Difference between revisions of "Simulation of pandemic spread"
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'''RO = 0.9 - 2.1''' <ref>EISENBERG, Joseph, 2020. R0: How scientists quantify the intensity of an outbreak like coronavirus and its pandemic potential: The pursuit: University of Michigan School of Public Health: Coronavirus: Pandemic. R0: How Scientists Quantify the Intensity of an Outbreak Like Coronavirus and Its Pandemic Potential | The Pursuit | University of Michigan School of Public Health | Coronavirus | Pandemic [online] [vid. 11. leden 2024]. Získáno z: https://sph.umich.edu/pursuit/2020posts/how-scientists-quantify-outbreaks.html</ref><br> | '''RO = 0.9 - 2.1''' <ref>EISENBERG, Joseph, 2020. R0: How scientists quantify the intensity of an outbreak like coronavirus and its pandemic potential: The pursuit: University of Michigan School of Public Health: Coronavirus: Pandemic. R0: How Scientists Quantify the Intensity of an Outbreak Like Coronavirus and Its Pandemic Potential | The Pursuit | University of Michigan School of Public Health | Coronavirus | Pandemic [online] [vid. 11. leden 2024]. Získáno z: https://sph.umich.edu/pursuit/2020posts/how-scientists-quantify-outbreaks.html</ref><br> | ||
'''Fatality rate = 0.000005'''<ref>Center for Disease Control and Prevention., 2023. Preliminary estimated influenza-related illnesses, medical visits, hospitalizations, and deaths in the United States – 2021-2022 influenza season. Centers for Disease Control and Prevention [online] [vid. 11. leden 2024]. Získáno z: https://www.cdc.gov/flu/about/burden/2021-2022.htm | '''Fatality rate = 0.000005'''<ref>Center for Disease Control and Prevention., 2023. Preliminary estimated influenza-related illnesses, medical visits, hospitalizations, and deaths in the United States – 2021-2022 influenza season. Centers for Disease Control and Prevention [online] [vid. 11. leden 2024]. Získáno z: https://www.cdc.gov/flu/about/burden/2021-2022.htm | ||
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===Measles=== | ===Measles=== | ||
Revision as of 14:51, 11 January 2024
Title: Simulation of pandemic spread
Author: Daniel Kopecký
Method: Agent-based model
Tool: NetLogo
Contents
Introduction and problem definition
Not so long ago we had the COVID-19 pandemic, which showed us the shortcomings in dealing with this type of problem. Pandemic propagation simulation can be a key tool to model, analyse and predict the evolution of a pandemic. This model deals specifically with viral diseases. The aim of this simulation is to be able to predict the spread of a pandemic virus, whereby using appropriate values, it can simulate the approximate development of a pandemic in the Czechia.
Method
An agent-based model in NetLogo is used to simulate the spread of the pandemic. This allows us to code our own scenario using different variables and helps us to get closer to the real pandemic evolution.
Model
The model contains a map of the Czechia, where the selected population is randomly scattered at the beginning of the simulation. The inhabitants can move freely within the entire rendered territory. The selected population is already infected and by running the simulation they can infect other uninfected citizens. The model offers to create a custom scenario where the user sets their own variable values. It is also possible to choose from preset viruses that set the variables for the user.
Environment
The model is reserved for the Czechia. This was achieved using a silhouette of the Czech Republic that was uploaded to NetLogo using this code: import-pcolors "cesko.png".
Agents
People are represented by agents who have three different colors. People who are healthy, but not immune, are green. These people can also be infected by infected individuals. The red colour is used for people who are infected. So these people can spread the virus, get better or die from the virus. Individuals who have had the virus and are now immune to the virus are blue.
Movement
People move randomly around the territory of the Czechia. During their movement they may meet other individuals from whom they could potentially be infected.
Spread of infection
The original infected individuals can infect any individual that comes into their vicinity. The variables qurantine.effort, trasmission.rate, and the number of infected around an uninfected individual affect whether an individual becomes infected. If an individual has already been infected but has recovered, this means that it has immunity and therefore cannot be re-infected. Immunity can be turned off when setting up the model.
Recovery from infection
Individuals who are infected have a chance to recover, which is set by the recovery.rate variable. If they recover and immunity is on, the individual's color will change to blue. If immunity is off, the color of the individual will change to green.
Death
If an individual is infected, there is a chance that they will die. This is affected by two variables, namely healthcare.capacity and infected-mortality. Healthcare.capacity is the spare capacity of healthcare facilities. The value ranges from 0 to 1 and represents the maximum % of the population that can be hospitalized at one time. If the % of infected in the population exceeds the capacity limit of the healthcare facilities, the probability of an individual dying rises.
End of the simulation
The simulation ends when the virus stops spreading and the number of infected drops to zero, or when the number of infected equals the population size, or when the virus kills off the entire population.
Predefined viruses
The model offers the possibility to choose a predefined virus or to choose a custom scenario where the user defines their own variable values. Once a virus is selected, its transmission rate and mortality rate are set.
Covid-19
Covid-19, caused by the novel coronavirus SARS-CoV-2, emerged in late 2019 and quickly developed into a global pandemic. It primarily spreads through respiratory droplets, causing a range of symptoms from mild respiratory issues to severe pneumonia, with a heightened risk for older adults and those with underlying health conditions.
R0 = 0.71 [1]
Fatality rate = 0.0004 [2]
Spanish Influenza
The Spanish Influenza, which occurred in 1918, was an exceptionally deadly H1N1 influenza A virus that caused a devastating pandemic.
R0 = 1.2 - 3.0 [3]
Fatality rate = 0.03 [4]
Seasonal Influenza
Seasonal Influenza, or the flu, is an annual respiratory illness caused by influenza viruses. It typically circulates during the colder months and can lead to fever, cough, and muscle aches.
RO = 0.9 - 2.1 [5]
Fatality rate = 0.000005[6]
Measles
Measles is a highly contagious viral infection, primarily affecting children, characterized by a distinctive rash and fever.
R0 = 12-18[7]
Fatality rate = 0.001[8]
SARS
SARS (Severe Acute Respiratory Syndrome) emerged in 2002, caused by a coronavirus (SARS-CoV), resulting in severe respiratory distress.
Ebola
Ebola, a severe and often fatal viral hemorrhagic fever, gained global attention during outbreaks in Africa. It causes internal bleeding and multiple organ failure, with a high mortality rate.
Variables
- init-population - Population at the beginning of the simulation
- init-infected - Number of infected at the start of the simulation
- recovery.rate - Rate of recovery of infected individuals
- init-immune - Number of immune individuals at the beginning of the simulation
- quarantine.effort - Quarantine effort (affects the chance of infecting an individual)
- transmission.rate - Rate of virus transmission between individuals
- infected-mortality - Virus mortality rate
- healthcare.capacity - Capacity of health facilities (affects the rate of death of individuals)
- immunity? - Turns immunity on or off
Output
Plots
Results
Conclusion
NetLogo File
Sources
- ↑ COVDATA. [online]. [citováno: 11.1.2023]. Dostupné z: https://www.covdata.cz/cesko.php
- ↑ Edouard Mathieu, Hannah Ritchie, Lucas Rodés-Guirao, Cameron Appel, Charlie Giattino, Joe Hasell, Bobbie Macdonald, Saloni Dattani, Diana Beltekian, Esteban Ortiz-Ospina and Max Roser (2020) - "Coronavirus Pandemic (COVID-19)". Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/coronavirus' [Online Resource]
- ↑ Emilia Vynnycky, Amy Trindall, Punam Mangtani, Estimates of the reproduction numbers of Spanish influenza using morbidity data, International Journal of Epidemiology, Volume 36, Issue 4, August 2007, Pages 881–889, https://doi.org/10.1093/ije/dym071
- ↑ Taubenberger, J. K., & Morens, D. M. (2006). 1918 Influenza: the Mother of All Pandemics. Emerging Infectious Diseases, 12(1), 15-22. https://doi.org/10.3201/eid1201.050979.
- ↑ EISENBERG, Joseph, 2020. R0: How scientists quantify the intensity of an outbreak like coronavirus and its pandemic potential: The pursuit: University of Michigan School of Public Health: Coronavirus: Pandemic. R0: How Scientists Quantify the Intensity of an Outbreak Like Coronavirus and Its Pandemic Potential | The Pursuit | University of Michigan School of Public Health | Coronavirus | Pandemic [online] [vid. 11. leden 2024]. Získáno z: https://sph.umich.edu/pursuit/2020posts/how-scientists-quantify-outbreaks.html
- ↑ Center for Disease Control and Prevention., 2023. Preliminary estimated influenza-related illnesses, medical visits, hospitalizations, and deaths in the United States – 2021-2022 influenza season. Centers for Disease Control and Prevention [online] [vid. 11. leden 2024]. Získáno z: https://www.cdc.gov/flu/about/burden/2021-2022.htm
- ↑ Guerra, F. M., Bolotin, S., Lim, G., Heffernan, J., Deeks, S. L., Li, Y., & Crowcroft, N. S. (2017). The basic reproduction number (R0) of measles: a systematic review. The Lancet. Infectious diseases, 17(12), e420–e428. https://doi.org/10.1016/S1473-3099(17)30307-9
- ↑ CENTERS FOR DISEASE CONTROL AND PREVENTION, 2019. Measles. Centers for Disease Control and Prevention [online] [vid. 11. leden 2024]. Získáno z: https://www.cdc.gov/globalhealth/newsroom/topics/measles/index.html
