Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression

Depression is a common illness worldwide with doubtless severe implications. Due to the absence of early identification and treatment for depression, millions of individuals worldwide suffer from mental illnesses. It might be difficult to identify those who are experiencing mental health illnesses and to provide them with the early help that they need. Additionally, depression may be associated with thoughts of suicide. Currently, there are no clinically specific diagnostic biomarkers that can identify the severity and type of depression. In this research paper, the novel particle swarm-cuckoo search (PS-CS) optimization algorithm is proposed instead of the traditional backpropagation algorithm for training deep neural networks. The backpropagation algorithm is widely used for supervised learning in deep neural networks, but it has limitations in terms of convergence speed and the possibility of getting trapped in local optima. These problems were addressed by using a deep neural network architecture for depression detection tasks along with the PS-CS optimization technique. The PS-CS algorithm combines the strengths of both particle swarm optimization and cuckoo search algorithms, which allows for a more efficient and effective optimization of the network parameters. We also evaluated how well the suggested methods performed against the most widely used classification models, including (K-nearest neighbor) KNN, (support vector regression) SVR, and decision trees, as well as the most widely used deep learning models, including residual neural network (ResNet), visual geometry group (VGG), and simple neural network (LeNet). The findings show that the suggested method, PS-CS, in conjunction with the CNN model, outperformed all other models, achieving the maximum accuracy of 99.5%. Other models, such as the KNN, decision trees, and logistic regression, achieved lower accuracies ranging from 69% to 97%.

Nitrogen dioxide as proxy indicator of air pollution from fossil fuel burning in New Delhi during lockdown phases of COVID-19 pandemic period: impact on weather as revealed by Sentinel-5 precursor (5p) spectrometer sensor.

There has been a long-lasting impact of the lockdown imposed due to COVID-19 on several fronts. One such front is climate which has seen several implications. The consequences of climate change owing to this lockdown need to be explored taking into consideration various climatic indicators. Further impact on a local and global level would help the policymakers in drafting effective rules for handling challenges of climate change. For in-depth understanding, a temporal study is being conducted in a phased manner in the New Delhi region taking NO2 concentration and utilizing statistical methods to elaborate the quality of air during the lockdown and compared with a pre-lockdown period. In situ mean values of the NO2 concentration were taken for four different dates, viz. 4th February, 4th March, 4th April, and 25th April 2020. These concentrations were then compared with the Sentinel (5p) data across 36 locations in New Delhi which are found to be promising. The results indicated that the air quality has been improved maximum in Eastern Delhi and the NO2 concentrations were reduced by one-fourth than the pre-lockdown period, and thus, reduced activities due to lockdown have had a significant impact. The result also indicates the preciseness of Sentinel (5p) for NO2 concentrations.

Sweetness and positivity together aren't a happy ending: Case controlled study amongst severe COVID-19 for impact of diabetes mellitus on survival.

Background: India is the epicenter of diabetes mellitus (DM). The relationship between COVID and DM in age/gender-matched non-diabetics has not been studied yet. The role of DM in predicting the disease severity and outcome in COVID patients might provide new insight for effective management. Methods: We conducted a prospective comparative study at a COVID care center from 25th April-31st May 2021. Among 357 severe-COVID patients screened, all consecutive diabetes (n-113) and age/gender-matched non-diabetes (n-113) patients were recruited. All diabetics and non-diabetics at admission were subjected to high resolution computed tomography (HRCT) chest and inflammatory markers (C-reactive protein (CRP), D-dimer, ferritin, interleukin-6 (IL-6), lactate dehydrogenase (LDH), Neutrophil-Lymphocyte Ratio (NLR)) before starting anti- COVID therapy. Statistical analysis was done using JMP 15·0 ver·3·0·0. Results: The prevalence of DM among the screened population (n-357) was 38·37%. The mean age of the study population was 61y with male preponderance (57%). There was no statistical difference in the HRCT-score or inflammatory markers in the two groups except for higher NLR (p-0·0283) in diabetics. Diabetics had significantly inferior overall survival (OS) (p-0·0251) with a 15d-OS of diabetics vs. non-diabetics being 58·87%, 72·67%, and 30d-OS of diabetics vs. non-diabetics being 46·76%, 64·61%, respectively. The duration of the hospital stay was not statistically different in the two groups (p-0·2). Conclusion: The mortality is significantly higher in severe-COVID patients with DM when compared to age/gender-matched non-diabetics. There was no significant difference in most inflammatory markers/CT at admission between the two groups.

Multifaceted Impact of Lockdown During COVID-19 on Food Security and Smallholder Agricultural Systems Review and Meta-Analysis: SARS-CoV-2 and Enveloped Virus Detection in Feces and Wastewater

Summary One of the most serious pandemic situations created in history over the past 100?years due to an outbreak of the novel disease COVID-19 (coronavirus disease-19), in which, the entire population worldwide is experiencing lockdown and all activities have been disrupted. The first case of COVID-19 or coronavirus disease in India was reported on 30 January 2020. In this paper, we have reported a case study to address the question of how the lockdown ensuing from the outbreak of COVID-19 in India is affecting the economy of the Indian farmers. The cumulative report of the number of confirmed cases along with those of recoveries and deaths reported per day from 30 January 2020 to 21 April 2020 (80?days) was used for this analysis. The Ministry of Health and Family Welfare, Government of India, has confirmed that a total of 15?300 active cases of COVID-19 and 592 deaths have been reported in India till 21 April 2020. Different agro-farming societies and farmers' groups have informed that the production of cereals and pulses during the Rabi season (2019?2020) is high owing to a relatively longer period of cold weather. While the Indian government has ensured an adequate supply of all essentials, sporadic incidences of panic involving rapid purchase of goods and groceries by the population have been observed. However, farmers in all states of India cannot avail facilities such as accessibility to market yards, ease of procuring inputs, and selling of their produced goods equally. Comparatively, these facilities were found to be better in the state of Uttar Pradesh and the region of Bundelkhand, although a crisis in disposing summer vegetables such as cucurbits was witnessed in Bundelkhand. Detection and quantification of viruses supplies key information on their spread and allows risk assessment for public health. In wastewater, existing detection methods have been focusing on non-enveloped enteric viruses due to enveloped virus transmission, such as coronaviruses, by the fecal-oral route being less likely. Since the beginning of the SARS-CoV-2 pandemic, interest and importance of enveloped virus detection in wastewater has increased. Here, quantitative studies on SARS-CoV-2 occurrence in feces and raw wastewater and other enveloped viruses via quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR) during the early stage of the pandemic until April 2021 are reviewed, including statistical evaluation of the positive detection rate and efficiency throughout the detection process involving concentration, extraction, and amplification stages. Optimized and aligned sampling protocols and concentration methods for enveloped viruses, along with SARS-CoV-2 surrogates, in wastewater environments may improve low and variable recovery rates providing increased detection efficiency and comparable data on viral load measured across different studies.

Short-Term Statistical Forecasts of COVID-19 Infections in India.

COVID-19 cases in India have been steadily increasing since January 30, 2020 and have led to a government-imposed lockdown across the country to curtail community transmission with significant impacts on societal systems. Forecasts using mathematical-epidemiological models have played and continue to play an important role in assessing the probability of COVID-19 infection under specific conditions and are urgently needed to prepare health systems for coping with this pandemic. In many instances, however, access to dedicated and updated information, in particular at regional administrative levels, is surprisingly scarce considering its evident importance and provides a hindrance for the implementation of sustainable coping strategies. Here we demonstrate the performance of an easily transferable statistical model based on the classic Holt-Winters method as means of providing COVID-19 forecasts for India at different administrative levels. Based on daily time series of accumulated infections, active infections and deaths, we use our statistical model to provide 48-days forecasts (28 September to 15 November 2020) of these quantities in India, assuming little or no change in national coping strategies. Using these results alongside a complementary SIR model, we find that one-third of the Indian population could eventually be infected by COVID-19, and that a complete recovery from COVID-19 will happen only after an estimated 450 days from January 2020. Further, our SIR model suggests that the pandemic is likely to peak in India during the first week of November 2020.

Model-based ensembles: Lessons learned from retrospective analysis of COVID-19 infection forecasts across 10 countries.

Mathematical models of different types and data intensities are highly used by researchers, epidemiologists, and national authorities to explore the inherently unpredictable progression of COVID-19, including the effects of different non-pharmaceutical interventions. Regardless of model complexity, forecasts of future COVID-19 infections, deaths and hospitalization are associated with large uncertainties, and critically depend on the quality of the training data, and in particular how well the recorded national or regional numbers of infections, deaths and recoveries reflect the the actual situation. In turn, this depends on, e.g., local test and abatement strategies, treatment capacities and available technologies. Other influencing factors including temperature and humidity, which are suggested by several authors to affect the spread of COVID-19 in some countries, are generally only considered by the most complex models and further serve to inflate the uncertainty. Here we use comparative and retrospective analyses to illuminate the aggregated effect of these systematic biases on ensemble-based model forecasts. We compare the actual progression of active infections across ten of the most affected countries in the world until late November 2020 with "re-forecasts" produced by two of the most commonly used model types: (i) a compartment-type, susceptible-infected-removed (SIR) model; and (ii) a statistical (Holt-Winters) time series model. We specifically examine the sensitivity of the model parameters, estimated systematically from different subsets of the data and thereby different time windows, to illustrate the associated implications for short- to medium-term forecasting and for probabilistic projections based on (single) model ensembles as inspired by, e.g., weather forecasting and climate research. Our findings portray considerable variations in forecasting skill in between the ten countries and demonstrate that individual model predictions are highly sensitive to parameter assumptions. Significant skill is generally only confirmed for short-term forecasts (up to a few weeks) with some variation across locations and periods.

Highlighting the compound risk of COVID-19 and environmental pollutants using geospatial technology.

The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections.

Multi-level impacts of the COVID-19 lockdown on agricultural systems in India: The case of Uttar Pradesh

When on March 24, 2020 the Government of India ordered a complete lockdown of the country as a response to the COVID-19 pandemic, it had serious unwanted implications for farmers and the supply chains for agricultural produce. This was magnified by the fact that, as typically in developing countries, India's economy is strongly based on farming, industrialization of its agricultural systems being only modest. This paper reports on the various consequences of the COVID-19 lockdown for farming systems in India, including the economy, taking into account the associated emergency responses of state and national governments. Combining quantitative and qualitative sources of information with a focus on the Indian state of Uttar Pradesh, including expert elicitation and a survey of farmers, the paper identifies and analyzes the different factors that contributed to the severe disruption of farming systems and the agricultural sector as a whole following the lockdown. Among other issues, our study finds that the lack of migrant labor in some regions and a surplus of workers in others greatly affected the April harvest, leading to a decline in agricultural wages in some communities and an increase in others, as well as to critical losses of produce. Moreover, the partial closure of rural markets and procurement options, combined with the insufficient supply of products, led to shortages of food supplies and dramatically increased prices, which particularly affected urban dwellers and the poor. We argue that the lessons learned from the COVID-19 crisis could fuel the development of new sustainable agro-policies and decision-making in response not only to future pandemics but also to the sustainable development of agricultural systems in India and in developing countries in general.

Recent advances in vaccine and immunotherapy for COVID-19.

The COVID-19 pandemic caused by SARS-CoV-2 has resulted in millions of cases and hundreds of thousands of deaths. Beyond there being no available antiviral therapy, stimulating protective immunity by vaccines is the best option for managing future infections. Development of a vaccine for a novel virus is a challenging effort that may take several years to accomplish. This mini-review summarizes the immunopathological responses to SARS-CoV-2 infection and discusses advances in the development of vaccines and immunotherapeutics for COVID-19.

Relevance of SARS-CoV-2 related factors ACE2 and TMPRSS2 expressions in gastrointestinal tissue with pathogenesis of digestive symptoms, diabetes-associated mortality, and disease recurrence in COVID-19 patients.

COVID-19 is caused by a new strain of coronavirus called SARS-coronavirus-2 (SARS-CoV-2), which is a positive sense single strand RNA virus. In humans, it binds to angiotensin converting enzyme 2 (ACE2) with the help a structural protein on its surface called the S-spike. Further, cleavage of the viral spike protein (S) by the proteases like transmembrane serine protease 2 (TMPRSS2) or Cathepsin L (CTSL) is essential to effectuate host cell membrane fusion and virus infectivity. COVID-19 poses intriguing issues with imperative relevance to clinicians. The pathogenesis of GI symptoms, diabetes-associated mortality, and disease recurrence in COVID-19 are of particular relevance because they cannot be sufficiently explained from the existing knowledge of the viral diseases. Tissue specific variations of SARS-CoV-2 cell entry related receptors expression in healthy individuals can help in understanding the pathophysiological basis the aforementioned collection of symptoms. ACE2 mediated dysregulation of sodium dependent glucose transporter (SGLT1 or SLC5A1) in the intestinal epithelium also links it to the pathogenesis of diabetes mellitus which can be a possible reason for the associated mortality in COVID-19 patients with diabetes. High expression of ACE2 in mucosal cells of the intestine and GB make these organs potential sites for the virus entry and replication. Continued replication of the virus at these ACE2 enriched sites may be a basis for the disease recurrence reported in some, thought to be cured, patients. Based on the human tissue specific distribution of SARS-CoV-2 cell entry factors ACE2 and TMPRSS2 and other supportive evidence from the literature, we hypothesize that SARS-CoV-2 host cell entry receptor-ACE2 based mechanism in GI tissue may be involved in COVID-19 (i) in the pathogenesis of digestive symptoms, (ii) in increased diabetic complications, (iii) in disease recurrence.

Possible routes of SARS-CoV-2 invasion in brain: In context of neurological symptoms in COVID-19 patients.

Manifestation of neurological symptoms in certain patients of coronavirus disease-2019 (COVID-19) has warranted for their virus-induced etiogenesis. SARS-CoV-2, the causative agent of COVID-19, belongs to the genus of betacoronaviruses which also includes SARS-CoV-1 and MERS-CoV; causative agents for severe acute respiratory syndrome (SARS) in 2002 and Middle East respiratory syndrome (MERS) in 2012, respectively. Studies demonstrating the neural invasion of SARS-CoV-2 in vivo are still scarce, although such characteristics of certain other betacoronaviruses are well demonstrated in the literature. Based on the recent evidence for the presence of SARS-CoV-2 host cell entry receptors in specific components of the human nervous and vascular tissue, a neural (olfactory and/or vagal), and a hematogenous-crossing the blood-brain barrier, routes have been proposed. The neurological symptoms in COVID-19 may also arise as a consequence of the "cytokine storm" (characteristically present in severe disease) induced neuroinflammation, or co-morbidities. There is also a possibility that, there may be multiple routes of SARS-CoV-2 entry into the brain, or multiple mechanisms can be involved in the pathogenesis of the neurological symptoms. In this review article, we have discussed the possible routes of SARS-CoV-2 brain entry based on the emerging evidence for this virus, and that available for other betacoronaviruses in literature.

Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.

BACKGROUND: The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. OBJECTIVE: The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. METHODS: The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. RESULTS: The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. CONCLUSIONS: The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.