Epidemiological Analysis of SARS-CoV-2 Transmission Dynamics in the State of Odisha, India: A Yearlong Exploratory Data Analysis

Nasker, Sourya Subhra; Nanda, Ananya; Balamurugan, Ramadass; Nayak, Sasmita

doi:10.3390/ijerph182111203

Cited by 2 publications

(2 citation statements)

References 55 publications

(73 reference statements)

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“…In the state of Odisha, the first wave of started in June 2020 lasting until November 2020 with maximum peak in the month of September. The first COVID-19 wave in Odisha, declined by early December 2020 [21]. 1).…”

Section: Resultsmentioning

confidence: 99%

Comparison of COVID19(SARS-COV-2) infection, based on gender, age group and sequential waves by RT-PCR analysis among the population of western districts of Odisha

Panda¹,

Lopamudra²,

Dalai³

et al. 2023

JOAPR

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As SARS-CoV-2 has rapidly spread all over the world, it is evident that patient’s gender, age, and comorbidity might render these patients more vulnerable to either increased mortality or increased risk of infection. In the first wave of pandemic, percentage of infection was 5% in May 2020, which rose to 23% in August. Towards the end of the year, it was around 1.8%. In second wave, infection gradually increases to 3.4% in the later part of March 2021, reaching maximum of 31% in April. of the recorded positive cases, infection rate in male population was comparatively higher them the female. In September 2020 where the rate of infection was highest in the first wave the male infectivity rate is 16% compared to 7.13% in females. In April 2021, male infection rate was at 21% compared to 9.7% in the female population. The positivity rate in male population was at 3.7% in third wave of infection. During this period the positivity rate in female population was at 1.87%. Our study shows the positivity rare was maximum in the age group of 18-29(25.5%) followed by 30-39(23.5%) and 40-49(17.2%) in 2020. Similar trend was observed in the year 2021 where the infection rate was 24.2%,23.6%,18.46% in the age group of 18-29,30-39 and 40-49 respectively. However, in 2022 maximum infection was recorded in the age group of 18-29 at around 29%, followed by age group of 30-39(20.9%) and 6-17(15%).

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Section: Resultsmentioning

confidence: 99%

Comparison of COVID19(SARS-COV-2) infection, based on gender, age group and sequential waves by RT-PCR analysis among the population of western districts of Odisha

Panda¹,

Lopamudra²,

Dalai³

et al. 2023

JOAPR

View full text Add to dashboard Cite

show abstract

“…Variables in the research datasets were converted into different categorized data, new indicators were developed using ratios, percentages, and sums, and the results were presented through descriptive tables and charts 3 [56,66,76] Graphical trajectory analysis was used to estimate the time lag between peak days of cases and of deaths. 1 [30] Probability distribution was used to estimate the probability of occurrence of extreme epidemics /COVID-19 resurgence 2 [60,61] In addition to descriptive tables and charts, other statistical analysis were also used, such as ANOVAs, regressions, and the like.…”

Section: Exploratory Data Analysismentioning

confidence: 99%

COVID-19 Pandemic Risk Assessment: Systematic Review

Chu,

Kwok,

Chan

et al. 2024

RMHP

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Background:The COVID-19 pandemic presents the possibility of future large-scale infectious disease outbreaks. In response, we conducted a systematic review of COVID-19 pandemic risk assessment to provide insights into countries' pandemic surveillance and preparedness for potential pandemic events in the post-COVID-19 era. Objective: We aim to systematically identify relevant articles and synthesize pandemic risk assessment findings to facilitate government officials and public health experts in crisis planning. Methods: This study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and included over 620,000 records from the World Health Organization COVID-19 Research Database. Articles related to pandemic risk assessment were identified based on a set of inclusion and exclusion criteria. Relevant articles were characterized based on study location, variable types, data-visualization techniques, research objectives, and methodologies. Findings were presented using tables and charts. Results: Sixty-two articles satisfying both the inclusion and exclusion criteria were identified. Among the articles, 32.3% focused on local areas, while another 32.3% had a global coverage. Epidemic data were the most commonly used variables (74.2% of articles), with over half of them (51.6%) employing two or more variable types. The research objectives covered various aspects of the COVID-19 pandemic, with risk exposure assessment and identification of risk factors being the most common theme (35.5%). No dominant research methodology for risk assessment emerged from these articles. Conclusion: Our synthesized findings support proactive planning and development of prevention and control measures in anticipation of future public health threats.

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Epidemiological Analysis of SARS-CoV-2 Transmission Dynamics in the State of Odisha, India: A Yearlong Exploratory Data Analysis

Cited by 2 publications

References 55 publications

Comparison of COVID19(SARS-COV-2) infection, based on gender, age group and sequential waves by RT-PCR analysis among the population of western districts of Odisha

Comparison of COVID19(SARS-COV-2) infection, based on gender, age group and sequential waves by RT-PCR analysis among the population of western districts of Odisha

COVID-19 Pandemic Risk Assessment: Systematic Review

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