Visualization and machine learning for forecasting of COVID-19 in Senegal

In this article, we give visualization and different machine learning technics for two weeks and 40 days ahead forecast based on public data. On July 15, 2020, Senegal reopened its airspace doors, while the number of confirmed cases is still increasing. The population no longer respects hygiene measures, social distancing as at the beginning of the contamination. Negligence or tiredness to always wear the masks? We make forecasting on the inflection point and possible ending time.

[1]  N. Hengartner,et al.  The Novel Coronavirus, 2019-nCoV, is Highly Contagious and More Infectious Than Initially Estimated , 2020, medRxiv.

[2]  B. Ndiaye,et al.  Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting , 2020, 2004.01574.

[3]  R. Tibshirani,et al.  An introduction to the bootstrap , 1993 .

[4]  Diaraf Seck,et al.  Comparative prediction of confirmed cases with COVID-19 pandemic by machine learning, deterministic and stochastic SIR models , 2020, 2004.13489.

[5]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[6]  S. Sarr,et al.  Impact of contamination factors on the COVID-19 evolution in Senegal , 2020, 2006.16326.

[7]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[8]  M. A. M. T. Balde,et al.  Fitting SIR model to COVID-19 pandemic data and comparative forecasting with machine learning , 2020, medRxiv.

[9]  Susan A. Murphy,et al.  Monographs on statistics and applied probability , 1990 .

[10]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[11]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[13]  W. O. Kermack,et al.  Contributions to the mathematical theory of epidemics—I , 1991, Bulletin of mathematical biology.

[14]  Yoav Freund,et al.  Large Margin Classification Using the Perceptron Algorithm , 1998, COLT' 98.

[15]  Karen A. F. Copeland Local Polynomial Modelling and its Applications , 1997 .

[16]  Mouhamadou A.M.T. Balde,et al.  Impact studies of nationwide measures COVID-19 anti-pandemic: compartmental model and machine learning , 2020, Communications in Mathematical Biology and Neuroscience.

[17]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..