Assessing the accuracy of California county level COVID-19 hospitalization forecasts to inform public policy decision making.

BACKGROUND: The COVID-19 pandemic has highlighted the role of infectious disease forecasting in informing public policy. However, significant barriers remain for effectively linking infectious disease forecasts to public health decision making, including a lack of model validation. Forecasting model performance and accuracy should be evaluated retrospectively to understand under which conditions models were reliable and could be improved in the future. METHODS: Using archived forecasts from the California Department of Public Health's California COVID Assessment Tool ( https://calcat.covid19.ca.gov/cacovidmodels/ ), we compared how well different forecasting models predicted COVID-19 hospitalization census across California counties and regions during periods of Alpha, Delta, and Omicron variant predominance. RESULTS: Based on mean absolute error estimates, forecasting models had variable performance across counties and through time. When accounting for model availability across counties and dates, some individual models performed consistently better than the ensemble model, but model rankings still differed across counties. Local transmission trends, variant prevalence, and county population size were informative predictors for determining which model performed best for a given county based on a random forest classification analysis. Overall, the ensemble model performed worse in less populous counties, in part because of fewer model contributors in these locations. CONCLUSIONS: Ensemble model predictions could be improved by incorporating geographic heterogeneity in model coverage and performance. Consistency in model reporting and improved model validation can strengthen the role of infectious disease forecasting in real-time public health decision making.

Implementation beyond the clinic: Community-driven utilization of research evidence from PC CARES, a suicide prevention program.

While implementation and dissemination of research is a rapidly growing area, critical questions remain about how, why, and under what conditions everyday people integrate and utilize research evidence. This mixed-methods study investigates how participants of Promoting Community Conversations About Research to End Suicide (PC CARES) make sense of and use research evidence about suicide prevention in their own lives. PC CARES is a health intervention addressing the need for culturally responsive suicide prevention practices in rural Alaska through a series of community Learning Circles. We analyzed PC CARES transcripts and surveys for 376 participants aged 15+ across 10 Northwest Alaska Native villages. Quantitative analysis showed significant correlations between five utilization of research evidence (URE) factors and participants' intent to use research evidence from PC CARES Learning Circles. Key qualitative themes from Learning Circle transcripts expanded upon these URE constructs and included navigating discordant information, centering relationships, and Indigenous worldviews as key to interpreting research evidence. We integrate and organize our findings to inform two domains from the Consolidated Framework for Research Implementation: (1) intervention characteristics and (2) characteristics of individuals, with emphasis on findings most relevant for community settings where self-determined, evidence-informed action is especially important for addressing health inequities.

Elements of disease in a changing world: modelling feedbacks between infectious disease and ecosystems.

An overlooked effect of ecosystem eutrophication is the potential to alter disease dynamics in primary producers, inducing disease-mediated feedbacks that alter net primary productivity and elemental recycling. Models in disease ecology rarely track organisms past death, yet death from infection can alter important ecosystem processes including elemental recycling rates and nutrient supply to living hosts. In contrast, models in ecosystem ecology rarely track disease dynamics, yet elemental nutrient pools (e.g. nitrogen, phosphorus) can regulate important disease processes including pathogen reproduction and transmission. Thus, both disease and ecosystem ecology stand to grow as fields by exploring questions that arise at their intersection. However, we currently lack a framework explicitly linking these disciplines. We developed a stoichiometric model using elemental currencies to track primary producer biomass (carbon) in vegetation and soil pools, and to track prevalence and the basic reproduction number (R0 ) of a directly transmitted pathogen. This model, parameterised for a deciduous forest, demonstrates that anthropogenic nutrient supply can interact with disease to qualitatively alter both ecosystem and disease dynamics. Using this element-focused approach, we identify knowledge gaps and generate predictions about the impact of anthropogenic nutrient supply rates on infectious disease and feedbacks to ecosystem carbon and nutrient cycling.

A mechanistic, stigmergy model of territory formation in solitary animals: Territorial behavior can dampen disease prevalence but increase persistence.

Although movement ecology has leveraged models of home range formation to explore the effects of spatial heterogeneity and social cues on movement behavior, disease ecology has yet to integrate these potential drivers and mechanisms of contact behavior into a generalizable disease modeling framework. Here we ask how dynamic territory formation and maintenance might contribute to disease dynamics in a territorial, solitary predator for an indirectly transmitted pathogen. We developed a mechanistic individual-based model where stigmergy-the deposition of signals into the environment (e.g., scent marking, scraping)-dictates local movement choices and long-term territory formation, but also the risk of pathogen transmission. Based on a variable importance analysis, the length of the infectious period was the single most important variable in predicting outbreak success, maximum prevalence, and outbreak duration. Host density and rate of pathogen decay were also key predictors. We found that territoriality best reduced maximum prevalence in conditions where we would otherwise expect outbreaks to be most successful: slower recovery rates (i.e., longer infectious periods) and higher conspecific densities. However, for slower pathogen decay rates, stigmergy-driven movement increased outbreak durations relative to random movement simulations. Our findings therefore support a limited version of the "territoriality benefits" hypothesis-where reduced home range overlap leads to reduced opportunities for pathogen transmission, but with the caveat that reduction in outbreak severity may increase the likelihood of pathogen persistence. For longer infectious periods and higher host densities, key trade-offs emerged between the strength of pathogen load, the strength of the stigmergy cue, and the rate at which those two quantities decayed; this finding raises interesting questions about the evolutionary nature of these competing processes and the role of possible feedbacks between parasitism and territoriality. This work also highlights the importance of considering social cues as part of the movement landscape in order to better understand the consequences of individual behaviors on population level outcomes.

Risk of non-syndromic orofacial clefts by maternal rural-urban residence and race/ethnicity: A population-based case-control study in Washington State 1989-2014.

BACKGROUND: Orofacial clefts (OFC) have multifactorial aetiology. Established risk factors explain a small proportion of cases. OBJECTIVES: To evaluate OFC risk by maternal rural residence and race/ethnicity, and test whether these associations changed after US-mandated folic acid fortification. METHODS: This population-based case-control study included all non-syndromic OFC cases among Washington State singleton livebirths between 1989-2014 and birth year-matched controls. Data sources included birth certificates and hospital records. Logistic regression estimated odds ratios (OR) and 95% confidence intervals (CI) for OFC by maternal rural-urban residence (adjusted for maternal race/ethnicity) and by maternal race/ethnicity. We evaluated additive and multiplicative effect measure modification by time of folic acid fortification (before vs. after). Probabilistic quantitative bias analysis accounted for potential differential case ascertainment for infants born to Black mothers. RESULTS: The overall non-syndromic OFC birth prevalence was 1.0 per 1000 livebirths (n = 2136 cases). Among controls (n = 25 826), 76% of mothers were urban residents and 72% were of White race/ethnicity. OFC risk was slightly higher for infants born to rural than to urban mothers, adjusting for race/ethnicity (OR 1.12, 95% CI 1.01, 1.25). The association was similar before and after US-mandated folic acid fortification. Compared with infants born to White mothers, OFC risk was higher for American Indian mothers (OR 1.73, 95% CI 1.35, 2.23) and lower for Black (OR 0.62, 95% CI 0.48, 0.81), Hispanic (OR 0.75, 95% CI 0.64, 0.87), and Asian/Pacific Islander (API) mothers (OR 0.87, 95% CI 0.74, 1.02). Bias analysis suggests the observed difference for Black mothers may be explained by selection bias. Post-fortification, the association of OFC with maternal API race/ethnicity decreased and with maternal Black race/ethnicity increased relative to maternal White race/ethnicity. CONCLUSIONS: Infants born to rural mothers and to American Indian mothers in Washington State during 1989-2014 were at higher OFC risk before and after US-mandated folic acid fortification.

Disease outbreak thresholds emerge from interactions between movement behavior, landscape structure, and epidemiology.

Disease models have provided conflicting evidence as to whether spatial heterogeneity promotes or impedes pathogen persistence. Moreover, there has been limited theoretical investigation into how animal movement behavior interacts with the spatial organization of resources (e.g., clustered, random, uniform) across a landscape to affect infectious disease dynamics. Importantly, spatial heterogeneity of resources can sometimes lead to nonlinear or counterintuitive outcomes depending on the host and pathogen system. There is a clear need to develop a general theoretical framework that could be used to create testable predictions for specific host-pathogen systems. Here, we develop an individual-based model integrated with movement ecology approaches to investigate how host movement behaviors interact with landscape heterogeneity (in the form of various levels of resource abundance and clustering) to affect pathogen dynamics. For most of the parameter space, our results support the counterintuitive idea that fragmentation promotes pathogen persistence, but this finding was largely dependent on perceptual range of the host, conspecific density, and recovery rate. For simulations with high conspecific density, slower recovery rates, and larger perceptual ranges, more complex disease dynamics emerged, and the most fragmented landscapes were not necessarily the most conducive to outbreaks or pathogen persistence. These results point to the importance of interactions between landscape structure, individual movement behavior, and pathogen transmission for predicting and understanding disease dynamics.

Genotype and sex-based host variation in behaviour and susceptibility drives population disease dynamics.

Host heterogeneity in pathogen transmission is widespread and presents a major hurdle to predicting and minimizing disease outbreaks. Using Drosophila melanogaster infected with Drosophila C virus as a model system, we integrated experimental measurements of social aggregation, virus shedding, and disease-induced mortality from different genetic lines and sexes into a disease modelling framework. The experimentally measured host heterogeneity produced substantial differences in simulated disease outbreaks, providing evidence for genetic and sex-specific effects on disease dynamics at a population level. While this was true for homogeneous populations of single sex/genetic line, the genetic background or sex of the index case did not alter outbreak dynamics in simulated, heterogeneous populations. Finally, to explore the relative effects of social aggregation, viral shedding and mortality, we compared simulations where we allowed these traits to vary, as measured experimentally, to simulations where we constrained variation in these traits to the population mean. In this context, variation in infectiousness, followed by social aggregation, was the most influential component of transmission. Overall, we show that host heterogeneity in three host traits dramatically affects population-level transmission, but the relative impact of this variation depends on both the susceptible population diversity and the distribution of population-level variation.

Dynamic, spatial models of parasite transmission in wildlife: Their structure, applications and remaining challenges.

Individual differences in contact rate can arise from host, group and landscape heterogeneity and can result in different patterns of spatial spread for diseases in wildlife populations with concomitant implications for disease control in wildlife of conservation concern, livestock and humans. While dynamic disease models can provide a better understanding of the drivers of spatial spread, the effects of landscape heterogeneity have only been modelled in a few well-studied wildlife systems such as rabies and bovine tuberculosis. Such spatial models tend to be either purely theoretical with intrinsic limiting assumptions or individual-based models that are often highly species- and system-specific, limiting the breadth of their utility. Our goal was to review studies that have utilized dynamic, spatial models to answer questions about pathogen transmission in wildlife and identify key gaps in the literature. We begin by providing an overview of the main types of dynamic, spatial models (e.g., metapopulation, network, lattice, cellular automata, individual-based and continuous-space) and their relation to each other. We investigate different types of ecological questions that these models have been used to explore: pathogen invasion dynamics and range expansion, spatial heterogeneity and pathogen persistence, the implications of management and intervention strategies and the role of evolution in host-pathogen dynamics. We reviewed 168 studies that consider pathogen transmission in free-ranging wildlife and classify them by the model type employed, the focal host-pathogen system, and their overall research themes and motivation. We observed a significant focus on mammalian hosts, a few well-studied or purely theoretical pathogen systems, and a lack of studies occurring at the wildlife-public health or wildlife-livestock interfaces. Finally, we discuss challenges and future directions in the context of unprecedented human-mediated environmental change. Spatial models may provide new insights into understanding, for example, how global warming and habitat disturbance contribute to disease maintenance and emergence. Moving forward, better integration of dynamic, spatial disease models with approaches from movement ecology, landscape genetics/genomics and ecoimmunology may provide new avenues for investigation and aid in the control of zoonotic and emerging infectious diseases.

COVID-19 Vaccination and Incidence of Pediatric SARS-CoV-2 Infection and Hospitalization.

Importance: A SARS-CoV-2 vaccine was approved for adolescents aged 12 to 15 years on May 10, 2021, with approval for younger age groups following thereafter. The population level impact of the pediatric COVID-19 vaccination program has not yet been established. Objective: To identify whether California's pediatric COVID-19 immunization program was associated with changes in pediatric COVID-19 incidence and hospitalizations. Design, Setting, and Participants: A case series on COVID-19 vaccination including children aged 6 months to 15 years was conducted in California. Data were obtained on COVID-19 cases in California between April 1, 2020, and February 27, 2023. Exposure: Postvaccination evaluation periods spanned 141 days (June 10 to October 29, 2021) for adolescents aged 12 to 15 years, 199 days (November 29, 2021, to June 17, 2022) for children aged 5 to 11 years, and 225 days (July 17, 2022, to February 27, 2023) for those aged 6 to 59 months. During these periods, statewide vaccine coverage reached 53.5% among adolescents aged 12 to 15 years, 34.8% among children aged 5 to 11 years, and 7.9% among those aged 6 to 59 months. Main Outcomes and Measures: Age-stepped implementation of COVID-19 vaccination was used to compare observed county-level incidence and hospitalization rates during periods when each age group became vaccine eligible to counterfactual rates predicted from observations among other age groups. COVID-19 case and hospitalization data were obtained from the California reportable disease surveillance system. Results: Between April 1, 2020, and February 27, 2023, a total of 3 913 063 pediatric COVID-19 cases and 12 740 hospitalizations were reported in California. Reductions of 146 210 cases (95% prediction interval [PI], 136 056-158 948) were estimated among adolescents aged 12 to 15 years, corresponding to a 37.1% (35.5%-39.1%) reduction from counterfactual predictions. Reductions of 230 134 (200 170-265 149) cases were estimated among children aged 5 to 11 years, corresponding to a 23.7% (20.6%-27.3%) reduction from counterfactual predictions. No evidence of reductions in COVID-19 cases statewide were found among children aged 6 to 59 months (estimated averted cases, -259; 95% PI, -1938 to 1019), although low transmission during the evaluation period may have limited the ability to do so. An estimated 168 hospitalizations (95% PI, 42-324) were averted among children aged 6 to 59 months, corresponding to a 24.4% (95% PI, 6.1%-47.1%) reduction. In meta-analyses, county-level vaccination coverage was associated with averted cases for all age groups. Despite low vaccination coverage, pediatric COVID-19 immunization in California averted 376 085 (95% PI, 348 355-417 328) reported cases and 273 (95% PI, 77-605) hospitalizations among children aged 6 months to 15 years over approximately 4 to 7 months following vaccination availability. Conclusions and Relevance: The findings of this case series analysis of 3 913 063 cases suggest reduced pediatric SARS-CoV-2 transmission following immunization. These results support the use of COVID-19 vaccines to reduce COVID-19 incidence and hospitalization in pediatric populations.

Centering Community Strengths and Resisting Structural Racism to Prevent Youth Suicide: Learning from American Indian and Alaska Native Communities.

The persistence of extreme suicide disparities in American Indian and Alaska Native (AI/AN) youth signals a severe health inequity with distinct associations to a colonial experience of historical and on-going cultural, social, economic, and political oppression. To address this complex issue, we describe three AI/AN suicide prevention efforts that illustrate how strengths-based community interventions across the prevention spectrum can buffer suicide risk factors associated with structural racism. Developed and implemented in collaboration with tribal partners using participatory methods, the strategies include universal, selective, and indicated prevention elements. Their aim is to enhance systems within communities, institutions, and families by emphasizing supportive relationships, cultural values and practices, and community priorities and preferences. These efforts deploy collaborative, local approaches, that center on the importance of tribal sovereignty and self-determination, disrupting the unequal power distribution inherent in mainstream approaches to suicide prevention. The examples emphasize the centrality of Indigenous intellectual traditions in the co-creation of healthy developmental pathways for AI/AN young people. A central component across all three programs is a deep commitment to an interdependent or collective orientation, in contrast to an individual-based mental health suicide prevention model. This commitment offers novel directions for the entire field of suicide prevention and responds to calls for multilevel, community-driven public health strategies to address the complexity of suicide. Although our focus is on the social determinants of health in AI/AN communities, strategies to address the structural violence of racism as a risk factor in suicide have broad implications for all suicide prevention programming.

Structural violence of racism and colonization are social determinants of suicide.Collaborative and power-sharing implementation strategies can disrupt oppression.Strengths-based collectivist strategies can buffer structural suicide risk.

The illusion of personal health decisions for infectious disease management: disease spread in social contact networks.

Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals' infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies.

An Evaluation of Local Implementing Partner Performance During the First 2 Years of the USAID/PEPFAR Transition.

INTRODUCTION: Locally led and owned development is considered the best practice for international aid. As an implementing agency for the U.S. President's Emergency Plan for AIDS Relief (PEPFAR), the U.S. Agency for International Development (USAID) supported the goal of transitioning 70% of its portfolio funding directly to local organizations by 2020, including partner country governments. However, limited evidence or evaluation exists on how such a transition can help achieve HIV-related health outcomes. METHODS: We evaluated monitoring, evaluation, and reporting performance; calculated indicators; and quality of service across the HIV/AIDS treatment cascade for local and international partners in the USAID/PEPFAR portfolio implementing similar programs during the U.S. Government fiscal years (FY) 2019 to 2020 (October 1, 2018-September 30, 2020). We compared results aggregated globally, by country, and across individual partners. RESULTS: Globally, local partners met a lower proportion of their treatment targets than international partners and did not meet targets for pre-exposure prophylaxis or voluntary medical male circumcision in FY2020. However, local partners exceeded targets in programs supporting orphans, vulnerable children, and key populations affected by HIV/AIDS. Local partners also had testing positivity, linkage rates, and viral load suppression that were equivalent to or higher than that of international partners. Based on available assessments, local partners displayed quality of service delivery comparable to international partners. CONCLUSION: Local partners faced challenges, including unfamiliarity with USAID funding, increasing targets across several indicators, and the syndemics of HIV/AIDS and COVID-19. A higher percentage of targets and funding led South African local partners to yield an outsized effect on global percent target achievement. While these findings should be interpreted cautiously due to limited sample size and short time horizon, they are a key first step in evaluating the local partner transition support of the long-term goal of sustained epidemic control of HIV/AIDS.

Reconciling contrasting effects of nitrogen on host immunity and pathogen transmission using stoichiometric models.

Hosts rely on the availability of nutrients for growth, and for defense against pathogens. At the same time, changes in host nutrition can alter the dynamics of pathogens that rely on their host for reproduction. For primary producer hosts, enhanced nutrient loads may increase host biomass or pathogen reproduction, promoting faster density-dependent pathogen transmission. However, the effect of elevated nutrients may be reduced if hosts allocate a growth-limiting nutrient to pathogen defense. In canonical disease models, transmission is not a function of nutrient availability. Yet, including nutrient availability is necessary to mechanistically understand the response of infection to changes in the environment. Here, we explore the implications of nutrient-mediated pathogen infectivity and host immunity on infection outcomes. We developed a stoichiometric disease model that explicitly integrates the contrasting dependencies of pathogen infectivity and host immunity on nitrogen (N) and parameterized it for an algal-host system. Our findings reveal dynamic shifts in host biomass build-up, pathogen prevalence, and the force of infection along N supply gradients with N-mediated host infectivity and immunity, compared with a model in which the transmission rate was fixed. We show contrasting responses in pathogen performance with increasing N supply between N-mediated infectivity and N-mediated immunity, revealing an optimum for pathogen transmission at intermediate N supply. This was caused by N limitation of the pathogen at a low N supply and by pathogen suppression via enhanced host immunity at a high N supply. By integrating both nutrient-mediated pathogen infectivity and host immunity into a stoichiometric model, we provide a theoretical framework that is a first step in reconciling the contrasting role nutrients can have on host-pathogen dynamics.

Conceptualizing Indigenous strengths-based health and wellness research using group concept mapping.

BACKGROUND: In recent years public health research has shifted to more strengths or asset-based approaches to health research but there is little understanding of what this concept means to Indigenous researchers. Therefore our purpose was to define an Indigenous strengths-based approach to health and well-being research. METHODS: Using Group Concept Mapping, Indigenous health researchers (N = 27) participated in three-phases. Phase 1: Participants provided 218 unique responses to the focus prompt "Indigenous Strengths-Based Health and Wellness Research…" Redundancies and irrelevant statements were removed using content analysis, resulting in a final set of 94 statements. Phase 2: Participants sorted statements into groupings and named these groupings. Participants rated each statement based on importance using a 4-point scale. Hierarchical cluster analysis was used to create clusters based on how statements were grouped by participants. Phase 3: Two virtual meetings were held to share and invite researchers to collaboratively interpret results. RESULTS: A six-cluster map representing the meaning of Indigenous strengths-based health and wellness research was created. Results of mean rating analysis showed all six clusters were rated on average as moderately important. CONCLUSIONS: The definition of Indigenous strengths-based health research, created through collaboration with leading AI/AN health researchers, centers Indigenous knowledges and cultures while shifting the research narrative from one of illness to one of flourishing and relationality. This framework offers actionable steps to researchers, public health practitioners, funders, and institutions to promote relational, strengths-based research that has the potential to promote Indigenous health and wellness at individual, family, community, and population levels.

Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics.

Social associations in common carp (Cyprinus carpio): Insights from induced feeding aggregations for targeted management strategies.

Heterogeneity in social interactions can have important consequences for the spread of information and diseases and consequently conservation and invasive species management. Common carp (Cyprinus carpio) are a highly social, ubiquitous, and invasive freshwater fish. Management strategies targeting foraging carp may be ideal because laboratory studies have suggested that carp can learn, have individual personalities, a unique diet, and often form large social groups. To examine social feeding behaviors of wild carp, we injected 344 carp with passive integrated transponder (PIT) tags and continuously monitored their feeding behaviors at multiple sites in a natural lake in Minnesota, USA. The high-resolution, spatio-temporal data were analyzed using a Gaussian mixture model (GMM). Based on these associations, we analyzed group size, feeding bout duration, and the heterogeneity and connectivity of carp social networks at foraging sites. Wild carp responded quickly to bait, forming aggregations most active from dusk to dawn. During the 2020 baiting period (20 days), 133 unique carp were detected 616,593 times. There was some evidence that feeding at multiple sites was constrained by basin geography, but not distance alone. GMM results suggested that feeding bouts were short, with frequent turnover of small groups. Individual foraging behavior was highly heterogeneous with Gini coefficients of 0.79 in 2020 and 0.66 in 2019. "Superfeeders"-those contributing to 80% of total cumulative detections (top 18% and top 29% of foragers in 2020 and 2019 respectively)-were more likely to be detected earlier at feeding stations, had larger body sizes, and had higher network measures of degree, weighted degree, and betweenness than non-superfeeders. Overall, our results indicate that wild carp foraging is social, easily induced by bait, dominated by large-bodied individuals, and potentially predictable, which suggests social behaviors could be leveraged in management of carp, one of the world's most recognizable and invasive fish.

Bioaccumulation of Pathogen Exposure in Top Predators.

Predator-prey interactions present heightened opportunities for pathogen spillover, as predators are at risk of exposure to infectious agents harbored by prey. Epizootics with high morbidity and mortality have been recorded following prey-to-predator spillover events, which have had significant conservation implications for sensitive species. Using felids as a detailed case study, we have documented both virulent and clinically silent infections in apex predators following transfer of microbes from prey. We draw on these examples and others to examine the mechanisms that determine frequency and outcome of predator exposure to prey-based pathogens. We propose that predator-prey dynamics should be more thoroughly considered in empirical research and disease dynamic modeling approaches in order to reveal answers to outstanding questions relating to pathogen bioaccumulation.

Trade-offs with telemetry-derived contact networks for infectious disease studies in wildlife.

Network analysis of infectious disease in wildlife can reveal traits or individuals critical to pathogen transmission and help inform disease management strategies. However, estimates of contact between animals are notoriously difficult to acquire. Researchers commonly use telemetry technologies to identify animal associations; but such data may have different sampling intervals and often captures a small subset of the population. The objectives of this study were to outline best practices for telemetry sampling in network studies of infectious disease by determining (1) the consequences of telemetry sampling on our ability to estimate network structure, (2) whether contact networks can be approximated using purely spatial contact definitions, and (3) how wildlife spatial configurations may influence telemetry sampling requirements.We simulated individual movement trajectories for wildlife populations using a home range-like movement model, creating full location datasets and corresponding "complete" networks. To mimic telemetry data, we created "sample" networks by subsampling the population (10-100% of individuals) with a range of sampling intervals (every minute to every three days). We varied the definition of contact for sample networks, using either spatiotemporal or spatial overlap, and varied the spatial configuration of populations (random, lattice, or clustered). To compare complete and sample networks, we calculated seven network metrics important for disease transmission and assessed mean ranked correlation coefficients and percent error between complete and sample network metrics.Telemetry sampling severely reduced our ability to calculate global node-level network metrics, but had less impact on local and network-level metrics. Even so, in populations with infrequent associations, high intensity telemetry sampling may still be necessary. Defining contact in terms of spatial overlap generally resulted in overly connected networks, but in some instances, could compensate for otherwise coarse telemetry data.By synthesizing movement and disease ecology with computational approaches, we characterized trade-offs important for using wildlife telemetry data beyond ecological studies of individual movement, and found that careful use of telemetry data has the potential to inform network models. Thus, with informed application of telemetry data, we can make significant advances in leveraging its use for a better understanding and management of wildlife infectious disease.

Lung mediated auditory contrast enhancement improves the Signal-to-noise ratio for communication in frogs.

Environmental noise is a major source of selection on animal sensory and communication systems. The acoustic signals of other animals represent particularly potent sources of noise for chorusing insects, frogs, and birds, which contend with a multi-species analog of the human "cocktail party problem" (i.e., our difficulty following speech in crowds). However, current knowledge of the diverse adaptations that function to solve noise problems in nonhuman animals remains limited. Here, we show that a lung-to-ear sound transmission pathway in frogs serves a heretofore unknown noise-control function in vertebrate hearing and sound communication. Inflated lungs improve the signal-to-noise ratio for communication by enhancing the spectral contrast in received vocalizations in ways analogous to signal processing algorithms used in hearing aids and cochlear implants. Laser vibrometry revealed that the resonance of inflated lungs selectively reduces the tympanum's sensitivity to frequencies between the two spectral peaks present in conspecific mating calls. Social network analysis of continent-scale citizen science data on frog calling behavior revealed that the calls of other frog species in multi-species choruses can be a prominent source of environmental noise attenuated by the lungs. Physiological modeling of peripheral frequency tuning indicated that inflated lungs could reduce both auditory masking and suppression of neural responses to mating calls by environmental noise. Together, these data suggest an ancient adaptation for detecting sound via the lungs has been evolutionarily co-opted to create auditory contrast enhancement that contributes to solving a multi-species cocktail party problem.

Lessons from movement ecology for the return to work: Modeling contacts and the spread of COVID-19.

Human behavior (movement, social contacts) plays a central role in the spread of pathogens like SARS-CoV-2. The rapid spread of SARS-CoV-2 was driven by global human movement, and initial lockdown measures aimed to localize movement and contact in order to slow spread. Thus, movement and contact patterns need to be explicitly considered when making reopening decisions, especially regarding return to work. Here, as a case study, we consider the initial stages of resuming research at a large research university, using approaches from movement ecology and contact network epidemiology. First, we develop a dynamical pathogen model describing movement between home and work; we show that limiting social contact, via reduced people or reduced time in the workplace are fairly equivalent strategies to slow pathogen spread. Second, we develop a model based on spatial contact patterns within a specific office and lab building on campus; we show that restricting on-campus activities to labs (rather than labs and offices) could dramatically alter (modularize) contact network structure and thus, potentially reduce pathogen spread by providing a workplace mechanism to reduce contact. Here we argue that explicitly accounting for human movement and contact behavior in the workplace can provide additional strategies to slow pathogen spread that can be used in conjunction with ongoing public health efforts.