<img alt="" src="https://storage.yandexcloud.net/datasouls-competitions/sberbank-covid19-forecast/covid-scr-2.png" />

The participants need to build an algorithm for the most accurate prediction of the number of reported cases of COVID-19 dynamics for the next seven days. 
The competition aims at drawing attention to the pandemic forecasts building. It may probably help to find problems in the data sources or build a useful forecast using the most reliable data.

 
<a href="http://opendatascience.slack.com/archives/C011745MET0">The competition channel in&nbsp;Slack ODS:&nbsp;#proj_covid</a> 
<a href="http://covidforecast.sberbank.ai/">Description of the competition in Russian</a>

<img alt="" src="https://storage.yandexcloud.net/datasouls-competitions/sberbank-covid19-forecast/covid-scr-5.png" />

The main goal of the contest is open science development support, forecasting techniques development, and sharing experience in building predictive models. The tasks like that organized as public benchmarking allow scientists, researchers, and engineers to test and compare different approaches and together develop the best practices, at the same time making them available to the entire research community.

The next goal of the contest is to draw attention to the problem of pandemic surveillance and qualitative modeling of its spreading worldwide. The development of open qualitative predictive models that allow assessment of the immediate dynamics of epidemics supports future quick decision-making and effective responses aimed to prevent the next pandemic spread. 
 
<a href="https://storage.yandexcloud.net/datasouls-competitions/sberbank-covid19-forecast/COVID19_Data_Challenge_Rules_RU%20-_17.pdf">Contest rules RU</a> 
<a href="https://storage.yandexcloud.net/datasouls-competitions/sberbank-covid19-forecast/COVID-19_Rules_EN_17.pdf">Contest rules EN</a> 
<a href="https://www.sberbank.ru/ru/pol_pd">Processing of Personal Data Sberbank RU</a>

<h3>Prizes</h3>

<img alt="" src="https://storage.yandexcloud.net/datasouls-competitions/sberbank-covid19-forecast/covid-scr-1.png" />

The winners are determined in three stages:

&nbsp;

<ul>
	<li>Stage 1: for the week 13.04 - 19.04, the winners will be determined on Tuesday 21.04 (solutions sending deadline is 12.04, 11:59&nbsp;p.m., GMT +3 (Moscow)). 
	&nbsp;</li>
	<li>Stage 2: for the week 20.04 - 26.04, the winners are determined on Tuesday 28.04 (solutions sending deadline is 19.04, 11:59&nbsp;p.m., GMT +3 (Moscow)). 
	In the second stage of the competition, the results will be announced both according to country forecasts and for Russian regions. 
	Second stage prizes will be given to those who make the best forecasts for Russian regions. 
	&nbsp;</li>
	<li>Stage 3: for the week 27.04 - 03.05 winners are determined on Tuesday 05.05 (solutions sending deadline is 26.04, 11:59&nbsp;p.m., GMT +3 (Moscow)). 
	In the third (final) stage of the competition, the results will be announced both according to country forecasts and for Russian regions. 
	This stage prizes will be given to those who make the best forecasts for Russian regions. 
	&nbsp;</li>
</ul>

<h3>FAQ</h3>

What&nbsp;period is forecasted?

The forecast is produced for each day until the end of the competition. The intermediate results are given for one week. It may seem like a too-short planning horizon for such a long-distance problem. However, the epidemic&rsquo;s dynamic is strongly influenced by the quarantine measures taken in each country. It is not possible to predict the exact dates of their implementation as well as their effectiveness.&nbsp; 
Moreover, as of the contest starting date, there is no complete data on all the measures implemented, and the impact of the implemented measures has yet to be assessed.

Why predict if there is no confidence in the data validity?

Considering the contest task limitations, the algorithms and solutions developed in its course can still be useful, especially given the increase in data reliability. The competition is, first of all, a public benchmark of predictive models for COVID-19 epidemic data. The participants should focus primarily on modeling rather than on engineering aspects and limitations of systems and solutions, just like their models had to be immediately used in the real environment.

How were the evaluation metrics chosen? 
 
Task features cause the choice of metrics. First, the number of infected people grows exponentially. Second, the general development of the epidemic dynamics in any of the countries is more important than focusing on predicting the exact number of infected in most-pandemic countries. And third, different countries have different standards and approaches to COVID-19 testing, as well as different stages of the epidemic, so the problem needs a sustainable path.

Please note that the metrics may change during the contest. The final version will be announced no later than a week before the end of the competition. 
 
The organizers are waiting for suggestions for improvement from the Open Data Science community.

Use any data you can find to predict the future increase in the number of reported cases of COVID-19

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Use any data you can find to predict the future increase of the number of reported cases of COVID-19.

#	Teams (111)	Точность	Last	Entries
1	Niw	0.3726203475	4y	19
2	ZFTurbo (Роман Соловьев)	0.3963948074	4y	5
3	solemn_leader	0.3995833591	4y	3
4	Школьники Нонны	0.4110715196	4y	12
5	Philipp Lozovoy	0.4185969953	4y	11
6	Siberian GANs	0.4304787252	4y	8
7	Квентин Карантино	0.4374997843	4y	8
8	Topspin26	0.4531676923	4y	7
9	Alexei Chernobrovov	0.455656682	4y	12
10	Andrew Shevelev	0.4642920838	4y	3
11	KJ	0.4703927764	4y	6
12	Роман Пьянков	0.4941429448	4y	3
13	Олег Шаповалов	0.4996154069	4y	11
14	Renat Ramazanov	0.5143265391	4y	1
15	Кирилл Рыбкин	0.5268481508	4y	2
16	how	0.5333988307	4y	2
17	ihvh@github	0.5431243698	4y	12
18	bat_soup	0.555528638	4y	5
19	Medical Advisers Group	0.5600744662	4y	2
20	Jupyter Invaders	0.5613930541	4y	5
21	a18	0.5670564946	4y	4
22	Последний Боярин	0.5675621738	4y	3
23	drts	0.5721771588	4y	4
24	Alex	0.5762040914	4y	6
25	Ivan Komarov	0.5867689226	4y	2
26	Babusya	0.5931349983	4y	2
27	Victor Zaguskin	0.5946543435	4y	4
28	Михаил	0.6017631196	4y	3
29	Vladislav	0.6179009738	4y	17
30	JK	0.621123333	4y	2
31	Anatoly Ilyenkov	0.6265825307	4y	1
32	Team data has been deleted	0.6278237636	4y	2
33	Nikita Churkin	0.647591996	4y	2
34	supertoha	0.6501102477	4y	9
35	Sergei Dvindenko	0.6593224941	4y	2
36	Альберт Тухватуллин	0.669298106	4y	2
37	EkaterinaE	0.6766130021	4y	7
38	Konstantin Phd	0.7378857027	4y	6
39	Николай Брадис	0.7437817052	4y	4
40	Etvas Никто	0.7557889485	4y	3
41	Kirill Gerasimov	0.7728035492	4y	2
42	Twins	0.7832226224	4y	1
43	SG_da (Максим Сигида)	0.8183912617	4y	15
44	Invincible	0.8466846174	4y	3
45	Андрей Кузнецов	0.8475220699	4y	3
46	Electric Boogaloo	0.8810139644	4y	4
47	Nikolay Kobalo	0.9037624272	4y	3
48	SpaceDoge	0.9593949661	4y	10
49	MR M	1.0348883271	4y	2
50	Максим Шульга	1.1221059082	4y	2