Literature Review On The Application Of A Novel Cellular Automata Classifier for COVID-19 Prediction and Visualization
Khaoula Hidawi · Zenodo (CERN European Organization for Nuclear Research) · 2020
Numerous outbreak prediction models for COVID-19 are being used around the world each day to make important decisions and enforce confinement control measures. We can see for COVID-19 global pandemic prediction, that epidemiological and virologists are relying on statistical models to base their judgment and redirect their research. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. The current volume is an effort to bridge just that range of exploration, from nucleotide to an abstract concept, in contemporary AI research. That bridge must also join computer scientists with laboratory biochemists on how imputed knowledge will be used. A variety of target problems, and perhaps a hand-crafted representation for each, is embraced in the roster.[3] There is an obvious detriment to premature standardization, but it is daunting to see the difficulties of merging the hardwon insights, the cumulative world knowledge, that comes from each of these efforts to contain this pandemic, this literature review is simply an emphasis on one of the most important models that used cellular automata for predicting the cases of this pandemic which went unnoticed by many experts.