A goal programming model for two-stage COVID19 test sampling centers location-allocation problem.

The COVID19 virus, which first appeared in Wuhan, China, and has become a pandemic in a short time, has threatened the health system in many countries and put it into a bottleneck. Simultaneously, the second wave's expectation spread it necessary to plan the health services correctly. In this study, a location-allocation problem in the two-echelon system, which considers different test sampling alternatives, is examined to obtain test sampling centers' location-allocation. The problem is modeled as a goal programming model to create a network that tests samples at a minimum total distance, establishes a minimum number of test sampling centers, and reaches the distance of PCR test laboratories at minimum total distances. The proposed model is applied as a case study for the two cities located in Turkey, and the obtained locations and inventory levels of each location are presented. Besides, different scenarios are examined to understand the structure of the model. As a result, only testing in hospitals will increase the risk of contamination. Since testing at all points will not be possible administratively, it will be ensured that the most appropriate location-allocation decisions are taken by considering all the proposed model's objectives.

A predictive analytics model for COVID-19 pandemic using artificial neural networks

The COVID-19 pandemic spread rapidly around the world and is currently one of the most leading causes of death and heath disaster in the world. Turkey, like most of the countries, has been negatively affected by COVID-19. The aim of this study is to design a predictive model based on artificial neural network (ANN) model to predict the future number of daily cases and deaths caused byCOVID-19 in a generalized way to fit different countries’ spreads. In this study, we used a dataset between 11 March 2020 and 23 January 2021 for different countries. This study provides an ANN model to assist the government to take preventive action for hospitals and medical facilities. The results show that there is an 86% overall accuracy in predicting the mortality rate and 87% in predicting the number of cases.