PRE2019 3 Group16: Difference between revisions
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== Objectives == | == Objectives == | ||
== Users == | == Users == | ||
== Description of the Operation (Approach) == | |||
== Drone (State of the Art) == | |||
== | == Mathematical Model == | ||
== Feasibility of the Operation == | |||
== Planning == | == Planning == | ||
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== Who will do what == | == Who will do what == | ||
Efe Utku - Work on wiki page, | 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 | Roel den Hoet - Research of algorithms, implementation and testing of algorithms | ||
Venislav Varbanov - Research, implementation, testing and description of algorithms | Venislav Varbanov - Research, implementation, testing and description of algorithms | ||
Revision as of 17:34, 5 March 2020
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 |
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’’).
…
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 significant impacts on the society; one of them is the economical results 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 also 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.
The core aim 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 duration 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, duration of the epidemic with the drone operations and other logistic costs.
Objectives
Users
Description of the Operation (Approach)
Drone (State of the Art)
Mathematical Model
Feasibility of the Operation
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
Deliverables
Network and mathematical model of the disease
Optimization of drone fleet and operation base location
Simulation of case example
Basic model for drone including component list, applicable models and cost list
Who will do what
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
Venislav Varbanov - Research, implementation, testing and description of algorithms