The Justinianic Plague, the first part of the earliest of the three plague pandemics, has minimal historical documentation. Based on the limited primary sources, historians have argued both for and ...against the "maximalist narrative" of plague, i.e. that the Justinianic Plague had universally devastating effects throughout the Mediterranean region during the sixth century CE. Using primary sources of one of the pandemic's best documented outbreaks that took place in Constantinople during 542 CE, as well as modern findings on plague etiology and epidemiology, we developed a series of dynamic, compartmental models of disease to explore which, if any, transmission routes of plague are feasible. Using expected parameter values, we find that the bubonic and bubonic-pneumonic transmission routes exceed maximalist mortality estimates and are of shorter detectable duration than described by the primary sources. When accounting for parameter uncertainty, several of the bubonic plague model configurations yielded interquartile estimates consistent with the upper end of maximalist estimates of mortality; however, these models had shorter detectable outbreaks than suggested by the primary sources. The pneumonic transmission routes suggest that by itself, pneumonic plague would not cause significant mortality in the city. However, our global sensitivity analysis shows that predicted disease dynamics vary widely for all hypothesized transmission routes, suggesting that regardless of its effects in Constantinople, the Justinianic Plague would have likely had differential effects across urban areas around the Mediterranean. Our work highlights the uncertainty surrounding the details in the primary sources on the Justinianic Plague and calls into question the likelihood that the Justinianic Plague affected all localities in the same way.
ABSTRACT
A hallmark assumption of traditional approaches to disease modelling is that individuals within a given population mix uniformly and at random. However, this assumption does not always hold ...true; contact heterogeneity or preferential associations can have a substantial impact on the duration, size, and dynamics of epidemics. Contact heterogeneity has been readily adopted in epidemiological studies of humans, but has been less studied in wildlife. While contact network studies are becoming more common for wildlife, their methodologies, fundamental assumptions, host species, and parasites vary widely. The goal of this article is to review how contact networks have been used to study macro‐ and microparasite transmission in wildlife. The review will: (i) explain why contact heterogeneity is relevant for wildlife populations; (ii) explore theoretical and applied questions that contact networks have been used to answer; (iii) give an overview of unresolved methodological issues; and (iv) suggest improvements and future directions for contact network studies in wildlife.
Intermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes, including signal transduction and gene expression patterns, arising from specific oncogenotypes and cell ...lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here, we quantified metabolic features, mostly from the 13C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression, and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes.
Display omitted
•Cell-autonomous metabolic diversity is reported in over 80 lung cancer cell lines•Heterogeneous metabolic phenotypes support lung cancer cell growth•Relating metabolic and molecular data uncovers new aspects of metabolic regulation•Some metabolic features predict sensitivity to chemotherapy and targeted agents
Metabolic reprogramming influences therapeutic sensitivity in cancer, but the scope of metabolic diversity among cancer cells is unknown. Chen et al. characterized metabolic phenotypes in over 80 non-small cell lung cancer cell lines and then used genomics, transcriptomics, proteomics, and therapeutic sensitivities to uncover relationships between metabolism and orthogonal processes.
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.
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.
Advances in pathogen detection technologies have allowed more thorough characterization of infections in free-ranging wildlife.In well-studied predators like Puma concolor, multiple occurrences of spillover following consumption of reservoir hosts as prey have been observed.Outcomes of predator exposures to infectious agents harbored by prey vary, but rarely result in widespread disease.Models of disease transmission have largely considered predator behavior in the context of effects in the prey species, versus consequences to the predator.However, certain prey-transmitted infections can result in high mortality rates, sometimes with significant negative impacts on conservation of vulnerable/threatened species.
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.
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.
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.
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.
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.
1. 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. 2. 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. 3. 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. 4. 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.
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.
Highlights
Prioritizing local needs and contexts is key for implementing evidence‐supported practices in community settings.
There is limited research on how community‐driven implementation may improve uptake and advance health equity.
This study uses CFIR framework to assess community‐driven utilization of research evidence in community settings.
Quantitative data shows correlations between URE and participants' intentions to act on research evidence.
Qualitative findings describe how participants make meaning and plan to use presented research evidence.
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.
PDF
