Similarity maps and pairwise predictions for transmission dynamics of COVID-19 with neural networks

Abstract

On March 11, 2020, the World Health Organization declared COVID-19 as a pandemic. Since then, many countries have experienced the rapid transmission of this respiratory disease among their populations and have exercised many strategies to mitigate the spread of this disease. The prediction of the transmission dynamics serves important roles in designing mitigation strategies. However, due to the unknown characteristics of this disease, as well as the geographical and political factors, building efficient models of the dynamics for many countries is difficult. The objective of this study is to develop a transmission dynamics predictor that takes advantage of the time differences among many countries with respect to transmission of this disease, in that some countries experienced earlier outbreaks than others. The primary novelty of the proposed method is that, unlike many existing transmission predictors that require parameters based on prior knowledge of the epidemiology of past viruses, the proposed method only requires the transmission similarities between countries in the publicly available data for this current disease. In this paper, the viability and limitations of the proposed method are reported and discussed.

The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

“…More details about comparing methods can be seen in Galvan’s work [ 10 ]. Recently, Hartono [ 21 ] and Hu et al [ 26 ] went beyond topological visualization and clustering skills. In their studies, the authors assessed the ability to predict COVID-19 cases with approaches similar to SOM, using more sophisticated ones as TA-LSTM and MAE-k-means, to evaluate the transmission dynamics of COVID-19 in countries, provinces, cities, and regions.…”

“…Thus, locations with similarities can benefit from using analogous strategies to deal with the virus’ spread. In addition, topological maps allow the extraction of resources that can be used for the prediction task [ 21 , 26 ].…”

“…The TA is a novel topological neural network for similarity maps and pairwise predictions that was initially proposed by Hartono [ 21 ]. This algorithm was applied to evaluate the transmission dynamics of COVID-19 for more than 250 countries and territories.…”

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

“…According to the experimental results, vanilla, stacked, and bidirectional LSTM models outperformed traditional multilayer LSTM models in terms of performance measures. Hartono proposes an LSTM-based method as a transmission predictor of COVID-19 disease which only requires the transmission similarities between countries as inputs [ 96 ]. Firstly, a transmission dynamics map was generated by utilizing a topological ANN.…”

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

“…The approach then uses a fuzzy logic system to aggregate the response of these neural predictors. In [398] , Neural Networks and LSTM are used to build a model to forecast the pandemic.…”