Body Mass Index (BMI) and Coronavirus Disease 2019 (COVID-19): A Living Systematic Review

Castillo, José Miguel Soriano del; González, Aránzazu; Soler, Carla; Catalá-Gregori, A.I.; Merino-Torres, Juan Francisco

doi:10.20944/preprints202005.0166.v1

Cited by 4 publications

(4 citation statements)

References 65 publications

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“…The mean BMI value of severe COVID-19 patients ranged from 24.5 to 33.4 kg / m 2 , versus 22.0 to 24.3 kg / m 2 for non-severe patients was informed. The use of BMI is important during this pandemic, as it should be applied to nutritional support therapy during hospitalization of infected patients, as well as being considered in the home confinement population [30] . The COVID-19 pandemic intensifies the causes of vulnerability and deficiencies in global food systems, understood as all activities and processes that affect food production, distribution and consumption.…”

Section: Resultsmentioning

confidence: 99%

Food security in the time of Coronavirus (COVID-19). The Argentine case

Giannuzzi¹

2020

ijfnr

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The context of pandemic of the human coronavirus COVID-19, preventive strategies based on mandatory social isolation (quarantine) were imposed by governments to reduce transmission in the community. However, they have had a strong impact on the economies of the countries and on the people as massive layoffs, decreased wages, uncertainty, inability to work formally, increasing precariousness and inequality and food insecurity. Without a doubt, the pandemic surprised Argentina without the necessary tools to amortize the attacks of the disease, and all the collateral consequences that emerge from it. The concept of food insecurity represents a situation or a process experienced by households in which there is a limited and uncertain availability of the quantity and quality of food that allow covering the nutritional requirements of people, thus as an also limited and uncertain availability of the ability to acquire them in an acceptable way from a social and cultural perspective. This work addresses household food insecurity at the micro-social level based on their own surveys and describes the strategies carried out by households in the metropolitan area of Buenos Aires city, Argentina to withstand the effect. FI status was assessed among 200 adult university students a mean age of 28±6 years (57% female, 43% male) during isolation period in April -May 2020 using the food insecurity experience scale (FIES). The students responded affirmatively and with a higher percentage value to the items related to the lack of money to consume healthy and nutritious food. Food security status of the 200 students survey, 23.5 per cent (n = 47) were food secure. Food insecurity was reported by 76.5% (n = 153). Of these, 50% (n = 100) were mild food insecurity, 25.5% (n = 51) were moderate, while 1% (n = 2) were severe food insecurity. Food insecurity derived from access, as well as the difficulties of finding variety in food at affordable prices due to the obstacles imposed by the isolation affected food security. The changes on food intake during quarantine period was also analysed. In the current particular situation, it is advisable to improve the consumption of fresh food. Meat consumption has also been altered, shifting its consumption to cuts of less economic value. Among the reasons for this change in eating habits during this period, it is possible that the increase in stress, anxiety due to isolation, as well as the economic reasons and those that result from difficulties in accessing points of sale could explain these changes.

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Section: Resultsmentioning

confidence: 99%

Food security in the time of Coronavirus (COVID-19). The Argentine case

Giannuzzi¹

2020

ijfnr

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“…Furthermore, overcoming the population’s natural immunity, the type of strain is another numerator for the susceptible. Body mass index (BMI) is said to have an influence on developing or aggravating the disease, but the studies are complicated by the nutrition factors that should oppose the effect of BMI, and we chose not to consider this in our model [ 19 ].…”

Section: Prediction Models and Their Componentsmentioning

confidence: 99%

4P Model for Dynamic Prediction of COVID-19: a Statistical and Machine Learning Approach

et al. 2021

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Around the world, scientists are racing hard to understand how the COVID-19 epidemic is spreading and growing, thus trying to find ways to prevent it before medications are available. Many different models have been proposed so far correlating different factors. Some of them are too localized to indicate a general trend of the pandemic while some others have established transient correlations only. Hence, in this study, taking Bangladesh as a case, a 4P model has been proposed based on four probabilities (4P) which have been found to be true for all affected countries. Efficiency scores have been estimated from survey analysis not only for governing authorities on managing the situation ( P ( G )) but also for the compliance of the citizens (( P ( P )). Since immune responses to a specific pathogen can vary from person to person, the probability of a person getting infected (( P ( I )) after being exposed has also been estimated. And the vital one is the probability of test positivity (( P ( T )) which is a strong indicator of how effectively the infected people are diagnosed and isolated from the rest of the group that affects the rate of growth. All the four parameters have been fitted in a non-linear exponential model that partly updates itself periodically with everyday facts. Along with the model, all the four probabilistic parameters are engaged to train a recurrent neural network using long short-term memory neural network and the followed trial confirmed a ruling functionality of the 4Ps.

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“…Furthermore, overcoming the population's natural immunity, the type of strain is another numerator for the susceptible. Body Mass Index (BMI) is said to have an influence on developing or aggravating the disease, but the studies are complicated by the nutrition factors that should oppose the effect of BMI, and we chose not to consider this in our model [35].…”

Section: Prediction Models and Their Componentsmentioning

confidence: 99%

4P Model for Dynamic Prediction of the Covid-19: A Statistical and Machine Learning Approach

Hasan

Ahmmed

Chowdhury

et al. 2020

Preprint

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Introduction: Around the world, scientists are racing hard to understand how Covid-19 epidemic is spreading and growing, thus trying to find ways to prevent it before medications come to pass. Many different models have been proposed so far correlating different factors. Some of them are too localized to indicate a general trend of the pandemic while some others have established transient correlations only.Methods: Hence, in this study, a 4P model has been proposed based on four probabilities (4P) which has been found to be true for all affected countries taking Bangladesh as a case. Efficiency scores have been estimated from survey analysis not only for governing authorities on managing the situation (P(G)) but also for the compliance of the citizens ((P(P)). Since the immune responses of all the people are not uniform to a specific pathogen, the probability of a person getting infected ((P(I)) after being exposed has also been estimated. And the vital one is the probability of Test Positivity ((P(T)) which is a strong indicator of how effectively the infected people are diagnosed and isolated from the rest of the group that affects the rate of growth.Results and Conclusion: All the four parameters have been fitted in a non-linear exponential model that partly updates itself periodically with everyday facts. Along with the model, all the four probabilistic parameters are engaged to train a recurrent neural network using Long-Short Term Memory neural network and the followed trial confirmed a ruling functionality of the 4Ps.

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Body Mass Index (BMI) and Coronavirus Disease 2019 (COVID-19): A Living Systematic Review

Cited by 4 publications

References 65 publications

Food security in the time of Coronavirus (COVID-19). The Argentine case

Food security in the time of Coronavirus (COVID-19). The Argentine case

4P Model for Dynamic Prediction of COVID-19: a Statistical and Machine Learning Approach

4P Model for Dynamic Prediction of the Covid-19: A Statistical and Machine Learning Approach

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