Predictive models on COVID 19: What Africans should do?

Likassa, Habte Tadesse; Xain, Wen; Tang, Xuan; Gobebo, Gizachew

doi:10.1016/j.idm.2020.10.015

Cited by 8 publications

(12 citation statements)

References 31 publications

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“…To better present the COVID-19 pattern, we classified the case number into 10 levels: (0, 8], (8,16], (16,32] To clarify the spatial trend of the COVID-19 transmission, we applied the Anselin Local Moran's I statistic to delineate the spatial distribution of the weekly new and total cases. This statistic divides the study area into five categories of regions: 1) high-high cluster (severely infected area, cities with high cases surrounded by cities with high cases), 2) high-low outlier (spatial outlier, cities with high cases surrounded by cities with low cases), 3) low-high outlier (spatial outlier, cities with low cases surrounded by cities with high cases), 4) low-low cluster (secure area, clustered areas with similar low cases), and 5) not statistically significant area (the number of cases in cities was not statistically clustered).…”

Section: Analysis Methodsmentioning

confidence: 99%

“…With the rapid spread of COVID-19 pandemic in the world, it is of great significance to analyze its spatial evolution in China, especially the transmission pattern and spread trend, improving our understanding of the spatiotemporal mechanisms. Some have applied mathematical models to simulate and project the spread of COVID-19 [6][7][8]. Li et al conducted a preliminary evaluation of the epidemiological characteristics of COVID-19 [9], indicating that human-to-human transmission caused by close contacts began to emerge in mid-December and gradually spread over the following month.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal spread pattern of the COVID-19 cases in China

Feng

Tong

et al. 2020

PLoS ONE

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The COVID-19 pandemic is currently spreading widely around the world, causing huge threats to public safety and global society. This study analyzes the spatiotemporal pattern of the COVID-19 pandemic in China, reveals China’s epicenters of the pandemic through spatial clustering, and delineates the substantial effect of distance to Wuhan on the pandemic spread. The results show that the daily new COVID-19 cases mostly occurred in and around Wuhan before March 6, and then moved to the Grand Bay Area (Shenzhen, Hong Kong and Macau). The total COVID-19 cases in China were mainly distributed in the east of the Huhuanyong Line, where the epicenters accounted for more than 60% of the country’s total in/on 24 January and 7 February, half in/on 31 January, and more than 70% from 14 February. The total cases finally stabilized at approximately 84,000, and the inflection point for Wuhan was on 14 February, one week later than those of Hubei (outside Wuhan) and China (outside Hubei). The generalized additive model-based analysis shows that population density and distance to provincial cities were significantly associated with the total number of the cases, while distances to prefecture cities and intercity traffic stations, and population inflow from Wuhan after 24 January, had no strong relationships with the total number of cases. The results and findings should provide valuable insights for understanding the changes in the COVID-19 transmission as well as implications for controlling the global COVID-19 pandemic spread.

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Section: Analysis Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal spread pattern of the COVID-19 cases in China

Feng

Tong

et al. 2020

PLoS ONE

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“…Often during the cholera disease, this research-assisted pairing can be learned. Four diagnostic models, including lymphocytes, lactate dehydrogenase, C-reactive proteins, and neutrophils, have been developed and validated using the XGBoost algorithm [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Yang

Islam

Noor

et al. 2021

Computational and Mathematical Methods in Medicine

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Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people’s lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

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“…Likassa et al [24] also revealed that the spatial and temporal pattern of this novel virus was varying, spreading and covering the entire within a brief time. In the study, the fitting effect of the cubic model (R 2 =99.6%) was the best outperforming compared to the other six families of exponentials [24].…”

Section: Discussionmentioning

confidence: 98%

Time Series Models for COVID-19 New Cases in Top Seven Infected African Countries

Argawu

2021

JPRI

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Introduction: In Africa region on the date of December 09, 2021 at 14:46 GMT, the total cumulative cases of COVID-19 was 8,889,437 with total deaths and total recoveries of 224,731 (2.5% of death rate) and 8,185,382 (92% of recovery rate) respectively. Thus, this study aimed modelling and forecasting of COVID-19 new cases in top seven infected African countries using time series models. Methods: The top seven infected African countries COVID-19 new cases dataset was taken from our World COVID-19 dataset. The study period was from February 14 to September 06, 2020. Different time series models were used for modelling and forecasting of COVID-19 new cases data. Models comparisons were done by normalized BIC, root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared values. Results: The COVID-19 new cases data of Algeria, Egypt, Ethiopia, Morocco, and South Africa were fitted by ARIMA (0,1,0), ARIMA (0,1,0), Damped trend, Brown, and ARIMA (0,1,14) models in the study period, respectively. Whereas Ghana, and Nigeria COVID-19 new cases data were followed by simple exponential smoothing models. The 95% confidence levels for lowest to highest forecasted COVID-19 new cases were 258 to 197 with decreasing trend in Algeria, 63 to 933 with increasing trend in Egypt, 636 to 2,141 with increasing trend in Ethiopia, 0 to 1,022 with constant trend in Ghana, 1,900 to 2,807 with increasing trend in Morocco, 0 to 543 with constant trend in Nigeria, and 2,056 to 2,444 with increasing trend in South Africa for the next one month (from September 7 to October 6, 2020). Conclusion: The findings of the study used for preparedness planning against further spread of the COVID-19 epidemic in African countries. The author recommends that as many countries continue to relax restrictions on movement and mass gatherings, and more are opening their air spaces, and the countries’ other public and private sectors are reopening and then strong appropriate public health and social measures must be instituted on the ground again and again before the virus is distributed and attacked more and more peoples in the region. And, the researcher recommended that risk factors of COVID-19 new cases should be conducted for next time in Africa countries.

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Predictive models on COVID 19: What Africans should do?

Cited by 8 publications

References 31 publications

Spatiotemporal spread pattern of the COVID-19 cases in China

Spatiotemporal spread pattern of the COVID-19 cases in China

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Time Series Models for COVID-19 New Cases in Top Seven Infected African Countries

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