How to improve outbreak response: a case study of integrated outbreak analytics from Ebola in Eastern Democratic Republic of the Congo.

The emerging field of outbreak analytics calls attention to the need for data from multiple sources to inform evidence-based decision making in managing infectious diseases outbreaks. To date, these approaches have not systematically integrated evidence from social and behavioural sciences. During the 2018-2020 Ebola outbreak in Eastern Democratic Republic of the Congo, an innovative solution to systematic and timely generation of integrated and actionable social science evidence emerged in the form of the Cellulle d'Analyse en Sciences Sociales (Social Sciences Analytics Cell) (CASS), a social science analytical cell. CASS worked closely with data scientists and epidemiologists operating under the Epidemiological Cell to produce integrated outbreak analytics (IOA), where quantitative epidemiological analyses were complemented by behavioural field studies and social science analyses to help better explain and understand drivers and barriers to outbreak dynamics. The primary activity of the CASS was to conduct operational social science analyses that were useful to decision makers. This included ensuring that research questions were relevant, driven by epidemiological data from the field, that research could be conducted rapidly (ie, often within days), that findings were regularly and systematically presented to partners and that recommendations were co-developed with response actors. The implementation of the recommendations based on CASS analytics was also monitored over time, to measure their impact on response operations. This practice paper presents the CASS logic model, developed through a field-based externally led consultation, and documents key factors contributing to the usefulness and adaption of CASS and IOA to guide replication for future outbreaks

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

Hypothesis‐free deep survival learning applied to the tumour microenvironment in gastric cancer

The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we propose new ways to predict risk for cancer-specific death from digital images of immunohistochemically (IHC) stained tissue microarrays (TMAs). Specifically, we evaluated a cohort of 248 gastric cancer patients using convolutional neural networks (CNNs) in an end-to-end weakly supervised scheme independent of subjective pathologist input. To account for the time-to-event characteristic of the outcome data, we developed new survival models to guide the network training. In addition to the standard H&amp;E staining, we investigated the prognostic value of a panel of immune cell markers (CD8, CD20, CD68) and a proliferation marker (Ki67). Our CNN-derived risk scores provided additional prognostic value when compared to the gold standard prognostic tool TNM stage. The CNN-derived risk scores were also shown to be superior when systematically compared to cell density measurements or a CNN score derived from binary 5-year survival classification, which ignores time-to-event. To better understand the underlying biological mechanisms, we qualitatively investigated risk heat maps for each marker which visualised the network output. We identified patterns of biological interest that were related to low risk of cancer-specific death such as the presence of B-cell predominated clusters and Ki67 positive sub-regions and showed that the corresponding risk scores had prognostic value in multivariate Cox regression analyses (Ki67&amp;CD20 risks: hazard ratio (HR) = 1.47, 95% confidence interval (CI) = 1.15-1.89,p= 0.002; CD20&amp;CD68 risks: HR = 1.33, 95% CI = 1.07-1.67,p= 0.009). Our study demonstrates the potential additional value that deep learning in combination with a panel of IHC markers can bring to the field of precision oncology.</p

Complex refractive indices and single scattering albedo of global dust aerosols in the shortwave spectrum and relationship to size and iron content

International audienceThe optical properties of airborne mineral dust depend on its mineralogy, size distribution, shape, and might vary between different source regions. To date, large differences in refractive index values found 29 in the literature have not been fully explained. In this paper we present a new dataset of complex re-30 fractive indices (m=n-ik) and single scattering albedos (SSA) for 19 mineral dust aerosols over the 370-31 950 nm range in dry conditions. Dust aerosols were generated from natural parent soils from eight 32 source regions (Northern Africa, Sahel, Middle East, Eastern Asia, North and South America, Southern 33 Africa, and Australia). These were selected to represent the global scale variability of the dust mineral-34 ogy. Dust was re-suspended into a 4.2 m 3 smog chamber where its spectral shortwave scattering (βsca) 35 and absorption (βabs) coefficients, number size distribution, and bulk composition were measured. The 36 complex refractive index was estimated by Mie calculations combining optical and size data, while the 37 spectral SSA was directly retrieved from βsca and βabs measurements. Our results show that the imagi-38 nary part of the refractive index (k) and the SSA largely vary from sample to sample, with values for k 39 in the range 0.001 to 0.009 at 370 nm and 0.0003 to 0.002 at 950 nm, and values for SSA in the range 40 0.70 to 0.96 at 370 nm and 0.95 to 0.99 at 950 nm. In contrast, the real part of the refractive index (n) 41 is mostly source (and wavelength) independent, with an average value between 1.48 and 1.55. The 42 sample-to-sample variability in our dataset of k and SSA is mostly related to differences in the dust's 43 iron content. In particular, a wavelength-dependent linear relationship is found between the magnitude 44 of k and SSA and the mass concentrations of both iron oxide and total elemental iron. As an intrinsic 45 Atmos. Chem. Phys. Discuss., https://doi

Laboratory estimate of the regional shortwave refractive index and single scattering albedo of mineral dust from major sources worldwide

International audienceMineral dust is one of the most abundant aerosol species in the atmosphere and strongly contributes to the global and regional direct radiative effect. Still large uncertainties persist on the magnitude and overall sign of the dust direct effect, where indeed one of the main unknowns is how much mineral dust absorbs light in the shortwave (SW) spectral range. Aerosol absorption is represented both by the imaginary part (k) of the complex refractive index or the single scattering albedo (SSA, i.e. the ratio of the scattering to extinction coefficient). In this study we present a new dataset of SW complex refractive indices and SSA for mineral dust aerosols obtained from in situ measurements in the 4.2 m3 CESAM simulation chamber at LISA (Laboratoire Interuniversitaire des Systemes Atmospheriques) in Créteil, France. Investigated dust aerosol samples were issued from major desert sources worldwide, including the African Sahara and Sahel, Eastern Asia, the Middle East, Southern Africa, Australia, and the Americas, with differing iron oxides content. Results from the present study provide a regional mapping of the SW absorption by dust and show that the imaginary part of the refractive index largely varies (by up to a factor 6, 0.003-0.02 at 370 nm and 0.001-0.003 at 950 nm) for the different source areas due to the change in the particle iron content. The SSA for dust varies between 0.75-0.90 at 370 nm and 0.95-0.99 at 950 nm, with the largest absorption observed for Sahelian and Australian dust aerosols. Our range of variability for k and SSA is well bracketed by already published literature estimates, but suggests that regional‒dependent values should be used in models. The possible relationship between k and the dust iron oxides and elemental iron content is investigated with the aim of providing a parameterization of the regional‒dependent dust absorption to include in climate models

Complex refractive indices and single scattering albedo of global dust aerosols in the shortwave spectrum and relationship to iron content and size

International audienceThe optical properties of airborne mineral dust depend on its mineralogy, size distribution, shape, and might vary between different source regions. To date, large differences in refractive index values found 29 in the literature have not been fully explained. In this paper we present a new dataset of complex re-30 fractive indices (m=n-ik) and single scattering albedos (SSA) for 19 mineral dust aerosols over the 370-31 950 nm range in dry conditions. Dust aerosols were generated from natural parent soils from eight 32 source regions (Northern Africa, Sahel, Middle East, Eastern Asia, North and South America, Southern 33 Africa, and Australia). These were selected to represent the global scale variability of the dust mineral-34 ogy. Dust was re-suspended into a 4.2 m 3 smog chamber where its spectral shortwave scattering (βsca) 35 and absorption (βabs) coefficients, number size distribution, and bulk composition were measured. The 36 complex refractive index was estimated by Mie calculations combining optical and size data, while the 37 spectral SSA was directly retrieved from βsca and βabs measurements. Our results show that the imagi-38 nary part of the refractive index (k) and the SSA largely vary from sample to sample, with values for k 39 in the range 0.001 to 0.009 at 370 nm and 0.0003 to 0.002 at 950 nm, and values for SSA in the range 40 0.70 to 0.96 at 370 nm and 0.95 to 0.99 at 950 nm. In contrast, the real part of the refractive index (n) 41 is mostly source (and wavelength) independent, with an average value between 1.48 and 1.55. The 42 sample-to-sample variability in our dataset of k and SSA is mostly related to differences in the dust's 43 iron content. In particular, a wavelength-dependent linear relationship is found between the magnitude 44 of k and SSA and the mass concentrations of both iron oxide and total elemental iron. As an intrinsic 45 Atmos. Chem. Phys. Discuss., https://doi