estimateR: an R package to estimate and monitor the effective reproductive number.

BACKGROUND: Accurate estimation of the effective reproductive number ([Formula: see text]) of epidemic outbreaks is of central relevance to public health policy and decision making. We present estimateR, an R package for the estimation of the reproductive number through time from delayed observations of infection events. Such delayed observations include confirmed cases, hospitalizations or deaths. The package implements the methodology of Huisman et al. but modularizes the [Formula: see text] estimation procedure to allow easy implementation of new alternatives to the currently available methods. Users can tailor their analyses according to their particular use case by choosing among implemented options. RESULTS: The estimateR R package allows users to estimate the effective reproductive number of an epidemic outbreak based on observed cases, hospitalization, death or any other type of event documenting past infections, in a fast and timely fashion. We validated the implementation with a simulation study: estimateR yielded estimates comparable to alternative publicly available methods while being around two orders of magnitude faster. We then applied estimateR to empirical case-confirmation incidence data for COVID-19 in nine countries and for dengue fever in Brazil; in parallel, estimateR is already being applied (i) to SARS-CoV-2 measurements in wastewater data and (ii) to study influenza transmission based on wastewater and clinical data in other studies. In summary, this R package provides a fast and flexible implementation to estimate the effective reproductive number for various diseases and datasets. CONCLUSIONS: The estimateR R package is a modular and extendable tool designed for outbreak surveillance and retrospective outbreak investigation. It extends the method developed for COVID-19 by Huisman et al. and makes it available for a variety of pathogens, outbreak scenarios, and observation types. Estimates obtained with estimateR can be interpreted directly or used to inform more complex epidemic models (e.g. for forecasting) on the value of [Formula: see text].

Antibiotic-degrading resistance changes bacterial community structure via species-specific responses.

Some bacterial resistance mechanisms degrade antibiotics, potentially protecting neighbouring susceptible cells from antibiotic exposure. We do not yet understand how such effects influence bacterial communities of more than two species, which are typical in nature. Here, we used experimental multispecies communities to test the effects of clinically important pOXA-48-plasmid-encoded resistance on community-level responses to antibiotics. We found that resistance in one community member reduced antibiotic inhibition of other species, but some benefitted more than others. Further experiments with supernatants and pure-culture growth assays showed the susceptible species profiting most from detoxification were those that grew best at degraded antibiotic concentrations (greater than zero, but lower than the starting concentration). This pattern was also observed on agar surfaces, and the same species also showed relatively high survival compared to most other species during the initial high-antibiotic phase. By contrast, we found no evidence of a role for higher-order interactions or horizontal plasmid transfer in community-level responses to detoxification in our experimental communities. Our findings suggest carriage of an antibiotic-degrading resistance mechanism by one species can drastically alter community-level responses to antibiotics, and the identities of the species that profit most from antibiotic detoxification are predicted by their intrinsic ability to survive and grow at changing antibiotic concentrations.

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2.

The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.

Over the past two and a half years, countries around the globe have struggled to control the transmission of the SARS-CoV-2 virus within their borders. To manage the situation, it is important to have an accurate picture of how fast the virus is spreading. This can be achieved by calculating the effective reproductive number (Re), which describes how many people, on average, someone with COVID-19 is likely to infect. If the Re is greater than one, the virus is infecting increasingly more people, but if it is smaller than one, the number of cases is declining. Scientists use various strategies to estimate the Re, which each have their own strengths and weaknesses. One of the main difficulties is that infections are typically recorded only when people test positive for COVID-19, are hospitalized with the virus, or die. This means that the data provides a delayed representation of when infections are happening. Furthermore, changes in these records occur later than measures that change the infection dynamics. As a result, researchers need to take these delays into account when estimating Re. Here, Huisman, Scire et al. have developed a new method for estimating the Re based on available data records, statistically taking into account the above-mentioned delays. An online dashboard with daily updates was then created so that policy makers and the population could monitor the values over time. For over two years, Huisman, Scire et al. have been applying their tool and dashboard to COVID-19 data from 170 countries. They found that public health interventions, such as mask requirements and lockdowns, did help reduce the Re in Europe. But the effects were not uniform across the globe, likely because of variations in how restrictions were implemented and followed during the pandemic. In early 2020, the Re only dropped below one after countries put lockdowns or other severe measures in place. The Re values added to the dashboard over the last two years have been used pro-actively to inform public health policies in Switzerland and to monitor the spread of SARS-CoV-2 in South Africa. The team has also recently released programming software based on this method that can be used to track future disease outbreaks, and extended the method to estimate the Re using SARS-CoV-2 levels in wastewater.

Comparing treatment strategies to reduce antibiotic resistance in an in vitro epidemiological setting.

The rapid rise of antibiotic resistance, combined with the increasing cost and difficulties to develop new antibiotics, calls for treatment strategies that enable more sustainable antibiotic use. The development of such strategies, however, is impeded by the lack of suitable experimental approaches that allow testing their effects under realistic epidemiological conditions. Here, we present an approach to compare the effect of alternative multidrug treatment strategies in vitro using a robotic liquid-handling platform. We use this framework to study resistance evolution and spread implementing epidemiological population dynamics for treatment, transmission, and patient admission and discharge, as may be observed in hospitals. We perform massively parallel experimental evolution over up to 40 d and complement this with a computational model to infer the underlying population-dynamical parameters. We find that in our study, combination therapy outperforms monotherapies, as well as cycling and mixing, in minimizing resistance evolution and maximizing uninfecteds, as long as there is no influx of double resistance into the focal treated community.

Quantifying the impact of quarantine duration on COVID-19 transmission.

The large number of individuals placed into quarantine because of possible severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) exposure has high societal and economic costs. There is ongoing debate about the appropriate duration of quarantine, particularly since the fraction of individuals who eventually test positive is perceived as being low. We use empirically determined distributions of incubation period, infectivity, and generation time to quantify how the duration of quarantine affects onward transmission from traced contacts of confirmed SARS-CoV-2 cases and from returning travellers. We also consider the roles of testing followed by release if negative (test-and-release), reinforced hygiene, adherence, and symptoms in calculating quarantine efficacy. We show that there are quarantine strategies based on a test-and-release protocol that, from an epidemiological viewpoint, perform almost as well as a 10-day quarantine, but with fewer person-days spent in quarantine. The findings apply to both travellers and contacts, but the specifics depend on the context.

The COVID-19 pandemic has led many countries to impose quarantines, ensuring that people who may have been exposed to the SARS-CoV-2 virus or who return from abroad are isolated for a specific period to prevent the spread of the disease. These measures have crippled travel, taken a large economic toll, and affected the wellbeing of those needing to self-isolate. However, there is no consensus on how long COVID-19 quarantines should be. Reducing the duration of quarantines could significantly decrease the costs of COVID-19 to the overall economy and to individuals, so Ashcroft et al. decided to examine how shorter isolation periods and test-and-release schemes affected transmission. Existing data on how SARS-CoV-2 behaves in a population were used to generate a model that would predict how changing quarantine length impacts transmission for both travellers and people who may have been exposed to the virus. The analysis predicted that shortening quarantines from ten to seven days would result in almost no increased risk of transmission, if paired with PCR testing on day five of isolation (with people testing positive being confined for longer). The quarantine could be cut further to six days if rapid antigen tests were used. Ashcroft et al.'s findings suggest that it may be possible to shorten COVID-19 quarantines if good testing approaches are implemented, leading to better economic, social and individual outcomes.

Resistance Gene Carriage Predicts Growth of Natural and Clinical Escherichia coli Isolates in the Absence of Antibiotics.

Bacterial pathogens that carry antibiotic resistance alleles sometimes pay a cost in the form of impaired growth in antibiotic-free conditions. This cost of resistance is expected to be a key parameter for understanding how resistance spreads and persists in pathogen populations. Analysis of individual resistance alleles from laboratory evolution and natural isolates has shown they are typically costly, but these costs are highly variable and influenced by genetic variation at other loci. It therefore remains unclear how strongly resistance is linked to impaired antibiotic-free growth in bacteria from natural and clinical scenarios, where resistance alleles are likely to coincide with other types of genetic variation. To investigate this, we measured the growth of 92 natural and clinical Escherichia coli isolates across three antibiotic-free environments. We then tested whether variation of antibiotic-free growth among isolates was predicted by their resistance to 10 antibiotics, while accounting for the phylogenetic structure of the data. We found that isolates with similar resistance profiles had similar antibiotic-free growth profiles, but it was not simply that higher average resistance was associated with impaired growth. Next, we used whole-genome sequences to identify antibiotic resistance genes and found that isolates carrying a greater number of resistance gene types grew relatively poorly in antibiotic-free conditions, even when the resistance genes they carried were different. This suggests that the resistance of bacterial pathogens is linked to growth costs in nature, but it is the total genetic burden and multivariate resistance phenotype that predict these costs, rather than individual alleles or mean resistance across antibiotics.IMPORTANCE Managing the spread of antibiotic resistance in bacterial pathogens is a major challenge for global public health. Central to this challenge is understanding whether resistance is linked to impaired bacterial growth in the absence of antibiotics, because this determines whether resistance declines when bacteria are no longer exposed to antibiotics. We studied 92 isolates of the key bacterial pathogen Escherichia coli; these isolates varied in both their antibiotic resistance genes and other parts of the genome. Taking this approach, rather than focusing on individual genetic changes associated with resistance as in much previous work, revealed that growth without antibiotics was linked to the number of specialized resistance genes carried and the combination of antibiotics to which isolates were resistant but was not linked to average antibiotic resistance. This approach provides new insights into the genetic factors driving the long-term persistence of antibiotic-resistant bacteria, which is important for future efforts to predict and manage resistance.

Antibacterial effects of antiretrovirals, potential implications for microbiome studies in HIV.

BACKGROUND: Despite being used by more than 18 million people our understanding of the extent of the effects of antiretrovirals on the human body and other organisms remains incomplete. In addition, the direct effect of antiretrovirals on the gut microbiota of HIV-infected individuals has been largely overlooked in microbiome studies concerned with HIV-infected individuals. METHODS: Here we tested 25 antiretrovirals on Bacillus subtilis and Escherichia coli using a broth microdilution assay to assess whether these drugs have an antibacterial effect. RESULTS: We found that several widely used antiretroviral drugs have in vitro antibacterial activity against both gram-positive and gram-negative commensal bacteria. Efavirenz inhibited the growth of B. subtilis with a minimum inhibitory concentration (MIC) of 16 µg/ml (in all three replicates), while 2',3'-dideoxyinosine and zidovudine inhibited the growth of E. coli with an MIC of 16-32 µg/ml and 0.016-0.125 µg/ml (respectively). CONCLUSIONS: Given the large and increasing number of individuals on antiretrovirals, and the lifelong nature of HIV treatment, this proof-of-concept report could have several potential implications, including an impact of antiretrovirals on bacterial coinfections, as well as potentials for drug discovery and repositioning.

Costs of antibiotic resistance - separating trait effects and selective effects.

Antibiotic resistance can impair bacterial growth or competitive ability in the absence of antibiotics, frequently referred to as a 'cost' of resistance. Theory and experiments emphasize the importance of such effects for the distribution of resistance in pathogenic populations. However, recent work shows that costs of resistance are highly variable depending on environmental factors such as nutrient supply and population structure, as well as genetic factors including the mechanism of resistance and genetic background. Here, we suggest that such variation can be better understood by distinguishing between the effects of resistance mechanisms on individual traits such as growth rate or yield ('trait effects') and effects on genotype frequencies over time ('selective effects'). We first give a brief overview of the biological basis of costs of resistance and how trait effects may translate to selective effects in different environmental conditions. We then review empirical evidence of genetic and environmental variation of both types of effects and how such variation may be understood by combining molecular microbiological information with concepts from evolution and ecology. Ultimately, disentangling different types of costs may permit the identification of interventions that maximize the cost of resistance and therefore accelerate its decline.

The cost of antibiotic resistance depends on evolutionary history in Escherichia coli.

BACKGROUND: The persistence of antibiotic resistance depends on the fitness effects of resistance elements in the absence of antibiotics. Recent work shows that the fitness effect of a given resistance mutation is influenced by other resistance mutations on the same genome. However, resistant bacteria acquire additional beneficial mutations during evolution in the absence of antibiotics that do not alter resistance directly but may modify the fitness effects of new resistance mutations. RESULTS: We experimentally evolved rifampicin-resistant and sensitive Escherichia coli in a drug-free environment, before measuring the effects of new resistance elements on fitness in antibiotic-free conditions. Streptomycin-resistance mutations had small fitness effects in rifampicin-resistant genotypes that had adapted to antibiotic-free growth medium, compared to the same genotypes without adaptation. We observed a similar effect when resistance was encoded by a different mechanism and carried on a plasmid. Antibiotic-sensitive bacteria that adapted to the same conditions showed the same pattern for some resistance elements but not others. CONCLUSIONS: Epistatic variation of costs of resistance can result from evolution in the absence of antibiotics, as well as the presence of other resistance mutations.