Risk assessment of Ebola virus disease spreading in Uganda using a two-layer temporal network

Riad, Mahbubul H.; Sekamatte, Musa; Ocom, Felix; Makumbi, Issa; Scoglio, Caterina

doi:10.1038/s41598-019-52501-1

Cited by 18 publications

(6 citation statements)

References 47 publications

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“…Since most infectious diseases, such as COVID-19 8 , 24 , require close contact to spread from one person to the next, neighbors in a contact network will help drive how the disease spreads. In other words, the outcome of an outbreak depends on when and how often nodes are in contact 17 , 18 . Additionally, our results support the observation that the position of a person in their contact network seems to be indicative of infection time too.…”

Section: Discussionmentioning

confidence: 99%

On the importance of structural equivalence in temporal networks for epidemic forecasting

Kister

Tonetto

2023

Sci Rep

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Understanding how a disease spreads in a population is a first step to preparing for future epidemics, and machine learning models are a useful tool to analyze the spreading process of infectious diseases. For effective predictions of these spreading processes, node embeddings are used to encode networks based on the similarity between nodes into feature vectors, i.e., higher dimensional representations of human contacts. In this work, we evaluated the impact of homophily and structural equivalence on embedding for disease spread prediction by testing them on real world temporal human contact networks. Our results show that structural equivalence is a useful indicator for the infection status of a person. Embeddings that are balanced towards the preservation of structural equivalence performed better than those that focus on the preservation of homophily, with an average improvement of 0.1042 in the f1-score (95% CI 0.051 to 0.157). This indicates that structurally equivalent nodes behave similarly during an epidemic (e.g., expected time of a disease onset). This observation could greatly improve predictions of future epidemics where only partial information about contacts is known, thereby helping determine the risk of infection for different groups in the population.

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Section: Discussionmentioning

confidence: 99%

On the importance of structural equivalence in temporal networks for epidemic forecasting

Kister

Tonetto

2023

Sci Rep

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“…In the fight against MERS Co-V, for example, AI-based models have been used to predict prognosis in patients' infection (in particular, patients' recovery) using patients' profession (e.g., whether healthcare workers or not), age, pre-existing healthcare conditions, and disease severity as model input parameters (John and Shaiba, 2019). Similar AI-based applications and methods have been developed for Ebola patients (Colubri et al, 2016;Riad et al, 2019). These and other similar tools can help, for example, to assess healthcare preparedness for a pandemic and to determine treatment methods and resource allocation during a pandemic, and some of the these algorithms could be adapted for decision making in the management of COVID-19 (Bansal et al, 2020).…”

Section: Ai For Prognosismentioning

confidence: 99%

The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype

Abdulkareem

Petersen

2021

Front. Artif. Intell.

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COVID-19 has created enormous suffering, affecting lives, and causing deaths. The ease with which this type of coronavirus can spread has exposed weaknesses of many healthcare systems around the world. Since its emergence, many governments, research communities, commercial enterprises, and other institutions and stakeholders around the world have been fighting in various ways to curb the spread of the disease. Science and technology have helped in the implementation of policies of many governments that are directed toward mitigating the impacts of the pandemic and in diagnosing and providing care for the disease. Recent technological tools, artificial intelligence (AI) tools in particular, have also been explored to track the spread of the coronavirus, identify patients with high mortality risk and diagnose patients for the disease. In this paper, areas where AI techniques are being used in the detection, diagnosis and epidemiological predictions, forecasting and social control for combating COVID-19 are discussed, highlighting areas of successful applications and underscoring issues that need to be addressed to achieve significant progress in battling COVID-19 and future pandemics. Several AI systems have been developed for diagnosing COVID-19 using medical imaging modalities such as chest CT and X-ray images. These AI systems mainly differ in their choices of the algorithms for image segmentation, classification and disease diagnosis. Other AI-based systems have focused on predicting mortality rate, long-term patient hospitalization and patient outcomes for COVID-19. AI has huge potential in the battle against the COVID-19 pandemic but successful practical deployments of these AI-based tools have so far been limited due to challenges such as limited data accessibility, the need for external evaluation of AI models, the lack of awareness of AI experts of the regulatory landscape governing the deployment of AI tools in healthcare, the need for clinicians and other experts to work with AI experts in a multidisciplinary context and the need to address public concerns over data collection, privacy, and protection. Having a dedicated team with expertise in medical data collection, privacy, access and sharing, using federated learning whereby AI scientists hand over training algorithms to the healthcare institutions to train models locally, and taking full advantage of biomedical data stored in biobanks can alleviate some of problems posed by these challenges. Addressing these challenges will ultimately accelerate the translation of AI research into practical and useful solutions for combating pandemics.

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“…In February 2021, a new EVD case was detected in Butembo, a city in DRC's North Kivu Province. Unstable conditions due to armed con ict, outbreaks of violence, and social/economic problems in affected areas complicated the public health response and increased the risk of disease spread both locally within the DRC and to other countries in the region such as Uganda, Rwanda, Burundi, Zambia, South Sudan, and Central African Republic (Riad et al, 2019; Schmidt-Sane et al, 2020; Wadoum et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Understanding Knowledge, Attitudes and Practices on Ebola Virus Disease: A Multi-Site Mixed Methods Survey on Preparedness in Rwanda

Karuhije

Nkeshimana

Zakham

et al. 2023

Preprint

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Background: The overall goal of this survey was to understand the Ebola Virus Disease (EVD) - related knowledge, attitudes, and practices in Rwanda. Methods: This cross-sectional mixed-methods survey was conducted in five selected districts: Rusizi, Karongi, Rubavu, Burera and Gasabo. Quantitative data was collected from 1,010 participants using a structured questionnaire and Kobo Collect. Qualitative data was collected from 98 participants through Key Informant Interviews and Focus Group Discussion using a semi structured interview guide. Results: Among the 1,010 surveyed respondents, 56% were male, 70.3% were married, and 50% had primary education. An important finding was the high level of Ebola awareness and knowledge in all the five districts, with 99.6% reporting having previously heard of Ebola, which indicates previous awareness-raising efforts were successful. More than 54% of respondents indicated that Ebola is caused by a virus which originates from wild animal animals (42.1%). Furthermore, fever (85%), bleeding (87.7), and vomiting (40.2%) were cited as the primary signs and symptoms for Ebola. Most of the respondents were knowledgeable regarding prevention measures for Ebola. Despite this, 80% of the survey respondents had not received formal training or health education on Ebola. Many respondents (90%) believe that the country is at risk of an EVD outbreak and about 87.8% think that they are personally at risk of contracting Ebola. Most respondents reported adopting habits that included avoiding physical contact with the patients and reducing unnecessary movements/travel throughout the Ebola-affected regions. Additionally, most respondents (97.2%) believed that it is important to be vaccinated to prevent Ebola, and around 93.3% were ready and willing to get vaccinated once the EVD vaccine is available. While the radio is the preferred source for Ebola-related information, the most trusted sources are the ministry of health and governmental institutions, such as the Rwanda Biomedical Centre. Conclusion: Our results show that there was high EVD-related knowledge and awareness among the general population in Rwanda. However, for strong public health awareness, preparedness, and protection, there is a need to implement public sensitization programmes that address EVD-related misconceptions and discriminatory attitudes toward EVD patients.

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Risk assessment of Ebola virus disease spreading in Uganda using a two-layer temporal network

Cited by 18 publications

References 47 publications

On the importance of structural equivalence in temporal networks for epidemic forecasting

On the importance of structural equivalence in temporal networks for epidemic forecasting

The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype

Understanding Knowledge, Attitudes and Practices on Ebola Virus Disease: A Multi-Site Mixed Methods Survey on Preparedness in Rwanda

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