What is a model?
Importance of modeling
The pandemic
The importance of predictive modeling
Types of models
The results
References
Further reading
As some researchers commented, modeling the trajectory of the ongoing coronavirus disease 2019 (COVID-19) pandemic is like chasing a moving target.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly from Wuhan, China, where it was first detected, impacting every country. In March 2020, the World Health Organization (WHO) declared it a pandemic.
Since then, many research groups have predicted its course using modeling techniques.
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What is a model?
Modeling is a process, concept, or operation of a system, often mathematical, that investigates an idea using a representation of the thing examined to provide a view of its operation from one particular angle.
Importance of modeling
Models may be static or dynamic; the first describes the elements involved and their interactions, while the second describes how these elements act over time.
It is a process of iteration that uses mathematical, scientific, and technical knowledge to describe an area of interest, that is, a domain. It is not a reflection of reality but a way of thinking that enables a theory to be developed.
Modeling, therefore, includes setting up a plan, identifying the problem, selecting relevant parameters and values, and using computational or mathematical skills to describe the domain. The outcome may be a better understanding the scenario or building a new concept altogether.
Modeling helps confront the issues of building a system and finding a solution. It also allows people to communicate better about the idea being examined. Being an abstract concept, it avoids unessential details.
Models break up large and overwhelmingly complex tasks into smaller bits to achieve the final outcome. Therefore, They are a simplified version of reality, a salutary thing in that they help avoid unnecessary components and enhance planning efficiency.
However, accuracy must be balanced against simplicity so that the outcome can be approximated to a large extent, making it useful.
Models can be changed easily and at far less expense than operating systems. Any model must state the simplifying assumptions underlying it, with the consequences of these assumptions.
Epidemiological models are based on numerous modeling parameters defining disease spread and recovery from the infection, with other demographic characteristics of interest.
A new type of modeling emerged into the spotlight during the ongoing pandemic, dealing with large amounts of data run through statistics-based machine learning models to obtain accurate predictions of the trajectory of the COVID-19 pandemic.
The pandemic
During the current pandemic, scientists studied the microscopic lifecycle and behavior of SARS-CoV-2 and its effects on a seven-billion-strong global population.
Since these are both beyond the capacity of human sense organs to perceive, epidemiological modeling was used. This is quantitative large-scale data on how the virus spreads across communities, cities, and nations and how this is affected by human actions.
Early attempts to model the COVID-19 pandemic relied on limited data, leading to simple models using mathematical principles.
As more information came in, transmission rates, the basic and effective reproduction numbers, and public health interventions were all included to improve the visualization of viral spread routes and volumes.
The importance of predictive modeling
The most complex models aim to understand and predict what consequences could follow the introduction of one or more specific interventions. Such models emerged to predict the consequences of lockdowns, social distancing, and mask rules, as well as before and after vaccines were rolled out. Using simulation software, they helped represent how transmission occurred at the lowest level.
Information is key to a good model. Pandemic tracking from dozens of countries produces data used to create platforms for short-term predictions of the outbreak and long-term projections to help plan future preparedness and response strategies.
This could include predicting the short-term need for COVID-19 tests, hospital beds and intensive care unit (ICU) beds, ventilators, and hospital staff – physicians, nurses, and paramedical staff, for protective equipment and oxygen supplies.
This would help avoid crushing resource deficits in the face of an enormous demand, as happened in Italy early in the pandemic.
Another type of modeling uses the same data but couples it with public data on COVID-19 deaths, trying to see how infections during a given period will reflect on deaths a few weeks later, for instance. They then include the effect of interventions or increased mobility in the infected population.
Such predictions could help policymakers decide on interventions, considering their potential impact on public health and economic well-being. This could help safely phase out lockdown restrictions, introduce case detection methods, and implement locally relevant containment measures while keeping the transmission rate low.
Such decisions are essential in low-resource countries as the impact of lockdown on economic activity is much higher in such settings.
Finally, modeling helps understand and predict how disease severity varies among the infected population, how vaccination affects different subgroups according to demographic and other factors, as well as according to the number of doses and how new variants affect the pandemic trend.
What is Predictive Modeling and How Does it Work?
Types of models
Most infectious disease models use the SIR core or the expanded SEIR (referring to Susceptible, Exposed, Infected, and Recovered states) framework. This simple model treats the population in question as a mix of three (or four) groups: susceptible, infective, and removed.
Deterministic and stochastic models can be created using this platform for long- or short-term predictions.
Using the theoretical reproduction number calculated initially, this model tells scientists how the pandemic will likely progress using simple approximations of a complex process. It also shows the benefits that are probable from case isolation and quarantine.
The potential for isolation measures to separate infectives from susceptibles until they become removed is predicted. This is especially important early on when the number of infections appears small and manageable, ignoring the grim reality of exponential growth.
But this model can be tweaked, using added characteristics to define the people in these compartments further. For instance, it was realized early on that age was a significant risk factor for increased severity of illness and death in COVID-19. This is then reflected in the properties of the susceptible class.
Similarly, vaccinated and unvaccinated people had to be put in different compartments of such models to reflect how vaccine protection decreased over time and the benefits of vaccine boosters and sustained vaccine rollout in terms of containing the pandemic at manageable rates.
Other types of models include time series prediction models, grey models, and Markov chain models.
Some incorporate the emergence of variants of varying severity, their interaction with a partially immune population, the use of vaccines with varying efficacies, and the presence of vaccine hesitancy on a large scale.
The most specialized agent-based models use the very detailed characterization of individuals in each group, even their health profile, whether they live in households, their age, education and other sociodemographic parameters, occupation, number of people they interact with and in what capacity, their movements and social behavior.
Such models may begin to create more tailored predictions about the consequences of school or business closures or individual vs. household quarantine, for instance.
The limitations for all such models include the limitation of data collection, which multiple factors, including testing infrastructure, administrative differences and shortfalls, and differing guidelines at each step, have severely constrained.
The results
Modeling showed how many hitherto unrecognized factors contributed to viral transmission during this pandemic, including aerosol production, wind speed, and humidity, providing potentially modifiable risk factors.
Again, geographical modeling showed how virus path trajectories favored rapid and extensive transmission in densely populated, well-connected areas in contrast to islands and sparsely populated areas with very low traffic.
Thus, road, rail, ship, and air traffic were implicated in the initial and continuing rapid dissemination of the pandemic to frightening transmission levels.
With high infection and recovery rates, the effect of natural immunity was factored in. Combined with vaccine-induced immunity after the successful rollout of vaccines in 2021, this factor was further modified by the continuing emergence of concern variants and the immune response variability.
This model predicted a reduction in the prevalence of COVID-19 despite such escape variants while showing that hybrid immunity was likely to be more protective than natural immunity alone, both in the strength of protection and its breadth of coverage against variants.
Such research predicts endemic COVID-19, in other words. With the explosive spread of Omicron, however, other models point to the benefits of resumed mask use in avoiding millions of deaths globally.
This could also be achieved, though less remarkably, by increasing the production of antivirals capable of weakening this virus.
Overall, it appears that a longer period of coverage boosts the accuracy of the model predictions, while conversely, some predictions are not accurate or precise.
Similarly, the models become less accurate when the reproduction number changes drastically over time due to policy factors or other causes.
The several sources of uncertainty include variations in the parameters, data, and the public health interventions in force.
“This variability in outcomes highlights the challenges of modeling and forecasting the course of a pandemic during its early stages and with only limited data.”
This creates great challenges for those in charge of public health policy, which modeling seeks to mitigate with its available tools.
References
- What is modeling? Retrieved from https://www.open.edu/openlearn/science-maths-technology/computing-ict/models-and-modelling/content-section-2.1. Accessed on September 20, 2022.
- Modelling in science and technology. Retrieved from https://staff.fnwi.uva.nl/a.j.p.heck/Guide_on_modelling/Text/watismodelleren.html. Accessed on September 20, 2022.
- Zuo, Z. et al. (2021). Trajectory Simulation and Prediction of COVID-19 via Compound Natural Factor (CNF) Model in EDBF Algorithm. Earth’s Future. https://doi.org/10.1029/2020EF001936. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020EF001936
- Valjarevic, A. et al. (2020). Modelling and mapping of the COVID-19 trajectory and pandemic paths at global scale: A geographer's perspective. Open Geosciences. doi:10.1515/geo-2020-0156. https://ui.adsabs.harvard.edu/abs/2020OGeo...12..156V/abstract.
- Mullin, S. et al. (2022). Modeling pandemic to endemic patterns of SARS-CoV-2 transmission using parameters estimated from animal model data. PNAS. https://doi.org/10.1093/pnasnexus/pgac096. https://academic.oup.com/pnasnexus/article/1/3/pgac096/6625054
- Reiner, R. C. Jr. (2022). Forecasting the Trajectory of the COVID-19 Pandemic under Plausible Variant and Intervention Scenarios: A Global Modelling Study. SSRN. https://dx.doi.org/10.2139/ssrn.4126660. https://ssrn.com/abstract=4126660
- Al Dallal, A. et al. (2020). Trajectory analysis of the coronavirus pandemic and the impact of precautionary measures in the Kingdom of Bahrain. Arab Journal of Basic and Applied Sciences. https://doi.org/10.1080/25765299.2021.1886390. https://www.tandfonline.com/doi/full/10.1080/25765299.2021.1886390
- Czyzewski, A. Modelling an unprecedented pandemic: The vital role of team-based, collaborative epidemiology and disease modelling in managing pandemics. Available at https://www.imperial.ac.uk/stories/coronavirus-modelling/#Explained-Anatomy-of-a-model-VNpjbONLmY. Accessed on September 20, 2022.
- Gnanvi, J. E. et al. (2021). On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modelling techniques. Infectious Disease Modeling. https://doi.org/10.1016/j.idm.2020.12.008. https://www.sciencedirect.com/science/article/pii/S2468042720301123
- Zhao, H. et al. (2021). COVID-19: Short term prediction model using daily incidence data. PLOS ONE. https://doi.org/10.1371/journal.pone.0250110. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0250110
- Vakil, V. et al. (2022). Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19. International Journal of Environment Research and Public Health. https://doi.org/10.3390%2Fijerph19084541. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9032231/
Further reading
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