Novel deep learning approach to model and predict the spread of COVID-19

SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally, producing new variants and has become a pandemic. People have lost their lives not only due to the virus but also because of the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop robust artificial intelligence techniques to predict the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models are trained and tested on publicly available novel coronavirus dataset. The proposed models are evaluated by using Mean Absolute Error and compared with the existing methods for the prediction of the spread of COVID-19. Our experimental results demonstrate the superior prediction performance of the proposed models. The proposed DSPM and NRM achieve MAEs of 388.43 (error rate 1.6%) and 142.23 (0.6%), respectively compared to 6508.22 (27%) achieved by baseline SVM, 891.13 (9.2%) by Time-Series Model (TSM), 615.25 (7.4%) by LSTM-based Data-Driven Estimation Method (DDEM) and 929.72 (8.1%) by Maximum-Hasting Estimation Method (MHEM)

Budesonide + formoterol delivered via Spiromax(R) for the management of asthma and COPD: The potential impact on unscheduled healthcare costs of improving inhalation technique compared with Turbuhaler(R)

Contains fulltext : 178199.pdf (Publisher’s version ) (Open Access)BACKGROUND: Fixed-dose combinations of inhaled corticosteroids and long-acting beta2 agonists are commonly used for the treatment of asthma and COPD. However, the most frequently prescribed dry powder inhaler delivering this medicine - Symbicort(R) (budesonide and formoterol, BF) Turbuhaler(R) - is associated with poor inhalation technique, which can lead to poor disease control and high disease management costs. A recent study showed that patients make fewer inhaler errors when using the novel DuoResp(R) (BF) Spiromax(R) inhaler, compared with BF Turbuhaler(R). Therefore switching patients from BF Turbuhaler(R) to BF Spiromax(R) could improve inhalation technique, and potentially lead to better disease control and healthcare cost savings. METHODS: A model was developed to estimate the budget impact of reducing poor inhalation technique by switching asthma and COPD patients from BF Turbuhaler(R) to BF Spiromax(R) over three years in Germany, Italy, Sweden and the UK. The model estimated changes to the number, and associated cost, of unscheduled healthcare events. The model considered two scenarios: in Scenario 1, all patients were immediately switched from BF Turbuhaler(R) to BF Spiromax(R); in Scenario 2, 4%, 8% and 12% of patients were switched in years 1, 2 and 3 of the model, respectively. RESULTS: In Scenario 1, per patient cost savings amounted to euro60.10, euro49.67, euro94.14 and euro38.20 in Germany, Italy, Sweden and the UK, respectively. Total cost savings in each country were euro100.86 million, euro19.42 million, euro36.65 million and euro15.44 million over three years, respectively, with an estimated 597,754, 151,480, 228,986 and 122,368 healthcare events avoided. In Scenario 2, cost savings totalled euro8.07 million, euro1.55 million, euro2.93 million and euro1.23 million over three years, respectively, with 47,850, 12,118, 18,319, and 9789 healthcare events avoided. Savings per patient were euro4.81, euro3.97, euro7.53 and euro3.06. CONCLUSIONS: We demonstrated that reductions in poor inhalation technique by switching patients from BF Turbuhaler(R) to BF Spiromax(R) are likely to improve patients' disease control and generate considerable cost savings through healthcare events avoided