COVID 19 in INDIA: Strategies to combat from combination threat of life and livelihood

Krishnakumar, Balaji; Rana, Sravendra

doi:10.1016/j.jmii.2020.03.024

Cited by 90 publications

(60 citation statements)

References 7 publications

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“…Due to the lockdown effect, keeping the vast population of the country in mind, the mortality rate is considered still less in India if compared with some other countries like the USA, China, France, and Italy (Krishnakumar and Rana, 2020).…”

Section: Introductionmentioning

confidence: 99%

Air Quality, Atmospheric Variables and Spread of COVID-19 in Delhi (India): An Analysis

Dutta

Jinsart

2021

Aerosol Air Qual. Res.

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Based on a time series analysis of the criteria pollutants, meteorological parameters, and COVID-19 positive cases, this paper explores the impact of epidemic prevention and control actions on air quality for five different periods of COVID-19 outbreak in Delhi, India. The study found that under the epidemic control measure during 11 May-19 June 2020, the average concentrations of atmospheric air pollutants PM2.5, PM10, NO2, and CO were reduced to 42.15 µg m-3 , 128.68 µg m-3 , 27.31 ppb, and 0.83 ppm respectively, and were 73.85%, 46.48%, 63.43%, and 50.18% lower than the pre-COVID-19 level of January 2020, respectively. The steep fall of PM2.5, NO2, and CO was due to a drastic reduction in vehicular emission, but PM10 did not fall below the National Ambient Air Quality Standard. Between January 2020 to 11 May-19 June 2020 period, the pollutants O3 and SO2 increased significantly by 217.33% and 57.58 % respectively. The rise of SO2 in Delhi was due to long-distance transfer, power plant emissions, and biomass burning. The sharp increase of O3 happened due to accumulation in the atmosphere. During the peak COVID-19 phase (9 April-10 May 2020) Delhi had unprecedented improved AQI classes of II and III only. However, the pivotal pollutants in terms of their cumulative contribution to the AQI classes were PM10 and O3. Low RH and low-temperature situations exhibited positive correlations with the new COVID-19 infection cases in Delhi. Comparatively lower NO2 level in the air demonstrated a significant negative correlation with new COVID-19 cases while average SO2 concentration in the air, when increased to 24.05 ppb, showed a negative correlation with new COVID-19 cases in Delhi. This study indicated a possibility of O3 exhibiting a positive correlation with new COVID-19 cases under the condition of comparatively low temperature and low humidity.

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Section: Introductionmentioning

confidence: 99%

Air Quality, Atmospheric Variables and Spread of COVID-19 in Delhi (India): An Analysis

Dutta

Jinsart

2021

Aerosol Air Qual. Res.

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“…The proportion of those in larger cities was greater than those in medium and smaller cities. Mostly, daily wage workers work in major cities and are most affected by lockdown, which has the primary goal of social distancing, hence for them social distancing is stressful and a difficult pill to swallow [13,14]. Many people find it to have negative effects on their health.…”

Section: Discussionmentioning

confidence: 99%

Awareness regarding Coronavirus pandemic among the population of Sindh, Pakistan: A cross-sectional study

2020

SJCMS

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Methods: This is a cross-sectional descriptive study and was carried out in Sindh, Pakistan. 588 people from different cities of Sindh were asked to fill a questionnaire that tested their attitudes, knowledge, and practices related to the SARS-CoV-2 pandemic. Results: Television and other social media platforms have led to increased dissemination of knowledge among the population. Their increased understanding has led them to be more compliant with adopting precautionary measures, as frequent handwashing was seen in 89.8% and social distancing in 87.2%. The results also showed a greater prevalence of myths among the population. Conclusion: Increased access to correct knowledge can help to dissipate misconceptions and help spread accurate knowledge about the role that the public should play in reducing disease transmission.

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“…9 For India like country, the immediate requirement is to incorporate the visionary strategies to control the community spread. 10 There is therefore need for effective public health measures at community level including those recommended for containment and buffer zones. To achieve this, there is need for evidence based decision making at Central, State and District level.…”

Section: Introductionmentioning

confidence: 99%

Application of Mathematical Modeling in Public Health Decision Making Pertaining to Control of COVID-19 Pandemic in India

Kumar

Roy²

2020

EpidemInt

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The COVID-19 is highly infectious and possibility of spread is more, it is expecting to see large number of cases in the coming days. There is need for effective public health measures at community level including those recommended for containment and buffer zones. To achieve this, there is need for evidence based decision making at all level. Mathematical modeling can be important tool to achieve this. The objective of this research article is to sensitize public health decision makers at Centre, State and District level about application of Mathematical Modeling in decision making for prevention and control of COVID-19. Methodology: BAILEY'S MODEL was applied on secondary data for COVID-19. In this model, the removal rate is calculated which is the percentage of removed persons in the infected population. Further, regression analysis has been done, to show the linear relationship between the total infection rate and the total recovery rate. Linking of this model with decision making has been described. Findings: BAILEY'S MODEL described that when the number of infected is equal to the number of removed patients, the coefficient will reach 100% threshold and the epidemic will be extinguished. In this article as per the Regression Analysis (Linear) of Bailey's Relative Removal Rate (BMRRR), COVID 19, India, it is observed that the trend reached to 100 in the month of mid of September, 2020. So it may be interpreted that at that point of time, the number of infected will be equal to the number of removed patients, and that's why the coefficient will reach 100% threshold. Conclusion: This a very good model to support analysis and interpretation of State/District data (whenever numbers of cases are high) and it will also help in relevant decision making in control activities of COVID 19 Pandemic. This will further help government to take long-term disease prevention and intervention programs.

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COVID 19 in INDIA: Strategies to combat from combination threat of life and livelihood

Cited by 90 publications

References 7 publications

Air Quality, Atmospheric Variables and Spread of COVID-19 in Delhi (India): An Analysis

Air Quality, Atmospheric Variables and Spread of COVID-19 in Delhi (India): An Analysis

Awareness regarding Coronavirus pandemic among the population of Sindh, Pakistan: A cross-sectional study

Application of Mathematical Modeling in Public Health Decision Making Pertaining to Control of COVID-19 Pandemic in India

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