Data based model for predicting COVID-19 morbidity and mortality in metropolis

Barcellos, Demian da Silveira; Fernandes, Giovane Matheus Kayser; Souza, Fábio Teodoro de

doi:10.1038/s41598-021-04029-6

Cited by 18 publications

(12 citation statements)

References 41 publications

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“…Statistical analysis of confirmed COVID-19 data and local meteorological variables in Manaus revealed that low solar radiation cycles may lead to increased COVID-19 deaths due to reduced solar radiation. Dry spells may impair nasal functions that prevent viruses and bacteria from entering the body, leading to increased mortality from COVID-19 [ 47 ]. A study in Italy found that cities with high wind speeds had fewer COVID-19 infections, and inland cities with low wind speeds and high air pollution had higher COVID-19 infections [ 48 ].…”

Section: Results Of the Metrological Analysismentioning

confidence: 99%

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The Atmospheric Environment Effects of the COVID-19 Pandemic: A Metrological Study

Chen

Shi

2022

IJERPH

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Since the COVID-19 outbreak, the scientific community has been trying to clarify various problems, such as the mechanism of virus transmission, environmental impact, and socio-economic impact. The spread of COVID-19 in the atmospheric environment is variable and uncertain, potentially resulting in differences in air pollution. Many scholars are striving to explore the relationship between air quality, meteorological indicators, and COVID-19 to understand the interaction between COVID-19 and the atmospheric environment. In this study, we try to summarize COVID-19 studies related to the atmospheric environment by reviewing publications since January 2020. We used metrological methods to analyze many publications in Web of Science Core Collection. To clarify the current situation, hotspots, and development trends in the field. According to the study, COVID-19 research based on the atmospheric environment has attracted global attention. COVID-19 and air quality, meteorological factors affecting the spread of COVID-19, air pollution, and human health are the main topics. Environmental variables have a certain impact on the spread of SARS-CoV-2, and the prevalence of COVID-19 has improved the atmospheric environment to some extent. The findings of this study will aid scholars to understand the current situation in this field and provide guidance for future research.

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Section: Results Of the Metrological Analysismentioning

confidence: 99%

“…Dry air may impair nasal functions that prevent viruses and bacteria from entering the body, leading to increased mortality from COVID-19. Low solar radiation cycles may lead to higher COVID-19 mortality due to reduced solar radiation [ 47 ]. Rising temperatures could help curb the spread of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

The Atmospheric Environment Effects of the COVID-19 Pandemic: A Metrological Study

Chen

Shi

2022

IJERPH

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“…Other researchers used data mining to study environmental and meteorological variables and determine their correlation with the number of COVID-19 cases in three cities in Brazil. The model they developed was successful in predicting the number of cases and deaths in the cities they studied [ 16 ]. There was plenty of work carried out on predicting the number of cases.…”

Section: Introductionmentioning

confidence: 99%

A Pathfinding Algorithm for Lowering Infection Exposure of Healthcare Personnel Working in Makeshift Hospitals

2022

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Due to the recent COVID-19 outbreak, makeshift (MS) hospitals have become an important feature in healthcare systems worldwide. Healthcare personnel (HCP) need to be able to navigate quickly, effectively, and safely to help patients, while still maintaining their own well-being. In this study, a pathfinding algorithm to help HCP navigate through a hospital safely and effectively is developed and verified. Tests are run using a discretized 2D grid as a representation of an MS hospital plan, and total distance traveled and total exposure to disease are measured. The influence of the size of the 2D grid units, the shape of these units, and degrees of freedom in the potential movement of the HCP are investigated. The algorithms developed are designed to be used in MS hospitals where airborne illness is prevalent and could greatly reduce the risk of illness in HCP. In this study, it was found that the quantum-based algorithm would generate paths that accrued 50%–66% less total disease quantum than the shortest path algorithm with also about a 33%–50% increase in total distance traveled. It was also found that the mixed path algorithm-generated paths accrued 33%–50% less quantum, but only increased total distance traveled by 10%–20%.

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“…This challenge can be treated as the only limitation of the present study, developing the prediction models for COVID-19 cases. Prediction models [37][38][39] that combine several features to estimate the risk of infection have been developed, in the hope of assisting medical staff worldwide in trigging patients, especially in the context of limited health-care resources.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach to predict COVID-19 cases using neural networks and inverse problem

Paul

Lorin

2022

Preprint

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We derive a novel hybrid approach, a combination of neural networks and inverse problem, in order to forecast COVID-19 cases, and more generally any infectious disease. For this purpose, we extract a second order nonlinear differential equation for the total confirmed cases from a SIR-like model. That differential equation is the key factor of the present study. The neural network and inverse problems are used to compute the trial functions for total cases and the model parameters, respectively. The number of suspected and infected individuals can be found using the trial function of total confirmed cases. We divide the time domain into two parts, training interval (first 365/395 days) and test interval (first 366 to 395/ 396 to 450 days), and train the neural networks on the preassigned training zones. To examine the efficiency and effectiveness, we apply the proposed method to Canada, and use the Canadian publicly available database to estimate the parameters of the trial function involved with total cases. The trial functions of model parameters show that the basic reproduction number was closed to unity over a wide range, the first from 100 to 365 days of the current pandemic in Canada. The proposed prediction models, based on influence of previous time and social economic policy, show excellent agreement with the data. The test results revel that the single path prediction can forecast a period of 30 days, and forecasting using previous social and economical situation can forecast a range of 55 days.

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Data based model for predicting COVID-19 morbidity and mortality in metropolis

Cited by 18 publications

References 41 publications

The Atmospheric Environment Effects of the COVID-19 Pandemic: A Metrological Study

The Atmospheric Environment Effects of the COVID-19 Pandemic: A Metrological Study

A Pathfinding Algorithm for Lowering Infection Exposure of Healthcare Personnel Working in Makeshift Hospitals

A hybrid approach to predict COVID-19 cases using neural networks and inverse problem

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