Exploring excess mortality during the covid-19 pandemic with seasonal arima models

This chapter analyses the daily and the weekly deaths in Germany, Sweden and Spain between 2016 and 2019. It gives an estimate of the future number of deaths in 2020 in those countries, with a special focus on uncertainty, and thereby presents alternative models and methods for estimating the excess mortality in 2020, the year of the Covid-19 pandemic. Suitable seasonal auto-regressive integrated moving average (ARIMA) models are sought that allow the best possible fit to the available time series, in the sense that the properties of the resulting residual processes are compatible with those of white noise. It can be seen that only deaths in the age class 0-30 can be satisfactorily presented by a binomial mortality model. ARIMA is one of the most widely used forecasting methods for predicting univariate time series data. © ISTE Ltd 2022.

The Graph Convolutional Networks Framework for Predicting Pandemic Impact on Stock Prices

Covid-19 has dealt an unprecedented hit to the global economy and all industries, with varying degrees of decline from retail to real estate. This volatility is most evident in stock prices. Previous stock price forecasting methods typically used historical data for each stock as a separate input into the system. This paper proposes an attention-based parallel graph convolutional network framework, which consists of two parallel GCNs. The first GCN takes stock features as input, and the second GCN takes other industry features as input, and sets an attention model to reflect the pairwise interactions between networks. Experimental results on selected stock data show that the model outperforms both the LSTM model and the GCN model in accuracy and F1 score. © 2022 IEEE.

Analysis of Pharmacy and Laboratory Reagents Stock Management in a Portuguese Public Hospital: A Case Study

The current situation of healthcare units is characterised by the increasing cost of providing the respective care, the consequent deterioration of the financial situation, and the complicated and time-consuming processes. Together with rising demand, they may become factors contributing to a decrease in service demand. Due to this situation, more efficient and effective logistics and supply chain management are widely recognised as one of the main areas for improvement. To provide insight on which areas to improve, several objectives were looked at in this work, including the analysis of the methods and criteria for the selection of medicines in hospital pharmacies, the definition of obstacles to the rational management of stocks, and the analysis of historical data to forecast future demand for a Portuguese public hospital. The study revealed that some of the 1346 products present on the pharmacy's ERP do not have sufficient historical data to create an accurate forecast. In this context, and with a service level of 95%, 41% of products have a stock higher than what should be the maximum stock, amounting to approximately € 147.908.87 in fixed assets, and 11% of products were at risk of being out of stock at the time. The importance of the evolution of the core information system of hospitals was at stake, ensuring the technological sustainability of the ongoing digital transformation, alignment with ICT rationalisation measures, improvement of customer service, and improvement of the quality of information available to the user. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An inventory management model based on the 'just needed': The example of Hutchinson company during the COVID-19

In several economic sectors, the COVID-19 has impacted the organisation of supply chain. Companies had to face a dramatic decrease in their order books. Traditional sales forecasting methods were unable to anticipate this abrupt interruption in the delivery of finished products. The production schedule initially planned was revised downwards and the raw materials ordered, according to an MRP-type calculation, were not consumed. The increase in raw material stocks, due to its importance on the companies' cash flow, became a strategic and priority issue. It was in this context that the aeronautical group Hutchinson set up an inventory segmentation based on an indicator: the just needed. Our article describes this method, known internally as Stock Segmentation Raw Material (SSRM), and assesses its advantages and limitations. © 2022 IEEE.

Prediction of C&D, Grit, Asphalt and Treated Biomedical Wastes During COVID-19 Using Grey Model

Waste management has been recognized as a real issue in the current situation due to the COVID-19 impact on people’s lifestyles. Therefore, serious actions need to be taken to control and manage this impact on the environment. One of these important environmental programs is the investigation and research of generated wastes during the pandemic. Due to the COVID-19 pandemic, the types and amounts of waste generation have changed, therefore a way forward to reduce this impact is to investigate the data that coming from landfill to devise an appropriate approach. The goal of this study is to predict the amount of construction and demolition (C&D), Grit, Asphalt waste, and Treated Biomedical waste (TBW) before, during, and after pandemic using grey systems theory. The grey model is a relatively new forecasting method that has been employed for prediction in a small amount of data and is also used for uncertain systems. In this study, the data coming from Regina landfill is used to predict the amount of wastes generated during the pandemic using the grey model. The results will be compared with the existing regression-based waste model. Different measures like mean absolute percent error (MAPE) and root mean square error (RMSE) will be used to compare and evaluate the performance of these models. Finally, the best forecasting model will be chosen to predict the amount of waste generation for the future generation. © 2023, Canadian Society for Civil Engineering.

Performance Analysis of the Triple Exponential Smoothing Method during the Covid19 Pandemic on Tourist Visit Data

Data forecasting methods are essential in the business world to determine the company's future steps. However, the COVID-19 pandemic has hit the tourism economy hard, resulting in a slump in income. In this study, trials were conducted to analyze the reliability of forecasting methods on data affected by the COVID-19 pandemic. The method used is the Triple Exponential Smoothing method involving two models, namely Additive and Multiplicative. In this paper, the test is carried out using actual data derived from data from a service company engaged in tourist crossing transportation. Each method's alpha, beta, and gamma values are determined based on the parameters that produce the smallest error value. The experiment results show the predictability of the Triple Exponential Smoothing method by measuring the prediction error value based on the Mean Absolute Percentage Error (MAPE) value, which was 7.56% in the Additive model and 10.32% in the Multiplicative model before the pandemic happened. However, both methods' prediction measurements during a pandemic produce poor forecasts with an error percentage above 40%. Meanwhile, during the decline in pandemic cases, the value of the Triple Exponential Smoothing Multiplicative method was closer to the actual data with a prediction error value of 33.02%. Therefore, the Triple Exponential Smoothing Multiplicative method is more resistant and suitable for implementing into a forecasting system with actual data that influences pandemic events. © 2022 IEEE.

Impact of the COVID-19 pandemic on the conduct of clinical trials: a quantitative analysis.

Background: Globally, healthcare has shouldered much of the socioeconomic brunt of the COVID-19 pandemic leading to numerous clinical trials suspended or discontinued. Objective: To estimate the COVID-19 impact on the number of clinical trials worldwide. Methods: Data deposited by 219 countries in the ClinicalTrials.gov database (2007-2020) were interrogated using targeted queries. A time series model was fitted to the data for studies ongoing, initiated, or ended between 2007 Quarter (Q) 1 and 2019 Q4 to predict the expected trials number in 2020 in the COVID-19 absence. The predicted values were compared with the actual 2020 data to quantify the pandemic impact. Results: The ongoing registered trials number grew from 2007 Q1 (33,739) to 2019 Q4 (80,319). By contrast, there were markedly fewer ongoing trials in all four quarters of 2020 compared with forecasted values (1.6%-2.8% decrease). When excluding COVID-19-related studies, this disparity grew further (3.4%-5.8% decrease), to a peak of almost 5,000 fewer ongoing trials than estimated for 2020 Q2. The initiated non-COVID-19 trials number was higher than predicted in 2020 Q4 (9.9%). Conclusions: This pandemic has impacted clinical trials. Provided that current trends persist, clinical trial activities may soon recover to at least pre-COVID-19 levels.

COVID-19 epidemic forecasting based on a comprehensive ensemble method

Since December 2019, COVID-19 epidemic is continuing to spread globally. It not only jeopardizing the lives and health of people around the world seriously and putting a severe test on the public medical and health system, but also causes a huge impact on economic and trade activities and has a deep influence on the international community. In order to help researchers and policy makers understand the mechanism of virus transmission and adopt reasonable anti-epidemic policies to inhibit the further spread of the virus, some studies have adopted mathematical prediction models to simulate the spread of the virus and the development of the epidemic. However, the existing research has certain limitations, such as single method selection, excessive reliance on model parameters selection, and virus transmission and policy adjustments caused time variability of data. To solve the above problems, this paper proposes a comprehensive ensemble forecasting framework, which bases on six single prediction models, including time-varying Jackknife model averaging (TVJMA), time-varying parameters (TVP), time-varying parameter SIR (vSIR), logistic regression (LR), polynomial regression (PNR), autoregressive moving average (ARMA). The proposed method is used to predict the cumulative number of confirmed cases in the 6 most severely affected countries in different regions. Empirical results show that for a single prediction method, the TVJMA method outperforms the other five methods;the comprehensive ensemble forecasting method is significantly better than any single method in most cases, especially, the multi-model combined forecasting method based on error correction weights improves the prediction accuracy significantly. For different prediction steps, the comprehensive ensemble forecasting method is robust. © 2022, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.

Analysis and prediction of the Coronavirus epidemic in Tokyo Japan

The new crown epidemic is raging around the world, especially in Tokyo, Japan. After the Olympics, the situation of the new crown epidemic is not optimistic. At the same time, due to the emergence of more unstable factors, the number of newly confirmed and death cases is becoming more and more difficult to predict, which poses great challenges to the prevention and control of the epidemic. An effective forecasting method is urgently needed. In order to deal with the unpredictable Tokyo coronavirus epidemic, this article analyzes the existing coronavirus confirmed and death data and predicts the future trend of the coronavirus epidemic. This article first uses the ARIMA-GARCH model to make predictions, and obtains more accurate prediction results. Furthermore, this article uses the SIR model for fitting and prediction, and finally provides guidance on Tokyo's future anti-epidemic policy. © COPYRIGHT SPIE.

The value of probabilistic forecasting in emergency medical resource planning under uncertainty

Purpose: Most epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty. Design/methodology/approach: Two probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic. Findings: The managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density;(2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels;and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints. Originality/value: Very few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan. © 2022, Emerald Publishing Limited.

Artificial intelligence-based algorithm to track the probable COVID-19 cases using contact history of virus infected person

Currently, the world is facing major challenges in tackling COVID-19. It has affected many countries of the world in terms of human lives, economy and so many other aspects. Many organisations and scientists are working to find the way in which the spread of the COVID-19 can be minimised. One technology which can be effective in tackling this virus is Artificial Intelligence (AI). There are many ways in which AI can help in tackling with this virus. The foremost requirement of this situation is to find the cases of infections as early as possible so that it will not spread rapidly. In this paper, an artificial intelligence-based algorithm is proposed for the tracking of probable COVID-19 cases. The algorithm uses the mobile numbers of corona virus infected person as data for the forecasting. This technique will find the probable infected cases and help in controlling rapid spread of virus. This method will provide information regarding an infected person who had contacted to other persons by using forecasting method. As this is automated tracking system it will help in finding the probable virus infected cases with very short time.

The performance of phenomenological models in providing near-term Canadian case projections in the midst of the COVID-19 pandemic: March - April, 2020.

BACKGROUND: The COVID-19 pandemic has had an unprecedented impact on citizens and health care systems globally. Valid near-term projections of cases are required to inform the escalation, maintenance and de-escalation of public health measures, and for short-term health care resource planning. METHODS: Near-term case and epidemic growth rate projections for Canada were estimated using three phenomenological models: the logistic model, Generalized Richard's model (GRM) and a modified Incidence Decay and Exponential Adjustment (m-IDEA) model. Throughout the COVID-19 epidemic in Canada, these models have been validated against official national epidemiological data on an ongoing basis. RESULTS: The best-fit models estimated that the number of COVID-19 cases predicted to be reported in Canada as of April 1, 2020 and May 1, 2020 would be 11,156 (90 % prediction interval: 9,156-13,905) and 54,745 (90 % prediction interval: 54,252-55,239). The three models varied in their projections and their performance over the first seven weeks of their implementation. Both the logistic model and GRM under-predicted cases reported a week following the projection date in nearly all instances. The logistic model performed best at the early stages, the m-IDEA model performed best at the later stages, and the GRM performed most consistently during the full period assessed. CONCLUSIONS: All three models have yielded qualitatively comparable near-term forecasts of cases and epidemic growth for Canada. Under or over-estimation of projected cases and epidemic growth by these models could be associated with changes in testing policies and/or public health measures. Simple forecasting models can be invaluable in projecting the changes in trajectory of subsequent waves of cases to provide timely information to support the pandemic response.

A novel IDEA: The impact of serial interval on a modified-Incidence Decay and Exponential Adjustment (m-IDEA) model for projections of daily COVID-19 cases.

The SARS-CoV-2 virus causes the disease COVID-19, and has caused high morbidity and mortality worldwide. Empirical models are useful tools to predict future trends of disease progression such as COVID-19 over the near-term. A modified Incidence Decay and Exponential Adjustment (m-IDEA) model was developed to predict the progression of infectious disease outbreaks. The modification allows for the production of precise daily estimates, which are critical during a pandemic of this scale for planning purposes. The m-IDEA model was employed using a range of serial intervals given the lack of knowledge on the true serial interval of COVID-19. Both deterministic and stochastic approaches were applied. Model fitting was accomplished through minimizing the sum-of-square differences between predicted and observed daily incidence case counts, and performance was retrospectively assessed. The performance of the m-IDEA for projection cases in the near-term was improved using shorter serial intervals (1-4 days) at early stages of the pandemic, and longer serial intervals at mid- to late-stages (5-9 days) thus far. This, coupled with epidemiological reports, suggests that the serial interval of COVID-19 might increase as the pandemic progresses, which is rather intuitive: Increasing serial intervals can be attributed to gradual increases in public health interventions such as facility closures, public caution and social distancing, thus increasing the time between transmission events. In most cases, the stochastic approach captured the majority of future reported incidence data, because it accounts for the uncertainty around the serial interval of COVID-19. As such, it is the preferred approach for using the m-IDEA during dynamic situation such as in the midst of a major pandemic.