COVID-19 Death Cases Research Articles

Modeling and Prediction of the Covid-19 Cases With Deep Assessment Methodology and Fractional Calculus.

This study focuses on modeling, prediction, and analysis of confirmed, recovered, and death cases of COVID-19 by using Fractional Calculus in comparison with other models for eight countries including China, France, Italy, Spain, Turkey, the UK, and the US. First, the dataset is modeled using our previously proposed approach Deep Assessment Methodology, next, one step prediction of the future is made using two methods: Deep Assessment Methodology and Long Short-Term Memory. Later, a Gaussian prediction model is proposed to predict the short-term (30 Days) future of the pandemic, and prediction performance is evaluated. The proposed Gaussian model is compared to a time-dependent susceptible-infected-recovered (SIR) model. Lastly, an analysis of understanding the effect of history is made on memory vectors using wavelet-based denoising and correlation coefficients. Results prove that Deep Assessment Methodology successfully models the dataset with 0.6671%, 0.6957%, and 0.5756% average errors for confirmed, recovered, and death cases, respectively. We found that using the proposed Gaussian approach underestimates the trend of the pandemic and the fastest increase is observed in the US while the slowest is observed in China and Spain. Analysis of the past showed that, for all countries except Turkey, the current time instant is mainly dependent on the past two weeks where countries like Germany, Italy, and the UK have a shorter average incubation period when compared to the US and France.

Is Nigeria really on top of COVID-19? Message from effective reproduction number.

Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population.In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R0) - this also enables us to estimate the initial daily transmission rate (β0). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020.In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 is estimated to be 2.42 with credible interval: (2.37-2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%.Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

The Link between Humidity and COVID-19 Caused Death

Objective: The objectives were to understand the relationship between humidity and COVID-19 fatality and to provide clues for improving COVID-19 patient survival rate. Methods: Humidity data and confirmed COVID-19 death cases for all fifty US states and Washington DC were collected and analyzed, and the relationship between humidity and COVID-19 mortality was derived. Results: There is a positive correlation between humidity and COVID-19 patient fatality. Conclusion: COVID-19 mortality directly and positively correlates with the humidity of the environment the COVID-19 virus experiences. Such correlation could be used to reduce COVID-19 patient fatality and save lives.

Predictive modeling of COVID-19 death cases in Pakistan.

BackgroundThe world is presently facing the challenges posed by COVID-19 (2019-nCoV), especially in the public health sector, and these challenges are dangerous to both health and life. The disease results in an acute respiratory infection that may result in pain and death. In Pakistan, the disease curve shows a vertical trend by almost 256K established cases of the diseases and 6035 documented death cases till August 5, 2020.ObjectiveThe primary purpose of this study is to provide the statistical model to predict the trend of COVID-19 death cases in Pakistan. The age and gender of COVID-19 victims were represented using a descriptive study.Methodology: Three regression models, which include Linear, logarithmic, and quadratic, were employed in this study for the modelling of COVID-19 death cases in Pakistan. These three models were compared based on R2, Adjusted R2, AIC, and BIC criterions. The data utilized for the modelling was obtained from the National Institute of Health of Pakistan from February 26, 2020 to August 5, 2020.ConclusionThe finding deduced after the prediction modelling is that the rate of mortality would decrease by the end of October. The total number of deaths will reach its maximum point; then, it will gradually decrease. This indicates that the curve of total deaths will continue to be flat, i.e., it will shift to be constant, which is also the upper bound of the underlying function of absolute death.