Microscopic modeling of spatiotemporal epidemic dynamics

Conventional techniques of epidemic modeling are based on compartmental models, where population groups are transitioning from one compartment to another - for example, S, I, or R, (Susceptible, Infectious, or Recovered). Then, they focus on learning macroscopic properties of disease spreading, such as the transition rates between compartments. Although these models are useful in studying epidemic dynamics, they lack the granularity needed for analyzing individual behaviors during an epidemic and understanding the relationship between individual decisions and the spread of the disease. In this paper, we develop microscopic models of spatiotemporal epidemic dynamics informed by mobility patterns of individuals and their interactions. In contrast to macroscopic models, microscopic epidemic models focus on individuals and their properties, such as their activity level, mobility behaviors, and impact of mobility behavior changes. Our microscopic spatiotemporal epidemic model allows to: (i) assess the risk of infection of an individual based on mobility patterns;(ii) assess the risk of infection associated with specific geographic areas and points-of-interest (POIs);(iii) assess the risk of infection of a trip in an urban environment;(iv) provide trip recommendation for mitigating the risk of infection;and (v) assess targeted intervention strategies that aim to control the epidemic spreading. Our work provides an evidence-based data-driven model to inform individuals about the infection risks associated with their mobility behavior during a pandemic, providing at the same time safer alternatives. It can also inform public policy about the effectiveness of targeted intervention strategies that aim to contain or mitigate the epidemic spread compared to horizontal measures. © 2022 ACM.

Clustering and Visualizing of Chest X-ray Images for Covid-19 Detection

Corona pandemic showed how artificial intelligence has become a part of our daily lives and is breaking into all fields at a high rate and in different ways. Relying on the conventional techniques to test patients such as RT -PCR has two major drawbacks;a long time to get results and a lack of test kits. Therefore, data mining with machine learning techniques has been suggested to investigate covid-19. In this work, chest x-ray image-based covid-19 detection approach is proposed. Three types of x-ray images Covid-19, Pneumonia, and Normal, are used in two frameworks: image visualization and image segmentation. First, the x-ray samples are visualized using histograms to analyze the pixel-value distributions. The visualization approach helps covid-19 specialists to discover the intensity level of infection by examining the corresponding histograms. Second, a segmentation approach is developed with a k-mean algorithm to provide extra image tuning for infected areas. Three different centroids are used to provide different tuning granularity levels. The suggested frameworks give a fast and reliable methodology to help physicians to decide whether there is a virus or not in the x-ray sample. This is done statistically by histograms and visually by monitoring the segmented infected areas. © 2021 IEEE.

Role of Internet of Things and Artificial Intelligence in COVID-19 Pandemic Monitoring

Internet of Things (IoT) has become one of the important components in developing interconnected smart IoT devices. Data generated from the IoT devices increases rapidly due to the increase in the number of connected devices. The current COVID-19 outbreak condition has led to the need of the Healthcare IoT (H-IoT), which can provide an automatic solution for monitoring. Therefore, IoT data is extremely crucial to be analyzed. Artificial Intelligence (AI) has gained a lot of attentions for automatizing applications based on the big data generated from the IoT devices. This chapter presents the current development of AI applications for monitoring the pandemic. The role of IoT, data acquisition, preprocessing, and analysis is also described here. In depth, we elucidate few methods of data preprocessing using conventional techniques and Machine Learning (ML) algorithms, and data analysis using ML and Deep Learning (DL) algorithms. We list all techniques in handling data preprocessing and analysis, and the challenges of IoT and AI in the new way of living during pandemic which is also known as the era of new normal. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Overcoming challenges due to enhanced biomedical waste generation during COVID-19 pandemic.

Biomedical wastes (BMWs) are potentially infectious to the environment and health. They are co-dependent and accumulative during the ongoing coronavirus disease-2019(COVID-19) pandemic. In India the standard treatment processes of BMWs are incineration, autoclaving, shredding, and deep burial; however, incineration and autoclaving are the leading techniques applied by many treatment providers. These conventional treatment methods have several drawbacks in terms of energy, cost, and emission. But the actual problem for the treatment providers is the huge and non-uniform flow of the BMWs during the pandemic. The existing treatment methods are lacking flexibility for the non-uniform flow. The Government of India has provisionally approved some new techniques like plasma pyrolysis, sharp/needle blaster, and PIWS-3000 technologies on a trial basis. But they are all found to be inadequate in the pandemic. Therefore, there is an absolute requirement to micromanage the BMWs based on certain parameters for the possible COVID-19 like pandemic in the future. Segregation is a major step of the BMW management. Its guideline may be shuffled as segregation at the entry points followed by collection instead of the existing system of the collection followed by segregation. Other steps like transportation, location of treatment facilities, upgradation of the existing treatment facilities, and new technologies can solve the challenges up to a certain extent. Technologies like microwave treatment, alkaline hydrolysis, steam sterilization, biological treatment, catalytic solar disinfection, and nanotechnology have a lot of scopes for the treatment of BMWs. Hi-tech approaches in handling and transportation are found to be fruitful in the initial steps of BMW management. End products of the treated BMWs can be potentially fabricated for the application in the built environment. Some policies need to be re-evaluated by the health care facilities or government administrations for efficient BMW management.