(342aw) Multimodal Integrated Modelling for COVID-19 Health Risk Management | AIChE

(342aw) Multimodal Integrated Modelling for COVID-19 Health Risk Management


Sarigiannis, D. - Presenter, Aristotle University of Thessaloniki
Petridis, I., Aristotle University of Thessaloniki
Karakoltzidis, A., Aristotle University of Thessaloniki
Karakitsios, S., Aristotle University of Thessaloniki
Through the one-year experience of the pandemic, it has been evident that managing efficiently the public health risk of COVID-19 requires not only the ability to assess the extent of the contagion spread in the community, but to be able to predict the spread and the consequent pressure on the health services, and at the same time respect the social and financial viability kernel of the community and the local economy. Although strict and long-term lockdown is the most efficient practice for minimizing the contagion spread, the greatest challenge for the successful management of such a long-term sanitary crisis is the effective application of targeted interventions and closing of specific activities that are considered as the ones that contribute mostly to widespread contagion. On the other hand, it is important to be able to quantify the effect of the available artillery for combating the pandemic, including both vaccination, as well as non-pharmacological interventions, such as increased number of testing/rapid tests, indoor air purifiers, targeted containment measures and closing of specific activities, as well as the effect of seasonality on climate and transmission dynamics. In addition, the transmission dynamics of new mutants and the way they affect the contagion spread dynamics, or eventually the immunity have to be accounted as well. Accounting for all of the above, and aiming at the efficient management of the COVID-19 pandemic, we have developed a multi-modal computational tool for the evaluation of the public health risk from the COVID-19 epidemic in Greece, Italy and USA and we have evaluated the effectiveness of different non-pharmacological intervention scenarios for public health risk management. The computational tool for public health risk management from COVID-19 is called CORE: COVID Risk Evaluation model. It includes an advanced model of the spread of the epidemic, which is an evolution of the most advanced SEIR models available (Blackwood and Childs, 2018), also taking into account the implementation dynamics of non-pharmacological interventions such as virus detection testing geared towards the general population or targeted sub-population groups, circulation restriction and population containment measures (Maier and Brockmann, 2020), in the evolution of the dispersion (described as compartment X) and the final health risk assessment of the affected population. The SEIR-X dispersion model, which is the dispersion computation engine, has been extended to a multi-state population model to describe in detail the different possible states of the population based on the typology and severity of the symptoms of the disease (SEIR-X multi-state model, or SEIR-Xms). These estimates are then used as input to an already validated multi-stage SEIR-X model (CORE), which supports the management of the COVID-19 health risk by capturing reliably its dynamics, including the impact of risk reduction measures. The integrated model is already part of the OECD COVID-19 computational toolbox.

A key component for the identification of the effect of the targeted containment measures is the ability to properly account for the effect various activities may have on the effective contact among various population groups, accounting for their sociodemographic progfiles (i.e. age, occupation etc). Towards this aim, contact rates for the different population groups within the CORE model, are estimated using Agent-Based Modelling (ABM). ABM is a simulation technique that allows to explore and understand phenomena, where independent entities interact together, forming emerging behavior. While the direct representation of individuals’ actions is organisationally difficult, captures this process by managing information at the level of the autonomous decision-makers, called “agents” (Sarigiannis et al., 2018). The agents are software objects that have internal states and are programmed to react and act in their environment while following a set of behavioural rules. By simulating actions and interactions at the individual level, the diversity that exists among agents can be detected, as rise is given to the behaviour of the system as a whole (Macal and North, 2005, De Marchi and Page, 2014). The use of ABM modelling, allowed us to deliver estimates of the effective contact rate of the general population, as well as of the various educational grades, as well as several daily activities such as supermarkets, bars and restaurants, public transportation, health services, retails and large malls. Thus, by describing dynamically the interactions of the key components of the contact matrix, we were able to capture the non-linearities associated with changes in closing or opening of specific activities, e.g. the effect of opening the retail vs opening secondary education. All the above, allowed us to be able to evaluate various scenarios of containment and closing or opening of activities, supporting the efficient planning for combating COVID-19 spread dynamics. It is important to highlight, that CORE predictions accounted for all the dynamically changing external conditions, including the ones that go beyond human behaviour, such as climate and the presence of mutants with increased transmissibility in the community, as well as measures that effectively contribute to contagion control such as the opportunities provided by rapid tests and self-testing, the continuously increased rate of vaccination and the use of disinfection devices in public places and transportation. Another very interesting scenario that has been analysed by the CORE model for ensuring the financial sustainability and at the same time controlling the pandemic, was the management of vaccination prioritization, to occupants that highly contribute to contacts within the contact matrix. At the same time, beyond the successful analysis of containment scenarios, we were able to predict in long term the spread dynamics and the respective burden in the health services at specific milestones, such as the impact of touristic flows in the summer and the development of the second wave in early August 2020, the effect of the new mutations (of higher transmissibility) in the third wave in 2021, and the projections for the forthcoming summer.

Our most recent scenario analysis results demonstrate that the contribution of self-tests made frequently (once a week for the general population and twice a week for targeted sub-populations at higher risk of contagion) to reducing the spread of the pandemic is crucial. However, although the simulations have shown that that a large number of self-tests frequently made is capable of helping to control the pandemic, opening specific activities has to be allowed progressively. It has also been shown that at the current situation, where only 10% of the population has acquired artificial immunity through vaccination, self-tests are the most precious ally in combating this pandemic, while after 3 months, the combination of artificial immunity, with the summer climate conditions that do not favour transmission will help in pandemic control. The long-term prediction of the COVID-19 dynamics in Greece till the end of June, accounting for the currently proposed containment and openings scenario, vaccination and self-testing rate is illustrated below.