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Evolution is a key aspect of the biology of many pathogens, driving processes ranging from immune escape to changes in virulence. Because evolution is inherently subject to feedbacks, and because pathogen evolution plays out at scales ranging from within-host to between-host and beyond, evolutionary questions provide special challenges to the modelling community. In this article, we provide an overview of five challenges in modelling the evolution of pathogens and their hosts, and point to areas for development, focussing in particular on the issue of linking theory and data.

The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of 'leaving no one behind' has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and 'coldspots' of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 × 5 km2 in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators. [ABSTRACT FROM AUTHOR]

More than 50% of all animal species are insects that undergo complete metamorphosis. The key innovation of these holometabolous insects is a pupal stage between the larva and adult when most structures are completely rebuilt. Why this extreme lifestyle evolved is unclear. Here, we test the hypothesis that a trade-off between growth and differentiation explains the evolution of this novelty. Using a comparative approach, we find that holometabolous insects grow much faster than hemimetabolous insects. Using a theoretical model, we then show how holometaboly evolves under a growth-differentiation trade-off and identify conditions under which such temporal decoupling of growth and differentiation is favored. Our work supports the notion that the holometabolous life history evolved to remove developmental constraints on fast growth, primarily under high mortality., Competing Interests: Competing interests statement:The authors declare no competing interest.

The World Health Organization's Immunization and Vaccines-related Implementation Research Advisory Committee (IVIR-AC) serves to independently review and evaluate vaccine-related research to maximize the potential impact of vaccination programs. From 28 June - 1 July 2024, IVIR-AC was convened for an ad hoc meeting to discuss new evidence on criteria for rubella vaccine introduction and the risk of congenital rubella syndrome. This report summarizes background information on rubella virus transmission and the burden of congenital rubella syndrome, meeting structure and presentations, proceedings, and recommendations., Competing Interests: Declaration of competing interest P. L. was supported by the Bill & Melinda Gates Foundation for this work. S. S. was supported by the World Health Organization for this work. A. N. S. was financially supported by the World Health Organization for this work, and is additionally supported by the Bill & Melinda Gates Foundation, Gavi, the Vaccine Alliance, and the National Institutes of Health. K. F. is employed by the Bill & Melinda Gates Foundation. M. F. is supported by the National Science Foundation, the Bill & Melinda Gates Foundation, Gavi, the Vaccine Alliance, Centers for Disease Control and Prevention, and Imperial College London. E. V. was supported for this work by Gavi, the Vaccine Alliance via the Vaccine Impact Modeling Consortium (VIMC), which is jointly funded by Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation. C. J. E. M. has previously received travel funds to visit the Max Plank Institute of Evolutionary Anthropology. A. K. W. was supported for this work by Gavi, the Vaccine Alliance, is additionally supported by the Bill & Melinda Gates Foundation, and has previously received travel funds from Gavi, the Vaccine Alliance. M. K. was supported for this work by the Bill & Melinda Gates Foundation. A. P. is supported by Gavi, the Vaccine Alliance, Imperial College London, the Bill & Melinda Gates Foundation, and the World Health Organization. A. B. H. was supported by the Australian National Health and Medical Research Council for this work, is additionally supported by the Australian NSW Ministry of Health, PATH, the World Health Organization, and Gavi, the Vaccine Alliance, and has received consulting fees from the Australian NSW Ministry of Health, WHO Europe and Asian Development Bank. A. N. S. and M. F. report travel related support from the World Health Organization to attend previous IVIR-AC meetings. All other authors have no declarations., (Copyright © 2024. Published by Elsevier Ltd.)

Rubella infection during pregnancy can result in miscarriage or infants with a constellation of birth defects known as congenital rubella syndrome (CRS). When coverage is inadequate, rubella vaccination can increase CRS cases by increasing the average age of infection. Thus, the World Health Organisation recommends that countries introducing rubella vaccine be able to vaccinate at least 80% of each birth cohort. Previous studies have focused on national-level analyses and have overlooked sub-national variation in introduction risk. We characterised the sub-national heterogeneity in rubella transmission within Nigeria and modelled local rubella vaccine introduction under different scenarios to refine the set of conditions and strategies required for safe rubella vaccine use. Across Nigeria, the basic reproduction number ranged from 2.6 to 6.2. Consequently, the conditions for safe vaccination varied across states with low-risk areas requiring coverage levels well below 80 %. In high-risk settings, inadequate routine coverage needed to be supplemented by campaigns that allowed for gradual improvements in vaccination coverage over time. Understanding local heterogeneities in both short-term and long-term epidemic dynamics can permit earlier nationwide introduction of rubella vaccination and identify sub-national areas suitable for program monitoring, program improvement and campaign support., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Saad-Roy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity., Competing Interests: RS was employed by company Siemens Healthcare Diagnostics. JV was employed by company SIGA Technologies. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Lipsitch, Bassett, Brownstein, Elliott, Eyre, Grabowski, Hay, Johansson, Kissler, Larremore, Layden, Lessler, Lynfield, MacCannell, Madoff, Metcalf, Meyers, Ofori, Quinn, Bento, Reich, Riley, Rosenfeld, Samore, Sampath, Slayton, Swerdlow, Truelove, Varma and Grad.)

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., (© 2024 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

It is estimated that billions of people around the world are affected by micronutrient deficiencies. Madagascar is considered to be particularly nutritionally vulnerable, with nearly half of the population stunted, and parts of the country facing emergency, near famine-like conditions (IPC4). Although Madagascar is generally considered among the most undernourished of countries, empirical data in the form of biological samples to validate these claims are extremely limited. Our research drew data from three studies conducted between 2013-2020 and provided comprehensive biomarker profile information for 4,710 individuals from 30 communities in five different ecological regions during at least one time-point. Estimated prevalences of nutrient deficiencies and inflammation across various regions of rural Madagascar were of concern for both sexes and across all ages, with 66.5% of the population estimated to be deficient in zinc, 15.6% depleted in vitamin B 12 (3.6% deficient), 11.6% deficient in retinol, and lower levels of iron deficiency (as indicated by 11.7% deficient in ferritin and 2.3% deficient assessed by soluble transferrin receptors). Beyond nutrient status biomarkers, nearly one quarter of the population (24.0%) exhibited chronic inflammation based on high values of α-1-acid glycoprotein, and 12.3% exhibited acute inflammation based on high values of C-reactive protein. There is an 8-fold difference between the lowest and highest regional observed prevalence of vitamin B 12 deficiency, a 10-fold difference in vitamin A deficiency (based on retinol), and a 2-fold difference in acute inflammation (CRP) and deficiencies of zinc and iron (based on ferritin), highlighting strong geographical variations in micronutrient deficiencies across Madagascar., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Golden, Zamborain-Mason, Levis, Rice, Allen, Hampel, Hazen, Metcalf, Randriamady, Shahab-Ferdows, Wu and Haneuse.)

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., Competing Interests: Competing interests statement:The authors declare no competing interest.

Recent outbreaks of enterovirus D68 (EV-D68) infections, and their causal linkage with acute flaccid myelitis (AFM), continue to pose a serious public health concern. During 2020 and 2021, the dynamics of EV-D68 and other pathogens have been significantly perturbed by non-pharmaceutical interventions against COVID-19; this perturbation presents a powerful natural experiment for exploring the dynamics of these endemic infections. In this study, we analyzed publicly available data on EV-D68 infections, originally collected through the New Vaccine Surveillance Network, to predict their short- and long-term dynamics following the COVID-19 interventions. Although long-term predictions are sensitive to our assumptions about underlying dynamics and changes in contact rates during the NPI periods, the likelihood of a large outbreak in 2023 appears to be low. Comprehensive surveillance data are needed to accurately characterize future dynamics of EV-D68. The limited incidence of AFM cases in 2022, despite large EV-D68 outbreaks, poses further questions for the timing of the next AFM outbreaks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Theoretical models have successfully predicted the evolution of poultry pathogen virulence in industrialized farm contexts of broiler chicken populations. Whether there are ecological factors specific to more traditional rural farming that affect virulence is an open question. Within non-industrialized farming networks, live bird markets are known to be hotspots of transmission, but whether they could shift selection pressures on the evolution of poultry pathogen virulence has not been addressed. Here, we revisit predictions for the evolution of virulence for viral poultry pathogens, such as Newcastle's disease virus, Marek's disease virus, and influenza virus, H5N1, using a compartmental model that represents transmission in rural markets. We show that both the higher turnover rate and higher environmental persistence in markets relative to farms could select for higher optimal virulence strategies. In contrast to theoretical results modeling industrialized poultry farms, we find that cleaning could also select for decreased virulence in the live poultry market setting. Additionally, we predict that more virulent strategies selected in markets could circulate solely within poultry located in markets. Thus, we recommend the close monitoring of markets not only as hotspots of transmission, but as potential sources of more virulent strains of poultry pathogens., (© 2024. The Author(s).)

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., (© 2023 The Authors. Ecology Letters published by John Wiley & Sons Ltd.)

In disease ecology, pathogen transmission among conspecific versus heterospecific hosts is known to shape pathogen specialization and virulence, but we do not yet know if similar effects occur at the microbiome level. We tested this idea by experimentally passaging leaf-associated microbiomes either within conspecific or across heterospecific plant hosts. Although conspecific transmission results in persistent host-filtering effects and more within-microbiome network connections, heterospecific transmission results in weaker host-filtering effects but higher levels of interconnectivity. When transplanted onto novel plants, heterospecific lines are less differentiated by host species than conspecific lines, suggesting a shift toward microbiome generalism. Finally, conspecific lines from tomato exhibit a competitive advantage on tomato hosts against those passaged on bean or pepper, suggesting microbiome-level host specialization. Overall, we find that transmission mode and previous host history shape microbiome diversity, with repeated conspecific transmission driving microbiome specialization and repeated heterospecific transmission promoting microbiome generalism., Competing Interests: Author contributions K.M.M., S.E.L., C.J.E.M., and B.K. conceptualized and designed the experiments. K.M.M., A.L.S.V., J.K.S., and I.E.M. performed the experiments and processed the samples. K.M.M. performed data processing, analysis, and writing of the initial draft. All authors contributed to the writing and editing of the paper. Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Host genetics can shape microbiome composition, but to what extent it does, remains unclear. Like any other complex trait, this important question can be addressed by estimating the heritability (h 2 ) of the microbiome-the proportion of variance in the abundance in each taxon that is attributable to host genetic variation. However, unlike most complex traits, microbiome heritability is typically based on relative abundance data, where taxon-specific abundances are expressed as the proportion of the total microbial abundance in a sample., Results: We derived an analytical approximation for the heritability that one obtains when using such relative, and not absolute, abundances, based on an underlying quantitative genetic model for absolute abundances. Based on this, we uncovered three problems that can arise when using relative abundances to estimate microbiome heritability: (1) the interdependency between taxa can lead to imprecise heritability estimates. This problem is most apparent for dominant taxa. (2) Large sample size leads to high false discovery rates. With enough statistical power, the result is a strong overestimation of the number of heritable taxa in a community. (3) Microbial co-abundances lead to biased heritability estimates., Conclusions: We discuss several potential solutions for advancing the field, focusing on technical and statistical developments, and conclude that caution must be taken when interpreting heritability estimates and comparing values across studies. Video Abstract., (© 2023. BioMed Central Ltd., part of Springer Nature.)

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., Competing Interests: Declaration of interests No interests are declared., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Introduction: Timeliness of routine vaccination shapes childhood infection risk and thus is an important public health metric. Estimates of indicators of the timeliness of vaccination are usually produced at the national or regional level, which may conceal epidemiologically relevant local heterogeneities and makeitdifficultto identify pockets of vulnerabilities that could benefit from targeted interventions. Here, we demonstrate the utility of geospatial modelling techniques in generating high-resolution maps of the prevalence of delayed childhood vaccination in The Gambia. To guide local immunisation policy and prioritize key interventions, we also identified the districts with a combination of high estimated prevalence and a significant population of affected infants., Methods: We used the birth dose of the hepatitis-B vaccine (HepB0), third-dose of the pentavalent vaccine (PENTA3), and the first dose of measles-containing vaccine (MCV1) as examples to map delayed vaccination nationally at a resolution of 1 × 1-km 2 pixel. We utilized cluster-level childhood vaccination data from The Gambia 2019-20 Demographic and Health Survey. We adopted a fully Bayesian geostatistical model incorporating publicly available geospatial covariates to aid predictive accuracy. The model was implemented using the integrated nested Laplace approximation-stochastic partial differential equation (INLA-SPDE) approach., Results: We found significant subnational heterogeneity in delayed HepB0, PENTA3 and MCV1 vaccinations. Specificdistricts in the central and eastern regions of The Gambia consistentlyexhibited the highest prevalence of delayed vaccination, while the coastal districts showed alower prevalence forallthree vaccines. We also found that districts in the eastern, central, as well as in coastal parts of The Gambia had a combination of high estimated prevalence of delayed HepB0, PENTA3 and MCV1 and a significant population of affected infants., Conclusions: Our approach provides decision-makers with a valuable tool to better understand local patterns of untimely childhood vaccination and identify districts where strengthening vaccine delivery systems could have the greatest impact., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

Antibodies are a core element of the immune system's defense against infectious diseases. We hypothesize that antibody titres might therefore be an important predictor of survival in older individuals. This is important because biomarkers that robustly measure survival have proved elusive, despite their potential utility in health care settings. We present evidence supporting the hypothesis that influenza antibody titres are associated with overall survival of older individuals, and indicate a role for biological sex in modulating this association. Since antibody titres can be modulated by vaccination, these results have important implications for public health policy on influenza control in aging populations., Competing Interests: Conflict of interest: none

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Menezes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

As the SARS-CoV-2 trajectory continues, the longer-term immuno-epidemiology of COVID-19, the dynamics of Long COVID, and the impact of escape variants are important outstanding questions. We examine these remaining uncertainties with a simple modelling framework that accounts for multiple (antigenic) exposures via infection or vaccination. If immunity (to infection or Long COVID) accumulates rapidly with the valency of exposure, we find that infection levels and the burden of Long COVID are markedly reduced in the medium term. More pessimistic assumptions on host adaptive immune responses illustrate that the longer-term burden of COVID-19 may be elevated for years to come. However, we also find that these outcomes could be mitigated by the eventual introduction of a vaccine eliciting robust (i.e. durable, transmission-blocking and/or 'evolution-proof') immunity. Overall, our work stresses the wide range of future scenarios that still remain, the importance of collecting real-world epidemiological data to identify likely outcomes, and the crucial need for the development of a highly effective transmission-blocking, durable and broadly protective vaccine.

Mathematical models have played a crucial role in exploring and guiding pandemic responses. University campuses present a particularly well-documented case for institutional outbreaks, thereby providing a unique opportunity to understand detailed patterns of pathogen spread. Here, we present descriptive and modeling analyses of SARS-CoV-2 transmission on the Princeton University (PU) campus-this model was used throughout the pandemic to inform policy decisions and operational guidelines for the university campus. Epidemic patterns between the university campus and surrounding communities exhibit strong spatiotemporal correlations. Mathematical modeling analysis further suggests that the amount of on-campus transmission was likely limited during much of the wider pandemic until the end of 2021. Finally, we find that a superspreading event likely played a major role in driving the Omicron variant outbreak on the PU campus during the spring semester of the 2021-2022 academic year. Despite large numbers of cases on campus in this period, case levels in surrounding communities remained low, suggesting that there was little spillover transmission from campus to the local community., (© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.)

Characterizing the long-term kinetics of maternally derived and vaccine-induced measles immunity is critical for informing measles immunization strategies moving forward. Based on two prospective cohorts of children in China, we estimate that maternally derived immunity against measles persists for 2.4 months. Following two-dose series of measles-containing vaccine (MCV) at 8 and 18 months of age, the immune protection against measles is not lifelong, and antibody concentrations are extrapolated to fall below the protective threshold of 200 mIU/ml at 14.3 years. A catch-up MCV dose in addition to the routine doses between 8 months and 5 years reduce the cumulative incidence of seroreversion by 79.3-88.7% by the age of 6 years. Our findings also support a good immune response after the first MCV vaccination at 8 months. These findings, coupled with the effectiveness of a catch-up dose in addition to the routine doses, could be instrumental to relevant stakeholders when planning routine immunization schedules and supplemental immunization activities., (© 2023. The Author(s).)

Evidence that climate change will impact the ecology and evolution of individual plant species is growing. However, little, as yet, is known about how climate change will affect interactions between plants and their pathogens. Climate drivers could affect the physiology, and thus demography, and ultimately evolutionary processes affecting both plant hosts and their pathogens. Because the impacts of climate drivers may operate in different directions at different scales of infection, and, furthermore, may be nonlinear, abstracting across these processes may mis-specify outcomes. Here, we use mechanistic models of plant-pathogen interactions to illustrate how counterintuitive outcomes are possible, and we introduce how such framing may contribute to understanding climate effects on plant-pathogen systems. We discuss the evidence-base derived from wild and agricultural plant-pathogen systems that could inform such models, specifically in the direction of estimates of physiological, demographic and evolutionary responses to climate change. We conclude by providing an overview of knowledge gaps and directions for future research in this important area. This article is part of the theme issue 'Infectious disease ecology and evolution in a changing world'.

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., Competing Interests: Potential conflicts of interest. All authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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 R 0 infections, these return epidemics are of reduced equilibrium incidence and epidemic peak size. For high R 0 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.

Background: Over a life course, human adaptive immunity to antigenically mutable pathogens exhibits competitive and facilitative interactions. We hypothesize that such interactions may lead to cyclic dynamics in immune responses over a lifetime., Methods: To investigate the cyclic behavior, we analyzed hemagglutination inhibition titers against 21 historical influenza A(H3N2) strains spanning 47 years from a cohort in Guangzhou, China, and applied Fourier spectrum analysis. To investigate possible biological mechanisms, we simulated individual antibody profiles encompassing known feedbacks and interactions due to generally recognized immunological mechanisms., Results: We demonstrated a long-term periodicity (about 24 years) in individual antibody responses. The reported cycles were robust to analytic and sampling approaches. Simulations suggested that individual-level cross-reaction between antigenically similar strains likely explains the reported cycle. We showed that the reported cycles are predictable at both individual and birth cohort level and that cohorts show a diversity of phases of these cycles. Phase of cycle was associated with the risk of seroconversion to circulating strains, after accounting for age and pre-existing titers of the circulating strains., Conclusions: Our findings reveal the existence of long-term periodicities in individual antibody responses to A(H3N2). We hypothesize that these cycles are driven by preexisting antibody responses blunting responses to antigenically similar pathogens (by preventing infection and/or robust antibody responses upon infection), leading to reductions in antigen-specific responses over time until individual's increasing risk leads to an infection with an antigenically distant enough virus to generate a robust immune response. These findings could help disentangle cohort effects from individual-level exposure histories, improve our understanding of observed heterogeneous antibody responses to immunizations, and inform targeted vaccine strategy., Funding: This study was supported by grants from the NIH R56AG048075 (DATC, JL), NIH R01AI114703 (DATC, BY), the Wellcome Trust 200861/Z/16/Z (SR), and 200187/Z/15/Z (SR). This work was also supported by research grants from Guangdong Government HZQB-KCZYZ-2021014 and 2019B121205009 (YG and HZ). DATC, JMR and SR acknowledge support from the National Institutes of Health Fogarty Institute (R01TW0008246). JMR acknowledges support from the Medical Research Council (MR/S004793/1) and the Engineering and Physical Sciences Research Council (EP/N014499/1). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript., Competing Interests: BY, JR, HZ, CM, JH, KK, RS, CJ, YG No competing interests declared, BG BGC received financial research support through his institution from Merck for unrelated work, JL JL receives research support from CDC and NIH-NIGMS for for unrelated work, SR SR receives grants from Wellcome Trust, DC DATC received financial research support through his institution from Merck for unrelated work, (© 2022, Yang et al.)

Measles is a highly transmissible disease that requires high levels of vaccination coverage for control and elimination. Areas that are unable to achieve and maintain high coverage levels are at risk for measles outbreaks resulting in increased morbidity and mortality. Public health emergencies, such as the current COVID-19 pandemic, pose a threat to the functioning of health systems by disrupting immunization services which can derail measles vaccination efforts. Efforts to bridge coverage gaps in immunization include the rapid return to fully functioning services as well as deploying supplementary immunization activities (SIAs), which are additional vaccination campaigns intended to catch-up children who have missed routine services. However, SIAs, which to date tend to be national efforts, can be difficult to mobilize quickly, resource-intensive, and even more challenging to deploy during a public health crisis. By mapping expected burden of measles, more effective SIAs that are setting-specific and resource-efficient can be planned and mobilized. Using a spatial transmission model of measles dynamics, we projected and estimated the expected burden of national and local measles outbreaks in Zambia with the current COVID-19 pandemic as a framework to inform disruptions to routine vaccination. We characterize the impact of disruptions to routine immunization services on measles incidence, map expected case burden, and explore SIA strategies to mitigate measles outbreaks. We find that disruptions lasting six months or longer as well as having low MCV1 coverage prior to disruptions resulted in an observable increase of measles cases across provinces. Targeting provinces at higher risk of measles outbreaks for SIAs is an effective strategy to curb measles virus incidence following disruptions to routine immunization services., Competing Interests: Declaration of Competing Interest None. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

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., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.)

The absence of microbial exposure early in life leaves individuals vulnerable to immune overreaction later in life, manifesting as immunopathology, autoimmunity, or allergies. A key factor is thought to be a "critical window" during which the host's immune system can "learn" tolerance, and beyond which learning is no longer possible. Animal models indicate that many mechanisms have evolved to enable critical windows, and that their time limits are distinct and consistent. Such a variety of mechanisms, and precision in their manifestation suggest the outcome of strong evolutionary selection. To strengthen our understanding of critical windows, we explore their underlying evolutionary ecology using models encompassing demographic and epidemiological transitions, identifying the length of the critical window that would maximize fitness in different environments. We characterize how direct effects of microbes on host mortality, but also indirect effects via microbial ecology, will drive the optimal length of the critical window. We find that indirect effects such as magnitude of transmission, duration of infection, rates of reinfection, vertical transmission, host demography, and seasonality in transmission all have the effect of redistributing the timing and/or likelihood of encounters with microbial taxa across age, and thus increasing or decreasing the optimal length of the critical window. Declining microbial population abundance and diversity are predicted to result in increases in immune dysfunction later in life. We also make predictions for the length of the critical window across different taxa and environments. Overall, our modeling efforts demonstrate how critical windows will be impacted over evolution as a function of both host-microbiome/pathogen interactions and dispersal, raising central questions about potential mismatches between these evolved systems and the current loss of microbial diversity and/or increases in infectious disease., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors. Evolution Letters published by Wiley Periodicals LLC on behalf of Society for the Study of Evolution (SSE) and European Society for Evolutionary Biology (ESEB).)

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., Competing Interests: The authors have declared that no competing interests exist.

The potential for climate change to exacerbate the burden of human infectious diseases is increasingly recognized, but its effects on infectious diseases of plants have received less attention. Understanding the impacts of climate on the epidemiological dynamics of plant pathogens is imperative, as these organisms play central roles in natural ecosystems and also pose a serious threat to agricultural production and food security. We use the fungal 'flax rust' pathogen (Melampsora lini) and its subalpine wildflower host Lewis flax (Linum lewisii) to investigate how climate change might affect the dynamics of fungal plant pathogen epidemics using a combination of empirical and modeling approaches. Our results suggest that climate change will initially slow transmission at both the within- and between-host scales. However, moderate resurgences in disease spread are predicted as warming progresses, especially if the rate of greenhouse gas emissions continues to increase at its current pace. These findings represent an important step towards building a holistic understanding of climate effects on plant infectious disease that encompasses demographic, epidemiological, and evolutionary processes. A core result is that neglecting processes at any one scale of plant pathogen transmission may bias projections of climate effects, as climate drivers have variable and cascading impacts on processes underlying transmission that occur at different scales., (© 2022. The Author(s).)

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., (© 2022 John Wiley & Sons Ltd.)

Comparing age and sex differences in SARS-CoV-2 hospitalization and mortality with MERS-CoV, seasonal coronaviruses, influenza and other health outcomes opens the way to generating hypotheses as to underlying mechanisms driving disease risk. Using 60-year-olds as a reference age group, we find that relative rates of hospitalization and mortality associated with the emergent coronaviruses are lower during childhood and start to increase earlier (around puberty) as compared with influenza and seasonal coronaviruses. The changing distribution of disease risk by age for emerging pathogens appears to broadly track the gradual deterioration of the immune system (immunosenescence), which starts around puberty. By contrast, differences in severe disease risk by age from endemic pathogens are more decoupled from the immune ageing process. Intriguingly, age-specific sex differences in hospitalizations are largely similar across endemic and emerging infections. We discuss potential mechanisms that may be associated with these patterns., (© 2022 The Authors.)

Understanding viral evolution depends on a synthesis of evolutionary biology and immuno-epidemiology.

Inferring the relative strength (i.e. the ratio of reproduction numbers) and relative speed (i.e. the difference between growth rates) of new SARS-CoV-2 variants is critical to predicting and controlling the course of the current pandemic. Analyses of new variants have primarily focused on characterizing changes in the proportion of new variants, implicitly or explicitly assuming that the relative speed remains fixed over the course of an invasion. We use a generation-interval-based framework to challenge this assumption and illustrate how relative strength and speed change over time under two idealized interventions: a constant-strength intervention like idealized vaccination or social distancing, which reduces transmission rates by a constant proportion, and a constant-speed intervention like idealized contact tracing, which isolates infected individuals at a constant rate. In general, constant-strength interventions change the relative speed of a new variant, while constant-speed interventions change its relative strength. Differences in the generation-interval distributions between variants can exaggerate these changes and modify the effectiveness of interventions. Finally, neglecting differences in generation-interval distributions can bias estimates of relative strength.