Month: January 2023

Mass testing to end the COVID-19 public health threat

Cecile Philippe, Yaneer Bar-Yam, Stephane Bilodeau. Carlos Gershenson, Sunil K.Raina, Shu-Ti Chiou, Gunhild A. Nyborg, Matthias F.Schneider

The Lancet Regional Health – Europe
Volume 25, February 2023, 100574

After a period where many countries have let the SARS-CoV-2 virus spread more or less freely, individuals and communities are now grappling with the many negative health effects and economic ramifications from high levels of illness over long periods. As evidence of the detrimental long-term effects of the virus mount, it is increasingly clear that the policy vacuum comes at an unacceptable price both in the short and long term; its only justification would be if there was no other alternative that did not come at an even greater cost. Entering the cold season, the number of infections will most likely increase significantly in Europe (≈ one – two order of magnitude in 2021). While the world awaits and hopes for new and more effective vaccines, we need tools in the toolbox that can effectively control transmission of rapidly spreading new variants, especially if more pathogenic. Otherwise, we may face significant disruptions and enormous costs due to repeated waves of illness, with each wave increasing the numbers of workers thrown out of the workforce from long term health effects. Lockdowns, due to their social restrictions and high short-term economic costs, are no longer the best available option. We here point out that mass testing (regular asymptomatic screening of the general population) is an alternative approach that can dramatically reduce cases and quickly restore economic and social activity.

Read the full article at: www.sciencedirect.com

PhD opportunity at Sorbonne University: Transfer learning to inform the spread of other respiratory viruses : Application to Influenza using COVID19 and drug sales

In high-income countries, the COVID-19 pandemic fostered the generation of surveillance data at spatial and temporal resolution unseen before, providing comprehensive and accurate estimates of cases, detection capability, hospitalizations and deaths. At the same time, data describing behavioral response, mobility, mixing and compliance to public health measures have also become available with similar level of detail. Such an exhaustive picture of the unfurling of a pandemic was a first in human history, made possible because we live in the digital age. It does not imply that epidemiological surveillance will remain this way in the future. As COVID-19 becomes less virulent with vaccination and acquired immunity, political pressure is shifting away from comprehensive detection of cases, and individual willingness to get tested may also be declining. At the same time, corporate commitment to make proprietary data on human behavior available to scientific research (e.g., mobile phone data) is waning. This underpins the main scientific goal of this project: can we use the experience of “wartime” COVID-19 surveillance during years2020-2022 to improve epidemic understanding in the future “peacetime” period ? Typical data available for surveillance in peacetime is scarcer, for example syndromic surveillance for influenza and other respiratory viruses as reported in networks of general practitioners (GP), with limited virological confirmation. Other data sources, including participatory surveillance and drug sales, may complement such reports, but are less specific. Importantly, during the first 2 years of COVID-19, the aforementioned high-resolution data and the scarcer traditional data sources were observed together. We wish to exploit this overlap to build statistical and mathematical models that will extract more and better information from peacetime surveillance data. Specifically, we aim at generating estimates of incidence, severe cases, reproductive number that are better than those previously available in terms of spatial resolution, temporal resolution, predictive power (ability to make short-term forecasts and mid-term projections of epidemic activity). We will make use of AI/ML techniques to come up with models with which transfer of knowledge, for example from the dynamics of COVID-19 to that of Influenza, or from drug sales data to influenza, from mobility to infectious spread will make it possible to improve accurate estimation of influenza incidence and short term prediction. The impact of this project will be thus twofold. First, we will improve the knowledge and predictability of seasonal epidemic waves of airborne, directly transmitted pathogens. Second, we will provide with policymakers with new tools to inform public health response to seasonal acute respiratory illness.

More at: soundai.sorbonne-universite.fr

Collective Intelligence: Foundations + Radical Ideas June 19-22, 2023, Santa Fe, NM, USA

What is the nature of intelligence in social insect societies, adaptive matter, groups of cells like brains, sports teams, and AI, and how does it arise in these seemingly different kinds of collectives?

The Symposium & Short Course will search for unifying principles in collective intelligence by tackling its foundations, and explore radical ideas for harnessing collective potential. The event will begin with a broad discussion of first-principles approaches from the physical and natural sciences for deriving group performance from microscopic, individual-level behavior and interactions. Participants will debate the most promising measures of intelligence across systems and consider the dynamics of collective intelligence in changing environments. Finally, we will explore radical ideas for harnessing collective intelligence in human and hybrid systems and invite scholars, artists, writers, musicians, actors, directors, dancers, and inventors, in addition to scientists, to participate in this discussion.

APPLY by February 1st, 2023 for priority review.

More at: www.santafe.edu