A simplicial epidemic model for COVID-19 spread analysis.

Networks allow us to describe a wide range of interaction phenomena that occur in complex systems arising in such diverse fields of knowledge as neuroscience, engineering, ecology, finance, and social sciences. Until very recently, the primary focus of network models and tools has been on describing the pairwise relationships between system entities. However, increasingly more studies indicate that polyadic or higher-order group relationships among multiple network entities may be the key toward better understanding of the intrinsic mechanisms behind the functionality of complex systems. Such group interactions can be, in turn, described in a holistic manner by simplicial complexes of graphs. Inspired by these recently emerging results on the utility of the simplicial geometry of complex networks for contagion propagation and armed with a large-scale synthetic social contact network (also known as a digital twin) of the population in the U.S. state of Virginia, in this paper, we aim to glean insights into the role of higher-order social interactions and the associated varying social group determinants on COVID-19 propagation and mitigation measures.

Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations.

Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and have often lacked transparency in terms of prioritization of false-positive versus false-negative signals. They have also struggled to maintain relevance over time due to slow and infrequent updates addressing new variants and shifts in vaccine- and infection-induced immunity. We make two contributions to address these weaknesses. We first present a framework to evaluate predictive accuracy based on policy targets related to severe disease and mortality, allowing for explicit preferences toward false-negative versus false-positive signals. This approach allows policymakers to optimize metrics for specific preferences and interventions. Second, we propose a method to update risk thresholds in real time. We show that this adaptive approach to designating areas as "high risk" improves performance over static metrics in predicting 3-wk-ahead mortality and intensive care usage at both state and county levels. We also demonstrate that with our approach, using only new hospital admissions to predict 3-wk-ahead mortality and intensive care usage has performed consistently as well as metrics that also include cases and inpatient bed usage. Our results highlight that a key challenge for COVID-19 risk prediction is the changing relationship between indicators and outcomes of policy interest. Adaptive metrics therefore have a unique advantage in a rapidly evolving pandemic context.

Nonrandom foraging and resource distributions affect the relationships between host density, contact rates and parasite transmission.

Nonrandom foraging can cause animals to aggregate in resource dense areas, increasing host density, contact rates and pathogen transmission, but when should nonrandom foraging and resource distributions also have density-independent effects? Here, we used a factorial experiment with constant resource and host densities to quantify host contact rates across seven resource distributions. We also used an agent-based model to compare pathogen transmission when host movement was based on random foraging, optimal foraging or something between those states. Nonrandom foraging strongly depressed contact rates and transmission relative to the classic random movement assumptions used in most epidemiological models. Given nonrandom foraging in the agent-based model and experiment, contact rates and transmission increased with resource aggregation and average distance to resource patches due to increased host movement in search of resources. Overall, we describe three density-independent mechanisms by which host behaviour and resource distributions alter contact rate functions and pathogen transmission.

Comparative approaches in social network ecology.

Social systems vary enormously across the animal kingdom, with important implications for ecological and evolutionary processes such as infectious disease dynamics, anti-predator defence, and the evolution of cooperation. Comparing social network structures between species offers a promising route to help disentangle the ecological and evolutionary processes that shape this diversity. Comparative analyses of networks like these are challenging and have been used relatively little in ecology, but are becoming increasingly feasible as the number of empirical datasets expands. Here, we provide an overview of multispecies comparative social network studies in ecology and evolution. We identify a range of advancements that these studies have made and key challenges that they face, and we use these to guide methodological and empirical suggestions for future research. Overall, we hope to motivate wider publication and analysis of open social network datasets in animal ecology.

Exploring the intricacies of Pasteurella multocida dynamics in high-altitude livestock and its consequences for bovine health: A personal exploration of the yak paradox.

Pasturella multocida (P. multocida), a gram-negative bacterium, has long been a focus of interest in animal health because of its capacity to cause different infections, including hemorrhagic septicemia. Yaks, primarily found in high-altitude environments, are among the several livestock animals affected by these bacteria. Yaks are essential to the socioeconomic life of the people who depend on them since they are adapted to the cold and hypoxic conditions of highland environments. Nevertheless, these terrains exhibit a greater incidence of P. multocida despite the severe environmental complications. This predominance has been linked to the possible attenuation of the yak's immunological responses in such circumstances and the evolution of some bacterial strains to favor survival in the respiratory passages of the animals. Moreover, these particular strains threaten other cattle populations that interact with yaks, which might result in unanticipated outbreaks in areas previously thought to be low risk. Considering these findings, designing and executing preventative and control strategies suited explicitly for these distinct biological environments is imperative. Through such strategies, yaks' health will be guaranteed, and a larger bovine population will be safeguarded against unanticipated epidemics. The current review provides thorough insights that were previously dispersed among several investigations. Its distinct method of connecting the ecology of yaks with the dynamics of infection offers substantial background information for further studies and livestock management plans.

A model-based assessment of social isolation practices for COVID-19 outbreak response in residential care facilities.

BACKGROUND: Residential aged-care facilities (RACFs, also called long-term care facilities, aged care homes, or nursing homes) have elevated risks of respiratory infection outbreaks and associated disease burden. During the COVID-19 pandemic, social isolation policies were commonly used in these facilities to prevent and mitigate outbreaks. We refer specifically to general isolation policies that were intended to reduce contact between residents, without regard to confirmed infection status. Such policies are controversial because of their association with adverse mental and physical health indicators and there is a lack of modelling that assesses their effectiveness. METHODS: In consultation with the Australian Government Department of Health and Aged Care, we developed an agent-based model of COVID-19 transmission in a structured population, intended to represent the salient characteristics of a residential care environment. Using our model, we generated stochastic ensembles of simulated outbreaks and compared summary statistics of outbreaks simulated under different mitigation conditions. Our study focuses on the marginal impact of general isolation (reducing social contact between residents), regardless of confirmed infection. For a realistic assessment, our model included other generic interventions consistent with the Australian Government's recommendations released during the COVID-19 pandemic: isolation of confirmed resident cases, furlough (mandatory paid leave) of staff members with confirmed infection, and deployment of personal protective equipment (PPE) after outbreak declaration. RESULTS: In the absence of any asymptomatic screening, general isolation of residents to their rooms reduced median cumulative cases by approximately 27%. However, when conducted concurrently with asymptomatic screening and isolation of confirmed cases, general isolation reduced the median number of cumulative infections by only 12% in our simulations. CONCLUSIONS: Under realistic sets of assumptions, our simulations showed that general isolation of residents did not provide substantial benefits beyond those achieved through screening, isolation of confirmed cases, and deployment of PPE. Our results also highlight the importance of effective case isolation, and indicate that asymptomatic screening of residents and staff may be warranted, especially if importation risk from the outside community is high. Our conclusions are sensitive to assumptions about the proportion of total contacts in a facility accounted for by casual interactions between residents.

Evolutionary Invasion Analysis of Modern Epidemics Highlights the Context-Dependence of Virulence Evolution.

Models are often employed to integrate knowledge about epidemics across scales and simulate disease dynamics. While these approaches have played a central role in studying the mechanics underlying epidemics, we lack ways to reliably predict how the relationship between virulence (the harm to hosts caused by an infection) and transmission will evolve in certain virus-host contexts. In this study, we invoke evolutionary invasion analysis-a method used to identify the evolution of uninvadable strategies in dynamical systems-to examine how the virulence-transmission dichotomy can evolve in models of virus infections defined by different natural histories. We reveal peculiar patterns of virulence evolution between epidemics with different disease natural histories (SARS-CoV-2 and hepatitis C virus). We discuss the findings with regards to the public health implications of predicting virus evolution, and in broader theoretical canon involving virulence evolution in host-parasite systems.

Chaos in Opinion-Driven Disease Dynamics.

During the COVID-19 pandemic, it became evident that the effectiveness of applying intervention measures is significantly influenced by societal acceptance, which, in turn, is affected by the processes of opinion formation. This article explores one among the many possibilities of coupled opinion-epidemic systems. The findings reveal either intricate periodic patterns or chaotic dynamics, leading to substantial fluctuations in opinion distribution and, consequently, significant variations in the total number of infections over time. Interestingly, the model exhibits a protective pattern.

Resource selection by New York City deer reveals the effective interface between wildlife, zoonotic hazards and humans.

Although the role of host movement in shaping infectious disease dynamics is widely acknowledged, methodological separation between animal movement and disease ecology has prevented researchers from leveraging empirical insights from movement data to advance landscape scale understanding of infectious disease risk. To address this knowledge gap, we examine how movement behaviour and resource utilization by white-tailed deer (Odocoileus virginianus) determines blacklegged tick (Ixodes scapularis) distribution, which depend on deer for dispersal in a highly fragmented New York City borough. Multi-scale hierarchical resource selection analysis and movement modelling provide insight into how deer's movements contribute to the risk landscape for human exposure to the Lyme disease vector-I. scapularis. We find deer select highly vegetated and accessible residential properties which support blacklegged tick survival. We conclude the distribution of tick-borne disease risk results from the individual resource selection by deer across spatial scales in response to habitat fragmentation and anthropogenic disturbances.

Anthropogenic subsidies influence resource use during a mange epizootic in a desert coyote population.

Colonization of urban areas by synanthropic wildlife introduces novel and complex alterations to established ecological processes, including the emergence and spread of infectious diseases. Aggregation at urban resources can increase disease transfer, with wide-ranging species potentially infecting outlying populations. The garrison at the National Training Center, Fort Irwin, California, USA, was recently colonized by mange-infected coyotes (Canis latrans) that also use the surrounding Mojave Desert. This situation provided an ideal opportunity to examine the effects of urban resources on disease dynamics. We evaluated seasonal space use and determined the influence of anthropogenic subsidies, water sources, and prey density on urban resource selection. We found no difference in home range size between healthy and infected individuals, but infected residents had considerably more spatial overlap with one another than healthy residents. All coyotes selected for anthropogenic subsidies during all seasons, while infected coyotes seasonally selected for urban water sources, and healthy coyotes seasonally selected for urban areas with greater densities of natural prey. These results suggest that while all coyotes were selecting for anthropogenic subsidies, infected resident coyotes demonstrated a greater tolerance for other conspecifics, which could be facilitating the horizontal transfer of sarcoptic mange to non-resident coyotes. Conversely, healthy coyotes also selected for natural prey and healthy residents exhibited a lack of spatial overlap with other coyotes suggesting they were not reliant on anthropogenic subsidies and were maintaining territories. Understanding the association between urban wildlife, zoonotic diseases, and urban resources can be critical in determining effective responses for mitigating future epizootics.

Modelling the effect of travel-related policies on disease control in a meta-population structure.

Travel restrictions, while delaying the spread of an emerging disease from the source, could inflict substantial socioeconomic burden. Travel-related policies, such as quarantine and testing of travelers, may be considered as alternative strategies to mitigate the negative impact of travel bans. We developed a meta-population, delay-differential model to evaluate a strategy that combines testing of travelers prior to departure from the source of infection with quarantine and testing at exit from quarantine in the destination population. Our results, based on early parameter estimates of SARS-CoV-2 infection, indicate that testing travelers at exit from quarantine is more effective in delaying case importation than testing them before departure or upon arrival. We show that a 1-day quarantine with an exit test could outperform a longer, 3-day quarantine without testing in delaying the outbreak peak. Rapid, large-scale testing capacities with short turnaround times provide important means of detecting infectious cases and reducing case importation, while shortening quarantine duration for travelers at destination.

Decline of Marteilia cochillia in Ría de Arousa may be due to increased resistance in host Cerastoderma edule.

A huge, unprecedented mortality of cockle Cerastoderma edule caused by the protist Marteilia cochillia, which had never before been detected in Galicia (NW Spain), brought on a cockle fishery collapse in the Ría de Arousa (Galicia) in 2012. Since then, the disease dynamic pattern in the shellfish bed of Lombos do Ulla (at the inner area of that ria) involved an overwhelming annual wave of infections and subsequent cockle mass mortality that caused the near extinction of every cohort recruited to that bed. However, a pattern shift was detected among wild cohorts recruiting since 2016, with progressive declines of marteiliosis prevalence and increments in cockle survival. This suggested 2 non-exclusive hypotheses: increasing marteiliosis resistance through natural selection, and reduced abundance and/or virulence of the parasite. A field experiment was performed to assess these hypotheses by comparing marteiliosis prevalence and severity, as well as mortality, in cockles that naturally recruited to this bed in 2017 and 2018 with those of naïve cockles collected from a marteiliosis-free area and transplanted into Lombos do Ulla in 2017 and 2018. Marteiliosis prevalence and cumulative cockle mortality quickly reached very high values among the transplanted cockles, demonstrating that the parasite remained present and virulent in the area. Conversely, marteiliosis prevalence and cockle mortality were much lower in the cockles that recruited to Lombos do Ulla, suggesting increased resistance that may have been driven by natural selection. The young age at which cockles start reproduction and the very high mortality caused by marteiliosis may have enhanced natural selection.

Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data.

The United States experienced historically high numbers of measles cases in 2019, despite achieving national measles vaccination rates above the World Health Organization recommendation of 95% coverage with two doses. Since the COVID-19 pandemic began, resulting in suspension of many clinical preventive services, pediatric vaccination rates in the United States have fallen precipitously, dramatically increasing risk of measles resurgence. Previous research has shown that measles outbreaks in high-coverage contexts are driven by spatial clustering of nonvaccination, which decreases local immunity below the herd immunity threshold. However, little is known about how to best conduct surveillance and target interventions to detect and address these high-risk areas, and most vaccination data are reported at the state-level-a resolution too coarse to detect community-level clustering of nonvaccination characteristic of recent outbreaks. In this paper, we perform a series of computational experiments to assess the impact of clustered nonvaccination on outbreak potential and magnitude of bias in predicting disease risk posed by measuring vaccination rates at coarse spatial scales. We find that, when nonvaccination is locally clustered, reporting aggregate data at the state- or county-level can result in substantial underestimates of outbreak risk. The COVID-19 pandemic has shone a bright light on the weaknesses in US infectious disease surveillance and a broader gap in our understanding of how to best use detailed spatial data to interrupt and control infectious disease transmission. Our research clearly outlines that finer-scale vaccination data should be collected to prevent a return to endemic measles transmission in the United States.

Environmental reservoir dynamics predict global infection patterns and population impacts for the fungal disease white-nose syndrome.

Disease outbreaks and pathogen introductions can have significant effects on host populations, and the ability of pathogens to persist in the environment can exacerbate disease impacts by fueling sustained transmission, seasonal epidemics, and repeated spillover events. While theory suggests that the presence of an environmental reservoir increases the risk of host declines and threat of extinction, the influence of reservoir dynamics on transmission and population impacts remains poorly described. Here we show that the extent of the environmental reservoir explains broad patterns of host infection and the severity of disease impacts of a virulent pathogen. We examined reservoir and host infection dynamics and the resulting impacts of Pseudogymnoascus destructans, the fungal pathogen that causes white-nose syndrome, in 39 species of bats at 101 sites across the globe. Lower levels of pathogen in the environment consistently corresponded to delayed infection of hosts, fewer and less severe infections, and reduced population impacts. In contrast, an extensive and persistent environmental reservoir led to early and widespread infections and severe population declines. These results suggest that continental differences in the persistence or decay of P. destructans in the environment altered infection patterns in bats and influenced whether host populations were stable or experienced severe declines from this disease. Quantifying the impact of the environmental reservoir on disease dynamics can provide specific targets for reducing pathogen levels in the environment to prevent or control future epidemics.

The duration of travel impacts the spatial dynamics of infectious diseases.

Humans can impact the spatial transmission dynamics of infectious diseases by introducing pathogens into susceptible environments. The rate at which this occurs depends in part on human-mobility patterns. Increasingly, mobile-phone usage data are used to quantify human mobility and investigate the impact on disease dynamics. Although the number of trips between locations and the duration of those trips could both affect infectious-disease dynamics, there has been limited work to quantify and model the duration of travel in the context of disease transmission. Using mobility data inferred from mobile-phone calling records in Namibia, we calculated both the number of trips between districts and the duration of these trips from 2010 to 2014. We fit hierarchical Bayesian models to these data to describe both the mean trip number and duration. Results indicate that trip duration is positively related to trip distance, but negatively related to the destination population density. The highest volume of trips and shortest trip durations were among high-density districts, whereas trips among low-density districts had lower volume with longer duration. We also analyzed the impact of including trip duration in spatial-transmission models for a range of pathogens and introduction locations. We found that inclusion of trip duration generally delays the rate of introduction, regardless of pathogen, and that the variance and uncertainty around spatial spread increases proportionally with pathogen-generation time. These results enhance our understanding of disease-dispersal dynamics driven by human mobility, which has potential to elucidate optimal spatial and temporal scales for epidemic interventions.

Between lives and economy: COVID-19 containment policy in open economies.

This paper studies containment policies for combating a pandemic in an open-economy context. It does so via quantitative analyses using a model that incorporates a standard epidemiological compartmental model in a general equilibrium multi-country, multi-sector Ricardian model of international trade with input-output linkages. We quantitatively evaluate the long-run welfare and real-income losses due to the short-run pandemic shocks, and we study the role of trade in these effects. We devise a novel approach to computing national optimal policies. We find that (1) the long-run welfare and real-income losses due to just two years of pandemic shocks are substantial; (2) international trade helps buffer both the welfare and real-income losses, and it also saves lives; (3) the computed optimal policies indicate that most countries should have tightened their containment measures relative to what was done; and (4) compared to the case of autarky, the optimal policy under trade is generally more stringent.

Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace-of-life.

Pathogen transmission depends on host density, mobility and contact. These components emerge from host and pathogen movements that themselves arise through interactions with the surrounding environment. The environment, the emergent host and pathogen movements, and the subsequent patterns of density, mobility and contact form an 'epidemiological landscape' connecting the environment to specific locations where transmissions occur. Conventionally, the epidemiological landscape has been described in terms of the geographical coordinates where hosts or pathogens are located. We advocate for an alternative approach that relates those locations to attributes of the local environment. Environmental descriptions can strengthen epidemiological forecasts by allowing for predictions even when local geographical data are not available. Environmental predictions are more accessible than ever thanks to new tools from movement ecology, and we introduce a 'movement-pathogen pace of life' heuristic to help identify aspects of movement that have the most influence on spatial epidemiology. By linking pathogen transmission directly to the environment, the epidemiological landscape offers an efficient path for using environmental information to inform models describing when and where transmission will occur.

The relationship between pathogen life-history traits and metapopulation dynamics.

Plant pathogen traits, such as transmission mode and overwintering strategy, may have important effects on dispersal and persistence, and drive disease dynamics. Still, we lack insights into how life-history traits influence spatiotemporal disease dynamics. We adopted a multifaceted approach, combining experimental assays, theory and field surveys, to investigate whether information about two pathogen life-history traits - infectivity and overwintering strategy - can predict pathogen metapopulation dynamics in natural systems. For this, we focused on four fungal pathogens (two rust fungi, one chytrid fungus and one smut fungus) on the forest herb Anemone nemorosa. Pathogens infecting new plants mostly via spores (the chytrid and smut fungi) had higher patch occupancies and colonization rates than pathogens causing mainly systemic infections and overwintering in the rhizomes (the two rust fungi). Although the rust fungi more often occupied well-connected plant patches, the chytrid and smut fungi were equally or more common in isolated patches. Host patch size was positively related to patch occupancy and colonization rates for all pathogens. Predicting disease dynamics is crucial for understanding the ecological and evolutionary dynamics of host-pathogen interactions, and to prevent disease outbreaks. Our study shows that combining experiments, theory and field observations is a useful way to predict disease dynamics.

Influence of delayed density and ultraviolet radiation on caterpillar baculovirus infection and mortality.

Infectious disease is an important potential driver of population cycles but must occur through delayed density-dependent infection and resulting fitness effects. Delayed density-dependent infection by baculoviruses can be caused by environmental persistence of viral occlusion bodies (OBs), which can be influenced by environmental factors. Specifically, ultraviolet radiation is potentially important in reducing the environmental persistence of viruses by inactivating OBs. Delayed density-dependent viral infection has rarely been observed empirically at the population level although theory predicts that it is necessary for pathogens to drive population cycles. Similarly, field studies have not examined the effects of ultraviolet radiation on viral infection rates in natural animal populations. We tested if viral infection is delayed density-dependent with the potential to drive cyclic dynamics and if ultraviolet radiation influences viral infection levels. We censused 18 Ranchman's tiger moth (Arctia virginalis) populations across 9° of latitude over 2 years and quantified the effects of direct and delayed density and ultraviolet radiation on proportion infected by baculovirus, infection severity and survival to adulthood. Caterpillars were collected from field populations and reared in the laboratory. Baculovirus has not previously been described infecting A. virginalis, and we used genetic methods to confirm the identity of the virus. We found that proportion infected, infection severity and survival to adulthood exhibited delayed density dependence. Ultraviolet radiation in the previous summer decreased infection severity, which increased caterpillar survival probability. Structural equation modelling indicated that the effect of lagged density on caterpillar survival was mediated through proportion infected and infection severity and was 2.5-fold stronger than the indirect effect of ultraviolet. We successfully amplified polh, lef-8 and lef-9 viral genes from caterpillars, and BLAST results confirmed that the virus was a nucleopolyhedrovirus. Our findings provide clear evidence that delayed density-dependent mortality can arise through viral infection rate and severity in insects, which supports the role of viral disease as a mechanism, among others, that may drive insect population cycles. Furthermore, our findings support predictions that ultraviolet radiation can modify viral disease dynamics in insect populations, most likely through attenuating viral persistence in the environment.

Elevational Changes in Bacterial Microbiota Structure and Diversity in an Arthropod-Disease Vector.

Environmental conditions change rapidly along elevational gradients and have been found to affect community composition in macroscopic taxa, with lower diversity typically observed at higher elevations. In contrast, microbial community responses to elevation are still poorly understood. Specifically, the effects of elevation on vector-associated microbiota have not been studied to date, even though the within-vector microbial community is known to influence vector competence for a range of zoonotic pathogens. Here we characterize the structure and diversity of the bacterial microbiota in an important zoonotic disease vector, the sheep tick Ixodes ricinus, along replicated elevational gradient (630-1673 m) in the Swiss Alps. 16S rRNA sequencing of the whole within-tick bacterial microbiota of questing nymphs and adults revealed a decrease in Faith's phylogenetic microbial alpha diversity with increasing elevation, while beta diversity analyses revealed a lower variation in microbial community composition at higher elevations. We also found a higher microbial diversity later in the season and significant differences in microbial diversity among tick life stages and sexes, with lowest microbial alpha diversity observed in adult females. No associations between tick genetic diversity and bacterial diversity were observed. Our study demonstrates systematic changes in tick bacterial microbiota diversity along elevational gradients. The observed patterns mirror diversity changes along elevational gradients typically observed in macroscopic taxa, and they highlight the key role of environmental factors in shaping within-host microbial communities in ectotherms.