The data will be used by epidemiologist, statisticians, data scientists for assessing the risk of the Covid 2019 globally and would be used as a model to predict the case fatality rate along with the possible spread of the disease along with its attack rate.Data was in raw format. A detailed analysis is carried out from Epidemiology point of view and a datasheet is prepared through the identification of the Risk Factor in a Defined Population.The daily incidences of COVID-2019 from 11th January 2020 to 9th May 2020 were collected form the official covid dashboard of world health organization (WHO), i.e. https://covid19.who.int/explorer. The data is compiled in Excel 2016 and a database is created. The database is updated with the population of the countries and Case fatality rate, Basic Attack Rate (BAR) and Household Secondary Attack Rate (HSAR) is computed for all the countries.  

GeoCoV19 Dataset Description 

The GeoCoV19 Dataset comprises several TAR files, which contain zip files representing daily data. Each zip file contains a JSON with the following format:

{ "tweet_id": "122365517305623353", "created_at": "Sat Feb 01 17:11:42 +0000 2020", "user_id": "335247240", "geo_source": "user_location", "user_location": { "country_code": "br" }, "geo": {}, "place": { }, "tweet_locations": [ { "country_code": "it", "state": "Trentino-Alto", "county": "Pustertal - Val Pusteria" }, { "country_code": "us" }, { "country_code": "ru", "state": "Voronezh Oblast", "county": "Petropavlovsky District" }, { "country_code": "at", "state": "Upper Austria", "county": "Braunau am Inn" }, { "country_code": "it", "state": "Trentino-Alto", "county": "Pustertal - Val Pusteria" }, { "country_code": "cn" }, { "country_code": "in", "state": "Himachal Pradesh", "county": "Jubbal" } ] }

Description of all the fields in the above JSON 

Each JSON in the Geo file has the following eight keys:

1. Tweet_id: it represents the Twitter provided id of a tweet

2. Created_at: it represents the Twitter provided "created_at" date and time in UTC

3. User_id: it represents the Twitter provided user id

4. Geo_source: this field shows one of the four values: (i) coordinates, (ii) place, (iii) user_location, or (iv) tweet_text. The value depends on the availability of these fields. However, priority is given to the most accurate fields if available. The priority order is coordinates, places, user_location, and tweet_text. For instance, when a tweet has GPS coordinates, the value will be "coordinates" even though all other location fields are present. If a tweet does not have GPS, place, and user_location information, then the value of this field will be "tweet_text" if there is any location mention in the tweet text.

The remaining keys can have the following location_json inside them. Sample location_json: {"country_code":"us","state":"California","county":"San Francisco","city":"San Francisco"}. Depending on the available granularity, country_code, state, county or city keys can be missing in the location_json.

5. user_location: It can have a "location_json" as described above or an empty JSON {}. This field uses the "location" profile meta-data of a Twitter user and represents the user declared location in the text format. We resolve the text to a location.

6. geo: represents the "geo" field provided by Twitter. We resolve the provided latitude and longitude values to locations. It can have a "location_json" as described above or an empty JSON {}.

7. tweet_locations: This field can have an array of "location_json" as described above [location_json1, location_json2] or an empty array []. This field uses the tweet content (i.e., actual tweet message) to find toponyms. A tweet message can have several mentions of different locations (i.e., toponyms). That is why we have an array of locations representing all those toponyms in a tweet. For instance, in a tweet like "The UK has over 65,000 #COVID19 deaths. More than Qatar, Pakistan, and Norway.", there are four location mentions. Our tweet_locations array should represent these four separately.

8. place: It can have a "location_json" described above or an empty JSON {}. It represents the Twitter-provided "place" field.

Tweets hydrators:

CrisisNLP (Java): https://crisisnlp.qcri.org/#resource8

Twarc (Python): https://github.com/DocNow/twarc#dehydrate

Docnow (Desktop application): https://github.com/docnow/hydrator

If you have doubts or questions, feel free to contact us at: uqazi@hbku.edu.qa and mimran@hbku.edu.qa

Read the documentation properly and use the code snippet written in python to load data.

The script to load data is written in documentation.

Use the code snippet provided written in python to load data.

Purpose is to describe the dynamics of the COVID19 pandemics accounting for the mitigation measures, for the introduction or removal of the quarantine, and for the effect of vaccination when and if introduced.

Methods include the derivation of the Pandemic Equation describing the mitigation measures via the evolution of the growth time constant in the Pandemic Equation resulting in an asymmetric pandemic curve with a steeper rise than a decrease.

Results: The Pandemic equation predicts how the quarantine removal and business opening lead to a spike in the pandemic curve. The effective vaccination reduces the new daily infections predicted by the Pandemic equation to nearly zero. The pandemic curves in many localities have similar time dependencies but shifted in time. The Pandemic Equation parameters extracted from the well advanced pandemic curves can be used for predicting the pandemic evolution in the localities, where the pandemics is still in the initial stages.

Conclusion: Using the multiple pandemic locations for the parameter extraction allows for the uncertainty quantification in predicting the pandemic evolution using the introduced Pandemic Equation..

The First Few X cases and contacts (FFX) investigation protocol for Coronavirus Disease 2019 (COVID-19). This is about identification and tracing of cases and their close contacts in the general population or restricted to close settings (like households, health-care settings, schools). FFX is the primary investigation protocol to be initiated upon the identification of the initial laboratory-confirmed cases of COVID-19 in a country.

Two users were standing at a certain distance (d = {0.2:0.2:2, 3:5}) from each other. For each distance, the App was made to scan for incoming BLE signals for about 1min. The following information is logged: the truth distance, name of smartphone, MAC address of BLE chipset, the packet payload, RSS values, time elapsed, and timestamp.  Two phones were used: 1) gryphonelab, and 2) HTC One M9.

We consolidated the data and reorganized them while applying moving average to the raw RSS value.  The reorganized data has the following format:

• ·         device name,
• ·         time elapsed,
• ·         rss (raw RSS value),
• ·         mRSS10 (filtered RSS value with window size = 10),
• ·         mRSS100 (filtered RSS value with window size = 100),
• ·         distance, and
• ·         label

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