class_docstr: A dataset of mobility and history of reported cases of COVID-19
  in England NUTS3 regions, from 3 March to 12 of May. The dataset is
  segmented in days and the graph is directed and weighted. The graph
  indicates how many people moved from one region to the other each day,
  based on Facebook Data For Good disease prevention maps.
  The node features correspond to the number of COVID-19 cases
  in the region in the past **window** days. The task is to predict the
  number of cases in each node after 1 day. For details see this paper:
  `"Transfer Graph Neural Networks for Pandemic Forecasting." <https://arxiv.org/abs/2009.08388>`_
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code: def __init__(self):
      self._read_web_data()
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/dataset/encovid.py
imports: import json
  import urllib
  import numpy
  from ..signal import DynamicGraphTemporalSignal
label_desc: Use image node_id 1 to create a new EnglandCovidDatasetLoader object from inherited base classes: object with example: obj = EnglandCovidDatasetLoader()