Study of climatology parameters on COVID-19 outbreak in Jordan

Hamdan, M. A.; Dabbour, Loai

doi:10.1007/s12665-022-10348-2

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…19 In another study, climate indicators including average daily temperature, relative humidity, wind velocity, pressure, and concentrations of pollutants were used to predict COVID-19 active cases in the three cities in Jordan using Artificial Neural Networks (ANN) and multiple linear regression. 20 Moreover, Hussein et al proposed three approaches (short-term forecast (STF) model, long-term forecast (LTF) model, and hybrid forecast (HF) model) for predicting COVID-19 pandemic's development in Jordan over a long-term period that took into account various social circumstances like everyday life, curfews, lockdowns, and vaccination. 21 In this work we were able to build a final equation that can predict the probability of infection with COVID-19 in Karak city based on sex and age variables.…”

Section: Introductionmentioning

confidence: 99%

Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan

et al. 2023

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Background: On March 2020, World Health Organization (WHO) labeled coronavirus disease 2019 (COVID-19) as a pandemic. COVID-19 has rapidly increased in Jordan which resulted in the announcement of the emergency state on March 19th, 2020. Despite the variety of research being reported, there is no agreement on the variables that predict COVID-19 infection. This study aimed to test the predictors that probably contributed to the infection with COVID-19 using a binary logistic regression model. Methods: Based on data collected by Google sheet of COVID-19 infected and non-infected persons in Karak city, analysis was applied to predict COVID-19 infection probability using a binary logistic regression model. Results: A total of 386 participants have completed the questionnaire including 323 women and 63 men. Among the participants 295 (76.4%) were aged less than or equal 45 years old, and 91 (23.6%) were aged over 45 years old. Among the 386 participants a total of 275 were infected with COVID-19. The LR chi-square test was used to analyze every demographic characteristic (sex, age, job, smoking, chronic disease, yearly flu injection) in this study to find predictors of the likelihood of COVID-19 infection. The findings indicate that the participants' sex and age are the most important demographic determinants of infection. Cox & Snell R Square (R2 = 0.028) and Nagelkerke R Square (R2 = 0.039) indicators was used to measure model fineness with significant P-value < 0.05. Conclusions: Given a person's age and sex, the final model presented in this study can be used to calculate the probability of infection with COVID-19 in Karak city. This could help aid health-care management and policymakers in properly planning and allocating health-care resources.

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Section: Introductionmentioning

confidence: 99%

Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan

et al. 2023

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“…Research by Hamdan. M [22] showed that air pressure supported the virus's spread in Amman and Zarqa. Recent study indicated that the optimal temperature for spread of COVID-19 ranging from 41-57.2˚F [23].…”

Section: Introductionmentioning

confidence: 99%

A panel regression analysis for the COVID-19 epidemic in the United States

Guo

Duan

et al. 2022

PLoS ONE

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This study explored the roles of epidemic-spread-related behaviors, vaccination status and weather factors during the COVID-19 epidemic in 50 U.S. states since March 2020. Data from March 1, 2020 to February 5, 2022 were incorporated into panel model. The states were clustered by the k-means method. In addition to discussing the whole time period, we also took multiple events nodes into account and analyzed the data in different time periods respectively by panel linear regression method. In addition, influence of cluster grouping and different incubation periods were been discussed. Non-segmented analysis showed the rate of people staying at home and the vaccination dose per capita were significantly negatively correlated with the daily incidence rate, while the number of long-distance trips was positively correlated. Weather indicators also had a negative effect to a certain extent. Most segmental results support the above view. The vaccination dose per capita was unsurprisingly proved to be the most significant factor especially for epidemic dominated by Omicron strains. 7-day was a more robust incubation period with the best model fit while weather had different effects on the epidemic spread in different time period. The implementation of prevention behaviors and the promotion of vaccination may have a successful control effect on COVID-19, including variants’ epidemic such as Omicron. The spread of COVID-19 also might be associated with weather, albeit to a lesser extent.

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Comparisons of different statistical models for analyzing the effects of meteorological factors on COVID-19

Zheng,

Guo,

et al. 2023

Frigid Zone Medicine

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Objective This general non-systematic review aimed to gather information on reported statistical models examing the effects of meteorological factors on coronavirus disease 2019 (COVID-19) and compare these models. Methods PubMed, Web of Science, and Google Scholar were searched for studies on “meteorological factors and COVID-19” published between January 1, 2020, and October 1, 2022. Results The most commonly used approaches for analyzing the association between meteorological factors and COVID-19 were the linear regression model (LRM), generalized linear model (GLM), generalized additive model (GAM), and distributed lag non-linear model (DLNM). In addition to these classical models commonly applied in environmental epidemiology, machine learning techniques are increasingly being used to select risk factors for the outcome of interest and establishing robust prediction models. Conclusion Selecting an appropriate model is essential before conducting research. To ensure the reliability of analysis results, it is important to consider including non-meteorological factors (e.g., government policies on physical distancing, vaccination, and hygiene practices) along with meteorological factors in the model.

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Study of climatology parameters on COVID-19 outbreak in Jordan

Cited by 3 publications

References 20 publications

Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan

Predicting infection with COVID-19 disease using logistic regression model in Karak City, Jordan

A panel regression analysis for the COVID-19 epidemic in the United States

Comparisons of different statistical models for analyzing the effects of meteorological factors on COVID-19

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