Timeseries analyzer for coronavirus with stacked recurrent neural network and data crawler project. For further, you can read corresponding sections below.
For model, Keras library is used, and model structured as stacked recurrent neural network. In my implementation, there are three LSTM layers, and each of them has their own dropout layer for regularization. At the end, there is a single dense layer which is used for obtaining regression result.
For the feature vector, I decided to use previous days' data with specific day interval. In other words, the created RNN model maps occurred total death counts to next day's total death count. Size of feature vector can be configurable in implementation. You can change target directory i.e. target of training by configuring it in predictor file. I output crawler data so that it can be easily changed.
Note: Still there are few data we obtained around the world. The size of data may not be applicable for training of such a large network. I played layer counts, neuron counts of layers, activation functions and other hyper parameters; however, expected numbers could deviate unexpectedly. But at least, I thought that having initial implementation for such a case can be helpful for other curious people.
Note: Because of the above concern, I do not choose to train my models on particular country (because of lack of necessary data count). I choose to train my model with all data available for all countries so that maybe I can catch some point of direction increase in the virus spread speed collectively for all countries.
Several figures are placed here:
The project not just include predictor/analysis part for coronavirus, but also creation of dataset for the training. The main source for dataset is Worldometer web page Coronovirus section.
Crawling is done with the following procedure:
- Firstly, all the available counties are fetched and filtered from the table.
- Then, web scrapping procedure starts for each fetched country.
- Each country's detail page parsed and daily data is obtained from graphs included in the detail page of particular country.
Under crawler package, one could find the following files.
Synchronous scrapper class where HTTP requests are forwarded via urllib module of python. For parsing, beautifulsoup4 module is used.
Pretty similar to synchronous counterpart with just aiohttp take place of synchronous network access and main loop of asyncio controls the flow.
Output of obtained data is written into CSV formatted files under resources directory which can be found at the same level with src directory. Current version of CSV files have two columns of data where the first column includes date information and the second one consists corresponding values for particular information (ex: Active Cases or Daily Deaths).
File structure will be as follows.
$ ls
LICENSE README.md resources src
$ ls resources/
'Active Cases' 'Daily Deaths' 'Daily New Cases' 'New Cases vs. New Recoveries' 'Outcome of total closed cases (recovery rate vs death rate)' 'Total Cases' 'Total Deaths'
$ ls resources/Active\ Cases/
afghanistan.csv bermuda.csv china.csv ethiopia.csv guyana.csv kyrgyzstan.csv moldova.csv panama.csv seychelles.csv thailand.csv
albania.csv bhutan.csv colombia.csv faeroe_islands.csv haiti.csv laos.csv monaco.csv papua_new_guinea.csv sierra_leone.csv timor-leste.csv
algeria.csv bolivia.csv congo.csv falkland_islands.csv honduras.csv latvia.csv mongolia.csv paraguay.csv singapore.csv togo.csv
andorra.csv bosnia_and_herzegovina.csv costa_rica.csv fiji.csv hong_kong.csv lebanon.csv montenegro.csv peru.csv sint_maarten.csv trinidad_and_tobago.csv
angola.csv botswana.csv croatia.csv finland.csv hungary.csv liberia.csv montserrat.csv philippines.csv s._korea.csv tunisia.csv
anguilla.csv brazil.csv cuba.csv france.csv iceland.csv libya.csv morocco.csv poland.csv slovakia.csv turkey.csv
antigua_and_barbuda.csv british_virgin_islands.csv curaçao.csv french_guiana.csv india.csv liechtenstein.csv mozambique.csv portugal.csv slovenia.csv turks_and_caicos.csv
argentina.csv brunei.csv cyprus.csv french_polynesia.csv indonesia.csv lithuania.csv myanmar.csv qatar.csv somalia.csv uae.csv
armenia.csv bulgaria.csv czechia.csv gabon.csv iran.csv luxembourg.csv namibia.csv réunion.csv south_africa.csv uganda.csv
aruba.csv burkina_faso.csv denmark.csv gambia.csv iraq.csv macao.csv nepal.csv romania.csv south_sudan.csv uk.csv
australia.csv burundi.csv djibouti.csv georgia.csv ireland.csv madagascar.csv netherlands.csv russia.csv spain.csv ukraine.csv
austria.csv cabo_verde.csv dominica.csv germany.csv isle_of_man.csv malawi.csv new_caledonia.csv rwanda.csv sri_lanka.csv uruguay.csv
azerbaijan.csv cambodia.csv dominican_republic.csv ghana.csv israel.csv malaysia.csv new_zealand.csv saint_kitts_and_nevis.csv st._barth.csv usa.csv
bahamas.csv cameroon.csv drc.csv gibraltar.csv italy.csv maldives.csv nicaragua.csv saint_lucia.csv st._vincent_grenadines.csv uzbekistan.csv
bahrain.csv canada.csv ecuador.csv greece.csv ivory_coast.csv mali.csv niger.csv saint_martin.csv sudan.csv vatican_city.csv
bangladesh.csv car.csv egypt.csv greenland.csv jamaica.csv malta.csv nigeria.csv saint_pierre_miquelon.csv suriname.csv venezuela.csv
barbados.csv caribbean_netherlands.csv el_salvador.csv grenada.csv japan.csv martinique.csv north_macedonia.csv san_marino.csv sweden.csv vietnam.csv
belarus.csv cayman_islands.csv equatorial_guinea.csv guadeloupe.csv jordan.csv mauritania.csv norway.csv sao_tome_and_principe.csv switzerland.csv western_sahara.csv
belgium.csv chad.csv eritrea.csv guatemala.csv kazakhstan.csv mauritius.csv oman.csv saudi_arabia.csv syria.csv yemen.csv
belize.csv channel_islands.csv estonia.csv guinea-bissau.csv kenya.csv mayotte.csv pakistan.csv senegal.csv taiwan.csv zambia.csv
benin.csv chile.csv eswatini.csv guinea.csv kuwait.csv mexico.csv palestine.csv serbia.csv tanzania.csv zimbabwe.csv
palestine.csv serbia.csv tanzania.csv zimbabwe.csvFirst of all, the project needs Python>=3.6. To build and use the project, you need to follow following steps.
$ # You may want to activate your favorite virtual environment before doing installation
$ git clone [email protected]:yakuza8/coronavirus-timeseries-predictor.git
$ cd coronavirus-timeseries-predictor
$ pip install -r requirements.txt
$ pip install -e .There are several executable scripts that you may want to run. Let's give some examples of them.
$ # Navigate to crawler directory under src
$ cd src/crawler
$ python3.6 async_crawler.py # To get data under resources directory
$ cd ../predictor
$ python3.6 rnn_predictor.py # To train model