A dynamic neural network model for predicting risk of Zika in real-time

Public health surveillance systems are an important tool for disease distribution and burden of disease as well as enable efficient distribution of resources to fight a disease. The surveillance systems are used to detect, report, track a disease as well as assess the response to the disease and people’s attitudes. This paper provides a framework of review for purpose-oriented categorisation of public health surveillance systems. The framework for review of surveillance systems divides the systems into distribution or monitoring or prediction oriented. While there can be other categorisation based on data sources and data types used, the framework for review in this paper provides a cohesive system which can engulf such categories. The framework of review in this paper is purpose oriented, which categorises the surveillance system according to their stated objectives, which are the most important aspect of any public health surveillance system. This review and the framework of categorisation provide comprehensive details of the surveillance systems in terms of data types used, source of data and purpose of the surveillance system.

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

Late 2019, the world witnessed a new devastating outbreak: coronavirus (COVID19) that attacks the human lungs and causes respiratory infections like pneumonia pathologies. This disease is spreading rapidly in the globe, causing multiple damages, even on the healthcare sector, economic and social sectors. In this article, we present a framework dedicated to epidemiologists for the investigation of the Patient 0 infected and establish the propagation path in different areas of the country. A Content Based Medical Image Retrieval system is proposed based on the Stacked Auto-encoders for the recognition of the disease characteristics in medical images. Our model search target COVID Chest X-Ray and CT images using similarity measurements learned through an image database of different pathologies as SARS and other viral or bacterial species of pneumonia diseases.