BackgroundSince there is no specific treatment for coronavirus, there is an urgent need for global monitoring of people with Covid-19. The use of e-health services should be compatible with the diagnosis and control of the outbreak of zoonotic infectious diseases. The aim of this study is to provide a conceptual model based on health information technology services for Covid-19 disease management.MethodsThe present study is an applied descriptive study that was performed on a cross-sectional basis in a COVID-19 Center Hospital in Fars province of IRAN country in April 2020. The main tool of this research is a questionnaire that has been compiled by reviewing related articles in databases and surveying with experts in order to determine the necessary services in the management and control model of the prevalence of Covid19 disease. Then, in order to determine the necessary services in the conceptual model, this questionnaire was given to various specialists in the COVID-19 Center Hospital. Finally, based on the results of the questionnaire, a comprehensive conceptual model for the management and control of COVID 19 diseases is presented.ResultsThe proposed model consisted of three layers of cloud computing, fog and data acquisition. All services were approved by the surveyed participants. Among the services, Tele- monitoring for home quarantine, Electronic self-assessment, Telepsychology of patients in home and hospital quarantine, Tele prescription, Tele- information Tele- training have the highest agreement rate. proposed model is an integrated model. The innovation that can be mentioned in this research is the use of priority queue service as a service of the fog layer.ConclusionInformation communication technology tools have an important role in all aspects of contagious diseases management.

The new coronavirus has been spreading since the beginning of 2020 and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as data mining, enhanced intelligence, and other artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose and predict the COVID-19 epidemic. In this study, data mining models for early detection of Covid-19 in patients were developed using the epidemiological dataset of patients and individuals suspected of having Covid-19 in Iran. C4.5, support vector machine, Naive Bayes, logistic regression, Random Forest, and k-nearest neighbor algorithm were used directly on the dataset using Rapid miner to develop the models. By receiving clinical signs, this model diagnosis the risk of contracting the COVID-19 virus. Examination of the models in this study has shown that the support vector machine with 93.41% accuracy is more efficient in the diagnosis of patients with COVID-19 pandemic, which is the best model among other developed models. Keywords: COVID-19, Data mining, Machine Learning, Artificial Intelligence, Classification