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Objectives To highlight the importance of molecular testing on characterizing extensively drug-resistant (XDR) Salmonella Typhi (S. Typhi) and link it to the current outbreak in Sindh, Pakistan. Methods We report three travel-related typhoid fever cases caused by XDR S. Typhi that presented between January 2019 and August 2019. Antimicrobial susceptibility and genotyping with pulse-field gel electrophoresis (PFGE) were performed. Whole genome sequencing (WGS) was performed to characterise the genomic clonality in relation to the emerging outbreak of S. Typhi in Sindh, Pakistan and to study the molecular resistance profiles. Results Laboratory testing revealed resistance to all first-line antibiotics (i.e ampicillin, chloramphenicol and trimethoprim-sulfamethoxazole), as well as to quinolones and third-generation cephalosporins, leading the patients’ therapy to be changed to use carbapenems. Classical MLST (cMLST) revealed that the strains were of sequence type 1 (ST1) and the core genome sequence (cgWGS) analysis closely clustered our strains with internationally reported strains from Pakistan, India, and the UK. The strains were found to carry a blaCTX-15 gene-harbouring IncY plasmid, which encodes resistance to ceftriaxone. Conclusions This report alerts clinicians to the use of appropriate empirical treatments in such scenarios and highlights the significance of the global spread of XDR Salmonella Typhi.

Groundwater level (GWL) simulations allow the generation of reconstructions for exploring the past temporal variability of groundwater resources or provide the means for generating projections under climate change on decadal scales. In this context, analyzing GWLs affected by low-frequency variations is crucial. In this study, we assess the capabilities of three deep learning (DL) models (long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (BiLSTM)) in simulating three types of GWLs affected by varying low-frequency behavior: inertial (dominated by low-frequency), annual (dominated by annual cyclicity) and mixed (in which both annual and low-frequency variations have high amplitude). We also tested if maximal overlap discrete wavelet transform pre-processing (MODWT) of input variables helps to better identify the frequency content most relevant for the models (MODWT-DL models). Only external variables (i.e., precipitation, air temperature as raw data, and effective precipitation (EP)) were used as input. Results indicate that for inertial-type GWLs, MODWT-DL models with raw data were notably more accurate than standalone models. However, DL models performed well for annual-type GWLs, while using EP as input, with MODWT-DL models exhibiting only minor improvements. Using raw data as input improved MODWT-DL models compared to standalone models; nevertheless, all models using EP performed better for annual-type GWLs. For mixed-type GWLs, while using EP as input, MODWT-DL models performed well, with substantial improvements over standalone models. Using raw data as input, improvement of MODWT-DL models is marginal compared to that of standalone models; nevertheless, they perform better than standalone models with EP. The Shapley Additive exPlanations (SHAP) approach used to interpret models highlighted that they preferentially learned from low-frequency in precipitation data to achieve the best simulations for inertial and mixed GWLs. This study showed that MODWT-based input pre-processing is highly suitable to better simulate low-frequency varying GWLs.

Following the detection of the first human case of avian influenza A subtype H9N2 in 1998, more than 40 cases were diagnosed worldwide. However, the spread of the virus has been more remarkable and significant in global poultry populations, causing notable economic losses despite its low pathogenicity. Many surveillance studies and activities conducted in several countries have shown the predominance of this virus subtype. We present the case of a 14-month-old female in Oman with an A(H9N2) virus infection. This is the first human case of A(H9N2) reported from Oman and the Gulf Cooperation Countries, and Oman is the second country outside of southern and eastern Asia to report a case (cases have also been detected in Egypt). The patient had bronchial asthma and presented with a high-grade temperature and symptoms of lower respiratory tract infection that necessitated admission to a high dependency unit in a tertiary care hospital. It is of urgency that a multisector One Health approach be established to combat the threat of avian influenza at the animal–human interface. In addition to enhancements of surveillance and control in poultry, there is a need to develop screening and preventive programs for high-risk occupations. Keywords: Avian influenza A, One Health, A(H9N2), Virus infection, Oman

Highlights • There is 15 % excess mortality in ‘all-cause mortality’ in Oman during the study period, mainly in the >60-year-old age group. • 66 % of the excess death during the study period was attributed to COVID-19. • Excess mortality was found in the respiratory, infectious and unclassified diseases. • This excess mortality re-enforces the importance of a holistic approach in managing the syndemic effects of COVID-19., Background Mortality surveillance provides a crucial method for monitoring disease activity. Coronavirus disease 2019 (COVID-19) can cause excess mortality both directly and indirectly by increasing deaths from other diseases. The aim of this study was to investigate the effects of COVID-19 on mortality in Oman. Methods A cross-sectional retrospective analysis of mortality data from 1 January 2015 to 16 August 2020 was undertaken. Baseline mortality estimated using the Farrington flexible model and excess mortality were calculated for the pandemic period (16 March–16 August 2020) according to cause of death, place of death and age group. Results During the pandemic period, there was a 15% [95% confidence interval (CI) 14–17] increase in all-cause mortality from baseline. When classifying by cause, there was a 9% (95% CI 5–12) increase in deaths due to respiratory diseases, a 2% (95% CI 1–4) increase in deaths due to infectious diseases and a 9% (95% CI 8–11) increase in unclassified deaths. In terms of place of death, 12% (95% CI 11–14) of excess mortality occurred in hospitals and 7% (95% CI 5–8) occurred in homes during the pandemic period. Patients aged >60 years recorded a 15% (95% CI 13–16) increase in all-cause mortality during this period. Conclusion The COVID-19 pandemic has resulted in a 15% increase in all-cause mortality in Oman, mainly as a result of deaths from COVID-19. However, unclassified deaths, deaths due to respiratory diseases and deaths due to infectious diseases have also increased, enforcing the need for a holistic approach and appropriate coordination of health services during such health crises.

Highlights • Rt can be used to closely monitor the non-pharmaceutical interventions (NPI) of COVID-19 epidemic. • Generally, the activity of the epidemic in Oman. • Other factors, like the return of overseas students, have increased the epidemic activity. • Responses to NPI is different between migrants and natives., Background COVID-19’s emergence carries with it many uncertainties and challenges, one of which is epidemic management strategies. Oman has implemented non-pharmaceutical interventions (NPIs) to mitigate the impact of COVID-19. However, responses to NPIs may be different across different populations in a country with a large number of migrants like Oman. This study investigates the different responses to NPIs assessing the use of time-varying reproduction number (Rt) to monitor it. Methods Polymerase chain reaction (PCR) laboratory-confirmed COVID-19 data from Oman was used from February 24th to June 3rd, 2020 and included demographic and epidemiological information. Data were arranged into pairs of infector–infectee, and two main libraries of R software were used to estimate reproductive number (Rt). Rt was calculated for both Omanis and non-Omanis. Findings A total of 13,538 cases were included, 44·9% of which were Omanis. Among all, we identified 2769 infector–infectee pairs to calculate Rt. There was a sharp drop in Rt from 3·7, (95% confidence interval [CI] 2·8-4·6) mid-March to 1·4 (95% CI 1·2–1·7) in late March in response to NPIs. Then Rt decreased to 1·2 (95% CI 1·1–1·3) late April when it rose, corresponding to the easing up of NPIs. Comparing the two groups, the response to major public health controls was more evident in Omanis in reducing the Rt to 1·09 (95% CI 0·84–1·3) at the end of March. Interpretation Use of real-time estimation of Rt has allowed us to follow the effect of NPIs. The migrant population responds differently than the Omani population.

In this manuscript, we discuss the capabilities of a deep learning algorithm implemented with the Conventional Neural Network concept to characterize the hydraulic properties of aquifers. The algorithm called CNN-HT is designed to predict the inverse operator of hydraulic tomography using a synthetic training dataset in which the hydraulic head data associated with pumping tests are linked to hydraulic transmissivity field. This approach relies on an adaptation of the SegNet network that was initially developed to process image segmentation. The SegNet is composed of encoders and decoders networks. In the encoder, sequential operations with multiple filters, as convolution, batch normalization, max-pooling are performed to identify feature maps of the input data. In the decoder, the up-sampling, convolution, batch normalization and regression operations are used to prepare the output by recovering the loss of spatial resolution that occurred in the encoder process. In this adaptation, we used the least-square iterative formulation at the initial iteration with Jacobian matrix to resize the hydraulic head data to match the size of the output (transmissivity field). This protocol was applied to the hydraulic head data computed numerically by solving the groundwater flow equation for a given transmissivity field, generated geostatistically with Gaussian and spherical variograms. A part of this data was used for training the network and the other part to test its performance. The test step confirmed the effectiveness of this tool in reconstructing the main heterogeneities of the hydraulic properties, and its effectiveness is related to the nature and quantity of the training data. Moreover, the CNN-HT method provided inversion results of the same quality than those obtained with the Gauss-Newton algorithm using the finite difference or adjoint state method in the computation of the Jacobian matrix. However, the computational time is longer in CNN-HT but this time can be less or of the same order as that of Gauss-Newton using finite difference method.

(IF 3.67; Q1); International audience; In this work, we present a novel method to interpret, at a field scale, the preferential flows generated by harmonic pumping tests, in which the pumped flowrate varies according to a sinusoidal function with a given period. The experimental protocol relies on the application of harmonic pumping tests in a karstic field located near to Montpellier (Southern France) at 4 different boreholes, each time with a shorter and a longer period, and the analysis of the hydraulic responses recorded at the 13 observation wells. A qualitative analysis of the oscillatory component in the hydraulic responses, in term of amplitude decay and phase lag, permitted to propose a preliminary model of degree of connectivity between the boreholes, through the network of conduits. Then, a quantitative interpretation of the harmonic responses was applied to image the spatial heterogeneity of the hydraulic properties (hydraulic conductivity and storage coefficient) by using a deterministic inverse algorithm called CADI. This algorithm is based on an equivalent porous medium concept and parameterized by a Cellular Automata approach in order to provide a realistic reconstruction of the karstic network. This algorithm is linked to the groundwater flow equation, reformulated in frequency domain, to simulate the amplitudes and phase shifts responses to the harmonic pumping tests. The inverse process was successfully applied on the dataset collected with both periods, in a separate and joint way. The results obtained allowed for a discussion on the efficiency of the harmonic pumping tomography for the characterization of the karstic structures.

Background: Immunological cross-reactivity between common cold coronaviruses (CCC) and SARS-CoV-2 might account for the reduced incidence of COVID-19 in children. Evidence to support speculation includes in vitro evidence for humoral and cellular cross-reactivity with SARS-CoV-2 in specimens obtained before the pandemic started. Method: We used retrospective health insurance enrollment records, claims, and laboratory results to assemble a cohort of 869,236 insured individuals who had a PCR test for SARS-CoV-2. We estimated the effects of having clinical encounters for various diagnostic categories in the year preceding the study period on the risk of a positive test result. Findings: After adjusting for age, gender and care seeking behavior, we identified that individuals with diagnoses for common cold symptoms, including acute sinusitis, bronchitis, or pharyngitis in the preceding year had a lower risk of testing positive for SARS-CoV-2 (OR=0.76, 95%CI=0.75, 0.77). No reduction in the odds of a positive test for SARS-CoV-2 was seen in individuals under 18 years. The reduction in odds in adults remained stable for four years but was strongest in those with recent common cold symptoms. Interpretation: While this study cannot attribute this association to cross-immunity resulting from a prior CCC infection, it is one potential explanation. Regardless of the cause, the reduction in the odds of being infected by SARS-CoV-2 among those with a recent diagnosis of common cold symptoms may have a role in shifting future COVD-19 infection patterns from endemic to episodic.

(IF 3.73; Q1); International audience; We apply a stochastic Newton (SN) approach to solve a high-dimensional hydraulic inverse problem in highly heterogeneous geological media. By recognizing the connection between the cost function of deterministic optimizations and the posterior probability density of stochastic inversions, the Markov chain Monte Carlo (MCMC) sampler of SN is constructed by two parts: a deterministic part, which corresponds to a Newton step of deterministic optimization, and a stochastic part, which is a Gaussian distribution with the inverse of the local Hessian as the covariance matrix. The hybrid inverse method exploits the efficient tools for fast solution of deterministic inversions to improve the efficiency of the MCMC sampler. To address the ill-posedness of the inverse problem, a priori models, generated by a transition-probability geostatistical method, and conditioned to inter-well connection data, are used as regularization constraints.The effectiveness of the stochastic Newton method is first demonstrated by a synthetic test. The transmissivity field of the synthetic model is highly heterogeneous, and includes sharp variations. The inverse approach was then applied to a field hydraulic tomography investigation in a fractured and karstified aquifer to reconstruct its transmissivity field from a collection of real hydraulic head measurements. From the inversions, a series of transmissivity fields that produce good correlations between the inverted and the measured hydraulic heads were obtained. The inverse approach produced slightly different a posteriori transmissivity patterns for different a priori structure models of transmissivity; however, the trend and location of the high-transmissivity channels are consistent among various realizations. In addition, the uncertainty associated with each realization of the inverted transmissivity fields was quantified.