India's Pink-Eye Mystery: Decoding the 2023 Conjunctivitis Outbreak.

The pink eye outbreak in 2023 was caused due to humid weather conditions in most regions of India. The most affected states include Delhi, Gujrat (21% cases), Maharashtra (30%), Himachal Pradesh (4%) and Karnataka (4%). The epidemiological data indicates that males have a high prevalence rate as compared to females; urban areas were most affected, and professionals as well as students were the population group that had the highest prevalence rate. The most common clinical manifestations were the presence of red eye, eye discharge, grittiness, and eyelashes being stuck together. One of the hallmarks of histopathology is a cobblestone formation of flattened nodules with central vascular centers. Conjunctivitis is a virusmediated immune response accompanied by inflammation, which proceeds the immune reaction, giving rise to vasodilation, pseudo membrane formation, and conjunctivital discharge. The gold standard for the diagnosis of Conjunctivitis is the Adenoplus kit using PCR technology; apart from this slit lamp biomicroscope can be used for the evaluation. It is the need of the hour to spread awareness about the Pink Eye disease and the measures to prevent it.

The Risk of Crimean Congo Haemorrhagic Fever in India as a Growing Health Concern.

Every year witnesses an outbreak of some or the other zoonotic disease that causes the unparalled loss of human life. The year 2022 presented the outbreak of Crimean Congo haemorrhagic fever (CCHF), which brought unprecedented challenges to individuals as well as to the healthcare system all around the world, making it a serious health concern. Rising health concerns have highlighted the importance of managing and decreasing the further transmission of the CCHF virus. CCHF is one of tick-borne viral diseases, which spreads due to various reasons like changes in global warming, environmental influences, and other ecological factors. All these factors somehow impact the disease prevalence. This disease has a negative impact on both humans and livestock. The diverse climate and significant livestock population of India make it susceptible to the prevalence of CCHF. Therefore, it is the need of the hour to develop some strategies in order to tackle the challenges posed by CCHF. This article includes all the cases of CCHF that have occurred in India from the year 2011, along with the fatality rates associated with this disease. Also this study discusses the need to explore some specific drugs for the management and prevention of such diseases. In addition, the pathogenesis of the disease progression, along with some protective measures suggested by the government has been described for prevention of CCHF. Subsequently, this article attempted to draw attention towards the risk that may be posed by CCHF in the coming scenario, emphasizing the importance of taking proactive measures in anticipation of such risks.

A novel approach for COVID-19 Infection forecasting based on multi-source deep transfer learning.

COVID-19 is a contagious disease; so, predicting its future infections in a provincial region requires the consideration of the related data (i.e., rates of infection, mortality and recovery, etc.) over a period of time. Clearly, the COVID-19 data of a particular provincial region can be easily modelled as a time-series. However, predicting the future COVID-19 infections in a particular region is quite challenging when the availability of COVID-19 dataset of the province is of little quantity. Accordingly, ML models when deployed for such tasks usually results in low infection prediction accuracy. To overcome such issues of low variance and high bias in a model due to data scarcity, multi-source transfer learning (MSTL) along with deep learning may be quite useful and effective. Therefore, this paper proposes a novel technique based on multi-source deep transfer learning (MSDTL) to efficiently forecast the future COVID-19 infections in the provinces with insufficient COVID-19 data. The proposed approach is a novel contribution as it considers the fact that future COVID-19 transmission in a region also depends on its population density and economic conditions (GDP) for accurate forecasting of the infections to tackle the pandemic efficiently. The importance of this feature selection is experimentally proved in this paper. Our proposed approach employs the well-known recurrent neural network architecture, the Long-short term memory (LSTM), a popular deep-learning model for history-dependent tasks. A comparative analysis has been performed with existing state-of-art algorithms to portray the efficiency of LSTM. Thus, formation of MSDTL approach enhances the predictive precision capability of the LSTM. We evaluate the proposed methodology over the COVID-19 dataset from sixty-two provinces belonging to different nations. We then empirically evaluate the performance of the proposed approach using two different evaluation metrics, viz. The mean absolute percentage error and the coefficient of determination. We show that our proposed MSDTL based approach is better in terms of the accuracy of the future infection prediction, and produces improvements up to 96% over its without-TL counterpart.