Temperature, Population and Longitudinal Analysis to Predict Potential Spread for COVID-19

Dangi, Ravi Rai; Mathew, George

doi:10.2139/ssrn.3560786

Cited by 8 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, other studies came to the opposite conclusion (6 out of 61): a positive correlation between COVID-19 and temperature in Jakarta (Tosepu et al, 2020) and New York (Bashir et al, 2020), or no association (9 out of 61) in countries such as Spain (Briz-Redón and Serrano-Aroca, 2020), Iran (Ahmadi et al, 2020;Jahangiri et al, 2020), Nigeria (Taiwo and Fashola, 2020) and in a worldwide study (Jamil et al, 2020). Two worldwide analyses (Kassem,2020) and another one in China (Shahzad et al, 2020) found an unclear association between temperature and COVID-19, or an association depending on the temperature range (11 out of 61) in countries such as Brazil (Auler et al, 2020;Prata et al, 2020), China (Zhu and Xie, 2020) and India (Dangi and George, 2020).…”

Section: Findings 31 Meteorological Variablesmentioning

confidence: 98%

The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques

Briz‐Redón¹,

Serrano‐Aroca

2020

Progress in Physical Geography: Earth and Environment

138

147

View full text Add to dashboard Cite

The new SARS-CoV-2 coronavirus has spread rapidly around the world since it was first reported in humans in Wuhan, China, in December 2019 after being contracted from a zoonotic source. This new virus produces the so-called coronavirus 2019 or COVID-19. Although several studies have supported the epidemiological hypothesis that weather patterns may affect the survival and spread of droplet-mediated viral diseases, the most recent have concluded that summer weather may offer partial or no relief of the COVID-19 pandemic to some regions of the world. Some of these studies have considered only meteorological variables, while others have included non-meteorological factors. The statistical and modelling techniques considered in this research line have included correlation analyses, generalized linear models, generalized additive models, differential equations, or spatio-temporal models, among others. In this paper we provide a systematic review of the recent literature on the effects of climate on COVID-19’s global expansion. The review focuses on both the findings and the statistical and modelling techniques used. The disparate findings reported seem to indicate that the estimated impact of hot weather on the transmission risk is not large enough to control the pandemic, although the wide range of statistical and modelling approaches considered may have partly contributed to the inconsistency of the findings. In this regard, we highlight the importance of being aware of the limitations of the different mathematical approaches, the influence of choosing geographical units and the need to analyse COVID-19 data with great caution. The review seems to indicate that governments should remain vigilant and maintain the restrictions in force against the pandemic rather than assume that warm weather and ultraviolet exposure will naturally reduce COVID-19 transmission.

show abstract

Section: Findings 31 Meteorological Variablesmentioning

confidence: 98%

The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques

Briz‐Redón¹,

Serrano‐Aroca

2020

Progress in Physical Geography: Earth and Environment

138

147

View full text Add to dashboard Cite

show abstract

“…Dangi et al. [7] proposed a short term weather forecasting based method on wavelet denoising and catboost algorithm to predict the upcoming COVID-19 outbreak in 35 major cities in India (March and April 2020) by correlating the temperature factor of five major cities in the world. This study is based on population density and the correlation of temperature with selected cities where the COVID-19 outbreak has already become a pandemic.…”

Section: Related Workmentioning

confidence: 99%

Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches

Malki

Atlam

Hassanien

et al. 2020

Chaos, Solitons & Fractals

196

129

View full text Add to dashboard Cite

Nowadays, a significant number of infectious diseases such as human coronavirus disease (COVID-19) are threatening the world by spreading at an alarming rate. Some of the literatures pointed out that the pandemic is exhibiting seasonal patterns in its spread, incidence and nature of the distribution. In connection to the spread and distribution of the infection, scientific analysis that answers the questions whether the next summer can save people from COVID-19 is required. Many researchers have been exclusively asked whether high temperature during summer can slow down the spread of the COVID-19 as it has with other seasonal flues. Since there are a lot of questions that are unanswered right now, and many mysteries aspects about the COVID-19 that is still unknown to us, in-depth study and analysis of associated weather features are required. Moreover, understanding the nature of COVID-19 and forecasting the spread of COVID-19 request more investigation of the real effect of weather variables on the transmission of the COVID-19 among people. In this work, various regressor machine learning models are proposed to extract the relationship between different factors and the spreading rate of COVID-19. The machine learning algorithms employed in this work estimate the impact of weather variables such as temperature and humidity on the transmission of COVID-19 by extracting the relationship between the number of confirmed cases and the weather variables on certain regions. To validate the proposed method, we have collected the required datasets related to weather and census features and necessary prepossessing is carried out. From the experimental results, it is shown that the weather variables are more relevant in predicting the mortality rate when compared to the other census variables such as population, age, and urbanization. Thus, from this result, we can conclude that temperature and humidity are important features for predicting COVID-19 mortality rate. Moreover, it is indicated that the higher the value of temperature the lower number of infection cases.

show abstract

“…Many works have been reported in the literature to predict COVID-19 spreading. Dangi and George [6] used a novel weather forecasting method for predicting the outbreak of COVID-19 India. ey correlated the temperature factor with coronavirus cases of five most affected cities worldwide.…”

Section: Related Workmentioning

confidence: 99%

Impact of Weather Predictions on COVID‐19 Infection Rate by Using Deep Learning Models

et al. 2021

View full text Add to dashboard Cite

Nowadays, the whole world is facing a pandemic situation in the form of coronavirus diseases (COVID-19). In connection with the spread of COVID-19 confirmed cases and deaths, various researchers have analysed the impact of temperature and humidity on the spread of coronavirus. In this paper, a deep transfer learning-based exhaustive analysis is performed by evaluating the influence of different weather factors, including temperature, sunlight hours, and humidity. To perform all the experiments, two data sets are used: one is taken from Kaggle consists of official COVID-19 case reports and another data set is related to weather. Moreover, COVID-19 data are also tested and validated using deep transfer learning models. From the experimental results, it is shown that the temperature, the wind speed, and the sunlight hours make a significant impact on COVID-19 cases and deaths. However, it is shown that the humidity does not affect coronavirus cases significantly. It is concluded that the convolutional neural network performs better than the competitive model.

show abstract

Temperature, Population and Longitudinal Analysis to Predict Potential Spread for COVID-19

Cited by 8 publications

References 0 publications

The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques

The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques

Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches

Impact of Weather Predictions on COVID‐19 Infection Rate by Using Deep Learning Models

Contact Info

Product

Resources

About