Trends and Dynamics of COVID-19 in Zimbabwe: Implications for Selected Sustainable Development Goals

This study analysed the spatial and temporal trends and dynamics of COVID-19 to understand their implications on Sustainable Development Goals (SDGs) in Zimbabwe. Data on daily cases and mortality rates of COVID-19 were collected from the Worldometer website, whilst data on lockdown measures and travel restrictions were collected from Zimbabwe's Ministry of Health and Child Care. Exploratory and confirmatory analyses were employed on statistical data. COVID-19 statistical data were first tested for normality using the Kolmogorov-Smirnov test. Subsequently, the non-parametric Mann-Kendal (M-K) test was performed to determine the monthly average number of new cases and deaths trend from March 2020 to February 2022 using XLSTAT (2020). The study shows a significant increase (p = 0.00, α= 0.05) in COVID-19 cases between March 2020 and February 2022. The trend is characterised by sharp increases associated with wave periods. Although the results show no correlation between stringency index and COVID-19 cases, periods of high stringency are associated with a slightly lower number of cases. The spatial trends show that highly populated areas have high numbers of patient cases. Indeed, the lockdown measures put in place, among other factors, contributed to controlling the spread of the virus. The trends and dynamics of COVID-19 in Zimbabwe have implications for achieving SDG 1, SDG 2, SDG 3 and SDG 6. Thus, there is a need to factor in the temporal and spatial realities of COVID-19 in making a policy framework for effective control of the pandemic and promotion of sustainable development. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Citizenship Wanted, but Denied: The Racial Prerequisite Cases and Their Role in Institutionalizing Racism Against Asians: a Journal for Readers, Students and Teachers of History

This article explores the racial prerequisite cases, which were dubious court decisions that attempted to rationalize pseudo-scientific evidence and common knowledge as reasons for who could and could not be considered for, and who was ultimately denied, U.S. citizenship. These cases are historical antecedents of the current anti-Asian racism that has become even more pronounced as a result of COVID-19. Social studies educators can leverage these court cases to teach about controversial and relevant issues that connect to current immigration and citizenship policies, as well as provide historical context for contemporary discussions about race and racism.

Analysis of the COVID19 Pandemic Behaviour Based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models

A significant number of people infected by COVID19 do not get sick immediately but become carriers of the disease. These patients might have a certain incubation period. However, the classical compartmental model, SEIR, was not originally designed for COVID19. We used the simple, commonly used SEIR model to retrospectively analyse the initial pandemic data from Singapore. Here, the SEIR model was combined with the actual published Singapore pandemic data, and the key parameters were determined by maximizing the nonlinear goodness of fit R2 and minimizing the root mean square error. These parameters served for the fast and directional convergence of the parameters of an improved model. To cover the quarantine and asymptomatic variables, the existing SEIR model was extended to an infectious disease model with a greater number of population compartments, and with parameter values that were tuned adaptively by solving the nonlinear dynamics equations over the available pandemic data, as well as referring to previous experience with SARS. The contribution presented in this paper is a new model called the adaptive SEAIRD model;it considers the new characteristics of COVID19 and is therefore applicable to a population including asymptomatic carriers. The predictive value is enhanced by tuning of the optimal parameters, whose values better reflect the current pandemic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Covid-19 in Zimbabwe: Implications for the Commercial Agricultural Sector

COVID-19 (coronavirus disease), a highly infectious respiratory disease, emerged in 2019 in Wuhan City of Hubei Province, China. The pandemic has ravaged humanity and led to severe implications on the global economy. Zimbabwe, as part of the global community and her commercial agricultural sector in particular, was not spared from the high infectivity of the coronavirus and the associated disastrous impact of the pandemic. The aim of this chapter is to examine the impact of COVID-19 pandemic on the commercial agricultural sector of Zimbabwe. The methods used in this chapter involve extraction and analysis of data from publicly available data from the Ministry of Health and Child Care, Food and Agricultural Organisation (FAO) and Zimbabwe Statistical Agency (ZIMSTATS). The data extracted included cumulative cases of COVID-19 and agricultural output statistics prior and during the pandemic. To augment secondary data, a structured questionnaire was administered to prominent commercial famers (identified by the Agricultural Extension Workers) in each of the 61 districts of Zimbabwe, through phone calls or emails, in adherence to the COVID-19 regulations of minimizing close contact to reduce transmission. Apart from the analysis of such data, there is also a critical review of literature from published resources. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Emergency visits of otorhinolaryngology department during COVID-19 pandemic, Saudi Arabia, 2020

Aim: In this study, we aimed to assess the frequency of patient emergency visits to the Otorhinolaryngology (ORL) Department during coronavirus COVID-19 pandemic and compare it with that before coronavirus COVID-19. Material(s) and Method(s): A retrospective comparative study was performed at Al-Al-Hada Armed Forces Hospital, Taif (Saudi Arabia), and data regarding various diagnoses of ORL cases were collected from medical records of patients who visited/admitted to ORL-ED during the lockdown (Group 1) and those who visited/ admitted to ORL-ED before the pandemic (Group 2). Result(s): Group 2 had a significantly higher percentage of cases who had no ENT-related disorders, hypertrophy inferior turbinate (HIT), stridor, obstructive sleep apnea (OSA), epistaxis and who had no complications, had general ENT, foreign body ingestion-aspiration, trauma, otology and who had more than one disorder and Group 1 had a significantly higher percentage of those having nasal obstruction, tonsil hypertrophy grade 3, had emergency head and neck cancer, had deep neck space infections and who had complicated. Discussion(s): During coronavirus COVID-19 pandemic period, cold ENT visits were much less and foreign body ingestion remains the highest reason for ENT visits. Additionally, telemedicine has been shown to be effective in reducing ED visits during the pandemic period. Furthermore, older cases with chronic ENT problems who had regular follow-up ENT visits were less likely to visit ED during the pandemic.Copyright © 2022, Derman Medical Publishing. All rights reserved.

Prediction of COVID-19 Using the Artificial Neural Network (ANN) with K-Fold Cross-Validation

Background: COVID-19 is a disease that attacks the respiratory system and is highly contagious, so cases of the spread of COVID-19 are increasing every day. The increase in COVID-19 cases cannot be predicted accurately, resulting in a shortage of services, facilities and medical personnel. This number will always increase if the community is not vigilant and actively reduces the rate of adding confirmed cases. Therefore, public awareness and vigilance need to be increased by presenting information on predictions of confirmed cases, recovered cases, and cases of death of COVID-19 so that it can be used as a reference for the government in taking and establishing a policy to overcome the spread of COVID-19. Objective: This research predicts COVID-19 in confirmed cases, recovered cases, and death cases in Lampung Province Method: This study uses the ANN method to determine the best network architecture for predicting confirmed cases, recovered cases, and deaths from COVID-19 using the k-fold cross-validation method to measure predictive model performance. Results: The method used has a good predictive ability with an accuracy value of 98.22% for confirmed cases, 98.08% for cured cases, and 99.05% for death cases. Conclusion: The ANN method with k-fold cross-validation to predict confirmed cases, recovered cases, and COVID-19 deaths in Lampung Province decreased from October 27, 2021, to January 24, 2022. © 2023 The Authors. Published by Universitas Airlangga.

A review of some Covid-19 pandemic numbers in European Union, Canada, and Mexico

The rapidity and extent of Covid-19 infections have shown how a phenomenon that initially seemed geographically circumscribed quickly spread worldwide. In 2020, the spread of infection and the containment and management measures taken by local governments have been quite heterogeneous. Therefore, here we investigate the different ways of the spread of the infection in different areas, and specifically in Canada, Mexico, and the European Union states. For this purpose, for each area, official data on infection in 2020 are used to depict, analyze, and compare the monthly contagion's curves and the Rt index, both in absolute and relative terms. © Springer Nature Switzerland AG 2023. All rights reserved.

Machine learning-based diffusion model for prediction of coronavirus-19 outbreak.

The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model for prediction of number of coronavirus cases in four countries: India, France, China and Nepal. Diffusion prediction model works on the diffusion process of the human contact. Model considers two forms of spread: when the spread takes time after infecting one person and when the spread is immediate after infecting one person. It makes the proposed model different over other state-of-the art models. It is giving more accurate results than other state-of-the art models. The proposed diffusion prediction model forecasts the number of new cases expected to occur in next 4 weeks. The model has predicted the number of confirmed cases, recovered cases, deaths and active cases. The model can facilitate government to be well prepared for any abrupt rise in this pandemic. The performance is evaluated in terms of accuracy and error rate and compared with the prediction results of support vector machine, logistic regression model and convolution neural network. The results prove the efficiency of the proposed model.

Association of epidemiological and clinical features with PCR cycle threshold values of SARS-CoV-2 infection: a cross-sectional study.

The cycle threshold (Ct) in quantitative real-time reverse-transcriptase polymerase chain reaction (qRT-PCR) is inversely correlated to the amount of viral nucleic acid or viral load and can be regarded as an indicator of infectivity. We examined the association of socio-demographic and clinical characteristics of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) polymerase chain reaction (PCR) positive cases with PCR cycle threshold (Ct) values at the time of diagnosis. SARS-CoV-2 cases reported between 12 October 2020 and 24 January 2021 in Regensburg were analyzed employing bivariate and multivariable methods. We included 3,029 SARS-CoV-2 cases (31% asymptomatic at diagnosis) and analyzed the association of case characteristics with Ct values in 2,606 cases. Among symptomatic patients, cough (38.0%), rhinitis (32.4%), headache (32.0), and fever/chills (29.9%) were the most frequent complaints. Ct values ≤20 were more frequent in symptomatic cases (20.9% vs. 11.3%), whereas Ct values >30 were more common in asymptomatic cases (32.6% vs. 18.0%). Ct values >20 and ≤30 were most common in symptomatic and asymptomatic cases (48.0% vs 40.7%). We observed lower median Ct values of E and N gene in symptomatic cases. In a random forest model, the total number of symptoms, respiratory symptoms, and age were most strongly associated with low Ct values. In conclusion, certain symptoms and age were associated with lower Ct values. Ct values can be used as a pragmatic approach in estimating infectivity at the first notification of a case and, thus, in guiding containment measures.

Modified SEIR epidemic model including asymptomatic and hospitalized cases with correct demographic evolution

The aim of this study is to propose a modified Susceptible-Exposed-Infectious-Removed (SEIR) model that describes the time behaviour of symptomatic, asymptomatic and hospitalized patients in an epidemic, taking into account the effect of the demographic evolution. Unlike most of the recent studies where a constant ratio of new individuals is considered, we consider a more correct assumption that the growth ratio is proportional to the total population, following a Logistic law, as is usual in population growth studies for humans and animals. An exhaustive theoretical study is carried out and the basic reproduction number R0 is computed from the model equations. It is proved that if R0<1 then the disease-free manifold is globally asymptotically stable, that is, the epidemics remits. Global and local stability of the equilibrium points is also studied. Numerical simulations are used to show the agreement between numerical results and theoretical properties. The model is fitted to experimental data corresponding to the pandemic evolution of COVID-19 in the Republic of Cuba, showing a proper behaviour of infected cases which let us think that can provide a correct estimation of asymptomatic cases. In conclusion, the model seems to be an adequate tool for the study and control of infectious diseases.

Forecast for peak infections in the second wave of the Omicron after the adjustment of zero-COVID policy in the mainland of China.

On December 7, 2022, the Chinese government optimized the current epidemic prevention and control policy, and no longer adopted the zero-COVID policy and mandatory quarantine measures. Based on the above policy changes, this paper establishes a compartment dynamics model considering age distribution, home isolation and vaccinations. Parameter estimation was performed using improved least squares and Nelder-Mead simplex algorithms combined with modified case data. Then, using the estimated parameter values to predict a second wave of the outbreak, the peak of severe cases will reach on 8 May 2023, the number of severe cases will reach 206,000. Next, it is proposed that with the extension of the effective time of antibodies obtained after infection, the peak of severe cases in the second wave of the epidemic will be delayed, and the final scale of the disease will be reduced. When the effectiveness of antibodies is 6 months, the severe cases of the second wave will peak on July 5, 2023, the number of severe cases is 194,000. Finally, the importance of vaccination rates is demonstrated, when the vaccination rate of susceptible people under 60 years old reaches 98%, and the vaccination rate of susceptible people over 60 years old reaches 96%, the peak of severe cases in the second wave of the epidemic will be reached on 13 July 2023, when the number of severe cases is 166,000.

Epidemic characteristics analysis of COVID-19 cluster in Huizhou, Guangdong

Objective To analyze the epidemiological characteristics of COVID-19 cluster epidemic in Huizhou from January to February in 2020, and we provide experience and reference for the prevention and control of cluster epidemic. Methods Descriptive epidemiology was used to analyze the clusters of COVID-19 in Huizhou city. Results From January to February in 2020, a total of 19 COVID-19 cluster outbreaks were reported in Huizhou. The most common cluster outbreaks were in Huidong county (8 cases), Boluo county (3 cases) and Huiyang district (3 cases). There were 59 cases involved in 19 outbreaks, among which 46 were confirmed cases, and 13 were asymptomatic infected. The sex ratio of male to female was 0.84: 1, the age was 1-85 years old. The 19 cases of outbreaks were all caused by imported cases, among which 13 cases were imported from Wuhan (68.4%), 3 cases were imported from Hubei province except Wuhan (15.8%), and 3 cases were imported from other provinces and cities (15.8%). There were 13 cases (68.4%) in the first generation, and 6 cases (31.6%) in the second generation. Events exposed place were variety, including 3 (15.8%) simple family exposure, 13 (68.4%) joint exposure, exposure family, 1 (5.26%) of the joint exposure, family exposure, family dinners, 1 (5.26%) of the joint exposure, family exposure, exposure (hotel) exposed in public places, 1 (5.26%) of the collective unit (workplace) exposure. Conclusion All the COVID - 19 cluster outbreaks in Huizhou city were caused by imported cases, most of which occurred in the family and were caused by families living together and eating together. As the number of people returning to work, production and school increases, various prevention and control measures should be implemented in key areas, key populations and key places to prevent the outbreak from rebounding.Copyright © China Tropical Medicine 2021.

Measuring the Spread of COVID-19: Restrictions and Mobility in the Visegrad Countries (Czech Republic, Hungary, Poland, Slovakia)

The Visegrad Group, Visegrad Four, or V4, is a cultural and political alliance of four countries of Central Europe (Czech Republic, Hungary, Poland and Slovakia). The smallest country of them, Slovakia, recorded the first confirmed case of COVID-19 on March 6, 2020 in a 52-year-old man from the Bratislava region. However, it is a secondary transmission of the disease, as his son returned home from the risk area (Venice, Italy) (ÚVZSR, Retrieved from https://www.uvzsr.sk/index.php?option=com_contentandview=articleandid=4061:slovensko-zaznamenalo-prvy-potvrdeny-pripad-ochorenia-covid-19andcatid=250:koronavirus-2019-ncovandItemid=153, 2020). This record points to the gradual spread of the virus from the European outbreak in northern Italy and France (the first cases on January 24) to the neighbouring countries with the V4 countries emerging approximately 1 month apart. The first of the four countries was Czechia (3 cases) on March 1, followed by Hungary (2 cases) together with Poland (1 case) on March 4. In this study we will take a closer look at how quickly and in which regions of Czechia, Hungary, Poland and Slovakia the virus spread in contrast to the dates of implementation of individual measures and mobility changes during the COVID-19 period. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

SARS-CoV-2-related injury of biliary tract

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), which causes coronavirus disease-2019 (COVID-19), affects several organs and systems. The ways of the virus penetration into tissues have been studied. Liver is affected in 15 — 53 % of cases. Data on the RNA-seq sequencing in the Human Protein Atlas database con-firm the expression of ACE2 (angiotensin 2 converting enzyme receptor) in the liver and epithelium of the bile ducts and gallbladder. At the same time, a high frequency of ACE2 expression is observed only in cholangiocytes, but not in hepatocytes, Kupffer cells or endothelial cells. The amount of ACE2 in bile duct cells is significantly higher than in hepatocytes, and is comparable to the level in type 2 alveolar cells in the lungs, which makes the biliary tract a potential target for the virus. Binding of SARS-CoV-2 to gallbladder epithelial cells can lead to mucosal inflammation. A systematic review describes the formation of bile clots in small bile ducts. Presence of COVID-19 in the wall of the gallbladder (qrt-RCR) has been revealed. With this, acute cholecystitis develops clinically, and radiological investigations show thickening of the gallbladder wall, biliary sludge and perivesicular fluid. Onset of acute gangrenous cholecystitis as a late complication of COVID-19 infection is described. Histologically, inflammatory infiltrates are found in the gallbladder wall, which diffusely affect medium-sized arteries with obliteration of their lumen, ischemia of the bladder wall;the perineural inflammation take place. These features indicate vasculitis with thrombosis. Thus, despite the theoretical information about the biliary tract injury by the COVID-19 virus, this aspect of the infection has not been clinically studied, and the published papers are limited to the description of single clinical cases. In our opinion, a deeper and long-term study of the biliary tract pathology in COVID-19 infection is needed to provide rationale for the treatment with ursodeoxycholic acid. © 2020, Publishing Company VIT-A-POL. All rights reserved.

Newly Emerging Type 2 Diabetes in Covid-19 Patients: A Study in tertiary care hospital in North India

The COVID-19 pandemic has led to an increased risk of developing Type 2 Diabetes in patients with no prior history of the disease. In this systematic review, we aim to investigate the association between COVID-19 and newly diagnosed Type 2 Diabetes and identify the potential risk factors and underlying mechanisms of diabetes development in COVID-19 patients. Investigating the occurrence of diabetes following coronavirus infection was the study's goal. Out of the total 2700 cases, the mildly symptomatic (1628, 60.30%) and moderately symptomatic (815, 30.18%) patients received OPD treatment and experienced alleviation in 3-7 days;none of them required hospitalisation. Out of 2700 cases, 257 (or 9.52%) required hospitalisation due to serious illness. Our analysis shows that COVID-19 is associated with a higher risk of newly diagnosed Type 2 Diabetes, and that factors such as inflammation, insulin resistance, and genetic predisposition may contribute to the development of diabetes in COVID-19 patients. This study confirms the necessity of active glucose monitoring in hospitalised, critically unwell SARS-CoV-19-infected patients. [ FROM AUTHOR] Copyright of Journal of Cardiovascular Disease Research (Journal of Cardiovascular Disease Research) is the property of Journal of Cardiovascular Disease Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Description of the clinical picture of COVID-19 in patients with fatal outcome

In the Chelyabinsk region in 2020, 33 473 cases of a new coronavirus infection (COVID-19) were detected, of which 493 were fatal. The peak incidence of COVID-19 was recorded in November-December 2020. Purpose of research: description of the course of the infectious process of COVID-19 in patients with a fatal outcome. Material and methods. A retrospective analysis of case histories of patients with COVID-19 who had a lethal outcome was carried out using the method of continuous sampling from November 2020 to April 2021 on the basis of the Regional Infectious Diseases Center of the Regional Clinical Hospital No. 3 of the Chelyabinsk Region. Diagnosis was based on a comprehensive assessment of the anamnesis, clinical, laboratory and instrumental data in accordance with the Interim Guidelines "Prevention, Diagnosis and Treatment of Novel Coronavirus Infection (COVID-19)" version 9 dated 10/26/2020. Verification of the etiological diagnosis was carried out by PCR with reverse transcription to detect SARS-CoV-2 RNA in the nasopharyngeal secretion. The autopsy material was studied in the Pathoanatomical Department No. 9 of the Chelyabinsk Regional Pathological Bureau. Results and discussion. The average age of patients with a fatal outcome was 70 years, more often they were people with blood groups I and II, not vaccinated against COVID-19, pneumococcal infection and influenza;no gender differences were found. For patients with a fatal outcome from COVID-19, the presence of concomitant pathology is most characteristic: arterial hypertension (58%), atherosclerosis (45%), type 2 diabetes mellitus (41.5%), malignant neoplasms (17%). Among the "lethal patients", 50% had lung lesions at admission CT 2 and 25% had changes in the lungs consistent with CT 1;there was also a violation of cognitive functions in 21% of individuals. When comparing the analysis of studies of autopsy material and the clinical picture of patients, it was found that the morphological substrate of COVID-19 is both diffuse alveolar damage and simultaneous damage to the vascular bed, which is accompanied by damage to various organs and systems of the patient's body.Copyright © Eco-Vector, 2022.

Epidemiological characteristics of four times of community transmission of COVID-19 in Hebei

Objective To analyze the epidemiological characteristics of community transmission of the coronavirus disease 2019 (COVID-19) caused by four imported cases in Hebei Province, and to provide a scientific basis for the prevention and control of the disease. Methods Descriptive epidemiological methods were used to analyze the epidemiological characteristics of four community-transmitted COVID-19 outbreaks reported in the China Disease Control and Prevention Information System from January 1, 2020 to December 31, 2021 in Hebei Province. Results From January 1, 2020 to December 31, 2021, four community-transmitted COVID-19 outbreaks caused by imported COVID-19 occurred in Hebei Province, respectively related of Hubei (Wuhan) Province, Beijing Xinfadi market, Overseas cases and Ejina banner of Inner Mongolia Autonomous Region. Total of 1 656 cases (1 420 confirmed cases and 236 asymptomatic cases) were reported, including 375 cases in phase A (From January 22 to April 16, 2020), and phase B (from June 14 to June 24, 2020) 27 cases were reported, with 1 116 cases reported in the third phase (Phase C, January 2 to February 14, 2021), and 138 cases reported in the fourth phase (Phase D, October 23 to November 14, 2021). The 1 656 cases were distributed in 104 counties of 11 districts (100.00%), accounting for 60.46% of the total number of counties in the province. There were 743 male cases and 913 female cases, with a male to female ratio of 0.81:1. The minimum age was 13 days, the maximum age was 94 years old, and the average age (median) was 40.3 years old. The incidence was 64.01% between 30 and 70 years old. Farmers and students accounted for 54.41% and 14.73% of the total cases respectively. Of the 1 420 confirmed cases, 312 were mild cases, accounting for 21.97%;Common type 1 095 cases (77.11%);There was 1 severe case and 12 critical cases, accounting for 0.07% and 0.85%, respectively. 7 patients died from 61.0 to 85.7 years old. The mean (median) time from onset to diagnosis was 1.9 days (0-31 days), and the mean (median) time of hospital stay was 15 days (1.5-56 days). Conclusions Four times in Hebei province COVID-19 outbreak in scale, duration, population, epidemic and type of input source, there are some certain difference, but there are some common characteristics, such as the outbreak occurs mainly during the legal holidays or after starting and spreading epidemic area is mainly in rural areas, aggregation epidemic is the main mode of transmission, etc. To this end, special efforts should be made to strengthen the management of people moving around during holidays, and strengthen the implementation of epidemic prevention and control measures in places with high concentration of people. To prevent the spread of the epidemic, we will step up surveillance in rural areas, farmers' markets, medical workers and other key areas and groups, and ensure early detection and timely response.Copyright © 2022 China Tropical Medicine. All rights reserved.

Study of COVID-19 Impact on Medico Legal Cases Registered at Mediciti Hospital, Ghanpur Village, Medchal Mandal, Telangana

Background and Objectives: In every tertiary care hospital, casualty deals with the emergency cases of which the majority are Medico legal cases. The knowledge about the incidence of Medico legal cases is important to recognise the socioeconomic burden. The present study was conducted to scrutinize different Medicolegal cases at the emergency department of Mediciti Hospital, Medchal. The idea of the study was to find out frequency of several types of medico legal cases at casualty of Mediciti Hospital, Medchal. Material(s) and Method(s): It was a record based cross sectional study in which all the MLC cases registered in MLC record book from March 2020-March 2021 were analyzed. The data was collected on age, sex, type of Medico legal cases, road traffic accidents, mode of occurrence, month-wise distribution of medico legal cases and the time of occurrence. Results were expressed in numbers and percentages. Result(s): Out of all 355 registered medico legal cases, of which 258(73.2%) were males and 94(26.76%) were females. Maximum cases were from the age group of 20-29 years i.e., 127(43.09%). Majority of the MLC's registered we are due to road traffic accidents 144(39.7%) followed by Assault 49(13.8%), falls-43(12.7%), poisoning-43(12.1%), and injury at workplace-43(12.1%), accidental-14(3.9%), snake bite-8(2.2%) and others (7.4%). Conclusion(s): The present study shows RTAs account for a major part of MLCs. By proper education and training of safety measures among public decreases the cases. Enforcement of strict laws reduces the incidence of road traffic accidents. And also, strict laws should be amended to reduce the incidence of Assault.Copyright © 2023, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.

Relationship between COVID-19 and the Performance of Pharmaceutical Industry

The aim of the study was to determine how covid-19 affects the pharmaceutical stock market. The monthly data was used in this study span two years, from 2020 to 2022. For the study the independent variables were taken are daily new cases and daily deaths, market to book value and their effect on the market return of pharmaceutical industry. The results showed that the COVID-19 outbreak has had a significant impact on the sector's stock performance. Investors believe healthcare and pharmaceutical companies will benefit from this pandemic as they invest in research and development to prepare for the current epidemic and all subsequent pandemics. The results of this survey showed that investors will continue to invest in healthcare and pharmaceutical companies over time. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Predictive Information Workflow of Forecasting Number of COVID-19 Confirmed Cases

There are questions about how to accurately prepare with the correct number of resources for distribution in order to properly manage the healthcare resources (e.g., healthcare workers, Masks, ART-19 TestKit) required to tighten the grip on the COVID-19 pandemic. Mathematical and computational forecasting models have well served the means to address these questions, as well as the resulting advisories to governments. A workflow is proposed in this research, aiming to develop a forecasting simulation that makes accurate predictions on COVID-19 confirmed cases in Singapore. According to the analysis of the prior works, six candidate forecasting models are evaluated and compared in the workflow: polynomial regression, linear regression, SVM, Prophet, Holt's linear, and LSTM models. The study's goal is to determine the most suitable forecasting model for COVID-19 cases in Singapore. Two algorithms are also proposed to better compute the performance of two models: the order algorithm to determine optimal degree order for the polynomial regression model, and the optimizing algorithm for the Holt's linear model to calculate the optimal smoothing parameters. Observed from the experiment results with the COVID-19 dataset, the Prophet method model achieves the best performance with the lowest Root Mean Square Error (RMSE) score of 1557.744836 and Mean Absolute Percentage Error (MAPE) score of 0.468827, compared to the other five models. The Prophet method model achieving average accuracy range of 90% when forecasting the number of confirmed COVID-19 cases in Singapore for the next 87 days ahead. is chosen and recommended to be used as a system model for forecast the COVID-19 confirm cases in Singapore. The developed workflow will greatly assist the authorities in taking timely actions and making decisions to contain the COVID-19 pandemic. © 2022 IEEE.