Increase in traumatic injury burden amidst COVID-19 was disproportionately shouldered by racial and ethnic minority patients: An urban case study

Background: When the COVID-19 pandemic intersected with the longstanding global pandemic of traumatic injury, it exacerbated racial and ethnic disparities in injury burden. As Milwaukee, Wisconsin is a racially diverse yet segregated urban city due to historic and ongoing systemic efforts, this populace provided an opportunity to further characterize injury disparities. Method(s): We analyzed trauma registry data from the only adult Level 1 trauma center in Milwaukee, WI before and during the COVID-19 pandemic (N = 19,908 patients from 2015-2021). We retrospectively fit seasonal ARIMA models to monthly injury counts to determine baseline injury burden pre-COVID-19 (Jan 2015-Mar 2020). This baseline data was used to forecast injury by race and ethnicity from April 2020 to December 2021 and was compared to actual injury counts. Result(s): For all mechanisms of injury (MOI), counts during the pandemic were significantly higher than forecasted for Black or African American (mean absolute percentage error, MAPE = 23.17) and Hispanic or Latino (MAPE = 26.67) but not White patients (MAPE = 12.72). Increased injury for Black or African American patients was driven by increases in motor vehicle crashes (MVCs) and firearm-related injury;increased injury for Hispanic or Latino patients was driven by falls and MVCs. Conclusion(s): The exacerbation of injury burden disparities during COVID-19, particularly in specific MOI, underscores the need for primary injury prevention within specific overburdened communities. Injury prevention requires intervention through social determinants of health, including addressing the impact of structural racism, as primary drivers of injury burden disparities.Copyright © The Author(s) 2023.

Vaccine rate forecast for COVID-19 in Africa using hybrid forecasting models

Background: The public health sectors can use the forecasting applications to determine vaccine stock requirements to avoid excess or shortage stock. This prediction will ensure that immunization protection for COVID-19 is well-distributed among African citizens. Objective(s): The aim of this study is to forecast vaccination rate for COVID-19 in Africa Methods: The method used to estimate predictions is the hybrid forecasting models which predicts the COVID-19 vaccination rate (CVR). HARIMA is a hybrid of ARIMA and the Linear Regression model and HGRNN is a hybrid of Generalized Regression Neural Network (GRNN) and the Gaussian Process Regression (GPR) model which are used to improve predictive accuracy. Result(s): In this study, standard and hybrid forecasting models are used to evaluate new COVID-19 vaccine cases daily in May and June 2021. To evaluate the effectiveness of the models, the COVID-19 vaccine dataset for Africa was used, which included new vaccine cases daily from 13 January 2021 to 16 May 2021. Root Mean Squared Error (RMSE) and Error Percentage (EP) are used as evaluation measures in this process. The results obtained showed that the hybrid GRNN model performed better than the hybrid ARIMA model. Conclusion(s): HGRNN model provides accurate daily vaccinated case forecast, which helps to maintain optimal vaccine stock to avoid vaccine wastage and save many lives.Copyright © 2023 Dhamodharavadhani S et al.

Epidemiologic Assessement of Pediatric Inflammatory Bowel Disease Presentation in Nyc during Covid-19 Suggests Increased Ibd Presentation

SARS-nCoV2 may have increased capacity to generate autoimmune disease;multiple reports suggest increased risk of Type 1 Diabetes, and case reports suggest other autoimmune linkages. Inflammatory Bowel Disease (IBD) pathogenesis appears to be a mix of genetic susceptibility, microbial populations, and immune triggers such as infections. Given the perceived role of infection in pathogenesis, decreased incidence of all infections during the pandemic secondary to non-pharmaceutical interventions should decrease IBD incidence rates. The aim of this study was to evaluate the association between the Covid-19 pandemic and IBD presentation in NYC using data from new diagnoses at a consortium of institutions. Using EMR systems all diagnoses at 4 collaborating institutions were retrieved from 2015-2021. We fit time series model (ARIMA) to the quarterly number of cases of each disease for January 2016-March 2020 and forecast the subsequent 21 months. We not only did not observe a decline in pediatric IBD secondary to absent viral illness but noted a statistically significant increase in Crohn's Disease approximately 6 months after the initial 2020 COVID wave in NYC, and trends suggesting increases overall in IBD diagnoses above the existing trend towards increased disease presentation that pre-dated the pandemic. We also note suggestive but not significant trends towards overall increases in UC diagnostic rate. This data suggests that there may be a linkage between SARS-nCoV2 infection rates and subsequent pediatric IBD presentation, warranting further evaluation in the aftermath of the Omicron wave. (Figure Presented).

COMPARATIVE ANALYSIS OF EPIDEMIOLOGICAL, FORECASTING AND DEEP LEARNING MODELS FOR COVID19 SPREAD IN INDIA

Covid19 is affecting across many nations and most population of the world. As per WHO there are 270million confirmed with about 5.3 million fatalities as on December 15th, 2021. Many governments, organizations and local bodies have been applying various models in order to estimate the disease spread and appliede varied strategies to curb the spread. There are many models proposed by mathematicians and statisticians for the same. In the current work a comparison is done with mathematical disease spread models SIR, SIRD, classic time series forecasting modelARIMA, and artificial neural network models RNN, LSTM with Covid19 India data. The study investigates the effect of disease containment policies and vaccination drives for Covid19 data in the context of India using SIR Model. All the models are built for multiple time prediction windows starting from 5 days up to 45 days. The models are evaluated with MAE, MAPE and RMSE for multiple states and India level data. It is inferred that the prediction time of 5 days has best results for SIR model. The ARIMA model can predict withacceptable performance up to 30 days. RNN and LSTM models can predict for 5 days within acceptable performance. The best model that can predict longer durations and has good performance is ARIMA model. A detailed report on the model details and performance is the outcome of this study.

Performance improvement of medical blood handling for aphaeresis platelet usage

Objectives: Medical-use Blood Handling ought to be considered as Supply Chain Management including Donation Centers, Hospitals, and blood consumers so that the Quick Response to the change of blood usages and JIT provision can be accomplished. Among others,Aphaeresis Platelet (AP) is vital few of medical blood areas due to increasing demands on higher quality from medical personnel and patients. It makes the SCM more complicated because of its short life-time and infection-sensitivity. The medical centers and hospitals may incline safety inventory and usage amount of pallets due to a speculation of possible wastes. Hence, a prediction model can contribute greatly to the balance of demand and supply of medical-use blood and avoid the wastes of vital resources such as Aphaeresis Platelet regarding its safety lifecycle. Methods: In order to remedy the negative consequence on the performance of blood provision resulted from this speculation;we need to study the correlations between the characteristics of patients as well as hospitals and the usages of Platelet Concentrated, Aphaeresis Platelet, and Prestorage Leukocyte-reduced Aphaeresis Platelets where the rate of using platelet by patients with cancers play a crucial role in developing the model to estimate the demand quantity on Aphaeresis Platelets. Aiming at this, it is crucial to develop a prediction model based on the correlations between the characteristics of patients, healthcare institutions and of the utilization of various types of AP products. By means of previous knowledge and expert interviews, we have at first set up a research framework, which involves patient characteristics, type of hospitals, utilization (rate) and their relation to derive the forecasting model. After priori-analysis on the data of 6 years, we have at first test the data by means of regression, viz. ARIMA. Further studies by means of ANOVA can be conducted to search for the correlation between patient characteristics and utilization of AP, as well as the donation conditions and blood supply, where the characteristics of cancer. At last, we have done a cross-check on aforementioned data and interpretation of the result. Results: The result of this research shows at first that there is no influence of seasons (time) to the consumption of Aphaeresis Platelet in Taiwan while unexpected incidences such as COVID-19 do influence. As the second, medical institutions of different size and level do have different consuming behaviors to AP and patients under chemical therapies consume the largest portion of platelets, that is, 36.5%. More detailed, 25.92 of the patients with cancer consume platelet concentrated, 45.80% of them takes AP and 63.07% takes Prestorage Leukocyte-reduced Aphaeresis Platelets. The effects of perceived issues and potential development of performance improvement are worthy of further investigation. Conclusions: The result of this research shows at first that there is no influence of seasons (time) to the consumption of AP in Taiwan while unexpected incidences such as COVID-19 do influence. Medical institutions of different size and level do have different consuming behaviors to AP and patients under chemical therapies consume the largest portion of platelets.

COVID-19: some simple filtering algorithms, November 2021

Introduction/ Background: COVID-19 was declared a global pandemic on March 11, 2020 by the World Health Organization. Susceptible-Exposed- Infected-Recovered-Dead (SEIRD) has been used to predict its outbreak. However, it should be handled with precaution when it comes to predicting the end of the waves. Methods: In this study, we use some basic filters to show how bad the prediction of COVID-19 data can be. This study uses the publicly available data from the Center for Systems Science and Engineering (CSSE) through their github repository. Reinforcement learning, machine learning, exponential fitting, exponential smoothing and ARIMA are used on the same COVID data set and same time window. Their root mean square errors as well as their l2 errors are investigated as performance criteria. Results: Using the time horizon of 605 days, the RMSE are 0.6619 for reinforcement learning, 5.7549 for exponential smoothing, 274.3350 for machine learning, 274.3350 for single exponential and 137.5769 for ARIMA for short-term. On a longer-term basis, machine learning, exponential smoothing and single exponential were evaluated using RMSE and the results are 173.2891 for machine learning, 909.5221 for exponential smoothing and 289.2051 for single exponential. l2 errors were plotted on a graph as well. Impact: The filters used in this study do not allow us to estimate unreported cases, unreported deaths, hospitalized cases etc. “S+E+I+R+D=N” does not hold in the filter. The use of improved filtering techniques is to be investigated. Conclusion: The methods above can be reasonably good enough for short-term tracking and filtering by designing the parameters properly. For long-term forecasts, however, the trend is different. The basic machine learning method appears to be progressively performant as the training data size increases. The l1-norm needs to be investigated.

Temporal analysis of meningitis and its sociodemographic risk factors in Brazil over the last decade

Background and aims: This paper aims to evaluate the risk factors for hospitalizations for meningitis in Brazil and whether COVID-19 has an influence on this process. Methods: The patients' data is from the Notifiable Diseases Information System of Brazil's Ministry of Health. The lethality and odds ratio analyses were performed in the OpenEpi software using the Taylor Series with an IC95%. The temporal analysis is from January 2010 to November 2021, collected from the Hospitals' Information System of the Unified Health System. The statistical modelling used Gretl software and the US Census Bureau's X-13-ARIMASEATS tool (1.1). The adjustment statistics were calculated using MS Excel. We also checked the influence of COVID- 19 on the subject. Results: Aetiology, age, ethnicity, region and municipal income were considered statistically significant risk factors for unfavourable outcomes in meningitis. The sex category did not show a significant difference in meningitis lethality (Table 1). Regarding the temporal analysis, the best ARIMA models were (0,1,1,) x (0,0,0) for the North region and (0,1,1) x (0,1,1) for the others regions. All models proved to be more efficient than the naive prediction (MASE <1;Theil's U<1) and obtained R2 above 85% (Table 2). The trend of hospitalizations has been negative since 2020. Least squares regression showed that the COVID-19 was statistically significant in reducing hospitalization values in all Brazilian regions. Conclusion: It is possible that the measures against Sars- CoV-2 have contributed to reducing the hospitalizations by meningitis. (Figure Presented).