Cave beetle lineages gained genes before going down under: An example of repeated genomic exaptation?

The adaptation of animals to subterranean habitats like caves and aquifers stereotypically leads to dramatic trait-loss consequences like the lack of eyes and body pigmentation. These body plan regression trends are expected to be tied to gene loss as well. Indeed, previous studies documented the degeneration of vision genes in obligate cave dwellers. Contradicting this picture, the first broad-scale comparative transcriptome-wide study of gene content evolution in separate subterranean Australian and Mediterranean beetle clades unearthed evidence of global gene gain and retention. This suggests that the transition to cave life may be more contingent on gene repertoire expansion than contraction. Future studies, however, will need to examine how much the observed patterns of gene content evolution reflect subfunctionalization and fitness-securing genetic redundancy outcomes following gene duplication as opposed to adaptive trajectories.

Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to 2022.

BACKGROUND: Influenza-like illness (ILI) imposes a significant burden on patients, employers and society. However, there is no analysis and prediction at the hospital level in Chongqing. We aimed to characterize the seasonality of ILI, examine age heterogeneity in visits, and predict ILI peaks and assess whether they affect hospital operations. METHODS: The multiplicative decomposition model was employed to decompose the trend and seasonality of ILI, and the Seasonal Auto-Regressive Integrated Moving Average with exogenous factors (SARIMAX) model was used for the trend and short-term prediction of ILI. We used Grid Search and Akaike information criterion (AIC) to calibrate and verify the optimal hyperparameters, and verified the residuals of the multiplicative decomposition and SARIMAX model, which are both white noise. RESULTS: During the 12-year study period, ILI showed a continuous upward trend, peaking in winter (Dec. - Jan.) and a small spike in May-June in the 2-4-year-old high-risk group for severe disease. The mean length of stay (LOS) in ILI peaked around summer (about Aug.), and the LOS in the 0-1 and ≥ 65 years old severely high-risk group was more irregular than the others. We found some anomalies in the predictive analysis of the test set, which were basically consistent with the dynamic zero-COVID policy at the time. CONCLUSION: The ILI patient visits showed a clear cyclical and seasonal pattern. ILI prevention and control activities can be conducted seasonally on an annual basis, and age heterogeneity should be considered in the health resource planning. Targeted immunization policies are essential to mitigate potential pandemic threats. The SARIMAX model has good short-term forecasting ability and accuracy. It can help explore the epidemiological characteristics of ILI and provide an early warning and decision-making basis for the allocation of medical resources related to ILI visits.

Phenotypic characteristics and rehabilitation effect of children with regressive autism spectrum disorder: a prospective cohort study.

BACKGROUND: In this prospective cohort study, we determined the phenotypic characteristics of children with regressive autism spectrum disorder (ASD) and explored the effects of rehabilitation. METHODS: We recruited 370 children with ASD aged 1.5-7 years. Based on the Regression Supplement Form, the children were assigned to two groups: regressive and non-regressive. The core symptoms and neurodevelopmental levels of ASD were assessed before and after 1 year of behavioral intervention using the Autism Diagnostic Observation Schedule (ADOS), Social Response Scale (SRS), Children Autism Rating Scale (CARS), and Gesell Developmental Scale (GDS). RESULTS: Among the 370 children with ASD, 28.38% (105/370) experienced regression. Regression was primarily observed in social communication and language skills. Children with regressive ASD exhibited higher SRS and CARS scores and lower GDS scores than those with non-regressive ASD. After 1 year of behavioral intervention, the symptom scale scores significantly decreased for all children with ASD; however, a lesser degree of improvement was observed in children with regressive ASD than in those with non-regressive ASD. In addition, the symptom scores of children with regressive ASD below 4 years old significantly decreased, whereas the scores of those over 4 years old did not significantly improve. Children with regressive ASD showed higher core symptom scores and lower neurodevelopmental levels. Nevertheless, after behavioral intervention, some symptoms exhibited significant improvements in children with regressive ASD under 4 years of age. CONCLUSION: Early intervention should be considered for children with ASD, particularly for those with regressive ASD.

Coupling Different Road Traffic Noise Models with a Multilinear Regressive Model: A Measurements-Independent Technique for Urban Road Traffic Noise Prediction.

Road traffic noise is a severe environmental hazard, to which a growing number of dwellers are exposed in urban areas. The possibility to accurately assess traffic noise levels in a given area is thus, nowadays, quite important and, on many occasions, compelled by law. Such a procedure can be performed by measurements or by applying predictive Road Traffic Noise Models (RTNMs). Although the first approach is generally preferred, on-field measurement cannot always be easily conducted. RTNMs, on the contrary, use input information (amount of passing vehicles, category, speed, among others), usually collected by sensors, to provide an estimation of noise levels in a specific area. Several RTNMs have been implemented by different national institutions, adapting them to the local traffic conditions. However, the employment of RTNMs proves challenging due to both the lack of input data and the inherent complexity of the models (often composed of a Noise Emission Model-NEM and a sound propagation model). Therefore, this work aims to propose a methodology that allows an easy application of RTNMs, despite the availability of measured data for calibration. Four different NEMs were coupled with a sound propagation model, allowing the computation of equivalent continuous sound pressure levels on a dataset (composed of traffic flows, speeds, and source-receiver distance) randomly generated. Then, a Multilinear Regressive technique was applied to obtain manageable formulas for the models' application. The goodness of the procedure was evaluated on a set of long-term traffic and noise data collected in a French site through several sensors, such as sound level meters, car counters, and speed detectors. Results show that the estimations provided by formulas coming from the Multilinear Regressions are quite close to field measurements (MAE between 1.60 and 2.64 dB(A)), confirming that the resulting models could be employed to forecast noise levels by integrating them into a network of traffic sensors.

Uniqueness of Iris Pattern Based on the Auto-Regressive Model.

In this paper, we evaluate the uniqueness of a hypothetical iris recognition system that relies upon a nonlinear mapping of iris data into a space of Gaussian codewords with independent components. Given the new data representation, we develop and apply a sphere packing bound for Gaussian codewords and a bound similar to Daugman's to characterize the maximum iris population as a function of the relative entropy between Gaussian codewords of distinct iris classes. As a potential theoretical approach leading toward the realization of the hypothetical mapping, we work with the auto-regressive model fitted into iris data, after some data manipulation and preprocessing. The distance between a pair of codewords is measured in terms of the relative entropy (log-likelihood ratio statistic is an alternative) between distributions of codewords, which is also interpreted as a measure of iris quality. The new approach to iris uniqueness is illustrated using two toy examples involving two small datasets of iris images. For both datasets, the maximum sustainable population is presented as a function of image quality expressed in terms of relative entropy. Although the auto-regressive model may not be the best model for iris data, it lays the theoretical framework for the development of a high-performance iris recognition system utilizing a nonlinear mapping from the space of iris data to the space of Gaussian codewords with independent components.

Assessing greenhouse gas emissions in Cuban agricultural soils: Implications for climate change and rice (Oryza sativa L.) production.

Assessing the impact of greenhouse gas (GHG) emissions on agricultural soils is crucial for ensuring food production sustainability in the global effort to combat climate change. The present study delves to comprehensively assess GHG emissions in Cuba's agricultural soil and analyze its implications for rice production and climate change because of its rich agriculture cultivation tradition and diverse agro-ecological zones from the period of 1990-2022. In this research, based on Autoregressive Distributed Lag (ARDL) approach the empirical findings depicts that in short run, a positive and significant impact of 1.60 percent % in Cuba's rice production. The higher amount of atmospheric carbon dioxide (CO2) levels improves photosynthesis, and stimulates the growth of rice plants, resulting in greater grain yields. On the other hand, rice production index raising GHG emissions from agriculture by 0.35 % in the short run. Furthermore, a significant and positive impact on rice production is found in relation to the farm machinery i.e., 3.1 %. Conversely, an adverse and significant impact of land quality was observed on rice production i.e., -5.5 %. The reliability of models was confirmed by CUSUM and CUSUM square plot. Diagnostic tests ensure the absence of serial correlation and heteroscedasticity in the models. Additionally, the forecasting results are obtained from the three machine learning models i.e. feed forward neural network (FFNN), support vector machines (SVM) and adaptive boosting technique (Adaboost). Through the % MAPE criterion, it is evident that FFNN has achieved high precision (91 %). Based on the empirical findings, the study proposed the adoption of sustainable agricultural practices and incentives should be given to the farmers so that future generations inherit a world that is sustainable, and healthy.

Group Surrogate Data Generating Models and similarity quantification of multivariate time-series: A resting-state fMRI study.

Advancements in non-invasive brain analysis through novel approaches such as big data analytics and in silico simulation are essential for explaining brain function and associated pathologies. In this study, we extend the vector auto-regressive surrogate technique from a single multivariate time-series to group data using a novel Group Surrogate Data Generating Model (GSDGM). This methodology allowed us to generate biologically plausible human brain dynamics representative of a large human resting-state (rs-fMRI) dataset obtained from the Human Connectome Project. Simultaneously, we defined a novel similarity measure, termed the Multivariate Time-series Ensemble Similarity Score (MTESS). MTESS showed high accuracy and f-measure in subject identification, and it can directly compare the similarity between two multivariate time-series. We used MTESS to analyze both human and marmoset rs-fMRI data. Our results showed similarity differences between cortical and subcortical regions. We also conducted MTESS and state transition analysis between single and group surrogate techniques, and confirmed that a group surrogate approach can generate plausible group centroid multivariate time-series. Finally, we used GSDGM and MTESS for the fingerprint analysis of human rs-fMRI data, successfully distinguishing normal and outlier sessions. These new techniques will be useful for clinical applications and in silico simulation.

Application of SARIMA model in forecasting and analyzing inpatient cases of acute mountain sickness.

BACKGROUND: Acute Mountain Sickness (AMS) is typically triggered by hypoxia under high altitude conditions. Currently, rule of time among AMS inpatients was not clear. Thus, this study aimed to analyze the time distribution of AMS inpatients in the past ten years and construct a prediction model of AMS hospitalized cases. METHODS: We retrospectively collected medical records of AMS inpatients admitted to the military hospitals from January 2009 to December 2018 and analyzed the time series characteristics. Seasonal Auto-Regressive Integrated Moving Average (SARIMA) was established through training data to finally forecast in the test data set. RESULTS: A total of 22 663 inpatients were included in this study and recorded monthly, with predominant peak annually, early spring (March) and mid-to-late summer (July to August), respectively. Using the training data from January 2009 to December 2017, the model SARIMA (1, 1, 1) (1, 0, 1) 12 was employed to predict the test data from January 2018 to December 2018. In 2018, the total predicted value after adjustment was 9.24%, less than the actual value. CONCLUSION: AMS inpatients have obvious periodicity and seasonality. The SARIMA model has good fitting ability and high short-term prediction accuracy. It can help explore the characteristics of AMS disease and provide decision-making basis for allocation of relevant medical resources for AMS inpatients.

Spontaneously regressive multifocal bone pseudomyogenic hemangioendothelioma in a 17-year-old boy: a case report.

Pseudomyogenic hemangioendothelioma (PMH) is a rare vascular tumor that occurs in young mostly male patients. Seventy percent of PMH cases are multifocal and 25% involve bones. PMH is an indolent tumor with mild local aggressiveness and an unclear pathology. Only two cases of spontaneous regressive bone PMH have been reported. Here, we report the case of a 17-year-old boy with a multifocal bone PMH diagnosed from a chronic pain in his left knee. The PMH affected the right scapula, both humeri, the right olecranon, the second metacarpal bone, the second and fourth right ribs, the thoracic and lumbar spine, the pelvic ring, the left and right femoral neck, and the left patella. Every lesion presented with a lobulated, lytic pattern, sometimes with a peripheral sclerotic rim. MRI showed a tissue lesion with a low intensity on T1-weighted sequences and high intensity on T2-weighted sequences. Enhancement of T1 gadolinium fat-saturated sequences was bright. After discussion, a national specialized board decided to actively monitor the patient and start general chemotherapy in the case of progression. The disease was stable at 3 and 6 months and showed signs of regression at 1 year, which was further confirmed at 2 years. CT scan and MRI highlighted a progressive filling of the tumor with cancellous bone and a regression of the tissue contingent. This case report highlights to a new therapeutic approach for indolent PMH that does not prevent further treatment in the case of progression.

A Latent Auto-Regressive Approach for Bayesian Structural Equation Modeling of Spatially or Socially Dependent Data.

Spatial analytic approaches are classic models in econometric literature, but relatively new in social sciences. Spatial analysis models are synonymous with social network auto-regressive models which are also gaining popularity in the behavioral sciences. These models have two major benefits. First, dependent data, either socially or spatially, must be accounted for to acquire unbiased results. Second, analysis of the dependence provides rich additional information such as spillover effects. Structural Equation Models (SEM) are widely used in psychological research for measuring and testing multi-faceted constructs. So far, SEM that allow for spatial or social dependency are limited with regard to their flexibility, for example, when estimating nonlinear effects. Here, we provide a cohesive framework which can simultaneously estimate latent interaction/polynomial effects and account for spatial effects with both exogenous and endogenous latent variables, the Bayesian Spatial Auto-Regressive Structural Equation Model (BARDSEM). First, we briefly outline classic auto-regressive models. Next, we present the BARDSEM and introduce simulation results to exemplify its performance. Finally, we provide an empirical example using the spatially dependent extended US southern homicide data to show the rich interpretations that are possible using the BARDSEM. Finally, we discuss implications, limitations, and future research.

The Role of SNAP-25 in Autism Spectrum Disorders Onset Patterns.

Autism spectrum disorders (ASD) can present with different onset and timing of symptom development; children may manifest symptoms early in their first year of life, i.e., early onset (EO-ASD), or may lose already achieved skills during their second year of life, thus showing a regressive-type onset (RO-ASD). It is still controversial whether regression represents a neurobiological subtype of ASD, resulting from distinct genetic and environmental causes. We focused this study on the 25 kD synaptosomal-associated protein (SNAP-25) gene involved in both post-synaptic formation and adhesion and considered a key player in the pathogenesis of ASD. To this end, four single nucleotide polymorphisms (SNPs) of the SNAP-25 gene, rs363050, rs363039, rs363043, and rs1051312, already known to be involved in neurodevelopmental and psychiatric disorders, were analyzed in a cohort of 69 children with EO-ASD and 58 children with RO-ASD. Both the rs363039 G allele and GG genotype were significantly more frequently carried by patients with EO-ASD than those with RO-ASD and healthy controls (HC). On the contrary, the rs1051312 T allele and TT genotype were more frequent in individuals with RO-ASD than those with EO-ASD and HC. Thus, two different SNAP-25 alleles/genotypes seem to discriminate between EO-ASD and RO-ASD. Notably, rs1051312 is located in the 3' untranslated region (UTR) of the gene and is the target of microRNA (miRNA) regulation, suggesting a possible epigenetic role in the onset of regressive autism. These SNPs, by discriminating two different onset patterns, may represent diagnostic biomarkers of ASD and may provide insight into the different biological mechanisms towards the development of better tailored therapeutic and rehabilitative approaches.

Parallel decay of vision genes in subterranean water beetles.

In the framework of neutral theory of molecular evolution, genes specific to the development and function of eyes in subterranean animals living in permanent darkness are expected to evolve by relaxed selection, ultimately becoming pseudogenes. However, definitive empirical evidence for the role of neutral processes in the loss of vision over evolutionary time remains controversial. In previous studies, we characterized an assemblage of independently-evolved water beetle (Dytiscidae) species from a subterranean archipelago in Western Australia, where parallel vision and eye loss have occurred. Using a combination of transcriptomics and exon capture, we present evidence of parallel coding sequence decay, resulting from the accumulation of frameshift mutations and premature stop codons, in eight phototransduction genes (arrestins, opsins, ninaC and transient receptor potential channel genes) in 32 subterranean species in contrast to surface species, where these genes have open reading frames. Our results provide strong evidence to support neutral evolutionary processes as a major contributing factor to the loss of phototransduction genes in subterranean animals, with the ultimate fate being the irreversible loss of a light detection system.

Low-Complexity Self-Interference Cancellation for Multiple Access Full Duplex Systems.

Self-interference occurs when there is electromagnetic coupling between the transmission and reception of the same node; thus, degrading the RX sensitivity to incoming signals. In this paper we present a low-complexity technique for self-interference cancellation in multiple carrier multiple access systems employing whole band direct to digital sampling. In this scenario, multiple users are simultaneously received and transmitted by the system at overlapping arbitrary bandwidths and powers. Traditional algorithms for self-interference mitigation based on recursive least squares (RLS) or least mean squares (LMS), fail to provide sufficient rejection, since the incoming signal is far from being spectrally flat, which is critical for their performance. The proposed algorithm mitigates the interference by modeling the incoming multiple user signal as an autoregressive (AR) process and jointly estimates the AR parameters and self-interference. The resulting algorithm can be implemented using a low-complexity architecture comprised of only two RLS modules. The novel algorithm further satisfies low latency constraints and is adaptive, supporting time varying channel conditions. We compare this to many self-interference cancellation algorithms, mostly adopted from the acoustic echo cancellation literature, and show significant performance gain.

EP-ADTA: Edge Prediction-Based Adaptive Data Transfer Algorithm for Underwater Wireless Sensor Networks (UWSNs).

The underwater wireless sensor network is an important component of the underwater three-dimensional monitoring system. Due to the high bit error rate, high delay, low bandwidth, limited energy, and high dynamic of underwater networks, it is very difficult to realize efficient and reliable data transmission. Therefore, this paper posits that it is not enough to design the routing algorithm only from the perspective of the transmission environment; the comprehensive design of the data transmission algorithm should also be combined with the application. An edge prediction-based adaptive data transmission algorithm (EP-ADTA) is proposed that can dynamically adapt to the needs of underwater monitoring applications and the changes in the transmission environment. EP-ADTA uses the end-edge-cloud architecture to define the underwater wireless sensor networks. The algorithm uses communication nodes as the agents, realizes the monitoring data prediction and compression according to the edge prediction, dynamically selects the transmission route, and controls the data transmission accuracy based on reinforcement learning. The simulation results show that EP-ADTA can meet the accuracy requirements of underwater monitoring applications, dynamically adapt to the changes in the transmission environment, and ensure efficient and reliable data transmission in underwater wireless sensor networks.

Sweetened beverage taxes: Economic benefits and costs according to household income.

Taxing sweetened beverages has emerged as an important and effective policy for addressing their overconsumption. However, taxes may place a greater economic burden on people with lower incomes. We assess the degree to which sweetened beverage taxes in three large US cities placed an inequitable burden on populations with lower incomes by assessing spending on beverage taxes by income after taxes have been implemented, as well as any net transfer of funds towards lower income populations once allocation of tax revenue is considered. We find that while lower income populations pay a higher percentage of their income in beverage taxes, there is no difference in absolute spending on beverage taxes per capita, and that there is a sizable net transfer of funds towards programs targeting lower income populations. Thus, when considering both population-level taxes paid and sufficiently targeted allocations of tax revenues, a sweetened beverage tax may have characteristics of an equitable public policy.

Vertebrate features revealed in the rudimentary eye of the Pacific hagfish (Eptatretus stoutii).

Hagfish eyes are markedly basic compared to the eyes of other vertebrates, lacking a pigmented epithelium, a lens and a retinal architecture built of three cell layers: the photoreceptors, interneurons and ganglion cells. Concomitant with hagfish belonging to the earliest-branching vertebrate group (the jawless Agnathans), this lack of derived characters has prompted competing interpretations that hagfish eyes represent either a transitional form in the early evolution of vertebrate vision, or a regression from a previously elaborate organ. Here, we show the hagfish retina is not extensively degenerating during its ontogeny, but instead grows throughout life via a recognizable PAX6+ ciliary marginal zone. The retina has a distinct layer of photoreceptor cells that appear to homogeneously express a single opsin of the RH1 rod opsin class. The epithelium that encompasses these photoreceptors is striking because it lacks the melanin pigment that is universally associated with animal vision; notwithstanding, we suggest this epithelium is a homologue of gnathosome retinal pigment epithelium (RPE) based on its robust expression of RPE65 and its engulfment of photoreceptor outer segments. We infer that the hagfish retina is not entirely rudimentary in its wiring, despite lacking a morphologically distinct layer of interneurons: multiple populations of cells exist in the hagfish inner retina and subsets of these express markers of vertebrate retinal interneurons. Overall, these data clarify Agnathan retinal homologies, reveal characters that now appear to be ubiquitous across the eyes of vertebrates, and refine interpretations of early vertebrate visual system evolution.

A binary hidden Markov model on spatial network for amyotrophic lateral sclerosis disease spreading pattern analysis.

Amyotrophic lateral sclerosis (ALS) is a neurological disease that starts at a focal point and gradually spreads to other parts of the nervous system. One of the main clinical symptoms of ALS is muscle weakness. To study spreading patterns of muscle weakness, we analyze spatiotemporal binary muscle strength data, which indicates whether observed muscle strengths are impaired or healthy. We propose a hidden Markov model-based approach that assumes the observed disease status depends on two latent disease states. The model enables us to estimate the incidence rate of ALS disease and the probability of disease state transition. Specifically, the latter is modeled by a logistic autoregression in that the spatial network of susceptible muscles follows a Markov process. The proposed model is flexible to allow both historical muscle conditions and their spatial relationships to be included in the analysis. To estimate the model parameters, we provide an iterative algorithm to maximize sparse-penalized likelihood with bias correction, and use the Viterbi algorithm to label hidden disease states. We apply the proposed approach to analyze the ALS patients' data from EMPOWER Study.

Spatial skew-normal/independent models for nonrandomly missing clustered data.

Clinical studies on periodontal disease (PD) often lead to data collected which are clustered in nature (viz. clinical attachment level, or CAL, measured at tooth-sites and clustered within subjects) that are routinely analyzed under a linear mixed model framework, with underlying normality assumptions of the random effects and random errors. However, a careful look reveals that these data might exhibit skewness and tail behavior, and hence the usual normality assumptions might be questionable. Besides, PD progression is often hypothesized to be spatially associated, that is, a diseased tooth-site may influence the disease status of a set of neighboring sites. Also, the presence/absence of a tooth is informative, as the number and location of missing teeth informs about the periodontal health in that region. In this paper, we develop a (shared) random effects model for site-level CAL and binary presence/absence status of a tooth under a Bayesian paradigm. The random effects are modeled using a spatial skew-normal/independent (S-SNI) distribution, whose dependence structure is conditionally autoregressive (CAR). Our S-SNI density presents an attractive parametric tool to model spatially referenced asymmetric thick-tailed structures. Both simulation studies and application to a clinical dataset recording PD status reveal the advantages of our proposition in providing a significantly improved fit, over models that do not consider these features in a unified way.

Rickets manifestations in a child with metaphyseal anadysplasia, report of a spontaneously resolving case.

INTRODUCTION: Rickets is not an unusual diagnosis for pediatricians even currently in developed countries. Children typically present with leg bowing, enlargement of wrists, rachitic rosary (swelling of costochondral junctions) and/or waddling gait. But not every child with growth delay and enlarged metaphyses is diagnosed with rickets. Metaphyseal anadysplasia (MAD) is a disorder of variable severity with metaphyseal flaring and irregularities, without vertebral abnormalities. MAD is characterized by an early onset and a regressive course in late childhood without treatment, despite persistent short stature. Autosomal dominant or recessive variants in the matrix metalloproteinase 13 gene (MMP13) are responsible for these transient metaphyseal changes. CASE PRESENTATION: We report a new pathogenic heterozygous variant in MMP13 (NM_002427.4: c.216G>C, p.Gln72His) in a toddler, initially thought to have rickets, and his father, with MAD phenotypes. Additionally, we review the seven reported MMP13 variants. CONCLUSION: One should keep a wide differential diagnosis in cases of suspected rickets, including skeletal dysplasias which might have a regressive course.

Forecasting spread of COVID-19 using google trends: A hybrid GWO-deep learning approach.

The recent outbreak of COVID-19 has brought the entire world to a standstill. The rapid pace at which the virus has spread across the world is unprecedented. The sheer number of infected cases and fatalities in such a short period of time has overwhelmed medical facilities across the globe. The rapid pace of the spread of the novel coronavirus makes it imperative that its' spread be forecasted well in advance in order to plan for eventualities. An accurate early forecasting of the number of cases would certainly assist governments and various other organizations to strategize and prepare for the newly infected cases, well in advance. In this work, a novel method of forecasting the future cases of infection, based on the study of data mined from the internet search terms of people in the affected region, is proposed. The study utilizes relevant Google Trends of specific search terms related to COVID-19 pandemic along with European Centre for Disease prevention and Control (ECDC) data on COVID-19 spread, to forecast the future trends of daily new cases, cumulative cases and deaths for India, USA and UK. For this purpose, a hybrid GWO-LSTM model is developed, where the network parameters of Long Short Term Memory (LSTM) network are optimized using Grey Wolf Optimizer (GWO). The results of the proposed model are compared with the baseline models including Auto Regressive Integrated Moving Average (ARIMA), and it is observed that the proposed model achieves much better results in forecasting the future trends of the spread of infection. Using the proposed hybrid GWO-LSTM model incorporating online big data from Google Trends, a reduction in Mean Absolute Percentage Error (MAPE) values for forecasting results to the extent of about 98% have been observed. Further, reduction in MAPE by 74% for models incorporating Google Trends was observed, thus, confirming the efficacy of utilizing public sentiments in terms of search frequencies of relevant terms online, in forecasting pandemic numbers.