The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim , an open-source model developed to help address these questions. Covasim includes demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing, hygiene measures, and protective equipment; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine disease dynamics and policy options in Africa, Europe, Oceania, and North America.

Abstract:The chronic lung diseases, asthma and chronic obstructive pulmonary disease (COPD), are common affecting over 500 million people worldwide and causing substantial morbidity and mortality. Asthma is typically associated with Th2-mediated eosinophilic airway inflammation, in contrast to neutrophilic inflammation observed commonly in COPD. However, there is increasing evidence that the eosinophil might play an important role in 10-40% of patients with COPD. Consistently in both asthma and COPD a sputum eosinophilia is associated with a good response to corticosteroid therapy and tailored strategies aimed to normalize sputum eosinophils reduce exacerbation frequency and severity. Advances in our understanding of the multistep paradigm of eosinophil recruitment to the airway, and the consequence of eosinophilic inflammation, has led to the development of new therapies to target these molecular pathways. In this article we discuss the mechanisms of eosinophilic trafficking, the tools to assess eosinophilic airway inflammation in asthma and COPD during stable disease and exacerbations and review current and novel anti-eosinophilic treatments.

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BackgroundSputum and blood eosinophil counts predict corticosteroid effects in COPD patients. Bacterial infection causes increased airway neutrophilic inflammation. The relationship of eosinophil counts with airway bacterial load in COPD patients is uncertain. We tested the hypothesis that bacterial load and eosinophil counts are inversely related.MethodsCOPD patients were seen at stable state and exacerbation onset. Sputum was processed for quantitative polymerase chain reaction detection of the potentially pathogenic microorganisms (PPM) H. influenzae, M. catarrhalis and S. pneumoniae. PPM positive was defined as total load ≥1 × 104copies/ml. Sputum and whole blood were analysed for differential cell counts.ResultsAt baseline, bacterial counts were not related to blood eosinophils, but sputum eosinophil % was significantly lower in patients with PPM positive compared to PPM negative samples (medians: 0.5% vs. 1.25% respectively, p = 0.01). Patients with PPM positive samples during an exacerbation had significantly lower blood eosinophil counts at exacerbation compared to baseline (medians: 0.17 × 109/L vs. 0.23 × 109/L respectively, p = 0.008), while no blood eosinophil change was observed with PPM negative samples.ConclusionsThese findings indicate an inverse relationship between bacterial infection and eosinophil counts. Bacterial infection may influence corticosteroid responsiveness by altering the profile of neutrophilic and eosinophilic inflammation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12931-017-0570-5) contains supplementary material, which is available to authorized users.

Insulin signaling in the central nervous system (CNS) regulates energy balance and peripheral glucose homeostasis. Rictor is a key regulatory/structural subunit of the mTORC2 complex and is required for hydrophobic motif site phosphorylation of Akt at serine 473. To examine the contribution of neuronal Rictor/mTORC2 signaling to CNS regulation of energy and glucose homeostasis, we utilized Cre-LoxP technology to generate mice lacking Rictor in all neurons, or in either POMC or AgRP expressing neurons. Rictor deletion in all neurons led to increased fat mass and adiposity, glucose intolerance and behavioral leptin resistance. Disrupting Rictor in POMC neurons also caused obesity and hyperphagia, fasting hyperglycemia and pronounced glucose intolerance. AgRP neuron specific deletion did not impact energy balance but led to mild glucose intolerance. Collectively, we show that Rictor/mTORC2 signaling, especially in POMC-expressing neurons, is important for central regulation of energy and glucose homeostasis.

Insulin represses gluconeogenesis, in part, by inhibiting the transcription of genes that encode rate-determining enzymes, such as phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (G-6-Pase). Glucocorticoids stimulate expression of the PEPCK gene but the repressive action of insulin is dominant. Here, we show that treatment of H4IIE hepatoma cells with the synthetic glucocorticoid, dexamethasone (dex), induces the accumulation of glucocorticoid receptor, as well as many transcription factors, coregulators, and RNA polymerase II, on the PEPCK gene promoter. The addition of insulin to dex-treated cells causes the rapid dissociation of glucocorticoid receptor, polymerase II, and several key transcriptional regulators from the PEPCK gene promoter. These changes are temporally related to the reduced rate of PEPCK gene transcription. A similar disruption of the G-6-Pase gene transcription complex was observed. Additionally, insulin causes the rapid demethylation of arginine-17 on histone H3 of both genes. This rapid, insulin-induced, histone demethylation is temporally related to the disruption of the PEPCK and G-6-Pase gene transcription complex, and may be causally related to the mechanism by which insulin represses transcription of these genes.

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