Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action

Abstract

The COVID-pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers' demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people's lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research.

Intergenerational contacts and Covid-19 spread: Omnipresent grannies or bowling together?

“…In these scenarios, the number of intergenerational ties does not seem to matter much, that is, only a few intergenerational ties are sufficient to rapidly generate high contamination rates among the elderly, and consequent high CFR levels. We therefore underline the call for more rigorous empirical research and cautiousness in interpretation others made before us (Belloc et al, 2020;Squazzoni et al 2020).…”

On the dynamics emerging from pandemics and infodemics

“…Currently, there is a multiplicity of (often draconic) measures taken by different governments which are decided upon under time pressure and under limited information: It is, therefore, likely that policy-makers are not fully aware of the impact of their decisions (Elsenbroich and Badham 2020). This might be explained by the fact that the models currently employed hardly consider the full range of social and behavioral complexity (Squazzoni et al 2020): They are well-suited for short-term policy-making which aims at reducing the speed at which the virus spreads. In order to provide proper policy advice for long-term decisions, however, extended models need to be developed in order to avoid poorly conceived policies which strike back through delayed behavioral effects.…”

Agent-based modeling for SARS-CoV-2 epidemic prediction and intervention assessment. A methodological appraisal

“…However, despite the promising record of using ABMs in effective epidemiological interventions, its use in informing proposed measures against the novel coronavirus epidemic has raised criticism. [9][10][11] Unfortunately for the assessment of healthcare interventions based on this type of epidemiological models, standard evidence hierarchies either exclude such studies all together or include theoretical or mechanistic inferences at the lowest level of the hierarchy. For example, the Oxford Centre for Evidence-Based Medicine lists mechanism-based reasoning at the lowest, fifth stage, 12 and National Institute for Health and Care Excellence(NICE) guidelines exclude both epidemiological mathematical models and mechanistic reasoning.…”