Abstract
Governments across the world are formulating and implementing medical, social, economic and other policies to manage the COVID-19 pandemic and protect their citizens. Many governments claim that their policies follow the best available scientific advice. Much of that advice comes from computational modeling. Two of the main types of model are presented: the SIR (Susceptible, Infected, Recovered) model developed by Kermack and McKendrick in the 1920s and the more recent Agent Based Models. The SIR model gives a good intuition of how epidemics spread; including how mass vaccination can contain them. It is less useful than Agent Based Modeling for investigating the effects of policies such as social distancing, self-isolation, wearing facemasks, and test-trace-isolate.
Politicians and the public have been perplexed to observe the lack of consensus in the scientific community and there being no single 'best science' to follow. The outcome of computational models depends on the assumptions made and the data used. Different assumptions will lead to different computational outcomes, especially when the available data are so poor. This leads some commentators to argue that the models are wrong and dangerous. Some may be, but computational modeling is one of the few ways available to explore and try to understand the space of possible futures. This lack of certainty means that computational modeling must be seen as just one of many inputs into the political decision making process. Politicians must balance all the competing inputs and make timely decisions based on their conclusions---be they right or wrong. In the same way that democracy is the least worst form of government, computational modeling may be the least worst way of trying to understand the future for policy making.
- Walker, P.G.T., Whittaker, C., Watson, O. et al. The global impact of COVID-19 and strategies for mitigation and suppression. Imperial College London (March 26, 2020). Google ScholarCross Ref
- WHO Director-General's opening remarks at the media briefing on COVID-19 -- 11 March 2020. World Health Organization, March 11, 2020.Google Scholar
- World Health Authority 'Coronavirus disease 2019 (COVID-19), Situation Report-73. World Health Organization. April 2, 2020.Google Scholar
- Horowitz, J. Italy's health care system groans under coronavirus a warning to the world. New York Times. March 12, 2020.Google Scholar
- House of Commons. Statement by The Secretary of State for Health and Social Care. Hansard, Volume 673. U.K. Parliament. March 11, 2020.Google Scholar
- Stone, J. Top scientists set up 'shadow' SAGE committee to advise government amid concerns over political interference The Independent Newspaper. May 3, 2020.Google Scholar
- BBC News. Coronavirus: U.K. firms slash more than 12,000 jobs in two days. July 1, 2020,Google Scholar
- BBC News. Coronavirus: U.K. failed to stockpile crucial PPE. April 28, 2020.Google Scholar
- Solé, R. et al. Phase transitions and complex systems. Complexity 1 (1996), 13--26.Google ScholarCross Ref
- San Miguel, M, Johnson, J. H., Kertesz, J., Kaski, K., Díaz-Guilera, A., MacKay, R. S., Loreto, V., Érdi, P., and Helbing, D. Challenges in complex systems science. The European Physical Journal Special Topics 214 (2012), 245--271.Google ScholarCross Ref
- Kermack, W. O. and McKendrick, A. G.A contribution to the mathematical theory of epidemics. Proc. R. Soc. A. 115, 772 (1927).Google Scholar
- Anderson, R. M. and May, R. M. Infectious Diseases Of Humans: Dynamics and control. Oxford University Press, 1992.Google Scholar
- Mahajan, V., Muller, E., and Bass, F. M. Diffusion of new products: empirical generalizations and managerial uses. Marketing Science 14, 3 (1995).Google ScholarDigital Library
- FutureLearn. Pandemics, Modelling, and Policy, Step 1.7. The Kermack-McKendrick SIR epidemic model. April 2020.Google Scholar
- Berenson, A. The failure of expert predictions and models. Hillsdale College. April 28, 2020Google Scholar
- Begly, S. Influential COVID-19 model uses flawed methods and shouldn't guide U.S. policies, critics say. STAT. April 17, 2020.Google Scholar
- Is England ending its lockdown too soon? The Economist. June 4, 2020.Google Scholar
- BBC News. COVID: U.K. seeing second wave, says Boris Johnson. September 18, 2020Google Scholar
- Gates, B. The next outbreak. We're not ready. TED Talk. March 15, 2015.Google Scholar
- Adam, D. The Limits of R. Nature 583, 7816 (2020), 346--348.Google ScholarCross Ref
Recommendations
COVID-19 on Facebook Ads: Competing Agendas around a Public Health Crisis
COMPASS '20: Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable SocietiesIn the age of social media, disasters and epidemics usher not only devastation and affliction in the physical world, but also prompt a deluge of information, opinions, prognoses and advice to billions of internet users. The coronavirus epidemic of 2019-...
Understanding and Analyzing COVID-19-related Online Hate Propagation Through Hateful Memes Shared on Twitter
ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningRecent studies regarding the COVID-19 pandemic have revealed the widespread propagation of hateful content during this period. While significant research has focused on COVID-19-related online hate in text (e.g., text-based tweets), the role of memes in ...
A novel machine learning-assisted policy recommendation method on COVID-19 vaccination campaign
DS-RT '21: Proceedings of the 2021 IEEE/ACM 25th International Symposium on Distributed Simulation and Real Time ApplicationsAs the most serious global infectious disease in the past 100 years, it has caused severe loss of life and property to countries and their people worldwide in the past year. As the most powerful tool in the fight against the epidemic, how to quickly ...
Comments