PRE2019 3 Group16
Group Members
| Name | Student Number | Study | |
|---|---|---|---|
| Efe Utku | 1284290 | Applied Physics | e.utku@student.tue.nl |
| Roel den Hoet | 1248170 | Computer Science | r.d.hoet@student.tue.nl |
| Venislav Varbanov | 1284401 | Computer Science | v.varbanov@student.tue.nl |
Problem Statement
Infectious disease outbreaks are a fundamental problem of humans. There are various settings, worldwide, that might lead to an epidemic or a pandemic. Although these outbreaks have various impacts on the society; one of them is the economical consequences of it. Here, we suggest a drone operation responsible for collecting and testing nasopharyngeal specimen from people living in preselected disease-prone regions and communities. By keeping a precise track of more people in less time compared to currently used strategies; we aim to decrease the effects of the outbreak on the community and to evaluate our results in terms of the economic impact of the strategy.
Subject
Epidemics are defined as local infectious disease outbreaks that occur in a community or region. These outbreaks have major impacts on the daily life of community members as economical, social and political issues. The economical problems are mostly due to measures taken to prevent the spreading of the disease: e.g. working, transportation and gatherings in public areas are halted. To minimize these impacts one must keep an up-to-date record of regions that are prone, people who might be infected and people who are more susceptible to infections; because in the bigger picture the main problem is to identify and track reported cases. Only this way, the spreading can be reduced and the distribution of new cases per day can be minimized.
An efficient way to do this is to detect the “local source” of an outbreak and reinvestigate the timeline of the spread. However, this approach has cost and logistic complications within, depending on the number of cases and days passed since the identification outbreak. So, we suggest an alternative way to keep an outbreak under control; which is to use an aerial drone-based operation for specimen collection and accurate case identification.
In core of the drone operations is to provide a faster logistic solution for case reporting; hence, providing a faster tactic to act and take precautions regarding the spread. This subject is going to be investigated in terms of its’ effects on different stakeholders and the its’ numerical impact on the way the disease spreads. The later, technical, part also consists of 2 components. First one is the mathematical model describing the population dynamics with and without the drone strategy, and the second one is an optimization problem to get a realistic point of view on the costs and possibilities of this strategy. Then, by combining these two technical components a feasibility study will be conducted to compare the total cost/ economical impact of the outbreak on the community and the total cost of the drone operations. The economic impact is going to be calculated based on GDP per capita per day and the progress of the epidemic without the drone operations. The cost of the drone operations is going to be calculated based on the cost of a single drone, number of drones operating, progress of the epidemic with the drone operations and other logistic costs.
Objectives
Deliverables
The list of the deliverables and their explanations are given below.
Mathematical Model and Simulation of Population Dynamics
An epidemiological compartmental model describing the population dynamics of a community. The model is given by a system of non-linear differential equations.
A MATLAB script for simulating mentioned mathematical model; which is used to investigate the impact of the drone operation on the spreading of the disease.
Optimization of drone fleet and operation base location
Feasibility Study of the Operation
Two cost analysis are performed for estimating the economical impact of an outbreak and the cost of drone operations; these studies are then combined to check the feasibility of the proposed approach.
User, Society and Enterprise
User
The users of our project are sick people, and people that are vulnerable to the disease. The users can now be diagnosed at home without having to travel to a hospital, which saves the sick people a trip to the hospital.
Society
For the society, keeping the possibly sick people inside will decrease the exposure of the healthy people to the virus, and hence this will decrease the spreading of the virus. More people in society will remain healthy, and the spreading of the virus can be stopped earlier thanks to the drones.
Enterprise
This invention will help drone manufacturing companies with new work, but it will also help the medical sector by providing more jobs on the operation bases for people to work on. There will also be an increased demand for nose swabs, as these are necessary for our product to work.
Description of the Operation
(This text can be divided to the sections below)
We operate from a drone base in a central location in the region. From this base, drones will fly towards people whose nose swabs we would like to collect. These people have been notified earlier by the use of a phone app. When the drone arrives, the person uses a nose swab to collect specimen, and gives this to the drone in a marked container. After the drone has collected all specimen on its route, it will return to the drone base. Here, all collected specimen will be tested. When the test is over, the results will be communicated to the user via the phone app.
Thru the phone app, the user will also receive a recommendation based on the results of the test. This recommendation will imply if (s)he should stay at home isolated/ should receive medical treatment. Also, at the end of specific time intervals users might also receive information regarding the isolated regions and districts close to them, which also counts as an intervention policy.
Pre-Test Phase
Selection of the Regions
Selection of People to be Tested
Test Phase
Approach and Return of Drones to Bases
How Tests are Conducted
Drone (State of the Art)
Population Dynamics
Mathematical Model
Results of the Simulation
Feasibility of the Operation
Economic Impact of an Outbreak
Cost of Drone Operations
Planning
Week 3: Make plan - research algorithm and model
Week 4: Research algorithm and drone - create model
Week 5: Implement algorithm - research drone
Week 6: Simulate algorithm - research drone
Week 7: Create presentation
Week 8: Give presentation
Milestones
Week 3: New subject chosen - plan made
Week 4: Research of algorithm done - model done
Week 5: Algorithm implemented and tested - drone research done
Week 6: Case example simulated - drone component list done
Week 7: Wiki finalized
Week 8: Presentation finalized
Task Division
Main
Efe Utku - Work on wiki page, Research on Mathematical Model/ Feasibility/ Drones, Population Dynamics Simulation
Roel den Hoet - Research of algorithms, implementation and testing of algorithms, work on wiki page
Venislav Varbanov - Research, implementation, testing and description of algorithms
Weekly Contribution
02- 08/03/2020
Efe:
-Written Problem Statement, Subject
-Updated the WikiPage Template
-Research on Epidemic Modeling and Adjusting the Model
-Worked on MATLAB Simulation for Pop. Dynamics
Roel:
- Researched algorithms
- Updated the wiki page on User, Society and Enterprise
Venislav:
09- 15/03/2020
Efe:
-Written Drone Ops. ,Pop. Dynamics Model
-Researched on test conducting, predictive methods for region/ people selection, existing datasets on epidemics
-Worked on MATLAB Simulation for Pop. Dynamics
Roel:
Venislav:
10- 16/03/2020
Algorithm notes (Venislav)
Input:
- social network: undirected graph, vertices represent people and have coordinates and condition, edges between people who often communicate
- number of drone bases, coordinates of each base, number of drones per base
- range, flight time(entire day?, or add recharge time), speed and capacity of drones
Output/score: total number of people that got sick and/or time until no more people could get sick
Two ways we can choose which people get sick:
1. People that got sick the previous day or earlier and are not yet diagnosed have a chance x of transmitting the disease to each neighbor. A person with n sick undiagnosed neighbors has chance of getting sick min(100%,n*x). (preferred by me)
2. Use provided formula to compute x - the increase of sick people in a day, and pick x random people with a sick undiagnosed neighbor (if there are enough such people) and make them sick.
Day 1: Initially some people are sick (P). They get some of their neighbors (N) sick (S) and their connections are removed. Each of N picks a time window from 08:00 to 00:00 the same day. The drones try to cover as many of N as possible (D).
Day 2: Until 08:00 results of D are ready and connections of identified sick people are removed. People of S-D get some of their neighbors sick (S’). We consider all neighbors (N’) of N-D. Each of N-D and N’ picks a time window from 08:00 to 00:00 the same day. The drones try to cover as many of N-D and N’ as possible (D’).
Day 3: Until 08:00 results of D’ are ready and connections of identified sick people are removed. People of S-D-D’ and S’-D’ get some of their neighbors sick (S’). People of S-D-D’ self-diagnose and remove their connections. Let M be the neighbors of S-D-D’. We consider all neighbors (N’’) of M-D’. Each of M-D’ and N’’ picks a time window from 08:00 to 00:00 the same day. The drones try to cover as many of M-D’ and N’’ as possible (D’’).
…