Exploring factors shaping antibiotic resistance patterns in Streptococcus pneumoniae during the 2020 COVID-19 pandemic.

Non-pharmaceutical interventions implemented to block SARS-CoV-2 transmission in early 2020 led to global reductions in the incidence of invasive pneumococcal disease (IPD). By contrast, most European countries reported an increase in antibiotic resistance among invasive Streptococcus pneumoniae isolates from 2019 to 2020, while an increasing number of studies reported stable pneumococcal carriage prevalence over the same period. To disentangle the impacts of the COVID-19 pandemic on pneumococcal epidemiology in the community setting, we propose a mathematical model formalizing simultaneous transmission of SARS-CoV-2 and antibiotic-sensitive and -resistant strains of S. pneumoniae. To test hypotheses underlying these trends five mechanisms were built into the model and examined: (1) a population-wide reduction of antibiotic prescriptions in the community, (2) lockdown effect on pneumococcal transmission, (3) a reduced risk of developing an IPD due to the absence of common respiratory viruses, (4) community azithromycin use in COVID-19 infected individuals, (5) and a longer carriage duration of antibiotic-resistant pneumococcal strains. Among 31 possible pandemic scenarios involving mechanisms individually or in combination, model simulations surprisingly identified only two scenarios that reproduced the reported trends in the general population. They included factors (1), (3), and (4). These scenarios replicated a nearly 50% reduction in annual IPD, and an increase in antibiotic resistance from 20% to 22%, all while maintaining a relatively stable pneumococcal carriage. Exploring further, higher SARS-CoV-2 R0 values and synergistic within-host virus-bacteria interaction mechanisms could have additionally contributed to the observed antibiotic resistance increase. Our work demonstrates the utility of the mathematical modeling approach in unraveling the complex effects of the COVID-19 pandemic responses on AMR dynamics.

Modeling the impact of urban and hospital eco-exposomes on antibiotic-resistance dynamics in wastewaters.

The emergence and selection of antibiotic resistance is a major public health problem worldwide. The presence of antibiotic-resistant bacteria (ARBs) in natural and anthropogenic environments threatens the sustainability of efforts to reduce resistance in human and animal populations. Here, we use mathematical modeling of the selective effect of antibiotics and contaminants on the dynamics of bacterial resistance in water to analyze longitudinal spatio-temporal data collected in hospital and urban wastewater between 2012 and 2015. Samples were collected monthly during the study period at four different sites in Haute-Savoie, France: hospital and urban wastewater, before and after water treatment plants. Three different categories of exposure variables were collected simultaneously: 1) heavy metals, 2) antibiotics and 3) surfactants for a total of 13 drugs/molecules; in parallel to the normalized abundance of 88 individual genes and mobile genetic elements, mostly conferring resistance to antibiotics. A simple hypothesis-driven model describing weekly antibiotic resistance gene (ARG) dynamics was proposed to fit the available data, assuming that normalized gene abundance is proportional to antibiotic resistant bacteria (ARB) populations in water. The detected compounds were found to influence the dynamics of 17 genes found at multiple sites. While mercury and vancomycin were associated with increased ARG and affected the dynamics of 10 and 12 identified genes respectively, surfactants antagonistically affected the dynamics of three genes. The models proposed here make it possible to analyze the relationship between the persistence of resistance genes in the aquatic environment and specific compounds associated with human activities from longitudinal data. Our analysis of French data over 2012-2015 identified mercury and vancomycin as co-selectors for some ARGs.

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report.

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

Assessing respiratory epidemic potential in French hospitals through collection of close contact data (April-June 2020).

The transmission risk of SARS-CoV-2 within hospitals can exceed that in the general community because of more frequent close proximity interactions (CPIs). However, epidemic risk across wards is still poorly described. We measured CPIs directly using wearable sensors given to all present in a clinical ward over a 36-h period, across 15 wards in three hospitals in April-June 2020. Data were collected from 2114 participants and combined with a simple transmission model describing the arrival of a single index case to the ward to estimate the risk of an outbreak. Estimated epidemic risk ranged four-fold, from 0.12 secondary infections per day in an adult emergency to 0.49 per day in general paediatrics. The risk presented by an index case in a patient varied 20-fold across wards. Using simulation, we assessed the potential impact on outbreak risk of targeting the most connected individuals for prevention. We found that targeting those with the highest cumulative contact hours was most impactful (20% reduction for 5% of the population targeted), and on average resources were better spent targeting patients. This study reveals patterns of interactions between individuals in hospital during a pandemic and opens new routes for research into airborne nosocomial risk.

Risk Factors for Community and Intrahousehold Transmission of SARS-CoV-2: Modeling in a Nationwide French Population-Based Cohort Study, the EpiCoV Study.

We assessed the risk of acquiring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from household and community exposure according to age, family ties, and socioeconomic and living conditions using serological data from a nationwide French population-based cohort study, the Epidémiologie et Conditions de Vie (EpiCoV) Study. A history of SARS-CoV-2 infection was defined by a positive anti-SARS-CoV-2 enzyme-linked immunosorbent assay immunoglobulin G result in November-December 2020. We applied stochastic chain binomial models fitted to the final distribution of household infections to data from 17,983 individuals aged ≥6 years from 8,165 households. Models estimated the competing risks of being infected from community and household exposure. The age group 18-24 years had the highest risk of extrahousehold infection (8.9%, 95% credible interval (CrI): 7.5, 10.4), whereas the oldest (≥75 years) and youngest (6-10 years) age groups had the lowest risk, at 2.6% (95% CrI: 1.8, 3.5) and 3.4% (95% CrI: 1.9, 5.2), respectively. Extrahousehold infection was also associated with socioeconomic conditions. Within households, the probability of person-to-person transmission increased with age, from 10.6% (95% CrI: 5.0, 17.9) among children aged 6-10 years to 43.1% (95% CrI: 32.6, 53.2) among adults aged 65-74 years. Transmission was higher between partners (29.9%, 95% CrI: 25.6, 34.3) and from mother to child (29.1%, 95% CrI: 21.4, 37.3) than between individuals related by other family ties. In 2020 in France, the main factors identified for extrahousehold SARS-CoV-2 infection were age and socioeconomic conditions. Intrahousehold infection mainly depended on age and family ties.

Determinants of worldwide antibiotic resistance dynamics across drug-bacterium pairs: a multivariable spatial-temporal analysis using ATLAS.

BACKGROUND: Antibiotic resistance (ABR) is a major concern for global health. However, factors driving its emergence and dissemination are not fully understood. Identification of such factors is crucial to explain heterogeneity in ABR rates observed across space, time, and species and antibiotics. METHODS: We analysed count data of clinical isolates from 51 countries over 2006-19 for thirteen drug-bacterium pairs taken from the ATLAS database. We characterised ABR spatial and temporal patterns and used a mixed-effect negative binomial model, accounting for country-year dependences with random effects, to investigate associations with potential drivers, including antibiotic sales, economic and health indicators, meteorological data, population density, and tourism. FINDINGS: ABR patterns were strongly country and drug-bacterium pair dependent. In 2019, median ABR rates ranged from 6·3% (IQR 19·7% [0·5-20·2]) for carbapenem-resistant Klebsiella pneumoniae to 80·7% (41·8% [50·4-92·2]) for fluoroquinolone-resistant Acinetobacter baumannii, with heterogeneity across countries. From 2006 to 2019, carbapenem resistance increased in more than 60% of investigated countries; no global trend was observed for other resistances. Multivariable analyses identified significant associations of ABR with country-level selecting antibiotic sales, but only in fluoroquinolone-resistant-Escherichia coli, fluoroquinolone-resistant-Pseudomonas aeruginosa, and carbapenem-resistant-A baumannii. We also found a correlation between temperature and resistance in Enterobacteriaceae and with the health system quality for all drug-bacterium pairs except Enterococci and Streptococcus pneumoniae pairs. Despite wide consideration of possible explanatory variables, drug-bacterium pair ABR rates still showed unexplained spatial random effects variance. INTERPRETATION: Our findings reflect the diversity of mechanisms driving global antibiotic resistance across pathogens and stress the need for tailored interventions to tackle bacterial resistance. FUNDING: Independent research Pfizer Global Medical Grant and ANR Labex IBEID.

Collateral impacts of pandemic COVID-19 drive the nosocomial spread of antibiotic resistance: A modelling study.

BACKGROUND: Circulation of multidrug-resistant bacteria (MRB) in healthcare facilities is a major public health problem. These settings have been greatly impacted by the Coronavirus Disease 2019 (COVID-19) pandemic, notably due to surges in COVID-19 caseloads and the implementation of infection control measures. We sought to evaluate how such collateral impacts of COVID-19 impacted the nosocomial spread of MRB in an early pandemic context. METHODS AND FINDINGS: We developed a mathematical model in which Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and MRB cocirculate among patients and staff in a theoretical hospital population. Responses to COVID-19 were captured mechanistically via a range of parameters that reflect impacts of SARS-CoV-2 outbreaks on factors relevant for pathogen transmission. COVID-19 responses include both "policy responses" willingly enacted to limit SARS-CoV-2 transmission (e.g., universal masking, patient lockdown, and reinforced hand hygiene) and "caseload responses" unwillingly resulting from surges in COVID-19 caseloads (e.g., abandonment of antibiotic stewardship, disorganization of infection control programmes, and extended length of stay for COVID-19 patients). We conducted 2 main sets of model simulations, in which we quantified impacts of SARS-CoV-2 outbreaks on MRB colonization incidence and antibiotic resistance rates (the share of colonization due to antibiotic-resistant versus antibiotic-sensitive strains). The first set of simulations represents diverse MRB and nosocomial environments, accounting for high levels of heterogeneity across bacterial parameters (e.g., rates of transmission, antibiotic sensitivity, and colonization prevalence among newly admitted patients) and hospital parameters (e.g., rates of interindividual contact, antibiotic exposure, and patient admission/discharge). On average, COVID-19 control policies coincided with MRB prevention, including 28.2% [95% uncertainty interval: 2.5%, 60.2%] fewer incident cases of patient MRB colonization. Conversely, surges in COVID-19 caseloads favoured MRB transmission, resulting in a 13.8% [-3.5%, 77.0%] increase in colonization incidence and a 10.4% [0.2%, 46.9%] increase in antibiotic resistance rates in the absence of concomitant COVID-19 control policies. When COVID-19 policy responses and caseload responses were combined, MRB colonization incidence decreased by 24.2% [-7.8%, 59.3%], while resistance rates increased by 2.9% [-5.4%, 23.2%]. Impacts of COVID-19 responses varied across patients and staff and their respective routes of pathogen acquisition. The second set of simulations was tailored to specific hospital wards and nosocomial bacteria (methicillin-resistant Staphylococcus aureus, extended-spectrum beta-lactamase producing Escherichia coli). Consequences of nosocomial SARS-CoV-2 outbreaks were found to be highly context specific, with impacts depending on the specific ward and bacteria evaluated. In particular, SARS-CoV-2 outbreaks significantly impacted patient MRB colonization only in settings with high underlying risk of bacterial transmission. Yet across settings and species, antibiotic resistance burden was reduced in facilities with timelier implementation of effective COVID-19 control policies. CONCLUSIONS: Our model suggests that surges in nosocomial SARS-CoV-2 transmission generate selection for the spread of antibiotic-resistant bacteria. Timely implementation of efficient COVID-19 control measures thus has 2-fold benefits, preventing the transmission of both SARS-CoV-2 and MRB, and highlighting antibiotic resistance control as a collateral benefit of pandemic preparedness.

Defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study.

BACKGROUND: Epidemiological surveillance relies on microbial strain typing, which defines genomic relatedness among isolates to identify case clusters and their potential sources. Although predefined thresholds are often applied, known outbreak-specific features such as pathogen mutation rate and duration of source contamination are rarely considered. We aimed to develop a hypothesis-based model that estimates genetic distance thresholds and mutation rates for point-source single-strain food or environmental outbreaks. METHODS: In this modelling study, we developed a forward model to simulate bacterial evolution at a specific mutation rate (µ) over a defined outbreak duration (D). From the distribution of genetic distances expected under the given outbreak parameters and sample isolation dates, we estimated a distance threshold beyond which isolates should not be considered as part of the outbreak. We embedded the model into a Markov Chain Monte Carlo inference framework to estimate the most probable mutation rate or time since source contamination, which are both often imprecisely documented. A simulation study validated the model over realistic durations and mutation rates. We then identified and analysed 16 published datasets of bacterial source-related outbreaks; datasets were included if they were from an identified foodborne outbreak and if whole-genome sequence data and collection dates for the described isolates were available. FINDINGS: Analysis of simulated data validated the accuracy of our framework in both discriminating between outbreak and non-outbreak cases and estimating the parameters D and µ from outbreak data. Precision of estimation was much higher for high values of D and µ. Sensitivity of outbreak cases was always very high, and specificity in detecting non-outbreak cases was poor for low mutation rates. For 14 of the 16 outbreaks, the classification of isolates as being outbreak-related or sporadic is consistent with the original dataset. Four of these outbreaks included outliers, which were correctly classified as being beyond the threshold of exclusion estimated by our model, except for one isolate of outbreak 4. For two outbreaks, both foodborne Listeria monocytogenes, conclusions from our model were discordant with published results: in one outbreak two isolates were classified as outliers by our model and in another outbreak our algorithm separated food samples into one cluster and human samples into another, whereas the isolates were initially grouped together based on epidemiological and genetic evidence. Re-estimated values of the duration of outbreak or mutation rate were largely consistent with a priori defined values. However, in several cases the estimated values were higher and improved the fit with the observed genetic distance distribution, suggesting that early outbreak cases are sometimes missed. INTERPRETATION: We propose here an evolutionary approach to the single-strain conundrum by estimating the genetic threshold and proposing the most probable cluster of cases for a given outbreak, as determined by its particular epidemiological and microbiological properties. This forward model, applicable to foodborne or environmental-source single point case clusters or outbreaks, is useful for epidemiological surveillance and may inform control measures. FUNDING: European Union Horizon 2020 Research and Innovation Programme.

The interactions of SARS-CoV-2 with cocirculating pathogens: Epidemiological implications and current knowledge gaps.

Despite the availability of effective vaccines, the persistence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) suggests that cocirculation with other pathogens and resulting multiepidemics (of, for example, COVID-19 and influenza) may become increasingly frequent. To better forecast and control the risk of such multiepidemics, it is essential to elucidate the potential interactions of SARS-CoV-2 with other pathogens; these interactions, however, remain poorly defined. Here, we aimed to review the current body of evidence about SARS-CoV-2 interactions. Our review is structured in four parts. To study pathogen interactions in a systematic and comprehensive way, we first developed a general framework to capture their major components: sign (either negative for antagonistic interactions or positive for synergistic interactions), strength (i.e., magnitude of the interaction), symmetry (describing whether the interaction depends on the order of infection of interacting pathogens), duration (describing whether the interaction is short-lived or long-lived), and mechanism (e.g., whether interaction modifies susceptibility to infection, transmissibility of infection, or severity of disease). Second, we reviewed the experimental evidence from animal models about SARS-CoV-2 interactions. Of the 14 studies identified, 11 focused on the outcomes of coinfection with nonattenuated influenza A viruses (IAVs), and 3 with other pathogens. The 11 studies on IAV used different designs and animal models (ferrets, hamsters, and mice) but generally demonstrated that coinfection increased disease severity compared with either monoinfection. By contrast, the effect of coinfection on the viral load of either virus was variable and inconsistent across studies. Third, we reviewed the epidemiological evidence about SARS-CoV-2 interactions in human populations. Although numerous studies were identified, only a few were specifically designed to infer interaction, and many were prone to multiple biases, including confounding. Nevertheless, their results suggested that influenza and pneumococcal conjugate vaccinations were associated with a reduced risk of SARS-CoV-2 infection. Finally, fourth, we formulated simple transmission models of SARS-CoV-2 cocirculation with an epidemic viral pathogen or an endemic bacterial pathogen, showing how they can naturally incorporate the proposed framework. More generally, we argue that such models, when designed with an integrative and multidisciplinary perspective, will be invaluable tools to resolve the substantial uncertainties that remain about SARS-CoV-2 interactions.

Ability of epidemiological studies to monitor HPV post-vaccination dynamics: a simulation study.

Genital human papillomavirus (HPV) infections are caused by a broad diversity of genotypes. As available vaccines target a subgroup of these genotypes, monitoring transmission dynamics of nonvaccine genotypes is essential. After reviewing the epidemiological literature on study designs aiming to monitor those dynamics, we evaluated their abilities to detect HPV-prevalence changes following vaccine introduction. We developed an agent-based model to simulate HPV transmission in a heterosexual population under various scenarios of vaccine coverage and genotypic interaction, and reproduced two study designs: post-vs.-prevaccine and vaccinated-vs.-unvaccinated comparisons. We calculated the total sample size required to detect statistically significant prevalence differences at the 5% significance level and 80% power. Although a decrease in vaccine-genotype prevalence was detectable as early as 1 year after vaccine introduction, simulations indicated that the indirect impact on nonvaccine-genotype prevalence (a decrease under synergistic interaction or an increase under competitive interaction) would only be measurable after >10 years whatever the vaccine coverage. Sample sizes required for nonvaccine genotypes were >5 times greater than for vaccine genotypes and tended to be smaller in the post-vs.-prevaccine than in the vaccinated-vs.-unvaccinated design. These results highlight that previously published epidemiological studies were not powerful enough to efficiently detect changes in nonvaccine-genotype prevalence.

Retrospective analysis of SARS-CoV-2 omicron invasion over delta in French regions in 2021-22: a status-based multi-variant model.

BACKGROUND: SARS-CoV-2 is a rapidly spreading disease affecting human life and the economy on a global scale. The disease has caused so far more then 5.5 million deaths. The omicron outbreak that emerged in Botswana in the south of Africa spread around the globe at further increased rates, and caused unprecedented SARS-CoV-2 infection incidences in several countries. At the start of December 2021 the first omicron cases were reported in France. METHODS: In this paper we investigate the spreading potential of this novel variant relatively to the delta variant that was also in circulation in France at that time. Using a dynamic multi-variant model accounting for cross-immunity through a status-based approach, we analyze screening data reported by Santé Publique France over 13 metropolitan French regions between 1st of December 2021 and the 30th of January 2022. During the investigated period, the delta variant was replaced by omicron in all metropolitan regions in approximately three weeks. The analysis conducted retrospectively allows us to consider the whole replacement time window and compare regions with different times of omicron introduction and baseline levels of variants' transmission potential. As large uncertainties regarding cross-immunity among variants persist, uncertainty analyses were carried out to assess its impact on our estimations. RESULTS: Assuming that 80% of the population was immunized against delta, a cross delta/omicron cross-immunity of 25% and an omicron generation time of 3.5 days, the relative strength of omicron to delta, expressed as the ratio of their respective reproduction rates, [Formula: see text], was found to range between 1.51 and 1.86 across regions. Uncertainty analysis on epidemiological parameters led to [Formula: see text] ranging from 1.57 to 2.34 on average over the metropolitan French regions, weighted by population size. CONCLUSIONS: Upon introduction, omicron spread rapidly through the French territory and showed a high fitness relative to delta. We documented considerable geographical heterogeneities on the spreading dynamics. The historical reconstruction of variant emergence dynamics provide valuable ground knowledge to face future variant emergence events.

Neonatal acquisition of extended-spectrum beta-lactamase-producing Enterobacteriaceae in the community of a low-income country (NeoLIC): protocol for a household cohort study in Moramanga, Madagascar.

INTRODUCTION: Data regarding the acquisition of extended-spectrum beta-lactamase-producing Enterobacteriaceae (ESBL-PE) in neonates at the community level are scarce in low-income and middle-income countries (LMICs), where the burden of neonatal sepsis is high.Our study aims at identifying and quantifying the role of the different routes of ESBL-PE transmission for neonates, which are still undefined in the community in LMICs. METHODS AND ANALYSIS: In a semirural community in Madagascar, 60 mothers and their neonates will be recruited at delivery, during which a maternal stool sample and meconium of the newborn will be collected. Home visits will be planned the day of the delivery and next at days 3, 7, 14, 21 and 28. Stool samples from the newborn, the mother and every other household member will be collected at each visit, as well as samples from the environment in contact with the newborn (food, surfaces and objects). Sociodemographic data and factors which might drive ESBL-PE acquisition will also be collected.We will analyse the isolated ESBL-PE using DNA sequencing methods to characterise clones, resistance genes and plasmids of ESBL-PE. To analyse these data globally, we will develop novel analytical approaches combining mathematical modelling and statistics. Finally, mathematical simulations will be performed to test different strategies of control of ESBL-PE transmission to neonates.In complement, we will conduct an anthropological investigation to understand local environments and practices that would contribute to neonatal ESBL-PE acquisition. In-depth interviews with members of 16 households will be conducted and 4 mother-newborn pairs will be followed by a participants' observations methodology. ETHICS AND DISSEMINATION: The study was approved by the ethical committee in Madagascar and by the institutional review board of Institut Pasteur, Paris, France.Findings will be reported to participating families, collaborators and local government; presented at national and international conferences and disseminated by peer-review publications.

Contact patterns and HPV-genotype interactions yield heterogeneous HPV-vaccine impacts depending on sexual behaviors: An individual-based model.

Human papillomaviruses are common sexually transmitted infections, caused by a large diversity of genotypes. In the context of vaccination against a subgroup of genotypes, better understanding the role of genotype interactions and human sexual behavior on genotype dynamics is essential. Herein, we present an individual-based model that integrates realistic heterosexual partnership behaviors and simulates interactions between vaccine and non-vaccine genotypes. Genotype interactions were considered, assuming a previous vaccine-genotype infection shortened (competition) or extended (synergy) the duration of a secondary non-vaccine-genotype infection. Sexual behavior determined papillomavirus acquisition and transmission: only 19.5% of active individuals at most 1 partner r during the year, but > 80% of those with ≥ 2 partners, were infected before vaccine introduction. The pre-vaccination situation was consistent with all genotype interaction scenarios. These genotype interactions, despite being undetectable during the pre-vaccination era, markedly impacted genotype prevalence after vaccination started, with a significant increase/decrease of non-vaccine genotypes prevalence for respectively competitive/synergistic interactions. These prevalence changes were more pronounced in individuals with ≤ 3 partners per year (up to 30% of prevalence modification assuming 65% vaccine coverage) but barely visible for individuals with > 3 partners per year (at most 0.30%). Results suggest the presence of genotype interaction, which is consistent with the pre-vaccine situation, may impact the dynamics of non-vaccine genotypes, particularly in less active individuals.

Measuring Basic Reproduction Number to Assess Effects of Nonpharmaceutical Interventions on Nosocomial SARS-CoV-2 Transmission.

Outbreaks of SARS-CoV-2 infection frequently occur in hospitals. Preventing nosocomial infection requires insight into hospital transmission. However, estimates of the basic reproduction number (R0) in care facilities are lacking. Analyzing a closely monitored SARS-CoV-2 outbreak in a hospital in early 2020, we estimated the patient-to-patient transmission rate and R0. We developed a model for SARS-CoV-2 nosocomial transmission that accounts for stochastic effects and undetected infections and fit it to patient test results. The model formalizes changes in testing capacity over time, and accounts for evolving PCR sensitivity at different stages of infection. R0 estimates varied considerably across wards, ranging from 3 to 15 in different wards. During the outbreak, the hospital introduced a contact precautions policy. Our results strongly support a reduction in the hospital-level R0 after this policy was implemented, from 8.7 to 1.3, corresponding to a policy efficacy of 85% and demonstrating the effectiveness of nonpharmaceutical interventions.

Modelling the impact of antimicrobial use and external introductions on commensal E. coli colistin resistance in small-scale chicken farms of the Mekong delta of Vietnam.

Colistin is a critically important antimicrobial for human medicine, and colistin-resistant Escherichia coli are commonly found in poultry and poultry products in Southeast Asia. Here, we aim at disentangling the within-farm and outside-farm drivers of colistin resistance in small-scale chicken farms of the Mekong delta of Vietnam. Nineteen Vietnamese chicken farms were followed up along a whole production cycle, during which weekly antimicrobial use data were recorded. At the beginning, middle and end of each production cycle, commensal E. coli samples from birds were collected, pooled and tested for colistin resistance. Twelve models were fitted to the data using an expectation-maximization algorithm and compared. We further tested the spatial clustering of the occurrence of resistance importations from external sources using the local Moran's I statistic. In the best model, colistin resistance in E. coli from chickens was found to be mostly affected by importations of resistance, and, to a lesser extent, by the use of antimicrobials in the last 1.73 weeks [0.00; 2.90], but not by the use of antimicrobials in day-olds, nor their colistin resistance carriage from hatchery. The occurrence of external source importations proved to be sometimes spatially clustered, suggesting a role of local environmental sources of colistin resistance.

Non-pharmaceutical interventions and COVID-19 vaccination strategies in Senegal: a modelling study.

BACKGROUND: When vaccines against the novel COVID-19 were available in Senegal, many questions were raised. How long should non-pharmaceutical interventions (NPIs) be maintained during vaccination roll-out? What are the best vaccination strategies? METHODS: In this study, we used an age-structured dynamic mathematical model. This model uses parameters based on SARS-CoV-2 virus, information on different types of NPIs, epidemiological and demographic data, some parameters relating to hospitalisations and vaccination in Senegal. RESULTS: In all scenarios explored, the model predicts a larger third epidemic wave of COVID-19 in terms of new cases and deaths than the previous waves. In a context of limited vaccine supply, vaccination alone will not be sufficient to control the epidemic, and the continuation of NPIs is necessary to flatten the epidemic curve. Assuming 20% of the population have been vaccinated, the optimal period to relax NPIs would be a few days from the last peak. Regarding the prioritisation of age groups to be vaccinated, the model shows that it is better to vaccinate individuals aged 5-60 years and not just the elderly (over 60 years) and those in high-risk groups. This strategy could be more cost-effective for the government, as it would reduce the high costs associated with hospitalisation. In terms of vaccine distribution, the optimal strategy would be to allocate full dose to the elderly. If vaccine doses are limited, half dose followed by full dose would be sufficient for people under 40 years because whether they receive half or full dose, the reduction in hospitalisations would be similar and their death-to-case ratio is very low. CONCLUSIONS: This study could be presented as a decision support tool to help devise strategies to control the COVID-19 pandemic and help the Ministry of Health to better manage and allocate the available vaccine doses.

Rapid antigen testing as a reactive response to surges in nosocomial SARS-CoV-2 outbreak risk.

Healthcare facilities are vulnerable to SARS-CoV-2 introductions and subsequent nosocomial outbreaks. Antigen rapid diagnostic testing (Ag-RDT) is widely used for population screening, but its health and economic benefits as a reactive response to local surges in outbreak risk are unclear. We simulate SARS-CoV-2 transmission in a long-term care hospital with varying COVID-19 containment measures in place (social distancing, face masks, vaccination). Across scenarios, nosocomial incidence is reduced by up to 40-47% (range of means) with routine symptomatic RT-PCR testing, 59-63% with the addition of a timely round of Ag-RDT screening, and 69-75% with well-timed two-round screening. For the latter, a delay of 4-5 days between the two screening rounds is optimal for transmission prevention. Screening efficacy varies depending on test sensitivity, test type, subpopulations targeted, and community incidence. Efficiency, however, varies primarily depending on underlying outbreak risk, with health-economic benefits scaling by orders of magnitude depending on the COVID-19 containment measures in place.

The Impact of Cocirculating Pathogens on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)/Coronavirus Disease 2019 Surveillance: How Concurrent Epidemics May Introduce Bias and Decrease the Observed SARS-CoV-2 Percentage Positivity.

BACKGROUND: Circulation of seasonal non-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) respiratory viruses with syndromic overlap during the coronavirus disease 2019 (COVID-19) pandemic may alter the quality of COVID-19 surveillance, with possible consequences for real-time analysis and delay in implementation of control measures. METHODS: Using a multipathogen susceptible-exposed-infectious-recovered (SEIR) transmission model formalizing cocirculation of SARS-CoV-2 and another respiratory virus, we assessed how an outbreak of secondary virus may affect 2 COVID-19 surveillance indicators: testing demand and positivity. Using simulation, we assessed to what extent the use of multiplex polymerase chain reaction tests on a subsample of symptomatic individuals can help correct the observed SARS-CoV-2 percentage positivity and improve surveillance quality. RESULTS: We find that a non-SARS-CoV-2 epidemic strongly increases SARS-CoV-2 daily testing demand and artificially reduces the observed SARS-CoV-2 percentage positivity for the duration of the outbreak. We estimate that performing 1 multiplex test for every 1000 COVID-19 tests on symptomatic individuals could be sufficient to maintain surveillance of other respiratory viruses in the population and correct the observed SARS-CoV-2 percentage positivity. CONCLUSIONS: This study showed that cocirculating respiratory viruses can distort SARS-CoV-2 surveillance. Correction of the positivity rate can be achieved by using multiplex polymerase chain reaction tests, and a low number of samples is sufficient to avoid bias in SARS-CoV-2 surveillance.

Lockdown impact on age-specific contact patterns and behaviours, France, April 2020.

BackgroundMany countries implemented national lockdowns to contain the rapid spread of SARS-CoV-2 and avoid overburdening healthcare capacity.AimWe aimed to quantify how the French lockdown impacted population mixing, contact patterns and behaviours.MethodsWe conducted an online survey using convenience sampling and collected information from participants aged 18 years and older between 10 April and 28 April 2020.ResultAmong the 42,036 survey participants, 72% normally worked outside their home, and of these, 68% changed to telework during lockdown and 17% reported being unemployed during lockdown. A decrease in public transport use was reported from 37% to 2%. Participants reported increased frequency of hand washing and changes in greeting behaviour. Wearing masks in public was generally limited. A total of 138,934 contacts were reported, with an average of 3.3 contacts per individual per day; 1.7 in the participants aged 65 years and older compared with 3.6 for younger age groups. This represented a 70% reduction compared with previous surveys, consistent with SARS-CoV2 transmission reduction measured during the lockdown. For those who maintained a professional activity outside home, the frequency of contacts at work dropped by 79%.ConclusionThe lockdown affected the population's behaviour, work, risk perception and contact patterns. The frequency and heterogeneity of contacts, both of which are critical factors in determining how viruses spread, were affected. Such surveys are essential to evaluate the impact of lockdowns more accurately and anticipate epidemic dynamics in these conditions.