Difference between revisions of "Covid-19"

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* [https://www.nice.org.uk/guidance/ng163 NICE guideline [NG163] - COVID-19 rapid guideline]
* [https://www.nice.org.uk/guidance/ng163 NICE guideline [NG163] - COVID-19 rapid guideline]
* [https://www.alnap.org/help-library/handbook-of-covid-19-prevention-and-treatment Handbook of COVID-19 Prevention and Treatment], also in [https://medf.kg.ac.rs/oglasna_tabla/Handbook_of_COVID-19_Prevention_and_Treatment_Srpski.pdf Serbian]
* [https://www.alnap.org/help-library/handbook-of-covid-19-prevention-and-treatment Handbook of COVID-19 Prevention and Treatment], also in [https://medf.kg.ac.rs/oglasna_tabla/Handbook_of_COVID-19_Prevention_and_Treatment_Srpski.pdf Serbian]
* [http://med.bg.ac.rs/wp-content/uploads/2020/04/Smernice-COVID-19-KBCDM.pdf Guidelines from KBC Dragiša Mišovi; hospital, in Serbian]
* [http://med.bg.ac.rs/wp-content/uploads/2020/04/Smernice-COVID-19-KBCDM.pdf Guidelines from KBC Dragiša Mišović hospital, in Serbian]

Revision as of 18:33, 4 May 2020

Open-Access Data and Computational Resources on Coronavirus disease 2019 (COVID-19)

COVID-19, Open Science and Open Data

We are witnessing a massive ongoing production of thousands of scientific papers on COVID-19. Rapidly evolving scientific opinions in these papers that are based on studies and emerging data and evidence as a part of the standard scientific process. Because of the scale of the effort and high interest of the public and impact on actions and policies that affect everyone, this production results in ambiguity and often confusion in the public. These findings and conclusions may be even weaponised in the public debate, which is quite common for climate science, but is new for epidemiology, virology and medicine in general. The attractiveness and popularity of the subject make it more prone to opportunistic production od papers, while the availability of preprints that did not go through the standards peer-review process adds a layer on uncertainty and increases the chances for questionable work to be used, popularised and relied upon. On the other hand, preprints allow quick sharing of information and accelerate research, as studies become available months before they are normally published; this also facilitates external validation and pruning by other researchers outside of the formal peer-review process.

Unlike natural disasters that are easy to witness, the less visible nature of the epidemic makes it easier to negate its effects and related facts. The conflicting data and messages may result in the opposite and also lead to clustering of around conflicting views and conclusions. While these divisions are normally gradually resolved in through the scientific process, they may have a great immediate impact on behaviours, social trust and healthcare and public health interventions. Sharing of data and papers therefore also increases the ability to understand and act rationally while reducing anxiety and uncertainty.

Inevitable difficulties related to the accuracy and timeliness of measurements and data complicate their interpretation, something that is obvious in the discussion of the spread of the infection, mortality rates, reinfection or percentage of asymptomatic carriers. Similar problems occur in the interpretation of epidemiological models, which are often interpreted as predictions and not as instruments to explore and quantify possibilities and policymakers’ actions. These issues result in an interpretation of warnings as exaggerated and false predictions. Preventive measures aim to prevent something from happening, which exposes them to disputes, objection and subsequent disregard, in particular upon prolonged or repetitive application.

The open science and open data may help in addressing these problems and concerns and help in addressing the current crisis of trust. This crisis affects the facts, validation process, experts and authorities, regardless of whether they are scientific, medical or societal. While the science operates with hypotheses, uncertainties and changing conclusions, others who are present in the public space may offer absolutes, overconfidence, inadequate simplifications or opportunistic scepticism. What the open science can help in is to develop new ways to communicate the facts and distinguish the valuable information, expertise and truth from everything else while providing the relevant, multi-disciplinary and integrative perspective. COVID-19 emphasises the need for the process that will ease dealing with uncertainties and accelerate the identification of important and valid messages and at the same time minimise the effects of uncertainties and changes in knowledge that diminish trust and increase vulnerability to falsehoods and disinformation.

General data resources

Overall resource lists and national resources

Daily countries aggregate data

ECDC data

Daily country regional or city aggregate data

  • Country...
    • Resource...

Per case country data


Multitype registries

Publications registries

Computational resources

Offering to researches from NI4OS countries, but potentially also to other resources, for all who are working in COVID-19 related fields.


  • Contact XY... and express your need by briefly describing
    • Area of research
    • Estimated computational load,
    • Execution environment (programming language, libraries),
    • Data exchange, etc.
  • The needs will be matched against the available resources and you will be responded within xxx
  • Details will be...

Other supporting resources

Data content

Desease-related data

  • WHO Case Record Form(CRF) describes key clinical information that can be useful in the general research, with the content that should be recorded: on admission; on admission to ICU and daily while the patient is in the ICU; and on discharge or death.
  • WHO Case Report Form lists the content that may be useful in the design of epidemiological research data capture.

The collected data should be aligned with the goals of the specific research, but also with the data required by applied Guidelines and protocols.

Data coding

Guidelines and protocols