Representative Public Health Surveys Pose Several Challenges: Lessons Learned Across 9 Communities During the COVID-19 Pandemic.

Community surveillance surveys offer an opportunity to obtain important and timely public health information that may help local municipalities guide their response to public health threats. The objective of this paper is to present approaches, challenges, and solutions from SARS-CoV-2 surveillance surveys conducted in different settings by 2 research teams. For rapid assessment of a representative sample, a 2-stage cluster sampling design was developed by an interdisciplinary team of researchers at Oregon State University between April 2020 and June 2021 across 6 Oregon communities. In 2022, these methods were adapted for New York communities by a team of veterinary, medical, and public health practitioners. Partnerships were established with local medical facilities, health departments, COVID-19 testing sites, and health and public safety staff. Field staff were trained using online modules, field manuals describing survey methods and safety protocols, and in-person meetings with hands-on practice. Private and secure data integration systems and public awareness campaigns were implemented. Pilot surveys and field previews revealed challenges in survey processes that could be addressed before surveys proceeded. Strong leadership, robust trainings, and university-community partnerships proved critical to successful outcomes. Cultivating mutual trust and cooperation among stakeholders is essential to prepare for the next pandemic.

Evaluation of a Wastewater-Based Epidemiological Approach to Estimate the Prevalence of SARS-CoV-2 Infections and the Detection of Viral Variants in Disparate Oregon Communities at City and Neighborhood Scales.

BACKGROUND: Positive correlations have been reported between wastewater SARS-CoV-2 concentrations and a community's burden of infection, disease or both. However, previous studies mostly compared wastewater to clinical case counts or nonrepresentative convenience samples, limiting their quantitative potential. OBJECTIVES: This study examined whether wastewater SARS-CoV-2 concentrations could provide better estimations for SARS-CoV-2 community prevalence than reported cases of COVID-19. In addition, this study tested whether wastewater-based epidemiology methods could identify neighborhood-level COVID-19 hotspots and SARS-CoV-2 variants. METHODS: Community SARS-CoV-2 prevalence was estimated from eight randomized door-to-door nasal swab sampling events in six Oregon communities of disparate size, location, and demography over a 10-month period. Simultaneously, wastewater SARS-CoV-2 concentrations were quantified at each community's wastewater treatment plant and from 22 Newport, Oregon, neighborhoods. SARS-CoV-2 RNA was sequenced from all positive wastewater and nasal swab samples. Clinically reported case counts were obtained from the Oregon Health Authority. RESULTS: Estimated community SARS-CoV-2 prevalence ranged from 8 to 1,687/10,000 persons. Community wastewater SARS-CoV-2 concentrations ranged from 2.9 to 5.1 log10 gene copies per liter. Wastewater SARS-CoV-2 concentrations were more highly correlated (Pearson's r=0.96; R2=0.91) with community prevalence than were clinically reported cases of COVID-19 (Pearson's r=0.85; R2=0.73). Monte Carlo simulations indicated that wastewater SARS-CoV-2 concentrations were significantly better than clinically reported cases at estimating prevalence (p<0.05). In addition, wastewater analyses determined neighborhood-level COVID-19 hot spots and identified SARS-CoV-2 variants (B.1 and B.1.399) at the neighborhood and city scales. DISCUSSION: The greater reliability of wastewater SARS-CoV-2 concentrations over clinically reported case counts was likely due to systematic biases that affect reported case counts, including variations in access to testing and underreporting of asymptomatic cases. With these advantages, combined with scalability and low costs, wastewater-based epidemiology can be a key component in public health surveillance of COVID-19 and other communicable infections. https://doi.org/10.1289/EHP10289.

Collective behaviour can stabilize ecosystems.

Collective behaviour is common in bacteria, plants and animals, and therefore occurs across ecosystems, from biofilms to cities. With collective behaviour, social interactions among individuals propagate to affect the behaviour of groups, whereas group-level responses in turn affect individual behaviour. These cross-scale feedback loops between individuals, populations and their environments can provide fitness benefits, such as the efficient exploitation of uncertain resources, as well as costs, such as increased resource competition. Although the social mechanics of collective behaviour are increasingly well-studied, its role in ecosystems remains poorly understood. Here we introduce collective movement into a model of consumer-resource dynamics to demonstrate that collective behaviour can attenuate consumer-resource cycles and promote species coexistence. We focus on collective movement as a particularly well-understood example of collective behaviour. Adding collective movement to canonical unstable ecological scenarios causes emergent social-ecological feedback, which mitigates conditions that would otherwise result in extinction. Collective behaviour could play a key part in the maintenance of biodiversity.

Global analysis of a predator-prey model with variable predator search rate.

We consider a modified Holling-type II predator-prey model, based on the premise that the search rate of predators is dependent on the prey density, rather than constant. A complete analysis of the global behavior of the model is presented, and shows that the model exhibits a dichotomy similar to the classical Holling-type II model: either the coexistence steady state is globally stable; or it is unstable, and then a unique, globally stable limit cycle exists. We discuss the similarities, but also important differences between our model and the Holling-type II model. The main differences are that: 1. The paradox of enrichment which always occurs in the Holling-type II model, does not always occur here, and 2. Even when the paradox of enrichment occurs, predators can adapt by lowering their search rate, and effectively stabilize the system.

Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities.

Influenza epidemics vary in intensity from year to year, driven by climatic conditions and by viral antigenic evolution. However, important spatial variation remains unexplained. Here we show predictable differences in influenza incidence among cities, driven by population size and structure. Weekly incidence data from 603 cities in the United States reveal that epidemics in smaller cities are focused on shorter periods of the influenza season, whereas in larger cities, incidence is more diffuse. Base transmission potential estimated from city-level incidence data is positively correlated with population size and with spatiotemporal organization in population density, indicating a milder response to climate forcing in metropolises. This suggests that urban centers incubate critical chains of transmission outside of peak climatic conditions, altering the spatiotemporal geometry of herd immunity.

Multiple Incursions and Recurrent Epidemic Fade-Out of H3N2 Canine Influenza A Virus in the United States.

Avian-origin H3N2 canine influenza virus (CIV) transferred to dogs in Asia around 2005, becoming enzootic throughout China and South Korea before reaching the United States in early 2015. To understand the posttransfer evolution and epidemiology of this virus, particularly the cause of recent and ongoing increases in incidence in the United States, we performed an integrated analysis of whole-genome sequence data from 64 newly sequenced viruses and comprehensive surveillance data. This revealed that the circulation of H3N2 CIV within the United States is typified by recurrent epidemic burst-fade-out dynamics driven by multiple introductions of virus from Asia. Although all major viral lineages displayed similar rates of genomic sequence evolution, H3N2 CIV consistently exhibited proportionally more nonsynonymous substitutions per site than those in avian reservoir viruses, which is indicative of a large-scale change in selection pressures. Despite these genotypic differences, we found no evidence of adaptive evolution or increased viral transmission, with epidemiological models indicating a basic reproductive number, R0, of between 1 and 1.5 across nearly all U.S. outbreaks, consistent with maintained but heterogeneous circulation. We propose that CIV's mode of viral circulation may have resulted in evolutionary cul-de-sacs, in which there is little opportunity for the selection of the more transmissible H3N2 CIV phenotypes necessary to enable circulation through a general dog population characterized by widespread contact heterogeneity. CIV must therefore rely on metapopulations of high host density (such as animal shelters and kennels) within the greater dog population and reintroduction from other populations or face complete epidemic extinction.IMPORTANCE The relatively recent appearance of influenza A virus (IAV) epidemics in dogs expands our understanding of IAV host range and ecology, providing useful and relevant models for understanding critical factors involved in viral emergence. Here we integrate viral whole-genome sequence analysis and comprehensive surveillance data to examine the evolution of the emerging avian-origin H3N2 canine influenza virus (CIV), particularly the factors driving ongoing circulation and recent increases in incidence of the virus within the United States. Our results provide a detailed understanding of how H3N2 CIV achieves sustained circulation within the United States despite widespread host contact heterogeneity and recurrent epidemic fade-out. Moreover, our findings suggest that the types and intensities of selection pressures an emerging virus experiences are highly dependent on host population structure and ecology and may inhibit an emerging virus from acquiring sustained epidemic or pandemic circulation.

Unreported cases in the 2014-2016 Ebola epidemic: Spatiotemporal variation, and implications for estimating transmission.

In the recent 2014-2016 Ebola epidemic in West Africa, non-hospitalized cases were an important component of the chain of transmission. However, non-hospitalized cases are at increased risk of going unreported because of barriers to access to healthcare. Furthermore, underreporting rates may fluctuate over space and time, biasing estimates of disease transmission rates, which are important for understanding spread and planning control measures. We performed a retrospective analysis on community deaths during the recent Ebola epidemic in Sierra Leone to estimate the number of unreported non-hospitalized cases, and to quantify how Ebola reporting rates varied across locations and over time. We then tested if variation in reporting rates affected the estimates of disease transmission rates that were used in surveillance and response. We found significant variation in reporting rates among districts, and district-specific rates of increase in reporting over time. Correcting time series of numbers of cases for variable reporting rates led, in some instances, to different estimates of the time-varying reproduction number of the epidemic, particularly outside the capital. Future analyses that compare Ebola transmission rates over time and across locations may be improved by considering the impacts of differential reporting rates.

A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak.

In recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging.

Spread of Canine Influenza A(H3N2) Virus, United States.

A canine influenza A(H3N2) virus emerged in the United States in February-March 2015, causing respiratory disease in dogs. The virus had previously been circulating among dogs in Asia, where it originated through the transfer of an avian-origin influenza virus around 2005 and continues to circulate. Sequence analysis suggests the US outbreak was initiated by a single introduction, in Chicago, of an H3N2 canine influenza virus circulating among dogs in South Korea in 2015. Despite local control measures, the virus has continued circulating among dogs in and around Chicago and has spread to several other areas of the country, particularly Georgia and North Carolina, although these secondary outbreaks appear to have ended within a few months. Some genetic variation has accumulated among the US viruses, with the appearance of regional-temporal lineages. The potential for interspecies transmission and zoonotic events involving this newly emerged influenza A virus is currently unknown.

Estimating the number of secondary Ebola cases resulting from an unsafe burial and risk factors for transmission during the West Africa Ebola epidemic.

BACKGROUND: Safely burying Ebola infected individuals is acknowledged to be important for controlling Ebola epidemics and was a major component of the 2013-2016 West Africa Ebola response. Yet, in order to understand the impact of safe burial programs it is necessary to elucidate the role of unsafe burials in sustaining chains of Ebola transmission and how the risk posed by activities surrounding unsafe burials, including care provided at home prior to death, vary with human behavior and geography. METHODOLOGY/PRINCIPAL FINDINGS: Interviews with next of kin and community members were carried out for unsafe burials in Sierra Leone, Liberia and Guinea, in six districts where the Red Cross was responsible for safe and dignified burials (SDB). Districts were randomly selected from a district-specific sampling frame comprised of villages and neighborhoods that had experienced cases of Ebola. An average of 2.58 secondary cases were potentially generated per unsafe burial and varied by district (range: 0-20). Contact before and after death was reported for 142 (46%) contacts. Caregivers of a primary case were 2.63 to 5.92 times more likely to become EVD infected compared to those with post-mortem contact only. Using these estimates, the Red Cross SDB program potentially averted between 1,411 and 10,452 secondary EVD cases, reducing the epidemic by 4.9% to 36.5%. CONCLUSIONS/SIGNIFICANCE: SDB is a fundamental control measure that limits community transmission of Ebola; however, for those individuals having contact before and after death, it was impossible to ascertain the exposure that caused their infection. The number of infections prevented through SDB is significant, yet greater impact would be achieved by early hospitalization of the primary case during acute illness.

Persistent Chaos of Measles Epidemics in the Prevaccination United States Caused by a Small Change in Seasonal Transmission Patterns.

Epidemics of infectious diseases often occur in predictable limit cycles. Theory suggests these cycles can be disrupted by high amplitude seasonal fluctuations in transmission rates, resulting in deterministic chaos. However, persistent deterministic chaos has never been observed, in part because sufficiently large oscillations in transmission rates are uncommon. Where they do occur, the resulting deep epidemic troughs break the chain of transmission, leading to epidemic extinction, even in large cities. Here we demonstrate a new path to locally persistent chaotic epidemics via subtle shifts in seasonal patterns of transmission, rather than through high-amplitude fluctuations in transmission rates. We base our analysis on a comparison of measles incidence in 80 major cities in the prevaccination era United States and United Kingdom. Unlike the regular limit cycles seen in the UK, measles cycles in US cities consistently exhibit spontaneous shifts in epidemic periodicity resulting in chaotic patterns. We show that these patterns were driven by small systematic differences between countries in the duration of the summer period of low transmission. This example demonstrates empirically that small perturbations in disease transmission patterns can fundamentally alter the regularity and spatiotemporal coherence of epidemics.

Contact heterogeneity, rather than transmission efficiency, limits the emergence and spread of canine influenza virus.

Host-range shifts in influenza virus are a major risk factor for pandemics. A key question in the study of emerging zoonoses is how the evolution of transmission efficiency interacts with heterogeneity in contact patterns in the new host species, as this interplay influences disease dynamics and prospects for control. Here we use a synergistic mixture of models and data to tease apart the evolutionary and demographic processes controlling a host-range shift in equine H3N8-derived canine influenza virus (CIV). CIV has experienced 15 years of continuous transfer among dogs in the United States, but maintains a patchy distribution, characterized by sporadic short-lived outbreaks coupled with endemic hotspots in large animal shelters. We show that CIV has a high reproductive potential in these facilities (mean R(0) = 3.9) and that these hotspots act as refugia from the sparsely connected majority of the dog population. Intriguingly, CIV has evolved a transmission efficiency that closely matches the minimum required to persist in these refugia, leaving it poised on the extinction/invasion threshold of the host contact network. Corresponding phylogenetic analyses show strong geographic clustering in three US regions, and that the effective reproductive number of the virus (R(e)) in the general dog population is close to 1.0. Our results highlight the critical role of host contact structure in CIV dynamics, and show how host contact networks could shape the evolution of pathogen transmission efficiency. Importantly, efficient control measures could eradicate the virus, in turn minimizing the risk of future sustained transmission among companion dogs that could represent a potential new axis to the human-animal interface for influenza.

Human mobility patterns predict divergent epidemic dynamics among cities.

The epidemic dynamics of infectious diseases vary among cities, but it is unclear how this is caused by patterns of infectious contact among individuals. Here, we ask whether systematic differences in human mobility patterns are sufficient to cause inter-city variation in epidemic dynamics for infectious diseases spread by casual contact between hosts. We analyse census data on the mobility patterns of every full-time worker in 48 Canadian cities, finding a power-law relationship between population size and the level of organization in mobility patterns, where in larger cities, a greater fraction of workers travel to work in a few focal locations. Similarly sized cities also vary in the level of organization in their mobility patterns, equivalent on average to the variation expected from a 2.64-fold change in population size. Systematic variation in mobility patterns is sufficient to cause significant differences among cities in infectious disease dynamics-even among cities of the same size-according to an individual-based model of airborne pathogen transmission parametrized with the mobility data. This suggests that differences among cities in host contact patterns are sufficient to drive differences in infectious disease dynamics and provides a framework for testing the effects of host mobility patterns in city-level disease data.

Fitting probability distributions to animal movement trajectories: using artificial neural networks to link distance, resources, and memory.

Animal movement paths are often thought of as a confluence of behavioral processes and landscape patterns. Yet it has proven difficult to develop frameworks for analyzing animal movement that can test these interactions. Here we describe a novel method for fitting movement models to data that can incorporate diverse aspects of landscapes and behavior. Using data from five elk (Cervus canadensis) reintroduced to central Ontario, we employed artificial neural networks to estimate movement probability kernels as functions of three landscape-behavioral processes. These consisted of measures of the animals' response to the physical spatial structure of the landscape, the spatial variability in resources, and memory of previously visited locations. The results support the view that animal movement results from interactions among elements of landscape structure and behavior, motivating context-dependent movement probabilities, rather than from successive realizations of static distributions, as some traditional models of movement and resource selection assume. Flexible, nonlinear models may thus prove useful in understanding the mechanisms controlling animal movement patterns.

Are there general mechanisms of animal home range behaviour? A review and prospects for future research.

Home range behaviour is a common pattern of space use, having fundamental consequences for ecological processes. However, a general mechanistic explanation is still lacking. Research is split into three separate areas of inquiry - movement models based on random walks, individual-based models based on optimal foraging theory, and a statistical modelling approach - which have developed without much productive contact. Here we review recent advances in modelling home range behaviour, focusing particularly on the problem of identifying mechanisms that lead to the emergence of stable home ranges from unbounded movement paths. We discuss the issue of spatiotemporal scale, which is rarely considered in modelling studies, as well as highlighting the need to consider more closely the dynamical nature of home ranges. Recent methodological and theoretical advances may soon lead to a unified approach, however, conceptually unifying our understanding of linkages among home range behaviour and ecological or evolutionary processes.