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Problem

Foreword

During the evolution of COVID-19 epidemics different countries have taken different measures to mitigate the diffusion of the epidemics, including restrictions to mobility and, more recently, vaccines. The latter are expected to lead to herd immunity of the corresponding populations.

In this project we shall ask you to consider as a paradigm two specific countries: United Kingdom (GBR) and Israel (ISR) which, followed different restriction policies, but in a similar time span, have already vaccinated a significant percentage of their population. For these two countries, we provide you with the data (D) below.

Data

The data are provided in .csv format and have been downloaded and selected from the COVID-19 Data Hub:

https://covid19datahub.io/index.html

Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Journal of Open Source Software 5(51):2376, doi: 10.21105/joss.02376

In the file the following variables are recorded daily, for the time frame Jan 22, 2020 – Apr 30, 2021:

Identifiers

Variable Description
id Unique country identifier.
date Observation date.

COVID-19 variables

Variable Description
deaths Cumulative number of deaths.
confirmed Cumulative number of confirmed cases.
tests Cumulative number of tests.
vaccines Cumulative number of doses administered. This is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime (e.g. people receive multiple doses). If a person receives one dose of the vaccine, this metric goes up by 1. If they receive a second dose, it goes up by 1 again.
recovered Cumulative number of patients released from hospitals or reported recovered.
hosp Number of hospitalized patients on date.
icu Number of hospitalized patients in ICUs on date.
vent Number of patients requiring invasive ventilation on date.
population Total population.

Policy measures

Variable Description
school_closing 0: No measures - 1: Recommend closing - 2: Require closing (only some levels or categories, e.g. just high school, or just public schools - 3: Require closing all levels. More details
workplace_closing 0: No measures - 1: Recommend closing (or work from home) - 2: require closing for some sectors or categories of workers - 3: require closing (or work from home) all-but-essential workplaces (e.g. grocery stores, doctors). More details
cancel_events 0: No measures - 1: Recommend cancelling - 2: Require cancelling. More details
gatherings_restrictions 0: No restrictions - 1: Restrictions on very large gatherings (the limit is above 1000 people) - 2: Restrictions on gatherings between 100-1000 people - 3: Restrictions on gatherings between 10-100 people - 4: Restrictions on gatherings of less than 10 people. More details
transport_closing 0: No measures - 1: Recommend closing (or significantly reduce volume/route/means of transport available) - 2: Require closing (or prohibit most citizens from using it). More details
stay_home_restrictions 0: No measures - 1: recommend not leaving house - 2: require not leaving house with exceptions for daily exercise, grocery shopping, and “essential” trips - 3: Require not leaving house with minimal exceptions (e.g. allowed to leave only once every few days, or only one person can leave at a time, etc.). More details
internal_movement_restrictions 0: No measures - 1: Recommend closing (or significantly reduce volume/route/means of transport) - 2: Require closing (or prohibit most people from using it). More details
international_movement_restrictions 0: No measures - 1: Screening - 2: Quarantine arrivals from high-risk regions - 3: Ban on high-risk regions - 4: Total border closure. More details
information_campaigns 0: No COVID-19 public information campaign - 1: public officials urging caution about COVID-19 - 2: coordinated public information campaign (e.g. across traditional and social media). More details
testing_policy 0: No testing policy - 1: Only those who both (a) have symptoms AND (b) meet specific criteria (e.g. key workers, admitted to hospital, came into contact with a known case, returned from overseas) - 2: testing of anyone showing COVID-19 symptoms - 3: open public testing (e.g. “drive through” testing available to asymptomatic people). More details
contact_tracing 0: No contact tracing - 1: Limited contact tracing, not done for all cases - 2: Comprehensive contact tracing, done for all cases. More details
stringency_index Stringency of governmental responses. More details

Task

Your task is to produce a model which fits the data and is able to investigate the effect of different policy measures to prevent the diffusion of the epidemics, including the vaccination campaign.

In order to work on the project, and depending on your background, you can use a deterministic mathematical model of epidemic response (SIR-like) or a stochastic model (agent-based models, random graphs, stochastic differential equations with Poissonian or Binomial noise, etc.).