Inferring COVID-19 testing and vaccination behavior from New Jersey testing data.

Characterizing the relationship between disease testing behaviors and infectious disease dynamics is of great importance for public health. Tests for both current and past infection can influence disease-related behaviors at the individual level, while population-level knowledge of an epidemic's course may feed back to affect one's likelihood of taking a test. The COVID-19 pandemic has generated testing data on an unprecedented scale for tests detecting both current infection (PCR, antigen) and past infection (serology); this opens the way to characterizing the complex relationship between testing behavior and infection dynamics. Leveraging a rich database of individualized COVID-19 testing histories in New Jersey, we analyze the behavioral relationships between PCR and serology tests, infection, and vaccination. We quantify interactions between individuals' test-taking tendencies and their past testing and infection histories, finding that PCR tests were disproportionately taken by people currently infected, and serology tests were disproportionately taken by people with past infection or vaccination. The effects of previous positive test results on testing behavior are less consistent, as individuals with past PCR positives were more likely to take subsequent PCR and serology tests at some periods of the epidemic time course and less likely at others. Lastly, we fit a model to the titer values collected from serology tests to infer vaccination trends, finding a marked decrease in vaccination rates among individuals who had previously received a positive PCR test. These results exemplify the utility of individualized testing histories in uncovering hidden behavioral variables affecting testing and vaccination.

The diversity of cGLR receptors: shedding new light on innate immunity.

The characterization of a new group of innate pattern recognition receptors detected in >500 species across the tree of life by Li et al. reveals surprising commonalities and peculiarities shared with other innate receptors. Receptor diversity within and among species opens the way to reconsidering the costs and benefits of innate immune recognition.

The evolution of powerful yet perilous immune systems.

The mammalian immune system packs serious punch against infection but can also cause harm: for example, coronavirus disease 2019 (COVID-19) made headline news of the simultaneous power and peril of human immune responses. In principle, natural selection leads to exquisite adaptation and therefore cytokine responsiveness that optimally balances the benefits of defense against its costs (e.g., immunopathology suffered and resources expended). Here, we illustrate how evolutionary biology can predict such optima and also help to explain when/why individuals exhibit apparently maladaptive immunopathological responses. Ultimately, we argue that the evolutionary legacies of multicellularity and life-history strategy, in addition to our coevolution with symbionts and our demographic history, together explain human susceptibility to overzealous, pathology-inducing cytokine responses. Evolutionary insight thereby complements molecular/cellular mechanistic insights into immunopathology.

Why do some coronaviruses become pandemic threats when others do not?

Despite multiple spillover events and short chains of transmission on at least 4 continents, Middle East Respiratory Syndrome Coronavirus (MERS-CoV) has never triggered a pandemic. By contrast, its relative, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has, despite apparently little, if any, previous circulation in humans. Resolving the unsolved mystery of the failure of MERS-CoV to trigger a pandemic could help inform how we understand the pandemic potential of pathogens, and probing it underscores a need for a more holistic understanding of the ways in which viral genetic changes scale up to population-level transmission.

Impact of waning immunity against SARS-CoV-2 severity exacerbated by vaccine hesitancy.

The SARS-CoV-2 pandemic has generated a considerable number of infections and associated morbidity and mortality across the world. Recovery from these infections, combined with the onset of large-scale vaccination, have led to rapidly-changing population-level immunological landscapes. In turn, these complexities have highlighted a number of important unknowns related to the breadth and strength of immunity following recovery or vaccination. Using simple mathematical models, we investigate the medium-term impacts of waning immunity against severe disease on immuno-epidemiological dynamics. We find that uncertainties in the duration of severity-blocking immunity (imparted by either infection or vaccination) can lead to a large range of medium-term population-level outcomes (i.e. infection characteristics and immune landscapes). Furthermore, we show that epidemiological dynamics are sensitive to the strength and duration of underlying host immune responses; this implies that determining infection levels from hospitalizations requires accurate estimates of these immune parameters. More durable vaccines both reduce these uncertainties and alleviate the burden of SARS-CoV-2 in pessimistic outcomes. However, heterogeneity in vaccine uptake drastically changes immune landscapes toward larger fractions of individuals with waned severity-blocking immunity. In particular, if hesitancy is substantial, more robust vaccines have almost no effects on population-level immuno-epidemiology, even if vaccination rates are compensatorily high among vaccine-adopters. This pessimistic scenario for vaccination heterogeneity arises because those few individuals that are vaccine-adopters are so readily re-vaccinated that the duration of vaccinal immunity has no appreciable consequences on their immune status. Furthermore, we find that this effect is heightened if vaccine-hesitants have increased transmissibility (e.g. due to riskier behavior). Overall, our results illustrate the necessity to characterize both transmission-blocking and severity-blocking immune time scales. Our findings also underline the importance of developing robust next-generation vaccines with equitable mass vaccine deployment.

Long-term benefits of nonpharmaceutical interventions for endemic infections are shaped by respiratory pathogen dynamics.

COVID-19 nonpharmaceutical interventions (NPIs), including mask wearing, have proved highly effective at reducing the transmission of endemic infections. A key public health question is whether NPIs could continue to be implemented long term to reduce the ongoing burden from endemic pathogens. Here, we use epidemiological models to explore the impact of long-term NPIs on the dynamics of endemic infections. We find that the introduction of NPIs leads to a strong initial reduction in incidence, but this effect is transient: As susceptibility increases, epidemics return while NPIs are in place. For low R0 infections, these return epidemics are of reduced equilibrium incidence and epidemic peak size. For high R0 infections, return epidemics are of similar magnitude to pre-NPI outbreaks. Our results underline that managing ongoing susceptible buildup, e.g., with vaccination, remains an important long-term goal.

Closing the gap in the Janzen-Connell hypothesis: What determines pathogen diversity?

The high tree diversity in tropical forests has long been a puzzle to ecologists. In the 1970s, Janzen and Connell proposed that tree species (hosts) coexist due to the stabilizing actions of specialized enemies. This Janzen-Connell hypothesis was subsequently supported by theoretical studies. Yet, such studies have taken the presence of specialized pathogens for granted, overlooking that pathogen coexistence also requires an explanation. Moreover, stable ecological coexistence does not necessarily imply evolutionary stability. What are the conditions that allow Janzen-Connell effects to evolve? We link theory from community ecology, evolutionary biology and epidemiology to tackle this question, structuring our approach around five theoretical frameworks. Phenomenological Lotka-Volterra competition models provide the most basic framework, which can be restructured to include (single- or multi-)pathogen dynamics. This ecological foundation can be extended to include pathogen evolution. Hosts, of course, may also evolve, and we introduce a coevolutionary model, showing that host-pathogen coevolution can lead to highly diverse systems. Our work unpacks the assumptions underpinning Janzen-Connell and places theoretical bounds on pathogen and host ecology and evolution. The five theoretical frameworks taken together provide a stronger theoretical basis for Janzen-Connell, delivering a wider lens that can yield important insights into the maintenance of diversity in these increasingly threatened systems.

Why are there so few (or so many) circulating coronaviruses?

Despite vast diversity in non-human hosts and conspicuous recent spillover events, only a small number of coronaviruses have been observed to persist in human populations. This puzzling mismatch suggests substantial barriers to establishment. We detail hypotheses that might contribute to explain the low numbers of endemic coronaviruses, despite their considerable evolutionary and emergence potential. We assess possible explanations ranging from issues of ascertainment, historically lower opportunities for spillover, aspects of human demographic changes, and features of pathogen biology and pre-existing adaptive immunity to related viruses. We describe how successful emergent viral species must triangulate transmission, virulence, and host immunity to maintain circulation. Characterizing the factors that might shape the limits of viral persistence can delineate promising research directions to better understand the combinations of pathogens and contexts that are most likely to lead to spillover.

serosim: An R package for simulating serological data arising from vaccination, epidemiological and antibody kinetics processes.

serosim is an open-source R package designed to aid inference from serological studies, by simulating data arising from user-specified vaccine and antibody kinetics processes using a random effects model. Serological data are used to assess population immunity by directly measuring individuals' antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological data and new analytical techniques used to interpret them are increasingly widespread. This creates a need for tools to simulate serological studies and the processes underlying observed titer values, as this will enable researchers to identify best practices for serological study design, and provide a standardized framework to evaluate the performance of different inference methods. serosim allows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological studies, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods.

Assessing the impact of preventative measures to limit the spread of Toxoplasma gondii in wild carnivores of Madagascar.

Novel multihost pathogens can threaten endangered wildlife species, as well as humans and domestic animals. The zoonotic protozoan parasite Toxoplasma gondii is transmitted by members of Felidae and can infect a large number of animal species, including humans. This parasite can have significant health consequences for infected intermediate hosts and could further endanger wild carnivore populations of Madagascar. Building on an empirical characterization of the prevalence of the pathogen in local mammals, we used mathematical models of pathogen transmission in a multihost community to compare preventative measures that aim to limit the spread of this parasite in wild carnivores. Specifically, we examined the effect of hypothetical cat vaccination and population control campaigns on reducing the risk of infection by T. gondii in wild Eupleridae. Our model predicted that the prevalence of exposure to T. gondii in cats would be around 72% and that seroprevalence would reach 2% and 43% in rodents and wild carnivores, respectively. Reducing the rodent population in the landscape by half may only decrease the prevalence of T. gondii in carnivores by 10%. Similarly, cat vaccination and reducing the population of definitive hosts had limited impact on the prevalence of T. gondii in wild carnivorans of Madagascar. A significant reduction in prevalence would require extremely high vaccination, low turnover, or both in the cat population. Other potential control methods of T. gondii in endangered Eupleridae include targeted vaccination of wild animals but would require further investigation. Eliminating the threat entirely will be difficult because of the ubiquity of cats and the persistence of the parasite in the environment.

Evaluación del impacto de las medidas preventivas para limitar el contagio de Toxoplasma gondii en los carnívoros silvestres de Madagascar Resumen Los patógenos novedosos con múltiples hospederos pueden amenazar tanto a las especies silvestres como a los humanos y a los animales domésticos. Los miembros de la familia Felidae transmiten el protozoario parásito Toxoplasma gondii, el cual puede infectar a un gran número de especies animales, incluyendo al humano. Este parásito puede generar consecuencias importantes para la salud en los hospederos intermediarios infectados y podría poner más en peligro a las poblaciones de carnívoros silvestres de Madagascar. Usamos modelos matemáticos de la transmisión de patógenos en una comunidad con múltiples hospederos a partir de una caracterización empírica de la prevalencia del patógeno en los mamíferos locales para comparar las medidas preventivas que buscan limitar la transmisión de este parásito en los carnívoros silvestres. En específico, examinamos el efecto de la vacunación hipotética de felinos y las campañas de control poblacional sobre la reducción del riesgo de infección de T. gondii en los Eupleridae silvestres. Nuestro modelo predijo que la prevalencia de la exposición a T. gondii en los felinos sería de un 72% y que la seroprevalencia llegaría al 2% y al 43% en los roedores y carnívoros silvestres, respectivamente. La reducción a la mitad de la población de roedores en el paisaje podría disminuir sólo en un 10% la prevalencia del protozoario en los carnívoros. De forma similar, la vacunación y la reducción de la población de hospederos definitivos tuvieron un impacto limitado sobre la prevalencia de T. gondii en los carnívoros silvestres de Madagascar. Una reducción significativa en la prevalencia requeriría que la población de felinos tuviera una vacunación extremadamente elevada, baja rotación, o ambas. Otros métodos potenciales de control de T. gondii en los Eupleridae incluyen la vacunación de animales silvestres, pero requieren de mayor investigación. La eliminación completa de la amenaza será difícil por la ubicuidad de los felinos y la persistencia del parásito en el ambiente.

Comparing and linking machine learning and semi-mechanistic models for the predictability of endemic measles dynamics.

Measles is one the best-documented and most-mechanistically-studied non-linear infectious disease dynamical systems. However, systematic investigation into the comparative performance of traditional mechanistic models and machine learning approaches in forecasting the transmission dynamics of this pathogen are still rare. Here, we compare one of the most widely used semi-mechanistic models for measles (TSIR) with a commonly used machine learning approach (LASSO), comparing performance and limits in predicting short to long term outbreak trajectories and seasonality for both regular and less regular measles outbreaks in England and Wales (E&W) and the United States. First, our results indicate that the proposed LASSO model can efficiently use data from multiple major cities and achieve similar short-to-medium term forecasting performance to semi-mechanistic models for E&W epidemics. Second, interestingly, the LASSO model also captures annual to biennial bifurcation of measles epidemics in E&W caused by susceptible response to the late 1940s baby boom. LASSO may also outperform TSIR for predicting less-regular dynamics such as those observed in major cities in US between 1932-45. Although both approaches capture short-term forecasts, accuracy suffers for both methods as we attempt longer-term predictions in highly irregular, post-vaccination outbreaks in E&W. Finally, we illustrate that the LASSO model can both qualitatively and quantitatively reconstruct mechanistic assumptions, notably susceptible dynamics, in the TSIR model. Our results characterize the limits of predictability of infectious disease dynamics for strongly immunizing pathogens with both mechanistic and machine learning models, and identify connections between these two approaches.

The impact of COVID-19 nonpharmaceutical interventions on the future dynamics of endemic infections.

Nonpharmaceutical interventions (NPIs) have been employed to reduce the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), yet these measures are already having similar effects on other directly transmitted, endemic diseases. Disruptions to the seasonal transmission patterns of these diseases may have consequences for the timing and severity of future outbreaks. Here we consider the implications of SARS-CoV-2 NPIs for two endemic infections circulating in the United States of America: respiratory syncytial virus (RSV) and seasonal influenza. Using laboratory surveillance data from 2020, we estimate that RSV transmission declined by at least 20% in the United States at the start of the NPI period. We simulate future trajectories of both RSV and influenza, using an epidemic model. As susceptibility increases over the NPI period, we find that substantial outbreaks of RSV may occur in future years, with peak outbreaks likely occurring in the winter of 2021-2022. Longer NPIs, in general, lead to larger future outbreaks although they may display complex interactions with baseline seasonality. Results for influenza broadly echo this picture, but are more uncertain; future outbreaks are likely dependent on the transmissibility and evolutionary dynamics of circulating strains.

Successive passaging of a plant-associated microbiome reveals robust habitat and host genotype-dependent selection.

There is increasing interest in the plant microbiome as it relates to both plant health and agricultural sustainability. One key unanswered question is whether we can select for a plant microbiome that is robust after colonization of target hosts. We used a successive passaging experiment to address this question by selecting upon the tomato phyllosphere microbiome. Beginning with a diverse microbial community generated from field-grown tomato plants, we inoculated replicate plants across 5 plant genotypes for 4 45-d passages, sequencing the microbial community at each passage. We observed consistent shifts in both the bacterial (16S amplicon sequencing) and fungal (internal transcribed spacer region amplicon sequencing) communities across replicate lines over time, as well as a general loss of diversity over the course of the experiment, suggesting that much of the naturally observed microbial community in the phyllosphere is likely transient or poorly adapted within the experimental setting. We found that both host genotype and environment shape microbial composition, but the relative importance of genotype declines through time. Furthermore, using a community coalescence experiment, we found that the bacterial community from the end of the experiment was robust to invasion by the starting bacterial community. These results highlight that selecting for a stable microbiome that is well adapted to a particular host environment is indeed possible, emphasizing the great potential of this approach in agriculture and beyond. In light of the consistent response of the microbiome to selection in the absence of reciprocal host evolution (coevolution) described here, future studies should address how such adaptation influences host health.

Assessing the Effects of Measles Virus Infections on Childhood Infectious Disease Mortality in Brazil.

BACKGROUND: Measles virus infection induces acute immunosuppression for weeks following infection, and also impairs preexisting immunological memory, resulting in "immune amnesia" that can last for years. Both mechanisms predispose the host to severe outcomes of subsequent infections. Therefore, measles dynamics could potentially affect the epidemiology of other infectious diseases. METHODS: To examine this hypothesis, we analyzed the annual mortality rates of children aged 1-9 years in Brazil from 1980 to 1995. We calculated the correlation between nonmeasles infectious disease mortality rates and measles mortality rates using linear and negative-binomial models, with 3 methods to control the confounding effects of time. We also estimated the duration of measles-induced immunomodulation. RESULTS: The mortality rates of nonmeasles infectious diseases and measles virus infection were highly correlated. This positive correlation remained significant after removing the time trends. We found no evidence of long-term measles immunomodulation beyond 1 year. CONCLUSIONS: These results support that measles virus infection could increase the mortality of other infectious diseases. The short lag identified for measles effects (<1 year) implies that acute immunosuppression was potentially driving this effect in Brazil. Overall, our study indicates disproportionate contributions of measles to childhood infectious disease mortality, highlighting the importance of measles vaccination.

Disentangling the dynamical underpinnings of differences in SARS-CoV-2 pathology using within-host ecological models.

Health outcomes following infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) are remarkably variable. The way the virus spreads inside hosts, and how this spread interacts with host immunity and physiology, is likely to determine variation in health outcomes. Decades of data and dynamical analyses of how other viruses spread and interact with host cells could shed light on SARS-CoV-2 within-host trajectories. We review how common axes of variation in within-host dynamics and emergent pathology (such as age and sex) might be combined with ecological principles to understand the case of SARS-CoV-2. We highlight pitfalls in application of existing theoretical frameworks relevant to the complexity of the within-host context and frame the discussion in terms of growing knowledge of the biology of SARS-CoV-2. Viewing health outcomes for SARS-CoV-2 through the lens of ecological models underscores the value of repeated measures on individuals, especially since many lines of evidence suggest important contingence on trajectory.

SARS-CoV-2 Outbreak Dynamics in an Isolated US Military Recruit Training Center With Rigorous Prevention Measures.

BACKGROUND: Marine recruits training at Parris Island experienced an unexpectedly high rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, despite preventive measures including a supervised, 2-week, pre-entry quarantine. We characterize SARS-CoV-2 transmission in this cohort. METHODS: Between May and November 2020, we monitored 2,469 unvaccinated, mostly male, Marine recruits prospectively during basic training. If participants tested negative for SARS-CoV-2 by quantitative polymerase chain reaction (qPCR) at the end of quarantine, they were transferred to the training site in segregated companies and underwent biweekly testing for 6 weeks. We assessed the effects of coronavirus disease 2019 (COVID-19) prevention measures on other respiratory infections with passive surveillance data, performed phylogenetic analysis, and modeled transmission dynamics and testing regimens. RESULTS: Preventive measures were associated with drastically lower rates of other respiratory illnesses. However, among the trainees, 1,107 (44.8%) tested SARS-CoV-2-positive, with either mild or no symptoms. Phylogenetic analysis of viral genomes from 580 participants revealed that all cases but one were linked to five independent introductions, each characterized by accumulation of mutations across and within companies, and similar viral isolates in individuals from the same company. Variation in company transmission rates (mean reproduction number R 0 ; 5.5 [95% confidence interval [CI], 5.0, 6.1]) could be accounted for by multiple initial cases within a company and superspreader events. Simulations indicate that frequent rapid-report testing with case isolation may minimize outbreaks. CONCLUSIONS: Transmission of wild-type SARS-CoV-2 among Marine recruits was approximately twice that seen in the community. Insights from SARS-CoV-2 outbreak dynamics and mutations spread in a remote, congregate setting may inform effective mitigation strategies.

Optimal immune specificity at the intersection of host life history and parasite epidemiology.

Hosts diverge widely in how, and how well, they defend themselves against infection and immunopathology. Why are hosts so heterogeneous? Both epidemiology and life history are commonly hypothesized to influence host immune strategy, but the relationship between immune strategy and each factor has commonly been investigated in isolation. Here, we show that interactions between life history and epidemiology are crucial for determining optimal immune specificity and sensitivity. We propose a demographically-structured population dynamics model, in which we explore sensitivity and specificity of immune responses when epidemiological risks vary with age. We find that variation in life history traits associated with both reproduction and longevity alters optimal immune strategies-but the magnitude and sometimes even direction of these effects depends on how epidemiological risks vary across life. An especially compelling example that explains previously-puzzling empirical observations is that depending on whether infection risk declines or rises at reproductive maturity, later reproductive maturity can select for either greater or lower immune specificity, potentially illustrating why studies of lifespan and immune variation across taxa have been inconclusive. Thus, the sign of selection on the life history-immune specificity relationship can be reversed in different epidemiological contexts. Drawing on published life history data from a variety of chordate taxa, we generate testable predictions for this facet of the optimal immune strategy. Our results shed light on the causes of the heterogeneity found in immune defenses both within and among species and the ultimate variability of the relationship between life history and immune specificity.

The required size of cluster randomized trials of nonpharmaceutical interventions in epidemic settings.

To control the SARS-CoV-2 pandemic and future pathogen outbreaks requires an understanding of which nonpharmaceutical interventions are effective at reducing transmission. Observational studies, however, are subject to biases that could erroneously suggest an impact on transmission, even when there is no true effect. Cluster randomized trials permit valid hypothesis tests of the effect of interventions on community transmission. While such trials could be completed in a relatively short period of time, they might require large sample sizes to achieve adequate power. However, the sample sizes required for such tests in outbreak settings are largely undeveloped, leaving unanswered the question of whether these designs are practical. We develop approximate sample size formulae and simulation-based sample size methods for cluster randomized trials in infectious disease outbreaks. We highlight key relationships between characteristics of transmission and the enrolled communities and the required sample sizes, describe settings where trials powered to detect a meaningful true effect size may be feasible, and provide recommendations for investigators in planning such trials. The approximate formulae and simulation banks may be used by investigators to quickly assess the feasibility of a trial, followed by more detailed methods to more precisely size the trial. For example, we show that community-scale trials requiring 220 clusters with 100 tested individuals per cluster are powered to identify interventions that reduce transmission by 40% in one generation interval, using parameters identified for SARS-CoV-2 transmission. For more modest treatment effects, or when transmission is extremely overdispersed, however, much larger sample sizes are required.

Maximizing and evaluating the impact of test-trace-isolate programs: A modeling study.

BACKGROUND: Test-trace-isolate programs are an essential part of coronavirus disease 2019 (COVID-19) control that offer a more targeted approach than many other nonpharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact. METHODS AND FINDINGS: We present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of completeness in case detection and contact tracing and speed of isolation and quarantine using parameters consistent with COVID-19 transmission (R0: 2.5, generation time: 6.5 days). We show that R is most sensitive to changes in the proportion of cases detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (<30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Results from this model also indicate that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of the impact of test-trace-isolate are sensitive to assumptions about COVID-19 natural history and adherence to isolation and quarantine, our qualitative findings are robust across numerous sensitivity analyses. CONCLUSIONS: Effective test-trace-isolate programs first need to be strong in the "test" component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic and can alleviate the need for more restrictive social distancing measures.