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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.

Abstract. We investigate the validity of applying the "local cubic law" (LCL) to flow in a fracture bounded by impermeable rock surfaces. A two-dimensional order-of-magnitude analysis of the Navier-Stokes equations yields three conditions for the applicability of LCL flow, as a leading-order approximation in a local fracture segment with parallel or nonparallel walls. These conditions demonstrate that the "cubic law" aperture should not be measured on a point-by-point basis but rather as an average over a certain length. Extending to the third dimension, in addition to defining apertures over segment lengths, we find that the geometry of the contact regions influences flow paths more significantly than might be expected from consideration of only the nominal area fraction of these contacts. Moreover, this latter effect is enhanced by the presence of non-LCL regions around these contacts. While contact ratios of 0.1-0.2 are usually assumed to have a negligible effect, our calculations suggest that contact ratios as low as 0.03-0.05 can be significant. Analysis of computer-generated fractures with self-affine walls demonstrates a nonlinear increase in contact area and a faster-than-cubic decrease in the overall hydraulic conductivity, with decreasing fracture aperture; these results are in accordance with existing experimental data on flow in fractures. Finally, our analysis of fractures with selfaffine walls indicates that the aperture distribution is not lognormal or gamma as is frequently assumed but rather truncated-normal initially and increasingly skewed with fracture closure.

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.

Background:Cohort studies of the relationship between air pollution exposure and chronic health effects require predictions of exposure over long periods of time.Objectives:We developed a unified modeling approach for predicting fine particulate matter, nitrogen dioxide, oxides of nitrogen, and black carbon (as measured by light absorption coefficient) in six U.S. metropolitan regions from 1999 through early 2012 as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).Methods:We obtained monitoring data from regulatory networks and supplemented those data with study-specific measurements collected from MESA Air community locations and participants’ homes. In each region, we applied a spatiotemporal model that included a long-term spatial mean, time trends with spatially varying coefficients, and a spatiotemporal residual. The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression. We estimated time trends from observed time series and used spatial smoothing methods to borrow strength between observations.Results:Prediction accuracy was high for most models, with cross-validation R2 (R2CV) > 0.80 at regulatory and fixed sites for most regions and pollutants. At home sites, overall R2CV ranged from 0.45 to 0.92, and temporally adjusted R2CV ranged from 0.23 to 0.92.Conclusions:This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants. We have generated participant-specific predictions for MESA Air to investigate health effects of long-term air pollution exposures. These successes highlight modeling advances that can be adopted more widely in modern cohort studies.Citation:Keller JP, Olives C, Kim SY, Sheppard L, Sampson PD, Szpiro AA, Oron AP, Lindström J, Vedal S, Kaufman JD. 2015. A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution. Environ Health Perspect 123:301–309; http://dx.doi.org/10.1289/ehp.1408145

Familial Mediterranean fever (FMF) is an autosomal recessive disease characterised by recurrent attacks of inflammation of serosal membranes. Amyloidosis leading to renal failure is the most severe complication in untreated patients. In Israel FMF is most frequent among Jews of North African origin. Recently the causative gene (MEFV) has been found and the common mutations characterised. The aim of this study was to investigate the carrier rates of the common MEFV mutations among 400 healthy members of four different ethnic groups (100 in each group) in Israel, and to compare the distribution of the different mutations between FMF carriers and patients. We found a high frequency of carriers among Jews from the various ethnic groups. In North African Jews it was 22%, in Iraqi Jews 39%, in Ashkenazi Jews 21%, and in Iranian Jews 6%. The distribution of the four most common MEFV mutations among healthy individuals (M694V 29%, V726A 16%, M680I 2% and E148Q 53%) was significantly different (P < 0.003) from that found in patients (M694V 84.4%, V726A 9.0%, M680I 0% and E148Q 6.6%). Six healthy asymptomatic individuals were found to carry mutations in both alleles: two homozygotes for E148Q and four compound heterozygotes E148Q/other. These results demonstrate a very high carrier rate among all Jewish ethnic groups. They confirm that mutation E148Q is associated with a milder phenotype, which explains the lower prevalence of FMF among the Ashkenazi and Iraqi Jews. This study raises the question of the need for molecular screening for

Exposure to traffic-related air pollution is associated with risk of cardiovascular disease and mortality. We examined whether exposure to diesel exhaust increased blood pressure in human subjects. We analyzed data from 45 nonsmoking subjects, age 18–49 in double-blinded, crossover exposure studies, randomized to order. Each subject was exposed to diesel exhaust, maintained at 200 μg/m3 of fine particulate matter, and filtered air for 120 minutes on days separated by at least two weeks. We measured blood pressure pre-exposure, at 30-minute intervals during exposure, and 3, 5, 7 and 24 hours from exposure initiation, and analyzed changes from pre-exposure values. Compared with filtered air, systolic blood pressure increased at all points measured during and after diesel exhaust exposure; the mean effect peaked between 30 and 60 minutes after exposure initiation (3.8 mmHg [95% CI: −0.4, 8.0] and 5.1 mmHg [95% CI: 0.7, 9.5] respectively). Sex and metabolic syndrome did not modify this effect. Combining readings between 30 and 90 minutes, diesel exhaust exposure resulted in a 4.4 mmHg increase in systolic blood pressure, adjusted for participant characteristics and exposure perception (95% CI: 1.1, 7.7, p=0.0009). There was no significant effect on heart rate or diastolic pressure. Diesel exhaust inhalation was associated with a rapid, measurable increase in systolic, but not diastolic, blood pressure in young nonsmokers, independent of perception of exposure. This controlled trial in humans confirms findings from observational studies. The effect may be important on a population basis given the worldwide prevalence of exposure to traffic-related air pollution.

Background Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. Methods The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a “snapshot” sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R2 and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. Results UK models consistently performed as well as or better than the analogous LUR models. The best CV R2 values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R2 values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R2 values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. Conclusion High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK.