ASEAN, include Indonesia, Malaysia, Philippines, Singapore, and Thailand, are the countries with ongoing transmission of SARS-COV-2, the virus that causes COVID-19. The confirmed cases in Indonesia and Philippines are the highest ranks among other ASEAN countries such as Malaysia, Thailand, and Singapore. To reduce the spread of the pandemic COVID-19, each country has implemented the lock-down policy differently, depending on its economic situation. Therefore, the study of the impact of lock-down across the world, particularly in ASEAN countries, is still relevant to do. In this study, we developed the lock-down model in ASEAN countries by using hybrid ARIMA-SVR and hybrid SEIR-ANN. The first hybrid is based on the time series model ARIMA, with the revision of the error is by using SVR. The second hybrid is based on the classical model of infectious diseases, SEIR, which we revise on the prediction part by using ANN. The hybrid is intended to revise the individual prediction model. The data collected per country was started from January 20, 2020 to August 5, 2020. The periods of lock-down in this study are divided into three, namely no lock-down, implemented lock-down, and the new normal periods. The strictness levels of lock-down were predicted for 60 days ahead. The results showed that the hybrid ARIMA-SVR had smaller RMSE compared with individual ARIMA, similarly, hybrid SEIR-ANN predicted S, E, I, and R more accurately compared with individual SEIR model. It has been also found that the lock down was most effectively implemented in Thailand, Singapore, and Malaysia, whereas Indonesia and Philippines were inefficient countries to enforce the restriction. It is indicated by the number of cases increased significantly during the restriction periods in both countries.

Lanczos iterative methods for solving a large sparse linear systems typically face the latent breakdown which strikes every time these methods are deployed. A number of approaches to deal with this issue have been investigated. One of them is by switching between the solvers preemptively breakdown. However, the problem is not fully solved yet. Here, we propose switching models combined with particular Lanczos iterative methods. The first model is by using the last iterate as the switching point with an unlimited number of iterations, the second model is by using the iterate with the minimum residual norm as the initial point and the third model is by using the iterate with the minimum of minimum residual norms as the switching point. These three models lead algorithms of SLULast, SLUMinRes, and SLUMoM, respectively. The parallel version of the proposed algorithms is also provided to speed up their convergence. In this case, we constructed the parallel of SLUMoM and we call it pSLUMoM. The numerical results showed that our switching models performed better than the existing switching strategy in terms of robustness and efficiency. In fact, under a parallel framework, pSLUMoM showed a performance gain of up to 50% in our experiments.

No abstract

No abstract