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Hassan J

Bio: Hassan J is an academic researcher from University of Peradeniya. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

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Posted ContentDOI
24 Nov 2020-medRxiv
TL;DR: This review presents a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19 and lists potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.
Abstract: The COVID-19 pandemic, within a short time span, has had a significant impact on every aspect of life in almost every country on the planet. As it evolved from a local epidemic isolated to certain regions of China, to the deadliest pandemic since the influenza outbreak of 1918, scientists all over the world have only amplified their efforts to combat it. In that battle, Artificial Intelligence, or AI, with its wide ranging capabilities and versatility, has played a vital role and thus has had a sizable impact. In this review, we present a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19. Furthermore, we catalogue the articles in these areas based on spatio-temporal modeling, intrinsic parameters, extrinsic parameters, dynamic parameters and multivariate inputs (to ascertain the penetration of AI usage in each sub area). The manner in which AI is used and the associated techniques utilized vary for each body of work. Majority of articles use deep learning models, compartment models, stochastic methods and numerous statistical methods. We conclude by listing potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.

8 citations


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01 Jan 2020
TL;DR: Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future.
Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

4,408 citations

Journal ArticleDOI
TL;DR: In this article, the authors focused on studies related to the COVID-19 contagion simulation through transport networks and found 15 spread models of contagion with the SEIR model being the most widely used, followed by mathematical-based risk models.
Abstract: The COVID-19 pandemic has generated a huge volume of research from various disciplines, such as health sciences, social sciences, mathematical modeling, social network analysis, complex systems, decision-making processes, computer simulation, economics, among many others. One of the key problems has been to understand the diffusion processes of the virus, which quickly spread worldwide through transport networks, mainly air flights. Almost two years after start the pandemic, it is necessary to collect and synthesize the existing work on this matter. This work focuses on studies related to the COVID-19 contagion simulation through transport networks. In particular, we are specially interested in the different datasets and epidemiological models used. The search methodology consists of four exhaustive searches in Google Scholar carried out between January 2020 and January 2021. Of the 1786 findings, we chose 54 articles related to Covid-19 contagion modeling and simulation through transport networks. The results show 30 different data sources for the collection of air flights and 11 additional sources for maritime and land transport. These datasets are usually complemented with other data sources, local and international, with demographic information, economic data, and statistics of traceability of the pandemic. The findings also found 15 spread models of contagion, with the SEIR model being the most widely used, followed by mathematical-based risk models. This diversity of results validates the need for these types of compilation efforts since researchers do not have a single centralized repository to collect air flight data.

3 citations

Posted ContentDOI
22 Apr 2020
TL;DR: It is found that in Lombardy restrictive containment measures should be prolonged at least until early July to avoid a resurgence of hospitalizations; on the other hand, in Emilia-Romagna the number of hospitalized cases could be kept under a reasonable amount with a higher contact rate.
Abstract: The outbreak of coronavirus disease 2019 (COVID-19) was identified in Wuhan, China, in December 2019. As of 17 April 2020, more than 2 million cases of COVID-19 have been reported worldwide. Northern Italy is one of the world’s centers of active coronavirus cases. In this study, we predicted the spread of COVID-19 and its burden on hospital care under different conditions of social distancing in Lombardy and Emilia-Romagna, the two regions of Italy most affected by the epidemic. To do this, we used a Susceptible-Exposed-Infectious-Recovered (SEIR) deterministic model, which encompasses compartments relevant to public health interventions such as quarantine. A new compartment L was added to the model for isolated infected population, i.e., individuals tested positives that do not need hospital care. We found that in Lombardy restrictive containment measures should be prolonged at least until early July to avoid a resurgence of hospitalizations; on the other hand, in Emilia-Romagna the number of hospitalized cases could be kept under a reasonable amount with a higher contact rate. Our results suggest that territory-specific forecasts under different scenarios are crucial to enhance or take new containment measures during the epidemic.

2 citations

Journal ArticleDOI
TL;DR: A Dynamic Bayesian Network (DBN) is developed to predict community-level relative risk of COVID-19 infection at the census tract scale in the U.S. state of Indiana.
Abstract: As COVID-19 continues to impact the United States and the world at large it is becoming increasingly necessary to develop methods which predict local scale spread of the disease. This is especially important as newer variants of the virus are likely to emerge and threaten community spread. We develop a Dynamic Bayesian Network (DBN) to predict community-level relative risk of COVID-19 infection at the census tract scale in the U.S. state of Indiana. The model incorporates measures of social and environmental vulnerability—including environmental determinants of COVID-19 infection—into a spatial temporal prediction of infection relative risk 1-month into the future. The DBN significantly outperforms five other modeling techniques used for comparison and which are typically applied in spatial epidemiological applications. The logic behind the DBN also makes it very well-suited for spatial-temporal prediction and for “what-if” analysis. The research results also highlight the need for further research using DBN-type approaches that incorporate methods of artificial intelligence into modeling dynamic processes, especially prominent within spatial epidemiologic applications.

2 citations

Journal ArticleDOI
TL;DR: In this paper, the spatial differentiation of the structure and homogeneity of the system in which SARS-CoV-2 occurs, as well as spatial concentration of people infected was investigated in a period of two infection waves in Germany.
Abstract: Among numerous publications about the SARS-CoV-2, many articles present research from the geographic point of view The cartographic research method used in this area of science can be successfully applied to analyze the spatiotemporal characteristics of the pandemic using limited data and can be useful for a quick and preliminary assessment of the spread of infections In this paper, research on the spatial differentiation of the structure and homogeneity of the system in which SARS-CoV-2 occurs, as well as spatial concentration of people infected was undertaken The phenomena were investigated in a period of two infection waves in Germany: in spring and autumn 2020 We applied the potential model, entropy, centrographic method, and Lorenz curve in spatial analysis The potentials model made it possible to distinguish core regions with a high level of the growth of new infections, along with areas of their impact, and regions with a low level of generation of new infections The entropy showed the spatial distribution of differentiation of the studied system and the change of these characteristics between spring and autumn The concentration method allowed for spatial and numerical demonstration of the concentration of infected population in a given area We wanted to show that it is possible to draw meaningful conclusions about the pandemic characteristics using only basic data about infections, along with proper cartographic methods The results can be used to designate the zones of the greatest threats, and thus, the areas where the most intense actions should be taken

2 citations