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Nowadays, COVID-19 is considered to be the biggest disaster that the world is facing. It has created a lot of destruction in the whole world. Due to this COVID-19, analysis has been done to predict the death rate and infected rate from the total population. To perform the analysis on COVID-19, regression analysis has been implemented by applying the differential equation and ordinary differential equation (ODE) on the parameters. The parameters taken for analysis are the number of susceptible individuals, the number of Infected Individuals, and the number of Recovered Individuals. This work will predict the total cases, death cases, and infected cases in the near future based on different reproductive rate values. This work has shown the comparison based on 4 different productive rates i.e. 2.45, 2.55, 2.65, and 2.75. The analysis is done on two different datasets; the first dataset is related to China, and the second dataset is associated with the world's data. The work has predicted that by 2020-08-12: 59,450,123 new cases and 432,499,003 total cases and 10,928,383 deaths.

New analysis bolsters idea that country’s seemingly low death rate was misleading

Although South Korea is a high-income country, its trauma system is comparable to low- and middle-income countries with high preventable trauma death rates of more than 30%. Since 2012, South Korea has established a national trauma system based on the implementation of regional trauma centers and improvement of the transfer system; this study aimed to evaluate its effectiveness. We compared the national preventable trauma death rates, transfer patterns, and outcomes between 2015 and 2017. The review of preventable trauma deaths was conducted by multiple panels, and a severity-adjusted logistic regression model was created to identify factors influencing the preventable trauma death rate. We also compared the number of trauma patients transferred to emergency medical institutions and mortality in models adjusted with injury severity scores. The preventable trauma death rate decreased from 2015 to 2017 (30.5% vs. 19.9%, p < 0.001). In the severity-adjusted model, the preventable trauma death risk had a lower odds ratio (0.68, 95% confidence interval: 0.53–0.87, p = 0.002) in 2017 than in 2015. Regional trauma centers received 1.6 times more severe cases in 2017 (according to the International Classification of Diseases Injury Severity Score [ICISS]; 23.1% vs. 36.5%). In the extended ICISS model, the overall trauma mortality decreased significantly from 2.1% (1008/47 806) to 1.9% (1062/55 057) (p = 0.041). The establishment of the national trauma system was associated with significant improvements in the performance and outcomes of trauma care. This was mainly because of the implementation of regional trauma centers and because more severe patients were transferred to regional trauma centers. This study might be a good model for low- and middle-income countries, which lack a trauma system.

AbstractPeople living with HIV (PLWH) are particularly vulnerable to worsened outcomes of COVID-19. Therefore, the purpose of this work was to provide a scoping review of the literature to assess the risk factors for COVID-19 mortality among PLWH. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR), searches were conducted in PubMed, Scopus, Global Health, and WHO Coronavirus Database. Articles were eligible for inclusion if they were in English, included PLWH who died after COVID-19 infection, and described risk factors for mortality. Results were descriptively synthesized and pooled thereafter. Study quality was assessed using the Joanna Brigg Institute’s critical appraisal tools. 20 studies were eligible for inclusion, with the pooled death rate being 11.7%. Age was a major risk factor, especially after 50 (23.2%) and after 70 (41.8%), and males had a death rate nearly double that of females. As total comorbidities increased, the death rate also greatly increased; among those with comorbidities, the highest fatality rates were those with cardiovascular disease (30.2%), chronic kidney disease (23.5%), obesity (22.4%), and diabetes (18.4%). Other risk factors for mortality among PLWH included having a Black racial background, being an injection drug user, being a smoker, and having a CD4 cell count below 200. There is a need to better study confounding factors, and to understand how vaccination influences mortality risk. Overall, the findings highlight a need to ensure that focus is placed on the varying demographics of PLWH amidst COVID-19 control efforts.

Abstract Background Populations affected by crises (armed conflict, food insecurity, natural disasters) are poorly covered by demographic surveillance. As such, crisis-wide estimation of population mortality is extremely challenging, resulting in a lack of evidence to inform humanitarian response and conflict resolution. Methods We describe here a ‘small-area estimation’ method to circumvent these data gaps and quantify both total and excess (i.e. crisis-attributable) death rates and tolls, both overall and for granular geographic (e.g. district) and time (e.g. month) strata. The method is based on analysis of data previously collected by national and humanitarian actors, including ground survey observations of mortality, displacement-adjusted population denominators and datasets of variables that may predict the death rate. We describe the six sequential steps required for the method’s implementation and illustrate its recent application in Somalia, South Sudan and northeast Nigeria, based on a generic set of analysis scripts. Results Descriptive analysis of ground survey data reveals informative patterns, e.g. concerning the contribution of injuries to overall mortality, or household net migration. Despite some data sparsity, for each crisis that we have applied the method to thus far, available predictor data allow the specification of reasonably predictive mixed effects models of crude and under 5 years death rate, validated using cross-validation. Assumptions about values of the predictors in the absence of a crisis provide counterfactual and excess mortality estimates. Conclusions The method enables retrospective estimation of crisis-attributable mortality with considerable geographic and period stratification, and can therefore contribute to better understanding and historical memorialisation of the public health effects of crises. We discuss key limitations and areas for further development.

Abstract Background COVID-19 restrictions and fear dramatically changed the use of medical care. Understanding the magnitude of cancelled and postponed appointments and associated factors can help identify approaches to mitigate unmet need. Objective To determine the proportion of medical visits cancelled or postponed and for whom. We hypothesized that adults with serious medical conditions and those with higher anxiety, depressive symptoms, and avoidance-oriented coping would have more cancellations/postponements. Design Four nationally representative cross-sectional surveys conducted online in May, July, October, and December 2020. Participants 59,747 US adults who completed 15-min online surveys. 69% cooperation rate. Measures Physical and mental health visits and cancer screening cancelled or postponed over prior 2 months. Plan to cancel or postpone visits over the next 2 months. Relationship with demographics, medical conditions, local COVID-19 death rate, anxiety, depressive symptoms, coping, intolerance of uncertainty, and perceived COVID-19 risk. Key Results Of the 58% (N = 34,868) with a medical appointment during the 2 months before the survey, 64% had an appointment cancelled or postponed in May, decreasing to 37% in December. Of the 41% of respondents with scheduled cancer screening, 20% cancelled/postponed, which was stable May to December. People with more medical conditions were more likely to cancel or postpone medical visits (OR 1.19 per condition, 95% CI 1.16, 1.22) and cancer screening (OR 1.20, 95% CI 1.15, 1.24). Race, ethnicity, and income had weak associations with cancelled/postponed visits, local death rate was unrelated, but anxiety and depressive symptoms were strongly related to cancellations, and this grew between May and December. Conclusions Cancelled medical care and cancer screening were more common among persons with medical conditions, anxiety and depression, even after accounting for COVID-19 deaths. Outreach and support to ensure that patients are not avoiding needed care due to anxiety, depression and inaccurate perceptions of risk will be important.

Introduction: Diagnosis of COVID-19 associated pulmonary aspergillosis remains unclear especially in non-immunocompromised patients. The aim of this study was to evaluate seven mycological criteria and their combination in a large homogenous cohort of patients. Methods: All successive patients (n=176) hospitalized for COVID-19 requiring mechanical ventilation and who clinically worsened despite appropriate standard of care were included over a one-year period. Direct examination, culture, Aspergillus qPCR ( Af -qPCR) and galactomannan was performed on all respiratory samples (n=350). Serum galactomannan, ß-D-glucan and plasma Af -qPCR were also assessed. Criteria were analyzed alone or in combination in relation to mortality rate. Results: Mortality was significantly different in patients with 0, ≤2 and ≥3 positive criteria (logrank test, p=0.04) with death rate of 43.1, 58.1 and 76.4% respectively. Direct examination, plasma qPCR and serum galactomannan were associated with a 100% mortality rate. Bronchoalveolar lavage (BAL) galactomannan and positive respiratory sample culture were often found as isolated markers (28.1 and 34.1%) and poorly repeatable when a second sample was obtained. Aspergillus DNA was detected in 13.1% of samples (46/350) with significantly lower Cq when associated with at least one other criteria (30.2 vs 35.8) (p<0.001). Conclusion: Combination of markers and/or blood biomarkers and/or direct respiratory sample examination seems more likely to identify patients with CAPA. Af -qPCR may help identifying false positive results of BAL galactomannan testing and culture on respiratory samples while quantifying fungal burden accurately.

The COVID-19 pandemic has plagued the entire world, including Indonesia. The step to breaking the spread chain of COVID-19 is to apply health protocols such as handwashing and social distancing. The Covid-19 case in Klaten Regency Central Java for ten weeks until early February 2021 with the status of the Red Zone and death rate is 14.1 percent. Ngerangan Tourism Village has a thematic kampong with culinary and nature attractions, such as Pecel Kampong. The number of visitors reaches 600 tourists per day. This causes tourist sites to be clusters most prone to the spread of COVID-19 and must be equipped with adequate health protocols. Therefore, this service aims to implement health protocols during the Covid 19 pandemic through hand washing and social distancing. The service method has three stages, namely information, guidance, and habitual independence. The output of this service is in the form of technology that is socialized and donated in handwashing behavior in the form of 3 portable footstep sinks, four jerry cans of handwashing soap, one hand washing educational banner, and one banner for implementing health protocols. Meanwhile, the technology that was socialized and granted in the implementation of Social Distancing was in the form of 1 banner for implementing social distancing and 20 stickers indicating the implementation of social distancing.

AbstractThe present study introduces a novel design of Morlet wavelet neural network (MWNN) models to solve a class of a nonlinear nervous stomach system represented with governing ODEs systems via three categories, tension, food and medicine, i.e., TFM model. The comprehensive detail of each category is designated together with the sleep factor, food rate, tension rate, medicine factor and death rate are also provided. The computational structure of MWNNs along with the global search ability of genetic algorithm (GA) and local search competence of active-set algorithms (ASAs), i.e., MWNN-GA-ASAs is applied to solve the TFM model. The optimization of an error function, for nonlinear TFM model and its related boundary conditions, is performed using the hybrid heuristics of GA-ASAs. The performance of the obtained outcomes through MWNN-GA-ASAs for solving the nonlinear TFM model is compared with the results of state of the article numerical computing paradigm via Adams methods to validate the precision of the MWNN-GA-ASAs. Moreover, statistical assessments studies for 50 independent trials with 10 neuron-based networks further authenticate the efficacy, reliability and consistent convergence of the proposed MWNN-GA-ASAs.

The explosion of coronavirus has posed challenges to public health infrastructure in India. This pandemic can be contained with social distancing and isolation. The analysis of human mobility trends plays a decisive role in the spread of the pandemic. These movement patterns are extracted from Google COVID-19 Community Mobile Reports. These reports help to analyze the human mobility trends to various frequently visited places across different states of India. This work focuses on analyzing mobility trends in India and their effect on the spread of pandemic in terms of number of active cases and death rate. The mobility patterns, number of tests conducted, population density across different states in India are explored to understand their effect on the severity of epidemic. These features are correlated using statistical methods. This study lays the foundation in building a framework to contain the contributors for the spread of pandemics and provide insights to the regulatory bodies to strategize enforcing or revoking lockdown restrictions across regions in the country.