https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases?force_layout=desktop docx (v2) --> The sent material is a good start for the co-operative part of the publication. Here: https://miau.my-x.hu/miau/quilt/2020/covid19_project/ https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases you can find a lot of data for countries. In the co-operative part of the publication, we need an OAM, where - the objects=rows are the countries (the more the more) - the columns are the attributes (e.g. ratio of deaths, infections, etc.) The above mentioned online folder demonstrates a parallel publication (still not closed) and some figures about circaseptan-rythm-possibilities... In our case, it is important, why should we collect an OAM (=big-data)? The parallel publication will derive the best/most-ideal country (see XLS). The learning material about H1N1 derived a potential scandal concerning the lacks in the prevention processes (c.f. vactination). Our goal here and now could be the derivation of a production function where the attributes are data about the health system/structure of the countries: https://apps.who.int/gho/data/node.main.HWFGRP?lang=en e.g. https://apps.who.int/gho/data/node.country.country-AUT?lang=en To be operative: - please, select indicators/attributes/variables and - please, download the appropriate data for at least 20 countries and 10 attributes (=OAM) Based on the statistics (Xi) about the health system (capacities, resources, potential) and based on the COVID-ratios concerning cases or deaths (Y), we will be able to derive a model. This model makes possible to evaluate countries: - if the estimated amount of Y is higher than the real Y-value (e.g. ratio of deaths) based on the resources (Xi), then the country has a good management - if the estimated amount of Y is lower than the real Y-value (e.g. ratio of deaths) based on the resources (Xi), then the country has a bad management - if the estimated amount of Y = the real Y-value (e.g. ratio of deaths) based on the resources (Xi), then the country has a norm-like management This OAM-or-big-data-based, solver-oriented analysis is the final goal of the course (delivered in a co-operative form). We have to complete the classic magic of words with AI-based analytical layers and their interpretations, because this is the 21th century! :-) ********* docx (v1) --> A big-data és a tér/idő-összehasonlítás már szépen formálódik! Amit nekem kell a tananyag még releváns kulcsszavai alapján (pl. objektivitás, automatizáció, MI) még hozzátennem, az egy új táblázat (OAM), melyet a meglévők alapján kellene elkészítenem, de (egyébként helyesen) tbálázatai képként vannak beillesztve, így kérném szépen a háttér XLS gyors megküldését. Hogy ne áruljak zsákba macskát - a még fontos táblázat az alábbi lenne: - sorok továbbra is az országok (objektumok) - oszlopok -- 3 db death rate: két dátum + change <--forrás: 1. táblázat -- 3 db deaths/population: két dátum + change (még számítandó) <--forrás: 2. táblázat -- 3 db cases/population: két dátum + change <--forrás: 4. táblázat Az így létrejött OAM alapján a következő kérdés válaszolható meg, ha mind a 3+3+3 attribútumhoz irányt rendelünk: melyik ország tekinthető a LEGJOBB helyzetben lévőnek mindösszesen? (Ha lenne pl. koronavírus-költségvetési (kiadási) adatunk minden országhoz, akkor lehetne arról is beszélni, melyik ország volt a leghatékonyabb? - ha lenne közhangulat-indexünk, akkor is valamiféle legjobb lelki hatékonyságú országról lehene nyilatkozni?!, stb.) <--ide illene a munkanélküliségi kérdés is, ami lehetne következmény változó (COCO_STD) és értékelési tényező (COCO_Y0)... Vagyis célegyenes! S bármi, ami még attribútumként feltárható csak növeli a dolgozat értékét - amit akár TDK-ként is be lehetne adni, ahol a TDK is tantárgynak számít idéntől, ha ez esetleg fontos lenne a kreditek gyűjtésekor... *** https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases https://apps.who.int/gho/data/node.main.HWFGRP?lang=en e.g. https://apps.who.int/gho/data/node.country.country-AUT?lang=en ­­­­­­­­­­­­­­********** the H1N1-project is already a kind of preparation work for the COVID19-case. https://miau.my-x.hu/miau/quilt/2020/quilt2/launching2020IV08/H1N1_v1.docx https://miau.my-x.hu/miau/quilt/2020/quilt2/launching2020IV08/part2d.html The following URL could be useful for us if we will focus on the COVID19-data: http://nrg.cs.ucl.ac.uk/mjh/covid19/ Of course, everybody can search for other sources! It is important, that we should have databases for downloading! You identified our objects in your last email. Objects will therefore be the countries! Question is: which countries (e.g. EU27/28 - or OECD or all where we have data)? The next step is to identify the attributes (Xi, and Y)! We need an OAM before we can decide about the further steps. Row-headers = countries (at least 27/28 or more) Column-headers = attributes (with unit!!!) - (at least 10) Cells = data Attributes should be relativized: it means - e.g. Luxembourg and e.g. China should also be comparable at any rate. Therefore, each attribute should have a unit like .../capita It is also important: we do not need here and now any subjective opinions/estimations/evaluations. We need data and we need to derive objective interpretations. If we had own (subjective) interpretations or subjective interpretations from other sources, we will capable of comparing them with the our objective results! :-)