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More than four decades after the eradication of smallpox, the ongoing 2022 monkeypox outbreak and increasing transmission events of other orthopoxviruses necessitate a greater understanding of the global distribution of susceptibility to orthopoxviruses. We aimed to characterise the current global landscape of smallpox vaccination history and orthopoxvirus susceptibility.We characterised the global landscape of smallpox vaccination at a subnational scale by integrating data on current demography with historical smallpox vaccination programme features (coverage and cessation dates) from eradication documents and published literature. We analysed this landscape to identify the factors that were most associated with geographical heterogeneity in current vaccination coverage. We considered how smallpox vaccination history might translate into age-specific susceptibility profiles for orthopoxviruses under different vaccination effectiveness scenarios.We found substantial global spatial heterogeneity in the landscape of smallpox vaccination, with vaccination coverage estimated to range from 7% to 60% within admin-1 regions (ie, regions one administrative level below country) globally, with negligible uncertainty (99·6% of regions have an SD less than 5%). We identified that geographical variation in vaccination coverage was driven mostly by differences in subnational demography. Additionally, we found that susceptibility for orthopoxviruses was highly age specific based on age at cessation and age-specific coverage; however, the age profile was consistent across vaccine effectiveness values.The legacy of smallpox eradication can be observed in the current landscape of smallpox vaccine protection. The strength and longevity of smallpox vaccination campaigns globally, combined with current demographic heterogeneity, have shaped the epidemiological landscape today, revealing substantial geographical variation in orthopoxvirus susceptibility. This study alerts public health decision makers to non-endemic regions that might be at greatest risk in the case of widespread and sustained transmission in the 2022 monkeypox outbreak and highlights the importance of demography and fine-scale spatial dynamics in predicting future public health risks from orthopoxviruses.US National Institutes of Health and US National Science Foundation.

ImportanceSince May 2022, human monkeypox (mpox) infections have spread rapidly outside endemic countries. On July 23, 2023, WHO declared a Public Health Emergency of international Concern because of the unprecedented global spread of mpox. Whereas there is an incomplete understanding of transmission routes, the spread of monkeypox virus (MPXV) through sexual contact networks of men who have sex with men (MSM) highlights the potential of transmission during sexual activity, either via bodily fluids or via direct contact. This research assesses the potential for MPXV to remain infectious in the environment on surfaces and in fluids under different environmental conditions.ObjectiveTo investigate the stability of MPXV on various surfaces, at different environmental conditions and in different bodily fluids (human blood, semen, serum, saliva, urine, and feces), and to assess decontamination of MPXV-contaminated wastewater via chlorination.Design, Setting, and ExposuresAll experiments involving viable MPXV were performed at the Rocky Mountains Laboratory under high containment conditions using MPXV strain hMPXV/USA/MA001. Infectious MPXV was quantified via plaque assay. Environmental decay rates were estimated using a Bayesian statistical model, with a Poisson likelihood for plaque counts.ResultsMPXV showed surface dependent stability, with faster decay at higher temperatures. Decay rates varied considerably depending on the medium in which virus was suspended, both overall MPXV displayed considerable environmental stability in bodily fluids and in particular proteinaceous fluids, such as blood and semen, lead to greater persistence. Wastewater chlorination was an effective decontamination technique.Conclusions and RelevanceMPXV retains its infectivity in the environment when it is deposited in blood, semen, and serum; environmental decay is more rapid in less proteinaceous fluids, particularly once residual liquid has evaporated. The observed persistence of MPXV implies environmental contamination in healthcare setting, could serve as vehicles of transmission and dissemination in these environments. Chlorination was effective in decontamination of wastewater. Key findings and public health implications; these results suggest that MPXV can have prolonged stability in the environment and are therefore directly relevant for hospital hygiene practices to prevent nosocomial transmission.

Respiratory viruses can transmit by multiple modes, including contaminated surfaces, commonly referred to as fomites. Efficient fomite transmission requires that a virus remain infectious on a given surface material over a wide range of environmental conditions, including different relative humidities. Prior work examining the stability of influenza viruses on surfaces has relied upon virus grown in media or eggs, which does not mimic the composition of virus-containing droplets expelled from the human respiratory tract. In this study, we examined the stability of the 2009 pandemic H1N1 (H1N1pdm09) virus on a variety of nonporous surface materials at four different humidity conditions. Importantly, we used virus grown in primary human bronchial epithelial (HBE) cultures from different donors to recapitulate the physiological microenvironment of expelled viruses. We observed rapid inactivation of H1N1pdm09 on copper under all experimental conditions. In contrast to copper, viruses were stable on polystyrene plastic, stainless steel, aluminum, and glass, at multiple relative humidity conditions, but greater decay on ABS plastic was observed at short time points. However, the half-lives of viruses at 23% relative humidity were similar among non-copper surfaces and ranged from 4.5 to 5.9 hours. Assessment of H1N1pdm09 longevity on nonporous surfaces revealed that virus persistence was governed more by differences among HBE culture donors than by surface material. Our findings highlight the potential role of an individual’s respiratory fluid on viral persistence and could help explain heterogeneity in transmission dynamics.IMPORTANCESeasonal epidemics and sporadic pandemics of influenza cause a large public health burden. Although influenza viruses disseminate through the environment in respiratory secretions expelled from infected individuals, they can also be transmitted by contaminated surfaces where virus-laden expulsions can deposit. Understanding virus stability on surfaces within the indoor environment is critical to assessing influenza transmission risk. We found that influenza virus stability is affected by the host respiratory secretion in which the virus is expelled, the surface material on which the droplet lands, and the ambient relative humidity of the environment. Influenza viruses can remain infectious on many common surfaces for prolonged periods, with half-lives of 4.5-5.9 hours. These data imply that influenza viruses are highly persistent in indoor environments in biologically-relevant matrices. Decontamination and engineering controls should be used to mitigate influenza virus transmission.

Leptospirosis is the most widespread zoonotic disease in the world, yet it is broadly understudied in multi-host wildlife systems. Knowledge gaps regardingLeptospiracirculation in wildlife, particularly in densely populated areas, contribute to frequent misdiagnoses in humans and domestic animals. We assessedLeptospiraprevalence levels and risk factors in five target wildlife species across the greater Los Angeles region: striped skunks (Mephitis mephitis), Northern raccoons (Procyon lotor), coyotes (Canis latrans), Virginia opossums (Didelphis virginiana), and fox squirrels (Sciurus niger). We sampled more than 960 individual animals, including over 700 from target species in the greater Los Angeles region, and an additional 260 sampled opportunistically from other regions and species. In the five target species, seroprevalences ranged from 5-60% and active infection prevalences ranged from 0.8-15.2% in all except fox squirrels (0%). Patterns of serologic reactivity suggest that mainland terrestrial wildlife, particularly mesocarnivores, could be the source of repeated observed introductions ofLeptospirainto local marine and island ecosystems. Overall, we found evidence of widespreadLeptospiraexposure in wildlife across Los Angeles and surrounding regions. This indicates exposure risk for humans and domestic animals and highlights that this pathogen can circulate endemically in many wildlife species even in densely populated urban areas.

SARS-CoV-2 is transmitted principally via air; contact and fomite transmission may also occur. Variants-of-concern (VOCs) are more transmissible than ancestral SARS-CoV-2. We find that early VOCs show greater aerosol and surface stability than the early WA1 strain, but Delta and Omicron do not. Stability changes do not explain increased transmissibility.

Studies of infectious disease ecology often rely heavily on knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody level data being one of the most promising sources of information. The use of antibody levels to back-calculate infection time requires the development of a host-pathogen system-specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data. Here, we present a way to do this in a Bayesian framework that facilitates the incorporation of all available information about potential infection times. We apply the model to estimate infection times of Channel Island foxes infected with Leptospira interrogans, leading to reductions of 51-92% in the window of possible infection times. Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements of infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. The method substantially simplifies the challenge of modeling antibody dynamics in the absence of individuals with known infection times, opens up new opportunities in wildlife disease ecology, and can even be applied to cross-sectional data once the model is trained.

Airborne transmission is one of the major routes contributing to the spread of SARS-CoV-2. Successful aerosol transmission occurs when people release respiratory particles carrying infectious virus in the fine aerosol size range. It remains poorly understood how infection influences the physiological host factors that are integral to this process. Here we assessed the changes in breathing, exhaled droplets, and released virus early after infection with the Alpha and Delta variants in the Syrian hamster. Infection with the two variants led to only nuanced differences in viral tissue titers, disease severity, or shedding magnitude. Both variants led to a short window of detectable virus in the air between 24 h and 48 h, which was poorly reflected by upper respiratory shedding measured in oropharyngeal swabs. The loss of viable air samples coincided with changes in airway constriction as measured by whole body plethysmography, and a decrease of fine aerosols produced in the 1-10 μm aerodynamic diameter range. We found that male sex was associated with greater viral replication in the upper respiratory tract and virus shedding in the air. This coincided with an exhaled particle profile shifted towards smaller droplets, independent of variant. Transmission efficiency of Alpha and Delta did not differ on average but exhibited clear variation among donor individuals, including a superspreading event. Transmission leading to substantial dual infections only occurred when both viruses were shed by the same donor and exposure was prolonged. These findings provide direct experimental evidence that quantitative and qualitative assessment of exhaled aerosols may be critical for understanding the limitations and determinants of efficient airborne transmission, thus allowing us to control the pandemic with non-pharmaceutical interventions.Airborne transmission is one of the major routes for SARS-CoV-2, however underlying host and virus parameters remain poorly understood. Here, we provide direct experimental evidence that the quantitative and qualitative assessment of exhaled aerosols are critical to understand the efficiency of SARS-CoV-2 airborne transmission. We show that after infection, the Alpha and Delta variants of concern displayed a short window of detectable virus in the air in contrast to prolonged shedding measured in oropharyngeal swabs. The limited window coincided with changes in airway constriction, and a sex dependent decrease of fine aerosols produced in the 1-10 μm aerodynamic diameter range. Dual airborne infections only occurred when both viruses were shed by the same donor and after prolonged exposure.

BackgroundMore than four decades after the eradication of smallpox, the ongoing 2022 monkeypox out-break and increasing transmission events of other orthopoxviruses necessitate a deeper understanding of the global distribution of susceptibility to orthopoxviruses, as shaped by the landscape of smallpox vaccination pre-eradication.MethodsWe characterize the fine-scale global spatial landscape of orthopoxvirus vulnerability based on geographical heterogeneity in demography and past smallpox vaccination program features, including vaccination coverage and cessation dates. For the United States, we also consider the role of immigration in shaping the landscape of protection.FindingsWe find significant global spatial heterogeneity in the landscape of orthopoxvirus susceptibility, with monkeypox susceptibility ranging from near 57% to near 96% within admin-1 regions globally, with negligible uncertainty in most regions. We identify that this variation is driven largely by differences in sub-national demography.InterpretationThe legacy of smallpox eradication can be observed in the current landscape of susceptibility for orthopoxviruses, including monkeypox. The strength and longevity of the smallpox vaccination campaign in each nation shape the epidemiological landscape today and reveal significant geographic variation in vulnerability. Our work alerts public health decision-makers to non-endemic regions that may be at greatest risk in the case of widespread and sustained transmission in the 2022 monkeypox outbreak and highlights the importance of demography and fine-scale spatial dynamics in predicting future public health risks from orthopoxviruses.FundingResearch reported in this publication was supported by the National Institutes of Health under award number R01GM123007 (SB) and National Science Foundation DEB-1557022 (JOL-S).Research in ContextEvidence before this studyWe searched the Red Book, WHO eradication documents, Bulletin of the World Health Organization and Morbidity and Mortality Weekly Report digital archives, and published literature and reports via Google Scholar and PubMed for data on smallpox vaccination coverage rates and cessation dates for each country. Search terms included: “smallpox vaccination cessation”,”end of smallpox vaccination”, “stop smallpox vaccination”, “smallpox vaccination coverage”, “smallpox scar surveys”, and “smallpox serum surveys”, combined with each country name. While the Red Book covers much of the smallpox eradication efforts in Africa, Asia, and South America, information on countries in Europe, Central America, the Middle East, and Oceania was sparse and scattered.Added value of this studyWe characterize the fine-scale global spatial landscape of orthopoxvirus vulnerability based on geographical heterogeneity in demography and past smallpox vaccination program features, including vaccination coverage and cessation dates. We find significant spatial heterogeneity in orthopoxvirus susceptibility, driven in large part by age structure, specifically what proportion of the population in a region was born before smallpox vaccination cessation. We contribute an open (and living) database of all subnational susceptibility estimates and uncertainties as an immediate resource for the global health community working on the monkeypox outbreak.Implications of all of the available evidenceOur findings highlight the need to consider spatial clustering of susceptible individuals and the importance of fine-scale spatial analysis in light of increased risk of orthopoxvirus emergence. In the event that transmission becomes widespread during the 2022 global monkeypox outbreak, our vulnerability map can inform public health efforts on identifying non-endemic regions and age cohorts at greatest risk, allocation of scarce vaccine supplies, and predicting transmission dynamics in concert with data on contact patterns, mobility and real-time prevalence.

The explosive outbreaks of COVID-19 seen in congregate settings such as prisons and nursing homes, has highlighted a critical need for effective outbreak prevention and mitigation strategies for these settings. Here we consider how different types of control interventions impact the expected number of symptomatic infections due to outbreaks. Introduction of disease into the resident population from the community is modeled as a stochastic point process coupled to a branching process, while spread between residents is modeled via a deterministic compartmental model that accounts for depletion of susceptible individuals. Control is modeled as a proportional decrease in the number of susceptible residents, the reproduction number, and/or the proportion of symptomatic infections. This permits a range of assumptions about the density dependence of transmission and modes of protection by vaccination, depopulation and other types of control. We find that vaccination or depopulation can have a greater than linear effect on the expected number of cases. For example, assuming a reproduction number of 3.0 with density-dependent transmission, we find that preemptively reducing the size of the susceptible population by 20% reduced overall disease burden by 47%. In some circumstances, it may be possible to reduce the risk and burden of disease outbreaks by optimizing the way a group of residents are apportioned into distinct residential units. The optimal apportionment may be different depending on whether the goal is to reduce the probability of an outbreak occurring, or the expected number of cases from outbreak dynamics. In other circumstances there may be an opportunity to implement reactive disease control measures in which the number of susceptible individuals is rapidly reduced once an outbreak has been detected to occur. Reactive control is most effective when the reproduction number is not too high, and there is minimal delay in implementing control. We highlight the California state prison system as an example for how these findings provide a quantitative framework for understanding disease transmission in congregate settings. Our approach and accompanying interactive website (https://phoebelu.shinyapps.io/DepopulationModels/) provides a quantitative framework to evaluate the potential impact of policy decisions governing infection control in outbreak settings.

Background: Despite significant advances in statistical approaches and data collection for analyzing wildlife movements over the last 50 years, there are limited analytical frameworks to be applied when spatial data are collected for purposes other than analyzing movement. Data collected for other purposes (e.g., sporadic captures or survival checks via telemetry) generally have lower temporal frequency or spatial precision than data collected to analyze fine-scale animal movement. The coarseness of the former renders them poorly suited for analysis using existing statistical tools. Methods: We propose a new way of estimating animal movement trajectories by integrating variable quality location data – including frequent but spatially coarse, irregularly-shaped polygon location data arising from VHF telemetry as well as less frequent, more spatially precise location data – using functional data analysis combined with a spatial resampling algorithm. We apply this method to analyze location data from the reintroduced Channel Island fox (Urocyon littoralis) subspecies population on Santa Rosa Island, California, which were collected from 2003-2012 for purposes other than movement analyses but provide an ideal case study to develop and test these novel methods. Results: By combining coarse-grained location knowledge, obtained through field notes and expert interpretation, with more precise location data, we reconstructed individual animal movement trajectories and demonstrated the utility of combining such data. Through the population ensemble of reconstructed trajectories, we learned that captive-born Channel Island foxes exhibited stronger seasonal movements than wild-born foxes, and most long-range movements occurred within the first two years of a fox’s time on the island. Conclusions: This methodology capitalizes on frequently overlooked but valuable spatial data, often found in field notes and expert knowledge, to reconstruct animal movements. Our approach has wide application to systems in which data of variable quality are collected for purposes other than studying movement and could have beneficial applications in species conservation, landscape ecology, disease management, and population monitoring.

Aerosol and Surface Stability of SARS-CoV-2 In this research letter, investigators report on the stability of SARS-CoV-2 and SARS-CoV-1 under experimental conditions. The viability of the two virus...

In the past two decades, three coronaviruses with ancestral origins in bats have emerged and caused widespread outbreaks in humans, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first SARS epidemic in 2002–2003, the appreciation of bats as key hosts of zoonotic coronaviruses has advanced rapidly. More than 4,000 coronavirus sequences from 14 bat families have been identified, yet the true diversity of bat coronaviruses is probably much greater. Given that bats are the likely evolutionary source for several human coronaviruses, including strains that cause mild upper respiratory tract disease, their role in historic and future pandemics requires ongoing investigation. We review and integrate information on bat–coronavirus interactions at the molecular, tissue, host and population levels. We identify critical gaps in knowledge of bat coronaviruses, which relate to spillover and pandemic risk, including the pathways to zoonotic spillover, the infection dynamics within bat reservoir hosts, the role of prior adaptation in intermediate hosts for zoonotic transmission and the viral genotypes or traits that predict zoonotic capacity and pandemic potential. Filling these knowledge gaps may help prevent the next pandemic., Bats harbour a multitude of coronaviruses and owing to their diversity and wide distribution are prime reservoir hosts of emerging viruses. Ruiz-Aravena, McKee and colleagues analyse the currently available information on bat coronaviruses and discuss their role in recent and potential future spillovers.

Since emerging in late 2019, SARS-CoV-2 has caused a global pandemic, and it may become an endemic human pathogen. Understanding the impact of environmental conditions on SARS-CoV-2 viability and its transmission potential is crucial to anticipating epidemic dynamics and designing mitigation strategies. Ambient temperature and humidity are known to have strong effects on the environmental stability of viruses (1) , but there is little data for SARS-CoV-2, and a general quantitative understanding of how temperature and humidity affect virus stability has remained elusive. Here, we characterise the stability of SARS-CoV-2 on an inert surface at a variety of temperature and humidity conditions, and introduce a mechanistic model that enables accurate prediction of virus stability in unobserved conditions. We find that SARS-CoV-2 survives better at low temperatures and extreme relative humidities; median estimated virus half-life was more than 24 hours at 10 °C and 40 % RH, but approximately an hour and a half at 27 °C and 65 % RH. Our results highlight scenarios of particular transmission risk, and provide a mechanistic explanation for observed superspreading events in cool indoor environments such as food processing plants. Moreover, our model predicts observations from other human coronaviruses and other studies of SARS-CoV-2, suggesting the existence of shared mechanisms that determine environmental stability across a number of enveloped viruses.

The explosive outbreaks of COVID-19 seen in congregate settings such as prisons and nursing homes, has highlighted a critical need for effective outbreak prevention and mitigation strategies for these settings. Here we consider how different types of control interventions impact the expected number of symptomatic infections due to outbreaks. Introduction of disease into the resident population from the community is modeled as a stochastic point process coupled to a branching process, while spread between residents is modeled via a deterministic compartmental model that accounts for depletion of susceptible individuals. Control is modeled as a proportional decrease in the number of susceptible residents, the reproduction number, and/or the proportion of symptomatic infections. This permits a range of assumptions about the density dependence of transmission and modes of protection by vaccination, depopulation and other types of control. We find that vaccination or depopulation can have a greater than linear effect on the expected number of cases. For example, assuming a reproduction number of 3.0 with density-dependent transmission, we find that preemptively reducing the size of the susceptible population by 20% reduced overall disease burden by 47%. In some circumstances, it may be possible to reduce the risk and burden of disease outbreaks by optimizing the way a group of residents are apportioned into distinct residential units. The optimal apportionment may be different depending on whether the goal is to reduce the probability of an outbreak occurring, or the expected number of cases from outbreak dynamics. In other circumstances there may be an opportunity to implement reactive disease control measures in which the number of susceptible individuals is rapidly reduced once an outbreak has been detected to occur. Reactive control is most effective when the reproduction number is not too high, and there is minimal delay in implementing control. We highlight the California state prison system as an example for how these findings provide a quantitative framework for understanding disease transmission in congregate settings. Our approach and accompanying interactive website (https://phoebelu.shinyapps.io/DepopulationModels/) provides a quantitative framework to evaluate the potential impact of policy decisions governing infection control in outbreak settings.

RNA viruses, such as hepatitis C virus (HCV), influenza virus, and SARS-CoV-2, are notorious for their ability to evolve rapidly under selection in novel environments. It is known that the high mutation rate of RNA viruses can generate huge genetic diversity to facilitate viral adaptation. However, less attention has been paid to the underlying fitness landscape that represents the selection forces on viral genomes, especially under different selection conditions. Here, we systematically quantified the distribution of fitness effects of about 1,600 single amino acid substitutions in the drug-targeted region of NS5A protein of HCV. We found that the majority of nonsynonymous substitutions incur large fitness costs, suggesting that NS5A protein is highly optimized. The replication fitness of viruses is correlated with the pattern of sequence conservation in nature, and viral evolution is constrained by the need to maintain protein stability. We characterized the adaptive potential of HCV by subjecting the mutant viruses to selection by the antiviral drug daclatasvir at multiple concentrations. Both the relative fitness values and the number of beneficial mutations were found to increase with the increasing concentrations of daclatasvir. The changes in the spectrum of beneficial mutations in NS5A protein can be explained by a pharmacodynamics model describing viral fitness as a function of drug concentration. Overall, our results show that the distribution of fitness effects of mutations is modulated by both the constraints on the biophysical properties of proteins (i.e., selection pressure for protein stability) and the level of environmental stress (i.e., selection pressure for drug resistance). IMPORTANCE Many viruses adapt rapidly to novel selection pressures, such as antiviral drugs. Understanding how pathogens evolve under drug selection is critical for the success of antiviral therapy against human pathogens. By combining deep sequencing with selection experiments in cell culture, we have quantified the distribution of fitness effects of mutations in hepatitis C virus (HCV) NS5A protein. Our results indicate that the majority of single amino acid substitutions in NS5A protein incur large fitness costs. Simulation of protein stability suggests viral evolution is constrained by the need to maintain protein stability. By subjecting the mutant viruses to selection under an antiviral drug, we find that the adaptive potential of viral proteins in a novel environment is modulated by the level of environmental stress, which can be explained by a pharmacodynamics model. Our comprehensive characterization of the fitness landscapes of NS5A can potentially guide the design of effective strategies to limit viral evolution.

Ecological and evolutionary processes govern the fitness, propagation, and interactions of organisms through space and time, and viruses are no exception. While coronavirus disease 2019 (COVID-19) research has primarily emphasized virological, clinical, and epidemiological perspectives, crucial aspects of the pandemic are fundamentally ecological or evolutionary. Here, we highlight five conceptual domains of ecology and evolution - invasion, consumer-resource interactions, spatial ecology, diversity, and adaptation - that illuminate (sometimes unexpectedly) the emergence and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We describe the applications of these concepts across levels of biological organization and spatial scales, including within individual hosts, host populations, and multispecies communities. Together, these perspectives illustrate the integrative power of ecological and evolutionary ideas and highlight the benefits of interdisciplinary thinking for understanding emerging viruses.

The spirochete bacterium Leptospira interrogans serovar Pomona is enzootic to California sea lions (CSL; Zalophus californianus) and causes periodic epizootics. Leptospirosis in CSL is associated with a high fatality rate in rehabilitation. Evidence-based tools for estimating prognosis and guiding early euthanasia of animals with a low probability of survival are critical to reducing the severity and duration of animal suffering. Classification and regression tree (CART) analysis of clinical data was used to predict survival outcomes of CSL with leptospirosis in rehabilitation. Classification tree outputs are binary decision trees that can be readily interpreted and applied by a clinician. Models were trained using data from cases treated from 2017 to 2018 at The Marine Mammal Center in Sausalito, CA, and tested against data from cases treated from 2010 to 2012. Two separate classification tree analyses were performed, one including and one excluding data from euthanized animals. When data from natural deaths and euthanasias were included in model-building, the best classification tree predicted outcomes correctly for 84.7% of cases based on four variables: appetite over the first 3 days in care, and blood urea nitrogen (BUN), creatinine, and sodium at admission. When only natural deaths were included, the best model predicted outcomes correctly for 87.6% of cases based on BUN and creatinine at admission. This study illustrates that CART analysis can be successfully applied to wildlife in rehabilitation to establish evidence-based euthanasia criteria with the goal of minimizing animal suffering. In the context of a large epizootic that challenges the limits of a facility's capacity for care, the models can assist in maximizing allocation of resources to those animals with the highest predicted probability of survival. This technique may be a useful tool for other diseases seen in wildlife rehabilitation.

The spirochete bacterium

Background For many emerging or re-emerging pathogens, cases in humans arise from a mixture of introductions (via zoonotic spillover from animal reservoirs or geographic spillover from endemic regions) and secondary human-to-human transmission. Interventions aiming to reduce incidence of these infections can be focused on preventing spillover or reducing human-to-human transmission, or sometimes both at once, and typically are governed by resource constraints that require policymakers to make choices. Despite increasing emphasis on using mathematical models to inform disease control policies, little attention has been paid to guiding rational disease control at the animal-human interface. Methods We introduce a modeling framework to analyze the impacts of different disease control policies, focusing on pathogens exhibiting subcritical transmission among humans (i.e. pathogens that cannot establish sustained human-to-human transmission). We quantify the relative effectiveness of measures to reduce spillover (e.g. reducing contact with animal hosts), human-to-human transmission (e.g. case isolation), or both at once (e.g. vaccination), across a range of epidemiological contexts. Results We provide guidelines for choosing which mode of control to prioritize in different epidemiological scenarios and considering different levels of resource and relative costs. We contextualize our analysis with current zoonotic pathogens and other subcritical pathogens, such as post-elimination measles, and control policies that have been applied. Conclusions Our work provides a model-based, theoretical foundation to understand and guide policy for subcritical zoonoses, integrating across disciplinary and species boundaries in a manner consistent with One Health principles.

Decontamination helps limit environmental transmission of infectious agents. It is required for the safe re-use of contaminated medical, laboratory and personal protective equipment, and for the safe handling of biological samples. Heat treatment is a common decontamination method, notably used for viruses. We show that for liquid specimens (here, solution of SARS-CoV-2 in cell culture medium), virus inactivation rate under heat treatment at 70°C can vary by almost two orders of magnitude depending on the treatment procedure, from a half-life of 0.86 min (95% credible interval: [0.09, 1.77]) in closed vials in a heat block to 37.00 min ([12.65, 869.82]) in uncovered plates in a dry oven. These findings suggest a critical role of evaporation in virus inactivation via dry heat. Placing samples in open or uncovered containers may dramatically reduce the speed and efficacy of heat treatment for virus inactivation. Given these findings, we reviewed the literature temperature-dependent coronavirus stability and found that specimen containers, and whether they are closed, covered, or uncovered, are rarely reported in the scientific literature. Heat-treatment procedures must be fully specified when reporting experimental studies to facilitate result interpretation and reproducibility, and must be carefully considered when developing decontamination guidelines.ImportanceHeat is a powerful weapon against most infectious agents. It is widely used for decontamination of medical, laboratory and personal protective equipment, and for biological samples. There are many methods of heat treatment, and methodological details can affect speed and efficacy of decontamination. We applied four different heat-treatment procedures to liquid specimens containing SARS-CoV-2. Our results show that the container used to store specimens during decontamination can substantially affect inactivation rate: for a given initial level of contamination, decontamination time can vary from a few minutes in closed vials to several hours in uncovered plates. Reviewing the literature, we found that container choices and heat treatment methods are only rarely reported explicitly in methods sections. Our study shows that careful consideration of heat-treatment procedure — in particular the choice of specimen container, and whether it is covered — can make results more consistent across studies, improve decontamination practice, and provide insight into the mechanisms of virus inactivation.

The unprecedented pandemic of SARS-CoV-2 has created worldwide shortages of personal protective equipment, in particular respiratory protection such as N95 respirators. SARS-CoV-2 transmission is frequently occurring in hospital settings, with numerous reported cases of nosocomial transmission highlighting the vulnerability of healthcare workers. In general, N95 respirators are designed for single use prior to disposal. Several groups have addressed the potential for re-use of N95 respirators from a mechanical or from a decontamination perspective. Here, we analyzed four different decontamination methods – UV radiation (260 – 285 nm), 70ºC heat, 70% ethanol and vaporized hydrogen peroxide (VHP) – for their ability to reduce contamination with infectious SARS-CoV-2 and their effect on N95 respirator function.

The coronavirus pandemic has created worldwide shortages of N95 respirators. We analyzed 4 decontamination methods for effectiveness in deactivating severe acute respiratory syndrome coronavirus 2 virus and effect on respirator function. Our results indicate that N95 respirators can be decontaminated and reused, but the integrity of respirator fit and seal must be maintained.

Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a major neglected tropical disease (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve individual-level and population-level understanding of this Leptospira reservoir system. We create a “host-pathogen space” by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to characterize changes in these values through time. Data from these individuals describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health but sampled only once. Our framework enables us to determine an individual’s location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and use these to impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space using quantitative biomarker data enables more nuanced understanding of an individual’s time course of infection, duration of immunity, and probability of being infectious. Such maps also make efficient use of limited data for rare or poorly understood diseases, by providing a means to rapidly assess the range and extent of potential clinical and immunological profiles. These approaches yield benefits for clinicians needing to triage patients, prevent transmission, and assess immunity, and for disease ecologists or epidemiologists working to develop appropriate risk management strategies to reduce transmission risk on a population scale (e.g. model parameterization using more accurate estimates of duration of immunity and infectiousness) and to assess health impacts on a population scale., Author summary A pathogen can cause variable disease severity across different host individuals, and these presentations change over the time-course from infection to recovery. In addition, different pathogens may induce similar clinical presentations. These facts complicate efforts to identify infections caused by rare or neglected pathogens and to understand factors governing disease spread in humans and animals, particularly when data are limited. These biological complexities are omitted from classical approaches to modeling infectious disease, which typically rely on discrete and well-defined disease states. Here we show that by analyzing multiple biomarkers of health and infection simultaneously, treating these values as quantitative rather than binary indicators, and including a modest amount of longitudinal sampling of hosts, we can create a map of the host-pathogen interaction that shows the full spectrum of disease presentations and opens doors for new insights and predictions. By accounting for individual variation and capturing changes through time since infection, this mapping framework enables more robust interpretation of cross-sectional data; e.g., to detect predictive relationships between biomarkers and key outcomes such as survival, or to assess whether observed disease is associated with the pathogen of interest. This approach can help epidemiologists, ecologists and clinicians to better study and manage the many infectious diseases that exhibit complex relationships with their hosts.

Our ability to understand and mitigate the spread of SARS-CoV-2 depends largely on antibody and viral RNA data that provide information about past exposure and shedding. Five months into the outbreak there is an impressive number of studies reporting antibody kinetics and RNA shedding dynamics, but extensive variation in detection assays, study group demographics, and laboratory protocols has presented a challenge for inferring the true biological patterns. Here, we combine existing data on SARS-CoV-2 IgG, IgM and RNA kinetics using a formal quantitative approach that enables integration of 3,214 data points from 516 individuals, published in 22 studies. This allows us to determine the mean values and distributions of IgG and IgM seroconversion times and titer kinetics, and to characterize how antibody and RNA detection probabilities change during the early phase of infection. We observe extensive variation in antibody response patterns and RNA detection patterns, explained by both individual heterogeneity and protocol differences such as targeted antigen and sample type. These results provide a robust reference for clinical management of individual patients, and a foundation for the mathematical models and serological surveys that underpin public health policies.

As the COVID-19 pandemic continues, mathematical epidemiologists share their views on what models reveal about how the disease has spread, the current state of play and what work still needs to be done., The contributors Alessandro Vespignani is an Italian-American physicist, currently Sternberg Family Distinguished University Professor of Physics, Computer Science and Health Sciences at Northeastern University in Boston, USA. He is the director of the Network Science Institute, and is best known for his work on complex networks and his contributions to computational epidemiology by developing specific tools for analysing the global spread of epidemics. Huaiyu Tian is a Professor of Ecology and Evolution of Infectious Disease at Beijing Normal University, China, and Oxford Martin Visiting Fellow at the University of Oxford. His interdisciplinary research focuses on the mechanistic processes that link biological and ecological change to disease dynamics. His lab combines geospatial computing, field surveillance, molecular epidemiology and ecological modelling. Christopher Dye is a Visiting Professor of Zoology and Oxford Martin Visiting Fellow at the University of Oxford. He is a former Director of Strategy at the World Health Organization, and is currently editor of coronavirusexplained.ukri.org/en/, a website that explains the science of coronavirus outbreaks, hosted by UK Research and Innovation (UKRI). James O. Lloyd-Smith is a professor in the Department of Ecology and Evolutionary Biology at the University of California, Los Angeles. His research explores the ecological and evolutionary dynamics of infectious disease in animal and human populations, with emphasis on the emergence of novel pathogens. His group combines mathematical models, statistical analysis, and laboratory, clinical and field studies to study diseases such as monkeypox, leptospirosis, influenza and now COVID-19. Rosalind M. Eggo is a mathematical modeller focusing on directly transmitted viral pathogens and the severe outcomes resulting from infection. She works at the London School of Hygiene and Tropical Medicine. Munik Shrestha is a postdoctoral researcher at Northeastern University in the Network Science Institute. He has contributed fundamental theory on message-passing algorithms for estimating the time-varying statistics of epidemics. He received his Ph.D. in physics from the University of New Mexico and was a Santa Fe Institute graduate fellow. Samuel V. Scarpino is an assistant professor in the Network Science Institute at Northeastern University, with appointments in marine and environmental sciences, physics, and health sciences. He is also an ISI Foundation complexity fellow. Scarpino earned a Ph.D. in evolution, ecology and behaviour from the University of Texas at Austin and was a Santa Fe Institute Omidyar postdoctoral fellow. Bernardo Gutierrez is a D.Phil. student in the Department of Zoology at the University of Oxford, and a guest researcher at Universidad San Francisco de Quito. He studies the evolution of emerging viruses and the integration of epidemiological and genomic data to track outbreaks. He began contributing to the Open COVID-19 Data Working Group during the start of the SARS-CoV-2 epidemic in January 2020. Moritz U. G. Kraemer is a research fellow at the University of Oxford and founder of the Open COVID-19 Data Working Group. Moritz is an epidemiologist working on the integration of multiple data streams to better understand the dynamics of emerging infectious diseases. Joseph Wu is a professor in the School of Public Health at the University of Hong Kong. He specializes in disease modelling and data science. Kathy Leung is an infectious disease epidemiologist at the University of Hong Kong. She specializes in mathematical modelling of communicable and non-communicable diseases, including COVID-19, influenza, Middle East respiratory syndrome, human papillomavirus, colorectal cancer and breast cancer. Gabriel M. Leung is an infectious disease epidemiologist, Dean of Medicine and Zimmern Professor of Population Health at the University of Hong Kong.

To the EditorA novel human coronavirus, now named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, referred to as HCoV-19 here) that emerged in Wuhan, China in late 2019 is now causing a pandemic1. Here, we analyze the aerosol and surface stability of HCoV-19 and compare it with SARS-CoV-1, the most closely related human coronavirus.2 We evaluated the stability of HCoV-19 and SARS-CoV-1 in aerosols and on different surfaces and estimated their decay rates using a Bayesian regression model (see Supplementary Appendix). All experimental measurements are reported as mean across 3 replicates.

Author(s): Gostic, Katelyn M; Gomez, Ana CR; Mummah, Riley O; Kucharski, Adam J; Lloyd-Smith, James O | Abstract: Traveller screening is being used to limit further global spread of 2019 novel coronavirus (nCoV) following its recent emergence. Here, we project the impact of different travel screening programs given remaining uncertainty around the values of key nCoV life history and epidemiological parameters. Even under best-case assumptions, we estimate that screening will miss more than half of infected travellers. Breaking down the factors leading to screening successes and failures, we find that most cases missed by screening are fundamentally undetectable, because they have not yet developed symptoms and are unaware they were exposed. These findings emphasize the need for measures to track travellers who become ill after being missed by a travel screening program. We make our model available for interactive use so stakeholders can explore scenarios of interest using the most up-to-date information. We hope these findings contribute to evidence-based policy to combat the spread of nCoV, and to prospective planning to mitigate future emerging pathogens.