USING DYNAMIC MODELS AND EMPIRICAL COVID-19 DATA TO SHOWCASE EFFECTIVE PANDEMIC PREVENTION MEASURES
A summary of work completed by WPI and Financial University Students to inform methods for preventing virus spread.
An Interactive Qualifying Project
The purpose of an Interactive Qualifying Project (IQP) is to collaborate with students from different backgrounds and disciplines to solve problems that impact society.
Abstract
Pandemics, such as Covid-19, transcend borders and require adequate intervention measures from countries if they are to be contained. In collaboration with students and professors from the Financial University in Moscow, our team assessed six major countries’ strategies for mitigating virus spread and developed dynamic pandemic models using AnyLogic, a modeling application. The international research and interactive models informed recommendations to governments on the following effective response protocols: contact tracing, prompt widespread testing, and uniform quarantining.
Recommendations
Quarantine/Self-Isolation
Multiple countries have proven that an efficient way to limit the spread of Covid-19 is quarantine and self-isolation procedures. People should be encouraged by the authorities to remain at home.
China's strict quarantine policy was a major contribution in their success in stopping the spread of Covid-19
Testing and Tracing
Testing for Covid-19 and tracing the sources of the spread has proven to be efficient in preventing the development of the Covid-19 pandemic. Governments should conduct as many test as possible, and using the information obtained track and isolate those that spread the disease.
South Korea employed a Covid-19 testing campaign, which helped trace the sources of the spread and stop the pandemic.
Social Distancing
Social distancing reduces the probability of the virus spreading from person to person by encouraging them to keep a minimum distance and avoid staying in groups and public places.
Multiple countries that our project team analyzed encouraged or enforced some sort of social distancing protocols, which were helpful in stopping the spread.
Promptness of Response
The behavior of a spreading virus can be modeled with a complex exponential function. The key word is "exponential". To have the greatest effect, measures have to be taken as quickly as possible after the pandemic starts spreading. The longer preventive measure are being set up, the lesser effect they will have on the situation.
South Korea was very quick to react to the threat, and as result they hit the plateau before other countries.
Country Comparison
China
Population in Millions
Percentage of Population over 60 Years
Average Population Density (persons/km2)
Date of First Infection
(m/d/yr)
Tests per Million Individuals (as of 4/17)
Deaths per Million Population 80 Days After First Infection
Infections per Million Population 80 Days After First Infection
1,439
17.3
148.0
11/17/20
N/A
<1
19
Germany
Population in Millions
Percentage of Population over 60 Years
Average Population Density (persons/km2)
Date of First Infection
(m/d/yr)
Tests per Million Individuals (as of 4/17)
Deaths per Million Population 80 Days After First Infection
Infections per Million Population 80 Days After First Infection
84
28.0
227.0
1/28/20
20,629
51
1683
Italy
Population in Millions
Percentage of Population over 60 Years
Average Population Density (persons/km2)
Date of First Infection
(m/d/yr)
Tests per Million Individuals (as of 4/17)
Deaths per Million Population 80 Days After First Infection
Infections per Million Population 80 Days After First Infection
60
22.8
206.0
1/31/20
19,490
402
3020
Republic of Korea
Population in Millions
Percentage of Population over 60 Years
Average Population Density (persons/km2)
Date of First Infection
(m/d/yr)
Tests per Million Individuals (as of 4/17)
Deaths per Million Population 80 Days After First Infection
Infections per Million Population 80 Days After First Infection
52
19.4
503.0
1/20/20
10,659
4
400
Russia
Population in Millions
Percentage of Population over 60 Years
Average Population Density (persons/km2)
Date of First Infection
(m/d/yr)
Tests per Million Individuals (as of 4/17)
Deaths per Million Population 80 Days After First Infection
Infections per Million Population 80 Days After First Infection
146
21.7
8.4
1/31/20
11,773
3
323
USA
Population in Millions
Percentage of Population over 60 Years
Average Population Density (persons/km2)
Date of First Infection
(m/d/yr)
Tests per Million Individuals (as of 4/17)
Deaths per Million Population 80 Days After First Infection
Infections per Million Population 80 Days After First Infection
331
20.8
35.8
1/21/20
10,333
66
1,528
Our team used data and information from six countries in order to link the resulting statistics to the measures that each government had taken to confront Covid-19. Based on these correlations recommendations were made.
Contributors
Svetlana
Nikitina
WPI
Project Advisor
Moscow Project Center Director and
Associate Teaching Professor of English at WPI
Anton
Alekseevich
Losev
FINU
Project Advisor
Specialist in computational and imitation models and their application in economic and financial spheres
Dmitri
Igorevich
Korovin
FINU
Project Advisor
PhD in economics, specialist in imitation modeling and professor
Kade Woolverton
WPI
Project Team Memeber
Sophomore in Civil Engineering
Hobbies: skiing, cycling, music
Matthew Withington
WPI
Project Team Memeber
Junior in Aerospace Engineering
Hobbies: Oyster Farming, weightlifting and video editing
Ivan
Nikulin
WPI
Project Team Memeber
Junior in Aerospace Engineering
Hobbies: snowboarding, soccer and fishing
Anna
Kozhieva
FINU
Project Team Memeber
Junior in Economics
Former member of kids Olympic reserve curling team
Karina
Nurgalieva
FINU
Project Team Memeber
Junior in Economics
Hobbies: photography, languages and playing the piano
Astra
Nikitina
FINU
Project Team Memeber
Junior in Economics
Hobbies: writing stories, karaoke and psychology
Olga
Skiba
FINU
Project Team Memeber
Junior in Applied Mathematics and Information Technologies
Her third project with WPI