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

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.

Seasonal and Climatic Variation in the Incidence of Adult Acute Appendicitis: A Seven Year Longitudinal Analysis

Introduction Acute appendicitis represents an extremely common surgical emergency, yet its aetiology remains uncertain. A multifactorial understanding of its causation has emerged along with increasing evidence of seasonal variation. This study seeks to find evidence for such a circannual trend within the UK, and further assess key meteorological indicators which may be causative of any such variation. Methods The patient records of a region health body in the North East of England were retrospectively assessed over a 7-year period. Incidence of acute appendicitis was recorded and averaged by month before undergoing statistical analysis for variation and correlation with average temperature, sunlight hours, and rainfall. Results The incidence of acute appendicitis revealed significant seasonal variation with only 38 total cases in the months of January compared to 73 in July, a 92.1% increase. Only a weak correlation was seen between incidence and average sunlight hours/rainfall, however a significant, positive correlation was found between incidence and average temperature ( r=0.58, p=0.48 ). Conclusion Compelling evidence is found to support the existence of a circannual trend for acute appendicitis. Data suggests a seasonal peak in the month of July, accompanied by a low in January, a finding that develops the understanding of this trend from previously equivocal research in the UK.

Comparison of Sungkai Tree-Ring Components and Meteorological Data from Western Java, Indonesia

In order to explore the potential of tree-ring components as climate proxies in Asian tropical area, we performed a systematic comparison between temporal variations in meteorological data – precipitation, relative humidity and sunlight hours – and those in treering parameters – ring width, mean earlywood vessel area and δ18O– in a sungkai disk collected from western Java, Indonesia. Ring width shows a significant positive correlation with precipitation in the last dry season prior to growth period. Ring width is also correlated inversely with sunlight hours in the last dry season. Mean earlywood vessel area shows a significant, positive correlation with precipitation and relative humidity during the rainy season of growth period. The δ18O and δ13C time series of alpha-cellulose samples, which divide each ring into three parts – earlywood, inner latewood and outer latewood – vary, furthermore, from 22‰ to 28‰ and from -28‰ to -24‰, respectively. δ13C results show distinct annual cycles, for which values of earlywood are highest, gradually followed by a decrease. Although δ18O has no such seasonal pattern, annual-averaged δ18O records show an inverse correlation with precipitation and relative humidity in the rainy season of growth period. As described above, multi-components of sungkai tree rings are expected to be useful in paleoclimate reconstruction on a seasonal scale.