Trouver et expliquer des stratégies de dépistage optimales avec un nombre de tests limité pendant l'épidémie de COVID-19

International audienceThe COVID-19 epidemics has now lasted for 2 years. A vaccine has been found, but other complementary measures are still required, in particular testing, tracing contacts, isolating infected individuals, and respecting sanitary measures (physical distancing, masks). However these measures are not always well accepted and many fake news circulate about the virus or the vaccine. We believe that explaining the mechanisms behind the epidemics and the reasons for the sanitary measures is key to protect the general population from disinformation. To this end, we have developed a simple agent-based epidemic simulator that includes various screening strategies. We show that it can be used to compare the efficiency of various targeting strategies, starting date, and number of daily tests. We also ran an optimisation algorithm that proves that the best strategies consist in testing widely and early. Our simulator is already available to play online, to raise awareness in the general population

Trouver et expliquer des stratégies de dépistage optimales avec un nombre de tests limité pendant l'épidémie de COVID-19

International audienceThe COVID-19 epidemics has now lasted for 2 years. A vaccine has been found, but other complementary measures are still required, in particular testing, tracing contacts, isolating infected individuals, and respecting sanitary measures (physical distancing, masks). However these measures are not always well accepted and many fake news circulate about the virus or the vaccine. We believe that explaining the mechanisms behind the epidemics and the reasons for the sanitary measures is key to protect the general population from disinformation. To this end, we have developed a simple agent-based epidemic simulator that includes various screening strategies. We show that it can be used to compare the efficiency of various targeting strategies, starting date, and number of daily tests. We also ran an optimisation algorithm that proves that the best strategies consist in testing widely and early. Our simulator is already available to play online, to raise awareness in the general population

Agent-based epidemics simulation to compare and explain screening and vaccination prioritization strategies

International audienceThis paper describes agent-based models of epidemics dynamics, willingly simplified with the goal not to predict the evolution of the epidemics, but to explain the underlying mechanisms in an interactive way. They allow to compare screening prioritization strategies, and vaccination priority strategies, on a virtual population. The models are implemented in Netlogo in two simulators, published online at https://nausikaa.net/index.php/simulating-epidemics/ to let people experiment. This paper reports on model design, implementation, and experimentations. We have compared screening strategies to assess the epidemics versus control it by quarantining infectious people; and we have compared vaccinating older people with more risk factors, versus younger people with more social contacts

Sharing Knowledge When it Cannot be Made Explicit: The Case of Product Lifecycle Management Systems

International audienceInformation systems often strengthen a preference for working alone: interoperability as much as interpretation variance restrain the ability of people and systems to interact and to work together within an extended enterprise. In this article, the authors propose to extend product lifecycle management (PLM) systems in order to share not only (1) knowledge that has been made explicit and which is strongly contextualized so that there is no interpretation variance, but also (2) knowledge that cannot be made explicit and which remains tacit knowledge, needing social interaction and shared understanding to be actually shared. The use of a collaborative platform is proposed in this article in order to allow stakeholders to produce a shared understanding of what a concept means through the use of ontologies. The conditions as well as the limits of the proposition are discussed at the end of this article

Knowledge Sharing in Design Based on Product Lifecycle Management System

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An agent-based model simulation of influenza interactions at the host level:insight into the influenza-related burden of pneumococcal infections

International audienceBackground: Host-level influenza virus–respiratory pathogen interactions are often reported. Although the exact biological mechanisms involved remain unelucidated, secondary bacterial infections are known to account for a large part of the influenza-associated burden, during seasonal and pandemic outbreaks. Those interactions probably impact the microorganisms’ transmission dynamics and the influenza public health toll. Mathematical models have been widely used to examine influenza epidemics and the public health impact of control measures. However, most influenza models overlooked interaction phenomena and ignored other co-circulating pathogens.Methods: Herein, we describe a novel agent-based model (ABM) of influenza transmission during interaction with another respiratory pathogen. The interacting microorganism can persist in the population year round (endemic type, e.g. respiratory bacteria) or cause short-term annual outbreaks (epidemic type, e.g. winter respiratory viruses). The agent-based framework enables precise formalization of the pathogens’ natural histories and complex within-host phenomena. As a case study, this ABM is applied to the well-known influenza virus–pneumococcus interaction, for which several biological mechanisms have been proposed. Different mechanistic hypotheses of interaction are simulated and the resulting virus-induced pneumococcal infection (PI) burden is assessed.Results: This ABM generates realistic data for both pathogens in terms of weekly incidences of PI cases, carriage rates, epidemic size and epidemic timing. Notably, distinct interaction hypotheses resulted in different transmission patterns and led to wide variations of the associated PI burden. Interaction strength was also of paramount importance: when influenza increased pneumococcus acquisition, 4–27% of the PI burden during the influenza season was attributable to influenza depending on the interaction strength.Conclusions: This open-source ABM provides new opportunities to investigate influenza interactions from a theoretical point of view and could easily be extended to other pathogens. It provides a unique framework to generate in silico data for different scenarios and thereby test mechanistic hypotheses

Knowledge Sharing Within Extended Enterprises: Case of Product Lifecycle Management systems

International audienceWhen it is made explicit by someone, knowledge becomes information source of knowledge for someone else. Thus knowledge sharing cannot be reduced to information sharing. The aim of this paper is promote knowledge sharing, whether tacit or "explicited" by individuals within extended enterprises. Product Lifecycle Management (PLM) systems aim at an integrated management of all product‐related information and processes within extended enterprises throughout the entire lifecycle of a product. In this paper, we propose (1) to outline a semantic interoperability between a collaborative platform and a Product Lifecycle Management (PLM) system, and (2) to highlight the conditions under which a piece of information shared through a PLM system may lead to one and only one interpretation. Step (1) allows individuals to construct a shared understanding, supporting tacit knowledge sharing, whereas step (2) leads to ensure explicited knowledge sharing, i.e. knowledge that has been made explicit by someone within a certain context. PLM systems are strongly integrated within extended enterprises and their use will illustrate in this paper how our approach supports knowledge sharing. The conditions and limits of our approach, as well as its study within industrial fields, are discussed at the end of this paper

Unraveling the Seasonal Epidemiology of Pneumococcus

International audienceInfections caused by Streptococcus pneumoniae \textemdash including invasive pneumococcal diseases (IPDs)\textemdash remain a significant public health concern worldwide. The marked winter seasonality of IPDs is a striking, but still enigmatic aspect of pneumococcal epidemiology in nontropical climates. Here we confronted age-structured dynamic models of carriage transmission and disease with detailed IPD incidence data to test a range of hypotheses about the components and the mechanisms of pneumococcal seasonality. We find that seasonal variations in climate, influenza-like illnesses, and interindividual contacts jointly explain IPD seasonality. We show that both the carriage acquisition rate and the invasion rate vary seasonally, acting in concert to generate the marked seasonality typical of IPDs. We also find evidence that influenza-like illnesses increase the invasion rate in an age-specific manner, with a more pronounced effect in the elderly than in other demographics. Finally, we quantify the potential impact of seasonally timed interventions, a type of control measures that exploit pneumococcal seasonality to help reduce IPDs. Our findings shed light on the epidemiology of pneumococcus and may have notable implications for the control of pneumococcal infections

Unraveling the Seasonal Epidemiology of Pneumococcus

International audienceInfections caused by Streptococcus pneumoniae \textemdash including invasive pneumococcal diseases (IPDs)\textemdash remain a significant public health concern worldwide. The marked winter seasonality of IPDs is a striking, but still enigmatic aspect of pneumococcal epidemiology in nontropical climates. Here we confronted age-structured dynamic models of carriage transmission and disease with detailed IPD incidence data to test a range of hypotheses about the components and the mechanisms of pneumococcal seasonality. We find that seasonal variations in climate, influenza-like illnesses, and interindividual contacts jointly explain IPD seasonality. We show that both the carriage acquisition rate and the invasion rate vary seasonally, acting in concert to generate the marked seasonality typical of IPDs. We also find evidence that influenza-like illnesses increase the invasion rate in an age-specific manner, with a more pronounced effect in the elderly than in other demographics. Finally, we quantify the potential impact of seasonally timed interventions, a type of control measures that exploit pneumococcal seasonality to help reduce IPDs. Our findings shed light on the epidemiology of pneumococcus and may have notable implications for the control of pneumococcal infections

Additional file 3: of An agent-based model simulation of influenza interactions at the host level: insight into the influenza-related burden of pneumococcal infections

Weekly incidence of simulated cases per 100,000 for the three possible pathogens. (A–D) For influenza (red), the following parameter values were used for the simulations: transmission probability 3.3% per contact-day; 23% of the population initially immunized; 20% case-reporting probability; no interaction mechanism activated between influenza and the second pathogen. For PI cases (black), the following parameter values were used: carriage rate 22% per contact-day; pathogenicity probability 0.0042% per day; no immunity; 100% case-reporting probability; no interaction mechanism activated (A), acquisition-interaction strength 50 (B), transmission-interaction strength 50 (C), and pathogenicity-interaction strength 50 (D). (E) Pneumococcal carriage prevalence for the baseline scenario (orange), the acquisition-interaction strength 50 (green), the transmission-interaction strength 50 (blue), and the pathogenicity-interaction strength 50 (purple); (F–H) For influenza (red) and a second epidemic pathogen (black) cases, the following parameter values were used for the latter: transmission probability 2.8% per contact-day; 25% of the population initially immunized; 20% case-reporting probability; no interaction mechanism activated (F), acquisition-interaction strength 25 (G), and cross-immunity–interaction strength 0.8 (H). The represented data were chosen for five among the 1000 simulated years for each scenario for their explicit representation of each interaction-mechanism effect on infection dynamics. (PNG 424 kb