The coronavirus disease that outbreak in 2019 has caused various health issues. According to the WHO, the first positive case was detected in Bangladesh on 7th March 2020, but while writing this paper in June 2021, the total confirmed, recovered, and death cases were 826922, 766266 and 13118, respectively. Due to the emergence of COVID-19 in Bangladesh, the country is facing a major public health crisis. Unfortunately, the country does not have a comprehensive health policy to address this issue. This makes it hard to predict how the pandemic will affect the population. Machine learning techniques can help us detect the disease's spread. To predict the trend, parameters, risks, and to take preventive measure in Bangladesh; this work utilized the Recurrent Neural Networks based Deep Learning methodologies like LongShort-Term Memory. Here, we aim to predict the epidemic's progression for a period of more than a year under various scenarios in Bangladesh. We extracted the data for daily confirmed, recovered, and death cases from March 2020 to August 2021. The obtained Root Mean Square Error (RMSE) values of confirmed, recovered, and death cases indicates that our result is more accurate than other contemporary techniques. This study indicates that the LSTM model could be used effectively in predicting contagious diseases. The obtained results could help in explaining the seriousness of the situation, also mayhelp the authorities to take precautionary steps to control the situation.
Considering the probable health risks due to radioactivity input via drinking tea, the concentrations of 226Ra, 232Th,40K and 137Cs radionuclides in the soil and the corresponding tea leaves of a large tea plantation were measured using high purity germanium (HPGe) γ-ray spectrometry. Different layers of soil and fresh tea leaf samples were collected from the Udalia Tea Estate (UTE) in the Fatickchari area of Chittagong, Bangladesh. The mean concentrations (in Bq/kg) of radionuclides in the studied soil samples were found to be 34 ± 9 to 45 ± 3 for 226Ra, 50 ± 13 to 63 ± 5 for 232Th, 245 ± 30 to 635 ± 35 for 40K and 3 ± 1 to 10 ± 1 for 137Cs, while the respective values in the corresponding tea leaf samples were 3.6 ± 0.7 to 5.7 ± 1.0, 2.4 ± 0.5 to 5.8 ± 0.9, 132 ± 25 to 258 ± 29 and <0.4. The mean transfer factors for 226Ra, 232Th and 40K from soil to tea leaves were calculated to be 0.12, 0.08 and 0.46, respectively, the complete range being 1.1 × 10-2 to 1.0, in accordance with IAEA values. Additionally, the most popularly consumed tea brands available in the Bangladeshi market were also analyzed and, with the exception of 40K, were found to have similar concentrations to the fresh tea leaves collected from the UTE. The committed effective dose via the consumption of tea was estimated to be low in comparison with the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) reference ingestion dose limit of 290 μSv/y. Current indicative tea consumption of 4 g/day/person shows an insignificant radiological risk to public health, while cumulative dietary exposures may not be entirely negligible, because the UNSCEAR reference dose limit is derived from total dietary exposures. This study suggests a periodic monitoring of radiation levels in tea leaves in seeking to ensure the safety of human health.