Tracking and Classifying Global COVID-19 Cases by using 1D Deep Convolution Neural Networks

Abstract

The novel coronavirus disease (COVID-19) and pandemic has taken the world by surprise and simultaneously challenged the health infrastructure of every country. Governments have resorted to draconian measures to contain the spread of the disease despite its devastating effect on their economies and education. Tracking the novel coronavirus 2019 disease remains vital as it influences the executive decisions needed to tighten or ease restrictions meant to curb the pandemic. One-Dimensional(1D) Convolution Neural Networks (CNN) have been used classify and predict several time-series and sequence data. Here 1D-CNN is applied to the time-series data of confirmed COVID-19 cases for all reporting countries and territories. The model performance was 90.5% accurate. The model was used to develop an automated AI tracker web app (AI Country Monitor) and is hosted on https://aicountrymonitor.org. This article also presents a novel concept of pandemic response curves based on cumulative confirmed cases that can be use to classify the stage of a country or reporting territory. It is our firm believe that this Artificial Intelligence COVID-19 tracker can be extended to other domains such as the monitoring/tracking of Sustainable Development Goals (SDGs) in addition to monitoring and tracking pandemics.

Applications of artificial intelligence in battling against covid-19: A literature review

“…One dimensional CNNs have shown success in performing analysis on time-series and sequence data. In [419] , a 1D CNN is applied to the time-series data of confirmed covid-19 cases for all countries and territories. The algorithm is used to track and classify progress of the pandemic in different countries.…”

A Combined Image Segmentation and Classification Approach for COVID-19 Infected Lungs

“…Increased CL brightens the image and flattens the histogram because the input image has a low intensity. The image is of dynamic range, as well as its contrast, increases as the BS increases [33]. The two parameters determined at the location with the biggest entropy curvature, using the image's entropy, produce subjectively good image quality.…”