Analysis of the Ebola Outbreak in 2014 and 2018 in West Africa and Congo by Using Artificial Adaptive Systems

Buscema, Massimo; Asadi–Zeydabadi, Masoud; Lodwick, Weldon A.; Nembot, Alphonse Nde; Bronstein, Alvin C.; Newman, Francis

doi:10.1080/08839514.2020.1747770

Cited by 10 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We analyzed the small dataset (48 real numbers) using a geographic profiling approach [2] , [11] , [12] , [13] , [14] . In particular, as mentioned above, we used the Topological Weighted Centroid (TWC), a special adaptive system, with a solid theoretical and mathematical background [3] and already successfully applied in various fields and published in numerous papers [4] , [5] , [6] , [7] [8] , [9] .…”

Section: Methodsmentioning

confidence: 99%

“…The algorithm used is based on geographic profiling using a topological approach. One of the advantages of this algorithm, called Topological Weighted Centroid (TWC) [3] , [4] , [5] , [6] , [7] , [8] , [9] , compared to standard methods of analysis of diffusion processes, lies in the fact that it requires very simple data: the coordinates of the places where the events of the process took place without any kind of other assumptions. For this reason, in conditions of poor data availability, it is particularly useful.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, the data used correspond to all places (by means of longitude and latitude) of Italy where at least one case of COVID-19 was detected until February 26th, 2020. The TWC has already been successfully applied several times to epidemic cases, in particular to Escherichia coli (Germany 2011), Chikungunya Fever (Italy 2007), Foot and Mouth Disease (UK 1968–1969), Food epidemic (Oahu 2010), Dengue fever (Brazil 2001), Listeria (US 2011) and Ebola (West Africa and Congo 2014–2018) epidemics [3] , [4] , [5] , [8] , [9] .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

COVID-19 in Italy and extreme data mining

Buscema

Torre

Breda

et al. 2020

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

In this article we want to show the potential of an evolutionary algorithm called Topological Weighted Centroid (TWC). This algorithm can obtain new and relevant information from extremely limited and poor datasets. In a world dominated by the concept of big (fat?) data we want to show that it is possible, by necessity or choice, to work profitably even on small data. This peculiarity of the algorithm means that even in the early stages of an epidemic process, when the data are too few to have sufficient statistics, it is possible to obtain important information. To prove our theory, we addressed one of the most central issues at the moment: the COVID-19 epidemic. In particular, the cases recorded in Italy have been selected. Italy seems to have a central role in this epidemic because of the high number of measured infections. Through this innovative artificial intelligence algorithm, we have tried to analyze the evolution of the phenomenon and to predict its future steps using a dataset that contained only geospatial coordinates (longitude and latitude) of the first recorded cases. Once the coordinates of the places where at least one case of contagion had been officially diagnosed until February 26th, 2020 had been collected, research and analysis was carried out on: outbreak point and related heat map (TWC alpha); probability distribution of the contagion on February 26th (TWC beta); possible spread of the phenomenon in the immediate future and then in the future of the future (TWC gamma and TWC theta); how this passage occurred in terms of paths and mutual influence (Theta paths and Markov Machine). Finally, a heat map of the possible situation towards the end of the epidemic in terms of infectiousness of the areas was drawn up. The analyses with TWC confirm the assumptions made at the beginning.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

COVID-19 in Italy and extreme data mining

Buscema

Torre

Breda

et al. 2020

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

show abstract

“…A myriad of machine learning models for healthcare have been developed recently, with a significant emphasis on aiding LMICs. Examples include COVID-19 forecast models in Iran [37] and India [38], Ebola forecast models for Africa [39], and automated malaria diagnostics in Uganda [40]. Various tuberculosis prediction studies in Brazil [41], South Africa [42] and Peru [43] have been conducted.…”

Section: Healthcare Ai In Low-income Settingsmentioning

confidence: 99%

From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven healthcare research for low-income settings

Krones,

Walker

2023

Preprint

View full text Add to dashboard Cite

This paper critically explores the opportunities and challenges of deploying Artificial Intelligence (AI) in healthcare. This study has two parallel components: (1) A narrative literature summary, which assesses the capacity of AI to aid in addressing the observed disparity in healthcare between high- and low-income countries. Despite the development of machine learning models for a wide range of diseases, many are never deployed in practice. We highlight various challenges that contribute to the lack of deployed models. A main challenge that is not always sufficiently addressed in the literature is the evaluation of model generalisation. For example, by using a multi-site set-up with test sets that were collected separately to the train and validation sets, or by using evaluation metrics which are both understandable and clinically applicable. Moreover, we discuss how the emerging trend of human-centred deployment research is a promising avenue for overcoming barriers towards deployment. (2) A case study on developing and evaluating a predictive AI model tailored for low-income environments. The focus of this case study is heart murmur detection in rural Brazil. Our Binary Bayesian ResNet model leverages overlapping log mel spectrograms of patient heart sound recordings and integrates demographic data and signal features via XGBoost to optimise performance. We discuss the model’s limitations, its robustness, and the obstacles preventing its practical application. We especially highlight how our model, and other state-of-the-art models, struggle to generalise to out-of-distribution data. The research accentuates the transformative potential of AI-enabled healthcare, particularly affordable point-of-care monitoring systems, in low-income settings. It also emphasises the necessity for effective implementation and integration strategies to guarantee the successful deployment of these technologies.

show abstract

“…Within this region, AI has been leveraged to support maternal and child health, 1 engagement with health programs, 2 and for the ongoing COVID-19 pandemic. 3 , 4 , 5 , 6 There has also been a significant focus on diseases that disproportionately affect communities in the Global South, such as dengue fever, 7 ebola, 8 tuberculosis, 9 , 10 , 11 , 12 and diabetic retinopathy, 13 a disease that causes damage to the blood vessels as a complication of diabetes. To address this issue, Bellemo et al.…”

Section: Introductionmentioning

confidence: 99%

Optimizing human-centered AI for healthcare in the Global South

Okolo

2022

Patterns

View full text Add to dashboard Cite

Over the past 60 years, artificial intelligence (AI) has made significant progress, but most of its benefits have failed to make a significant impact within the Global South. Current practices that have led to biased systems will prevent AI from being actualized unless significant efforts are made to change them. As technical advances in AI and an interest in solving new problems lead researchers and tech companies to develop AI applications that target the health of marginalized communities, it is crucially important to study how AI can be used to empower those on the front lines in the Global South and how these tools can be optimally designed for marginalized communities. This perspective examines the landscape of AI for healthcare in the Global South and the evaluations of such systems and provides tangible recommendations for AI practitioners and human-centered researchers to incorporate in the development of AI systems for use with marginalized populations.Concept: Basic principles of a new data science output observed and reported ll

show abstract

Analysis of the Ebola Outbreak in 2014 and 2018 in West Africa and Congo by Using Artificial Adaptive Systems

Cited by 10 publications

References 6 publications

COVID-19 in Italy and extreme data mining

COVID-19 in Italy and extreme data mining

From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven healthcare research for low-income settings

Optimizing human-centered AI for healthcare in the Global South

Contact Info

Product

Resources

About