trend. The testable predictions from other explanations including altered testing efficiency, time lag, improved treatment protocols and herd immunity are not consistently supported, or do not appear to make a major contribution to this trend although they may influence some other patterns of the epidemic.

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