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How to effectively keep track of diseases like COVID-19

An EU initiative is harnessing data mining and analytical techniques to utilise Big Data from multiple sources. This will improve the detection, monitoring and assessment of emerging diseases in Europe.

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As the impact and consequences of the coronavirus-related health crisis are being felt throughout the world, an international team of scientists is providing assistance to surveillance efforts aimed at tackling the outbreak. Partially supported by the EU-funded MOOD project, the team has issued several reports examining different aspects of the spread of COVID-19. In an article published in the journal ‘Science’, they utilised “a global metapopulation disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic.” The article states: “The model is calibrated based on internationally reported cases, and shows that at the start of the travel ban from Wuhan on 23 January 2020, most Chinese cities had already received many infected travelers. The travel quarantine of Wuhan delayed the overall epidemic progression by only 3 to 5 days in Mainland China, but has a more marked effect at the international scale, where case importations were reduced by nearly 80% until mid February.” According to the results, “sustained 90% travel restrictions to and from Mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.” The researchers emphasise that their analysis of the coronavirus “outbreak and the modeling assessment of the effects of travel limitations could be instrumental to national and international agencies for public health response planning.” The researchers also note that “while the Wuhan travel ban was initially effective at reducing international case importations, the number of cases observed outside Mainland China will resume its growth after 2-3 weeks from cases that originated elsewhere.” The scientists conclude that the extra “travel limitations up to 90% of the traffic have a modest effect unless paired with public health interventions and behavioral changes that achieve a considerable reduction in the disease transmissibility,” and emphasise that their results could help develop optimised containment schemes and mitigation policies both at national and international levels.

Risks in Africa

Another study partially supported by MOOD examined the preparedness of African countries against the novel coronavirus. The preliminary manuscript, a preprint that hasn’t been peer reviewed yet, was made available on the free online and archive distribution server ‘medRxiv’. “We determined the countries’ capacity to detect and respond to cases with two indicators: preparedness, using the WHO [World Health Organization] International Health Regulation Monitoring and Evaluation Framework; and vulnerability, with the Infectious Disease Vulnerability Index.” The article states: “Countries at the highest importation risk (Egypt, Algeria, Republic of South Africa) have moderate to high capacity to respond to outbreaks. Countries at moderate risk (Nigeria, Ethiopia, Sudan, Angola, Tanzania, Ghana, Kenya) have variable capacity and high vulnerability.” The MOOD (MOnitoring Outbreak events for Disease surveillance in a data science context) project will end in December 2023. It will develop “a framework and visualisation platform allowing real-time analysis and interpretation of epidemiological and genetic data in combination with environmental and socio-economic covariates,” as noted on the project website. “MOOD output will be designed and developed with end users to assure their routine use during and beyond MOOD. They will be tested and fine-tuned on air-borne, vector-borne, water-borne model diseases, including anti-microbial resistance.” For more information, please see: MOOD project website


MOOD, COVID-19, coronavirus, epidemic, outbreak, disease, health

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