A clarification of transmission terms in host-microparasite models: numbers, densities and areas

Caterpillar movement mediates spatially local interactions and determines the relationship between population density and contact

BACKGROUND

While interactions in nature are inherently local, ecological models often assume homogeneity across space, allowing for generalization across systems and greater mathematical tractability. Density-dependent disease models are a prominent example of models that assume homogeneous interactions, leading to the prediction that disease transmission will scale linearly with population density. In this study, we examined how the scale of larval butterfly movement interacts with the resource landscape to influence the relationship between larval contact and population density in the Baltimore checkerspot (Euphydryas phaeton). Our study was inspired by the recent discovery of a viral pathogen that is transmitted horizontally among Baltimore checkerspot larvae.

METHODS

We used multi-year larvae location data across six Baltimore checkerspot populations in the eastern U.S. to test whether larval nests are spatially clustered. We then integrated these spatial data with larval movement data in different resource contexts to investigate whether heterogeneity in spatially local interactions alters the assumed linear relationship between larval nest density and contact. We used Correlated Random Walk (CRW) models and field observations of larval movement behavior to construct Probability Distribution Functions (PDFs) of larval dispersal, and calculated the overlap in these PDFs to estimate conspecific contact within each population.

RESULTS

We found that all populations exhibited significant spatial clustering in their habitat use. Subsequent larval movement rates were influenced by encounters with host plants and larval age, and under many movement scenarios, the scale of predicted larval movement was not sufficient to allow for the "homogeneous mixing" assumed in density dependent disease models. Therefore, relationships between population density and larval contact were typically non-linear. We also found that observed use of available habitat patches led to significantly greater contact than would occur if habitat use were spatially random.

CONCLUSIONS

These findings strongly suggest that incorporating larval movement and spatial variation in larval interactions is critical to modeling disease outcomes in E. phaeton. Epidemiological models that assume a linear relationship between population density and larval contact have the potential to underestimate transmission rates, especially in small populations that are already vulnerable to extinction.

Forecasting Hepatitis C Virus Status for Children in the United States: A Modeling Study

BACKGROUND

Virtually all cases of hepatitis C virus (HCV) infection in children in the United States occur through vertical transmission, but it is unknown how many children are infected. Cases of maternal HCV infection have increased in the United States, which may increase the number of children vertically infected with HCV. Infection has long-term consequences for a child's health, but treatment options are now available for children ≥3 years old. Reducing HCV infections in adults could decrease HCV infections in children.

METHODS

Using a stochastic compartmental model, we forecasted incidence of HCV infections in children in the United States from 2022 through 2027. The model considered vertical transmission to children <13 years old and horizontal transmission among individuals 13-49 years old. We obtained model parameters and initial conditions from the literature and the Centers for Disease Control and Prevention's 2021 Viral Hepatitis Surveillance Report.

RESULTS

Model simulations assuming direct-acting antiviral treatment for children forecasted that the number of acutely infected children would decrease slightly and the number of chronically infected children would decrease even more. Alone, treatment and early screening in individuals 13-49 years old reduced the number of forecasted cases in children and, together, these policy interventions were even more effective.

CONCLUSIONS

Based on our simulations, acute and chronic cases of HCV infection are remaining constant or slightly decreasing in the United States. Improving early screening and increasing access to treatment in adults may be an effective strategy for reducing the number of HCV infected children in the United States.

Who acquires infection from whom? A sensitivity analysis of transmission dynamics during the early phase of the COVID-19 pandemic in Belgium

Age-related heterogeneity in a host population, whether due to how individuals mix and contact each other, the nature of host-pathogen interactions defining epidemiological parameters, or demographics, is crucial in studying infectious disease dynamics. Compartmental models represent a popular approach to address the problem, dividing the population of interest into a discrete and finite number of states depending on, for example, individuals' age and stage of infection. We study the corresponding linearised system whose operator, in the context of a discrete-time model, equates to a square matrix known as the next generation matrix. Performing formal perturbation analysis of the entries of the aforementioned matrix, we derive indices to quantify the age-specific variation of its dominant eigenvalue (i.e., the reproduction number) and explore the relevant epidemiological information we can derive from the eigenstructure of the matrix. The resulting method enables the assessment of the impact of age-related population heterogeneity on virus transmission. In particular, starting from an age-structured SEIR model, we demonstrate the use of this approach for COVID-19 dynamics in Belgium. We analyse the early stages of the SARS-CoV-2 spread, with particular attention to the pre-pandemic framework and the lockdown lifting phase initiated as of May 2020. Our results, influenced by our assumption on age-specific susceptibility and infectiousness, support the hypothesis that transmission was only influenced to a small extent by children in the age group [0,18) and adults over 60 years of age during the early phases of the pandemic and up to the end of July 2020.

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.

Improving estimates of waning immunity rates in stochastic SIRS models with a hierarchical framework

As most disease causing pathogens require transmission from an infectious individual to a susceptible individual, continued persistence of the pathogen within the population requires the replenishment of susceptibles through births, immigration, or waning immunity. Consider the introduction of an unknown infectious disease into a fully susceptible population where it is not known how long immunity is conferred once an individual recovers from infection. If, initially, the prevalence of disease increases (that is, the infection takes off), the number of infectives will usually decrease to a low level after the first major outbreak. During this post-outbreak period, the disease dynamics may be influenced by stochastic effects and there is a non-zero probability that the epidemic will die out. Die out in this period following the first major outbreak is known as an epidemic fade-out. If the disease does not die out, the susceptible population may be replenished by the waning of immunity, and a second wave may start. In this study, we investigate if the rate of waning immunity (and other epidemiological parameters) can be reliably estimated from multiple outbreak data, in which some outbreaks display epidemic fade-out and others do not. We generated synthetic outbreak data from independent simulations of stochastic SIRS models in multiple communities. Some outbreaks faded-out and some did not. We conducted Bayesian parameter estimation under two alternative approaches: independently on each outbreak and under a hierarchical framework. When conducting independent estimation, the waning immunity rate was poorly estimated and biased towards zero when an epidemic fade-out was observed. However, under a hierarchical approach, we obtained more accurate and precise posterior estimates for the rate of waning immunity and other epidemiological parameters. The greatest improvement in estimates was obtained for those communities in which epidemic fade-out was observed. Our findings demonstrate the feasibility and value of adopting a Bayesian hierarchical approach for parameter inference for stochastic epidemic models.

Stabilizing the return to normal behavior in an epidemic

Predicting the interplay between infectious disease and behavior has been an intractable problem because behavioral response is so varied. We introduce a general framework for feedback between incidence and behavior for an infectious disease. By identifying stable equilibria, we provide policy end-states that are self-managing and self-maintaining. We prove mathematically the existence of two new endemic equilibria depending on the vaccination rate: one in the presence of low vaccination but with reduced societal activity (the "new normal"), and one with return to normal activity but with vaccination rate below that required for disease elimination. This framework allows us to anticipate the long-term consequence of an emerging disease and design a vaccination response that optimizes public health and limits societal consequences.

Patterns and drivers of vector-borne microparasites in a classic metapopulation

Many organisms live in fragmented populations, which has profound consequences on the dynamics of associated parasites. Metapopulation theory offers a canonical framework for predicting the effects of fragmentation on spatiotemporal host–parasite dynamics. However, empirical studies of parasites in classical metapopulations remain rare, particularly for vector-borne parasites. Here, we quantify spatiotemporal patterns and possible drivers of infection probability for several ectoparasites (fleas, Ixodes trianguliceps and Ixodes ricinus) and vector-borne microparasites (Babesia microti, Bartonella spp., Hepatozoon spp.) in a classically functioning metapopulation of water vole hosts. Results suggest that the relative importance of vector or host dynamics on microparasite infection probabilities is related to parasite life-histories. Bartonella, a microparasite with a fast life-history, was positively associated with both host and vector abundances at several spatial and temporal scales. In contrast, B. microti, a tick-borne parasite with a slow life-history, was only associated with vector dynamics. Further, we provide evidence that life-history shaped parasite dynamics, including occupancy and colonization rates, in the metapopulation. Lastly, our findings were consistent with the hypothesis that landscape connectivity was determined by distance-based dispersal of the focal hosts. We provide essential empirical evidence that contributes to the development of a comprehensive theory of metapopulation processes of vector-borne parasites.

Mumps epidemic dynamics in the United States before vaccination (1923-1932)

Mumps is a vaccine-preventable, reemerging, and highly transmissible infectious disease. Widespread vaccination dramatically reduced cases; however, case counts have been increasing over the past 20 years. To provide a quantitative overview of historical mumps dynamics that can act as baseline information to help identify causes of mumps reemergence, we analyzed timeseries of cases reported from 1923 to 1932 in the United States. During that time, 239,230 mumps cases were reported in 70 cities. Larger cities reported annual epidemics and smaller cities reported intermittent, sporadic outbreaks. The critical community size above which transmission continuously occurred was likely between 365,583 and 781,188 individuals but could range as high as 3,376,438 individuals. Mumps cases increased as city size increased, suggesting density-dependent transmission. Using a density-dependent SEIR model, we calculated a mean effective reproductive number (Re) of 1.2. Re varied by city and over time, with periodic high values that could characterize short periods of very high transmission known as superspreading events. Case counts most often peaked in March, with higher-than-average transmission from December through April and showed a correlation with weekly births. While certain city pairs in Midwestern states had synchronous outbreaks, most outbreaks were less synchronous and not driven by distance between cities. This work demonstrates the importance of long-term infectious disease surveillance data and will inform future studies on mumps reemergence and control.

The Economic Evaluation of Mastitis Control Strategies in Holstein-Friesian Dairy Herds

The economic evaluation of mastitis control is challenging. The objective of this study was to perform the economic evaluation of mastitis control, under different intervention scenarios, quantifying the total cost of mastitis caused by S. aureus in Holstein cows in Argentina. A model was set for a dairy herd of Holstein cows endemically infected with S. aureus. A basic mastitis control plan including proper milking procedures, milking machine test, dry cow therapy, and treatment for clinical mastitis, was compared against other more complex and costly interventions, such as segregation and culling of chronically infected cows. Sensitivity analysis was performed by modifying the intramammary infection transition probabilities, economic parameters, and efficacy of treatment strategies. The basic mastitis control plan showed a median total cost of USD88.6/cow per year, which was close to the infected cows culling scenarios outputs. However, the segregation scenario was the most efficient, in which the total cost was reduced by about 50%. Such cost was more sensitive to probabilities and efficacy than the economic parameters. The model is flexible and can be customized by producers and veterinarians according to different control and herd settings.

Drivers of Batrachochytrium dendrobatidis infection load, with evidence of infection tolerance in adult male toads (Bufo spinosus)

Chytridiomycosis is affecting hundreds of amphibian species worldwide, but while in tropical areas, adult individuals have been the focus of most investigations, the exact role played by infection intensity of breeding adults is not well understood in temperate areas. We conducted mark-recapture-capture surveys during spiny common toad breeding seasons from 2006 to 2018 at the site of the first recorded outbreak of chytridiomycosis in Europe, the Peñalara Massif (Sierra de Guadarrama National Park, central Spain), and collected infection samples and several variables related to the reproductive effort of male individuals. We used general linear mixed models to evaluate the contribution of study variables on the infection loads of adult male toads exhibited at their capturing date. We also analysed the differences on several male characteristics between the pond with the largest breeding population against the rest of the ponds. We found that the duration of time spent in the waterbody and the condition of the host predicted infection loads. Animals of good physical condition, that spent longer in water, have higher infection levels than individuals with the opposite set of traits. The pond supporting the largest breeding population housed smaller male toads and in poorer condition. Our results are consistent with a shift in reproductive strategy in response to infection and potentially a strategy of tolerance, rather than resistance to infection. These findings have applications for disease mitigation and theoretical implications related to the trade-offs made and the evolution of traits in response to the disease.

Large scale systemic control short-circuits pathogen transmission by interrupting the sand rat (Psammomys obesus)-to-sand fly (Phlebotomus papatasi) Leishmania major transmission cycle

Systemic control uses the vertebrate hosts of zoonotic pathogens as "Trojan horses," killing blood-feeding female vectors and short-circuiting host-to-vector pathogen transmission. Previous studies focused only on the effect of systemic control on vector abundance at small spatial scales. None were conducted at a spatial scale relevant for vector control and none on the effect of systemic control on pathogen transmission rates. We tested the application of systemic control, using Fipronil-impregnated rodent baits, in reducing Leishmania major (Kinetoplastida: Trypanosomatidae; Yakimoff & Schokhor, 1914) infection levels within the vector, Phlebotomus papatasi (Diptera: Psychodidae; Scopoli, 1786) population, at the town-scale. We provided Fipronil-impregnated food-baits to all Psammomys obesus (Mammalia:Muridae; Cretzschmar, 1828), the main L. major reservoir, burrows along the southern perimeter of the town of Yeruham, Israel, and compared sand fly abundance and infection levels with a non-treated control area. We found a significant and substantial treatment effect on L. major infection levels in the female sand fly population. Sand fly abundance was not affected. Our results demonstrate, for the first time, the potential of systemic control in reducing pathogen transmission rates at a large, epidemiologically relevant, spatial scale.

Modeling disruption of Apis mellifera (honey bee) odorant-binding protein function with high-affinity binders

Chemical toxins pose a great threat to honey bee health because they affect memory and cognition, diminish immunity, and increase susceptibility to infection, resulting in decreased colony performance, reproduction, and survival. Although the behavioral effects of sub-lethal chemical exposure on honey bees have been intensively studied, how xenobiotics affect olfaction, at the molecular level, still needs to be elucidated. In the present work, in silico tools, such as molecular docking, binding free energy calculations, and molecular dynamics simulations are used to predict if environmental chemicals have stronger binding affinities to honey bee antennal odorant-binding protein 14 (OBP14) than the representative floral odors citralva, eugenol, and the fluorescent probe 1-N-phenylnaphthylamine. Based on structural analysis, 21 chemicals from crop pesticides, household appliances, cosmetics, food, public health-related products, and other sources, many of which are pervasive in the hive environment, have higher binding affinities than the floral odors. These results suggest that chemical exposures are likely to interfere with the honey bee's sense of smell and this disruptive mechanism may be responsible for the lower associative learning and memory based on olfaction found in bees exposed to pesticides. Moreover, bees mainly rely on olfactory cues to perceive their environment and orient themselves as well as to discriminate and identify their food, predators, nestmates, and diseased individuals that need to be removed with hygienic behavior. In summary, sub-lethal exposure to environmental toxins can contribute to colony collapse in several ways from the disruption of proper olfaction functioning.

Optimal spatial evaluation of a pro rata vaccine distribution rule for COVID-19

The COVID-19 Vaccines Global Access (COVAX) is a World Health Organization (WHO) initiative that aims for an equitable access of COVID-19 vaccines. Despite potential heterogeneous infection levels across a country, countries receiving allotments of vaccines may follow WHO's allocation guidelines and distribute vaccines based on a jurisdictions' relative population size. Utilizing economic-epidemiological modeling, we benchmark the performance of this pro rata allocation rule by comparing it to an optimal one that minimizes the economic damages and expenditures over time, including a penalty representing the social costs of deviating from the pro rata strategy. The pro rata rule performs better when the duration of naturally- and vaccine-acquired immunity is short, when there is population mixing, when the supply of vaccine is high, and when there is minimal heterogeneity in demographics. Despite behavioral and epidemiological uncertainty diminishing the performance of the optimal allocation, it generally outperforms the pro rata vaccine distribution rule.

Estimation of a Within-Herd Transmission Rate for African Swine Fever in Vietnam

We describe results from a panel study in which pigs from a 17-sow African swine fever (ASF) positive herd in Thái Bình province, Vietnam, were followed over time to record the date of onset of ASF signs and the date of death from ASF. Our objectives were to (1) fit a susceptible-exposed-infectious-removed disease model to the data with transmission coefficients estimated using approximate Bayesian computation; (2) provide commentary on how a model of this type might be used to provide decision support for disease control authorities. For the outbreak in this herd, the median of the average latent period was 10 days (95% HPD (highest posterior density interval): 2 to 19 days), and the median of the average duration of infectiousness was 3 days (95% HPD: 2 to 4 days). The estimated median for the transmission coefficient was 3.3 (95% HPD: 0.4 to 8.9) infectious contacts per ASF-infectious pig per day. The estimated median for the basic reproductive number, R₀, was 10 (95% HPD: 1.1 to 30). Our estimates of the basic reproductive number R₀ were greater than estimates of R₀ for ASF reported previously. The results presented in this study may be used to estimate the number of pigs expected to be showing clinical signs at a given number of days following an estimated incursion date. This will allow sample size calculations, with or without adjustment to account for less than perfect sensitivity of clinical examination, to be used to determine the appropriate number of pigs to examine to detect at least one with the disease. A second use of the results of this study would be to inform the equation-based within-herd spread components of stochastic agent-based and hybrid simulation models of ASF.

Animal movement and associated infectious disease risk in a metapopulation

Animal movements among habitat patches or populations are important for maintaining long-term genetic and demographic viability, but connectivity may also facilitate disease spread and persistence. Understanding factors that influence animal movements is critical to understanding potential transmission risk and persistence of communicable disease in spatially structured systems. We evaluated effects of sex, age and Mycoplasma ovipneumoniae infection status at capture on intermountain movements and seasonal movement rates observed in desert bighorn sheep (Ovis canadensis nelsoni) using global positioning system collar data from 135 individuals (27 males, 108 females) in 14 populations between 2013 and 2018, following a pneumonia outbreak linked to the pathogen M. ovipneumoniae in the Mojave Desert, California, USA. Based on logistic regression analysis, intermountain movements were influenced by sex, age and most notably, infection status at capture: males, older animals and uninfected individuals were most likely to make such movements. Based on multiple linear regression analysis, females that tested positive for M. ovipneumoniae at capture also had lower mean daily movement rates that were further influenced by season. Our study provides empirical evidence of a pathogenic infection decreasing an individual's future mobility, presumably limiting that pathogen's ability to spread, and ultimately influencing transmission risk within a spatially structured system.

High virulence is associated with pathogen spreadability in a songbird-bacterial system

How directly transmitted pathogens benefit from harming hosts is key to understanding virulence evolution. It is recognized that pathogens benefit from high within-host loads, often associated with virulence. However, high virulence may also directly augment spread of a given amount of pathogen, here termed 'spreadability'. We used house finches and the conjunctival pathogen Mycoplasma gallisepticum to test whether two components of virulence-the severity of conjunctival inflammation and behavioural morbidity produced-predict pathogen spreadability. We applied ultraviolet powder around the conjunctiva of finches that were inoculated with pathogen treatments of distinct virulence and measured within-flock powder spread, our proxy for 'spreadability'. When compared to uninfected controls, birds infected with a high-virulence, but not low-virulence, pathogen strain, spread significantly more powder to flockmates. Relative to controls, high-virulence treatment birds both had more severe conjunctival inflammation-which potentially facilitated powder shedding-and longer bouts on feeders, which serve as fomites. However, food peck rates and displacements with flockmates were lowest in high-virulence treatment birds relative to controls, suggesting inflammatory rather than behavioural mechanisms likely drive augmented spreadability at high virulence. Our results suggest that inflammation associated with virulence can facilitate pathogen spread to conspecifics, potentially favouring virulence evolution in this system and others.

Stability and Hopf bifurcation of an SIR epidemic model with density-dependent transmission and Allee effect

In this paper, an SIR model with a strong Allee effect and density-dependent transmission is proposed, and its characteristic dynamics are investigated. The elementary mathematical characteristic of the model is studied, including positivity, boundedness and the existence of equilibrium. The local asymptotic stability of the equilibrium points is analyzed using linear stability analysis. Our results indicate that the asymptotic dynamics of the model are not only determined using the basic reproduction number ${R_0}$. If ${R_0} < 1$, there are three disease-free equilibrium points, and a disease-free equilibrium is always stable. At the same time, the conditions for other disease-free equilibrium points to be bistable were determined. If ${R_0} > 1$ and in certain conditions, either an endemic equilibrium emerges and is locally asymptotically stable, or the endemic equilibrium becomes unstable. What must be emphasized is that there is a locally asymptotically stable limit cycle when the latter happens. The Hopf bifurcation of the model is also discussed using topological normal forms. The stable limit cycle can be interpreted in a biological significance as a recurrence of the disease. Numerical simulations are used to verify the theoretical analysis. Taking into account both density-dependent transmission of infectious diseases and the Allee effect, the dynamic behavior becomes more interesting than when considering only one of them in the model. The Allee effect makes the SIR epidemic model bistable, which also makes the disappearance of diseases possible, since the disease-free equilibrium in the model is locally asymptotically stable. At the same time, persistent oscillations due to the synergistic effect of density-dependent transmission and the Allee effect may explain the recurrence and disappearance of disease.

Season and prey identity mediate the effect of predators on parasites in rodents: a test of the healthy herds hypothesis

The healthy herds hypothesis (HHH) suggests that predators decrease parasitism in their prey. Repeated tests of this hypothesis across a range of taxa and ecosystems have revealed significant variation in the effect of predators on parasites in prey. Differences in the response to predators (1) between prey taxa, (2) between seasons, and (3) before and after catastrophic disturbance are common in natural systems, but typically ignored in empirical tests of the HHH. We used a predator exclusion experiment to measure the effect of these heterogeneities on the tri-trophic interaction among predators, parasites and prey. We experimentally excluded mammalian predators from the habitats of hispid cotton rats (Sigmodon hispidus) and cotton mice (Peromyscus gossypinus) and measured the effect of exclusion on gastrointestinal parasites in these rodents. Our experiment spanned multiple seasons and before and after a prescribed burn. We found that the exclusion of the same predators had opposite effects on the parasites of small mammal prey species. Additionally, we found that the effect of mammal exclusion on parasitism differed before versus after fire disturbance. Finally, we saw that the effect of predator exclusion was highly dependent on prey capture season. Significant effects of exclusion emerged primarily in the fall and winter months. The presence of so many different effects in one relatively simple system suggests that predator effects on parasites in prey are highly context dependent.

Mechanistic models of Rift Valley fever virus transmission: A systematic review

Rift Valley fever (RVF) is a zoonotic arbovirosis which has been reported across Africa including the northernmost edge, South West Indian Ocean islands, and the Arabian Peninsula. The virus is responsible for high abortion rates and mortality in young ruminants, with economic impacts in affected countries. To date, RVF epidemiological mechanisms are not fully understood, due to the multiplicity of implicated vertebrate hosts, vectors, and ecosystems. In this context, mathematical models are useful tools to develop our understanding of complex systems, and mechanistic models are particularly suited to data-scarce settings. Here, we performed a systematic review of mechanistic models studying RVF, to explore their diversity and their contribution to the understanding of this disease epidemiology. Researching Pubmed and Scopus databases (October 2021), we eventually selected 48 papers, presenting overall 49 different models with numerical application to RVF. We categorized models as theoretical, applied, or grey, depending on whether they represented a specific geographical context or not, and whether they relied on an extensive use of data. We discussed their contributions to the understanding of RVF epidemiology, and highlighted that theoretical and applied models are used differently yet meet common objectives. Through the examination of model features, we identified research questions left unexplored across scales, such as the role of animal mobility, as well as the relative contributions of host and vector species to transmission. Importantly, we noted a substantial lack of justification when choosing a functional form for the force of infection. Overall, we showed a great diversity in RVF models, leading to important progress in our comprehension of epidemiological mechanisms. To go further, data gaps must be filled, and modelers need to improve their code accessibility.

Quantifying intra- and inter-species contact rates at supplemental feeding sites in Ethiopia to inform rabies maintenance potential of multiple host species

Rabies, a multi-host pathogen responsible for the loss of roughly 59,000 human lives each year worldwide, continues to impose a significant burden of disease despite control efforts, especially in Ethiopia. However, how species other than dogs contribute to rabies transmission throughout Ethiopia remains largely unknown. In this study, we quantified interactions among wildlife species in Ethiopia with the greatest potential for contributing to rabies maintenance. We observed wildlife at supplemental scavenging sites across multiple landscape types and quantified transmission potential. More specifically, we used camera trap data to quantify species abundance, species distribution, and intra- and inter-species contacts per trapping night over time and by location. We derived a mathematical expression for the basic reproductive number (R₀ ) based on within- and between-species contract rates by applying the next generation method to the susceptible, exposed, infectious, removed model. We calculated R₀ for transmission within each species and between each pair of species using camera trap data in order to identify pairwise interactions that contributed the most to transmission in an ecological community. We estimated which species, or species pairs, could maintain transmission ( R 0 > 1 ${R_0} &gt; 1$ ) and which species, or species pairs, had contact rates too low for maintenance ( R 0 < 1 ${R_0} &lt; 1$ ). Our results identified multiple urban carnivores as candidate species for rabies maintenance throughout Ethiopia, with hyenas exhibiting the greatest risk for rabies maintenance through intra-species transmission. Hyenas and cats had the greatest risk for rabies maintenance through inter-species transmission. Urban and peri-urban sites posed the greatest risk for rabies transmission. The night-time hours presented the greatest risk for a contact event that could result in rabies transmission. Overall, both intra- and inter-species contacts posed risk for rabies maintenance. Our results can be used to target future studies and inform population management decisions.

Estimation of introduction time window of highly pathogenic avian influenza virus into broiler chicken farms during the 2020 - 2021 winter season outbreak in Japan

When an infectious disease occurs in an area, early detection of infected farms is important to respond quickly and contain the outbreak on a small scale. Estimating the time window for the introduction of the infection is important for its prevention and control. The aim of this study was to estimate the farm-specific time window from the introduction of the highly pathogenic avian influenza (HPAI) virus into poultry farms using field data from the HPAI H5N8 outbreak in the 2020-2021 winter season in Japan. Daily mortality data from 12 broiler chicken farms during the outbreak were used for the analysis. A mathematical model (Susceptible-Exposed-Infectious-Removed, SEIR model) was applied to generate the within-flock transmission of HPAI. The model-predicted mortality was fitted to the observed excess mortality data induced by HPAI to estimate the farm-specific transmission rate and the time of virus introduction. The estimated value of the transmission rate in each farm was 1.449 day^-1 in median (min: 0.661 day^-1, max: 3.387 day^-1). The time window from the introduction of the virus to notification in each farm was estimated at 14.0 days in median (min: 8.6 days, max: 24.1 days) in the deterministic model. In addition, in the stochastic model considering the randomness of transmission in the early phase of the outbreak, the upper value of 95 % credible interval of the time window ranged from 12 to 34 days, with a median of 21 days. The results suggest that although one to three weeks had elapsed on most farms until notification after the virus introduction, the time window could exceed three weeks considering the stochasticity of disease transmission. As for the potential farm characteristics affecting within-flock transmission, the transmission rate was smaller (p-value=0.02) and the estimated time window from introduction to notification was longer (p-value=0.02) when birds were older. This study provides reliable information for setting up a tracing period for a potential source farm and enhancing the efforts for early detection.

Dynamic Models of Within-Herd Transmission and Recommendation for Vaccination Coverage Requirement in the Case of African Swine Fever in Vietnam

African swine fever (ASF) is a highly contagious disease that is caused by the ASF virus (ASFV) with a high fatality rate in domestic pigs resulting in a high socio-economic impact. The pig business in Vietnam was recently affected by ASF for the first time. This study thus aimed to develop a disease dynamic model to explain how ASFV spreads in Vietnamese pig populations and suggest a protective vaccine coverage level required to prevent future outbreaks. The outbreak data were collected from ten private small-scale farms within the first wave of ASF outbreaks in Vietnam. Three methods were used to estimate the basic reproduction number (R₀), including the exponential growth method, maximum likelihood method, and attack rate method. The average R₀ values were estimated at 1.49 (95%CI: 1.05–2.21), 1.58 (95%CI: 0.92–2.56), and 1.46 (95%CI: 1.38–1.57), respectively. Based on the worst-case scenario, all pigs in a herd would be infected and removed within 50 days. We suggest vaccinating at least 80% of pigs on each farm once a commercially approved ASF vaccine is available. However, an improvement in biosecurity levels in small-scale farms is still greatly encouraged to prevent the introduction of the virus.

Transmission dynamics of ectoparasitic gyrodactylids (Platyhelminthes, Monogenea): An integrative review

Parasite transmission is the ability of pathogens to move between hosts. As a key component of the interaction between hosts and parasites, it has crucial implications for the fitness of both. Here, we review the transmission dynamics of Gyrodactylus species, which are monogenean ectoparasites of teleost fishes and a prominent model for studies of parasite transmission. Particularly, we focus on the most studied host–parasite system within this genus: guppies, Poecilia reticulata, and G. turnbulli/G. bullatarudis. Through an integrative literature examination, we identify the main variables affecting Gyrodactylus spread between hosts, and the potential factors that enhance their transmission. Previous research indicates that Gyrodactylids spread when their current conditions are unsuitable. Transmission depends on abiotic factors like temperature, and biotic variables such as gyrodactylid biology, host heterogeneity, and their interaction. Variation in the degree of social contact between hosts and sexes might also result in distinct dynamics. Our review highlights a lack of mathematical models that could help predict the dynamics of gyrodactylids, and there is also a bias to study only a few species. Future research may usefully focus on how gyrodactylid reproductive traits and host heterogeneity promote transmission and should incorporate the feedbacks between host behaviour and parasite transmission.

Host preferences inhibit transmission from potential superspreader host species

Host species that are particularly abundant, infectious and/or infected tend to contribute disproportionately to symbiont (parasite or mutualist) maintenance in multi-host systems. Therefore, in a facultative multi-host system where two host species had high densities, high symbiont infestation intensities and high infestation prevalence, we expected interspecific transmission rates to be high. Instead, we found that interspecific symbiont transmission rates to caged sentinel hosts were an order of magnitude lower than intraspecific transmission rates in the wild. Using laboratory experiments to decompose transmission rates, we found that opportunities for interspecific transmission were frequent, where interspecific and intraspecific contact rate functions were statistically indistinguishable. However, most interspecific contacts did not lead to transmission events owing to a previously unrecognized transmission barrier: strong host preferences. During laboratory choice experiments, the symbiont preferred staying on or dispersing to its current host species, even though the oligochaete symbiont is a globally distributed host generalist that can survive and reproduce on many snail host species. These surprising results suggest that when managing symbiont transmission, identifying key host species is still important, but it may be equally important to identify and manage transmission barriers that keep potential superspreader host species in check.

A model for leveraging animal movement to understand spatio-temporal disease dynamics

The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable model that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible model 'Movement-driven modelling of spatio-temporal infection risk' (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.

Parasite dynamics in North American monarchs predicted by host density and seasonal migratory culling

Insect-pathogen dynamics can show seasonal and inter-annual variation that covaries with fluctuations in insect abundance and climate. Long-term analyses are especially needed to track parasite dynamics in migratory insects, in part because their vast habitat ranges and high mobility might dampen local effects of density and climate on infection prevalence. Monarch butterflies (Danaus plexippus) are commonly infected with the protozoan Ophryocystis elektroscirrha (OE). Because this parasite lowers monarch survival and flight performance, and because migratory monarchs have experienced declines in recent decades, it is important to understand patterns and drivers of infection. 3. Here we compiled data on OE infection spanning 50 years, from wild monarchs sampled in the USA, Canada, and Mexico during summer breeding, fall migrating, and overwintering periods. We examined eastern versus western North American monarchs separately, to ask how abundance estimates, resource availability, climate, and breeding season length impact infection trends. We further assessed the intensity of migratory culling, which occurs when infected individuals are removed from the population during migration. 4. Average infection prevalence was four times higher in western compared to eastern subpopulations. In eastern North America, the proportion of infected monarchs increased three-fold since the mid-2000s. In the western region, the proportion of infected monarchs declined sharply from 2000-2015, and increased thereafter. For both eastern and western subpopulations, years with greater summer adult abundance predicted greater infection prevalence, indicating that transmission increases with host breeding density. Environmental variables (temperature and NDVI) were not associated with changes in infected adults. We found evidence for migratory culling of infected butterflies, based on declines in parasitism during fall migration. We estimated that tens of millions fewer monarchs reach overwintering sites in Mexico as a result of OE, highlighting the need to consider the parasite as a potential threat to the monarch population. 5. Increases in infection among eastern North American monarchs post-2002 suggest that changes to the host's ecology or environment have intensified parasite transmission. Further work is needed to examine the degree to which human practices, such as mass caterpillar rearing and the widespread planting of exotic milkweed, have contributed to this trend.

Ranavirus Amplification in Low-Diversity Amphibian Communities

In an era where emerging infectious diseases are a serious threat to biodiversity, epidemiological patterns need to be identified, particularly the complex mechanisms driving the dynamics of multi-host pathogens in natural communities. Many amphibian species have faced unprecedented population declines associated with diseases. Yet, specific processes shaping host-pathogen relationships within and among communities for amphibian pathogens such as ranaviruses (RV) remain poorly understood. To address this gap, we conducted a comprehensive study of RV in low-diversity amphibian communities in north-western Canada to assess the effects of biotic factors (species identity, species richness, abundance) and abiotic factors (conductivity, pH) on the pathogen prevalence and viral loads. Across 2 years and 18 sites, with communities of up to three hosts (wood frog, Rana sylvatica; boreal chorus frog, Pseudacris maculata; Canadian toad, Anaxyrus hemiophrys), we observed that RV prevalence nearly doubled with each additional species in a community, suggesting an amplification effect in aquatic, as well as terrestrial life-history stages. Infection intensity among infected wood frogs and boreal chorus frogs also significantly increased with an increase in species richness. Interestingly, we did not observe any effects of host abundance or abiotic factors, highlighting the importance of including host identity and species richness when investigating multi-host pathogens. Ultimately, only such a comprehensive approach can improve our understanding of complex and often highly context-dependent host-pathogen interactions.

Unveiling social distancing mechanisms via a fish-robot hybrid interaction

Pathogen transmission is a major limit of social species. Social distancing, a behavioural-based response to diseases, has been regularly reported in nature. However, the identification of distinctive stimuli associated with an infectious disease represents a challenging task for host species, whose cognitive mechanisms are still poorly understood. Herein, the social fish Paracheirodon innesi, was selected as model organism to investigate animal abilities in exploiting visual information to identify and promote social distancing towards potentially infected conspecifics. To address this, a robotic fish replica mimicking a healthy P. innesi subject, and another mimicking P. innesi with morphological and/or locomotion anomalies were developed. P. innesi individuals were attracted by the healthy fish replica, while they avoided the fish replica with morphological abnormalities, as well as the fish replica with an intact appearance, but performing locomotion anomalies (both symptoms associated with a microsporidian parasite infesting P. innesi and other fish). Furthermore, the fish replica presenting both morphology and locomotion anomalies in conjunction, triggered a significantly stronger social distancing response. This confirms the hypothesis that group living animals overgeneralize cues that can be related with a disease to minimize transmission, and highlights the important role of visual cues in infection risk contexts. This study prompts more attention on the role of behavioural-based strategies to avoid pathogen/parasite diffusion, and can be used to optimize computational approaches to model disease dynamics.

Within and between classroom transmission patterns of seasonal influenza among primary school students in Matsumoto city, Japan

Schools play a central role in the transmission of many respiratory infections. Heterogeneous social contact patterns associated with the social structures of schools (i.e., classes/grades) are likely to influence the within-school transmission dynamics, but data-driven evidence on fine-scale transmission patterns between students has been limited. Using a mathematical model, we analyzed a large-scale dataset of seasonal influenza outbreaks in Matsumoto city, Japan, to infer social interactions within and between classes/grades from observed transmission patterns. While the relative contribution of within-class and within-grade transmissions to the reproduction number varied with the number of classes per grade, the overall within-school reproduction number, which determines the initial growth of cases and the risk of sustained transmission, was only minimally associated with class sizes and the number of classes per grade. This finding suggests that interventions that change the size and number of classes, e.g., splitting classes and staggered attendance, may have a limited effect on the control of school outbreaks. We also found that vaccination and mask-wearing of students were associated with reduced susceptibility (vaccination and mask-wearing) and infectiousness (mask-wearing), and hand washing was associated with increased susceptibility. Our results show how analysis of fine-grained transmission patterns between students can improve understanding of within-school disease dynamics and provide insights into the relative impact of different approaches to outbreak control.

Counterintuitive scaling between population abundance and local density: Implications for modelling transmission of infectious diseases in bat populations

Models of host-pathogen interactions help to explain infection dynamics in wildlife populations and to predict and mitigate the risk of zoonotic spillover. Insights from models inherently depend on the way contacts between hosts are modelled, and crucially, how transmission scales with animal density. Bats are important reservoirs of zoonotic disease and are among the most gregarious of all mammals. Their population structures can be highly heterogeneous, underpinned by ecological processes across different scales, complicating assumptions regarding the nature of contacts and transmission. Although models commonly parameterise transmission using metrics of total abundance, whether this is an ecologically representative approximation of host-pathogen interactions is not routinely evaluated. We collected a 13-month dataset of tree-roosting Pteropus spp. from 2,522 spatially referenced trees across eight roosts to empirically evaluate the relationship between total roost abundance and tree-level measures of abundance and density-the scale most likely to be relevant for virus transmission. We also evaluate whether roost features at different scales (roost level, subplot level, tree level) are predictive of these local density dynamics. Roost-level features were not representative of tree-level abundance (bats per tree) or tree-level density (bats per m² or m³ ), with roost-level models explaining minimal variation in tree-level measures. Total roost abundance itself was either not a significant predictor (tree-level 3D density) or only weakly predictive (tree-level abundance). This indicates that basic measures, such as total abundance of bats in a roost, may not provide adequate approximations for population dynamics at scales relevant for transmission, and that alternative measures are needed to compare transmission potential between roosts. From the best candidate models, the strongest predictor of local population structure was tree density within roosts, where roosts with low tree density had a higher abundance but lower density of bats (more spacing between bats) per tree. Together, these data highlight unpredictable and counterintuitive relationships between total abundance and local density. More nuanced modelling of transmission, spread and spillover from bats likely requires alternative approaches to integrating contact structure in host-pathogen models, rather than simply modifying the transmission function.

Welfare economics of managing an epidemic: an exposition

This paper reviews recent findings on the normative analysis of private and governmental countermeasures against infectious diseases, focusing on COVID-19. Based on a model that relates the economic activity to infectious disease epidemics, policies that maximize social welfare are considered. Lockdowns in many countries are measures that restrict economic activity over a wide area, and the economic damage they cause is extremely large. Existing studies on the net benefit of lockdown implemented in 2020 have reached mixed conclusions as to whether it is warranted or not. Although the estimates of costs and effects are relatively stable, the setting of the value of a statistical life for converting effects into benefits has a wide range and is also likely to overestimate benefits. Therefore, a careful procedure for setting is particularly crucial to obtain a reliable evaluation of countermeasures. Compared to uniform restriction of activities, taking measures to restrict activities by selecting targets may improve efficiency. Attributes that can be used to select targets include those that can be identified at little or no cost, such as age and industry, and those that can only be identified at a cost, such as close contact with infectious individuals and the presence of pathogens. In comparison to lockdown, these measures may reduce human suffering and economic suffering. No trade-off exists between uniform activity restrictions and selective activity restrictions.

Identifying asymptomatic spreaders of antimicrobial-resistant pathogens in hospital settings

Antimicrobial-resistant organisms (AMROs) can colonize people without symptoms for long periods of time, during which these agents can spread unnoticed to other patients in healthcare systems. The accurate identification of asymptomatic spreaders of AMRO in hospital settings is essential for supporting the design of interventions against healthcare-associated infections (HAIs). However, this task remains challenging because of limited observations of colonization and the complicated transmission dynamics occurring within hospitals and the broader community. Here, we study the transmission of methicillin-resistant Staphylococcus aureus (MRSA), a prevalent AMRO, in 66 Swedish hospitals and healthcare facilities with inpatients using a data-driven, agent-based model informed by deidentified real-world hospitalization records. Combining the transmission model, patient-to-patient contact networks, and sparse observations of colonization, we develop and validate an individual-level inference approach that estimates the colonization probability of individual hospitalized patients. For both model-simulated and historical outbreaks, the proposed method supports the more accurate identification of asymptomatic MRSA carriers than other traditional approaches. In addition, in silica control experiments indicate that interventions targeted to inpatients with a high-colonization probability outperform heuristic strategies informed by hospitalization history and contact tracing.

Determination of vulnerable regions of SARS-CoV-2 in Malaysia using meteorology and air quality data

This study aims to explore the state-wise assessment of SARS-CoV-2 (COVID-19) pandemic spread in Malaysia with focus on influence of meteorological parameters and air quality. In this study, state-wise COVID-19 data, meteorological parameters and air quality index (AQI) were collected from March 13 to April 30, 2020, which encompass three movement control order (MCO) periods in the country. Overall, total infected cases were observed to be higher in MCO phase 1 and 2 and significantly reduced in MCO phase 3. Due to the variation in the spatial interval of population density and individual immunity, the relationship of these parameters to pandemic spread could not be achieved. The study infers that temperature (T) between 23 and 25 °C and relative humidity (RH) (70-80%) triggered the pandemic spread by increase in the infected cases in northern and central Peninsular Malaysia. Selangor, WP Kuala Lumpur and WP Putrajaya show significantly high infected cases and a definite trend was not observed with respect to a particular meteorological factor. It is identified that high precipitation (PPT), RH and good air quality have reduced the spread in East Malaysia. A negative correlation of T and AQI and positive correlation of RH with total infected cases were found during MCO phase 3. Principal component analysis (PCA) indicated that T, RH, PPT, dew point (DP) and AQI are the main controlling factors for the spread across the country apart from social distancing. Vulnerability zones were identified based on the spatial analysis of T, RH, PPT and AQI with reference to total infected cases. Based on time series analysis, it was determined that higher RH and T in Peninsular Malaysia and high amount of PPT, RH and good air quality in East Malaysia have controlled the spreading during MCO phase 3. The predominance of D614 mutant was observed prior to March and decreases at the end of March, coinciding with the fluctuation of meteorological factors and air quality. The outcome of this study gives a general awareness to the public on COVID-19 and the influence of meteorological factors. It will also help the policymakers to enhance the management plans against the pandemic spreading apart from social distancing in the next wave of COVID-19.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10668-021-01719-z.

Social fluidity mobilizes contagion in human and animal populations

Humans and other group-living animals tend to distribute their social effort disproportionately. Individuals predominantly interact with a small number of close companions while maintaining weaker social bonds with less familiar group members. By incorporating this behavior into a mathematical model, we find that a single parameter, which we refer to as social fluidity, controls the rate of social mixing within the group. Large values of social fluidity correspond to gregarious behavior, whereas small values signify the existence of persistent bonds between individuals. We compare the social fluidity of 13 species by applying the model to empirical human and animal social interaction data. To investigate how social behavior influences the likelihood of an epidemic outbreak, we derive an analytical expression of the relationship between social fluidity and the basic reproductive number of an infectious disease. For species that form more stable social bonds, the model describes frequency-dependent transmission that is sensitive to changes in social fluidity. As social fluidity increases, animal-disease systems become increasingly density-dependent. Finally, we demonstrate that social fluidity is a stronger predictor of disease outcomes than both group size and connectivity, and it provides an integrated framework for both density-dependent and frequency-dependent transmission.

EVALUATING THE THRESHOLD DENSITY HYPOTHESIS FOR MOOSE (ALCES ALCES), WHITE-TAILED DEER (ODOCOILEUS VIRGINIANUS), AND PARELAPHOSTRONGYLUS TENUIS

Despite the importance of the Parelaphostrongylus tenuis infection for moose (Alces alces) and white-tailed deer (Odocoileus virginianus) management, only one peer-reviewed study has evaluated the relationship between deer and moose densities and the potential for parasite-mediated competition between the species. Using polynomial-regression modeling, that study identified a deer-density threshold above which moose populations declined; however, the nature of the data and apparent outliers suggests the approach used to develop that threshold may not have been appropriate. We used the data from the original study to test whether alternative models, including linear models and negative binomial models would be less sensitive to outliers and could better explain that relationship. We found no evidence that moose density decreases as deer density increases. We concluded that, although the proposed moose-deer-P. tenuis relationship could be partially density dependent, additional factors, such as frequency dependence of disease transmission, gastropod abundance, and shared use of resources by moose and deer should also be considered.

Habitat loss and the risk of disease outbreak

Evidence suggests that emerging infectious diseases, such as COVID-19, originate from wildlife species, and that land-use change is an important pathway for pathogen transmission to humans. We first focus on zoonotic disease spillover and the rate at which primary human cases appear, demonstrating that a potential outbreak is directly related to the area of wildlife habitat. We then develop a model of the costs and benefits of land conversion that includes the effect of habitat size on the risk of disease outbreak. Our model and numerical simulations show that incorporating this risk requires more wildlife habitat conservation in the long run, and how much more should be conserved will depend on the initial habitat size. If the area is too small, then no conversion should take place. Any policy to control habitat loss, such as a tax imposed on the rents from converted land, should also vary with habitat area.

Human-Mosquito Contact: A Missing Link in Our Understanding of Mosquito-Borne Disease Transmission Dynamics

Despite the critical role that contact between hosts and vectors, through vector bites, plays in driving vector-borne disease (VBD) transmission, transmission risk is primarily studied through the lens of vector density and overlooks host-vector contact dynamics. This review article synthesizes current knowledge of host-vector contact with an emphasis on mosquito bites. It provides a framework including biological and mathematical definitions of host-mosquito contact rate, blood-feeding rate, and per capita biting rates. We describe how contact rates vary and how this variation is influenced by mosquito and vertebrate factors. Our framework challenges a classic assumption that mosquitoes bite at a fixed rate determined by the duration of their gonotrophic cycle. We explore alternative ecological assumptions based on the functional response, blood index, forage ratio, and ideal free distribution within a mechanistic host-vector contact model. We highlight that host-vector contact is a critical parameter that integrates many factors driving disease transmission. A renewed focus on contact dynamics between hosts and vectors will contribute new insights into the mechanisms behind VBD spread and emergence that are sorely lacking. Given the framework for including contact rates as an explicit component of mathematical models of VBD, as well as different methods to study contact rates empirically to move the field forward, researchers should explicitly test contact rate models with empirical studies. Such integrative studies promise to enhance understanding of extrinsic and intrinsic factors affecting host-vector contact rates and thus are critical to understand both the mechanisms driving VBD emergence and guiding their prevention and control.

'Keeping the kids at home' can limit the persistence of contagious pathogens in social animals

Social networks are considered to be 'highly modular' when individuals within one module are more connected to each other than they are to individuals in other modules. It is currently unclear how highly modular social networks influence the persistence of contagious pathogens that generate lifelong immunity in their hosts when between-group interactions are age dependent. This trait occurs in social species with communal nurseries, where juveniles are reared together for a substantial period in burrows or similar forms of containment and are thus in isolation from contact with individuals in other social groups. Our main objective was to determine whether, and to what extent, such age-dependent patterns of between-group interactions consistently increased the fade-out probability of contagious pathogens that generate lifelong immunity in their hosts. We hypothesised that in populations of species where juveniles are raised in communal nurseries, a high proportion of recovered adults in a group would form a 'protective barrier' around susceptible juveniles against pathogen transmission, thereby increasing the probability of epidemic fade-out in the population. To test this idea, we implemented a spatially implicit individual-based susceptible-infected-recovered (SIR) model for a large range of generic host and pathogen traits. Our results indicated that (a) the probability of epidemic fade-out was consistently higher in populations with communal nurseries, especially for highly contagious pathogens (high basic reproduction number, R₀ ) and (b) communal nurseries can counteract the cost of group living in terms of infection risk to a greater extent than variation in other traits. We discuss our findings in relation to herd immunity and outline the importance of considering the network structure of a given host population before implementing management measures such as vaccinations, since interventions focused on individuals with high between-group contact should be particularly effective for controlling pathogen spread in hosts with communal nurseries.

Evolutionary rescue in host-pathogen systems

Natural populations encounter a variety of threats that can increase their risk of extinction. Populations can avoid extinction through evolutionary rescue (ER), which occurs when an adaptive, genetic response to selection allows a population to recover from an environmental change that would otherwise cause extinction. While the traditional framework for ER was developed with abiotic risk factors in mind, ER may also occur in response to a biotic source of demographic change, such as the introduction of a novel pathogen. We first describe how ER in response to a pathogen differs from the traditional ER framework; density-dependent transmission, pathogen evolution, and pathogen extinction can change the strength of selection imposed by a pathogen and make host population persistence more likely. We also discuss several variables that affect traditional ER (abundance, genetic diversity, population connectivity, and community composition) that also directly affect disease risk resulting in diverse outcomes for ER in host-pathogen systems. Thus, generalizations developed in studies of traditional ER may not be relevant for ER in response to the introduction of a pathogen. Incorporating pathogens into the framework of ER will lead to a better understanding of how and when populations can avoid extinction in response to novel pathogens.

Population density and basic reproductive number of COVID-19 across United States counties

The basic reproductive number (R₀) is a function of contact rates among individuals, transmission probability, and duration of infectiousness. We sought to determine the association between population density and R₀ of SARS-CoV-2 across U.S. counties. We conducted a cross-sectional analysis using linear mixed models with random intercept and fixed slopes to assess the association of population density and R₀, and controlled for state-level effects using random intercepts. We also assessed whether the association was differential across county-level main mode of transportation percentage as a proxy for transportation accessibility, and adjusted for median household income. The median R₀ among the United States counties was 1.66 (IQR: 1.35–2.11). A population density threshold of 22 people/km² was needed to sustain an outbreak. Counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density. An increase in one unit of log population density increased R₀ by 0.16 (95% CI: 0.13 to 0.19). This association remained when adjusted for main mode of transportation and household income. The effect of population density on R₀ was not modified by transportation mode. Our findings suggest that dense areas increase contact rates necessary for disease transmission. SARS-CoV-2 R₀ estimates need to consider this geographic variability for proper planning and resource allocation, particularly as epidemics newly emerge and old outbreaks resurge.

Light pollution affects West Nile virus exposure risk across Florida

Emerging infectious diseases (EIDs) present global health threats, and their emergences are often linked to anthropogenic change. Artificial light at night (ALAN) is one form of anthropogenic change that spans beyond urban boundaries and may be relevant to EIDs through its influence on the behaviour and physiology of hosts and/or vectors. Although West Nile virus (WNV) emergence has been described as peri-urban, we hypothesized that exposure risk could also be influenced by ALAN in particular, which is testable by comparing the effects of ALAN on prevalence while controlling for other aspects of urbanization. By modelling WNV exposure among sentinel chickens in Florida, we found strong support for a nonlinear relationship between ALAN and WNV exposure risk in chickens with peak WNV risk occurring at low ALAN levels. Although our goal was not to discern how ALAN affected WNV relative to other factors, effects of ALAN on WNV exposure were stronger than other known drivers of risk (i.e. impervious surface, human population density). Ambient temperature in the month prior to sampling, but no other considered variables, strongly influenced WNV risk. These results indicate that ALAN may contribute to spatio-temporal changes in WNV risk, justifying future investigations of ALAN on other vector-borne parasites.

Changes in virus transmission dynamics following the emergence of RHDV2 shed light on its competitive advantage over previously circulating variants

Rabbit haemorrhagic disease virus (RHDV) is highly pathogenic to European rabbits. Until recently, only one serotype of RHDV was known, GI.1/RHDV. RHDV2/GI.2 is a novel virus that has rapidly spread and become the dominant pathogenic calicivirus in wild rabbits worldwide. It is speculated that RHDV2 has three competitive advantages over RHDV: (a) the ability to partially overcome immunity to other variants; (b) the ability to clinically infect young rabbits; and (c) a wider host range. These differences would be expected to influence virus transmission dynamics. We used markers of recent infection (IgM/IgA antibodies) to investigate virus transmission dynamics pre and post the arrival of RHDV2. Our data set contained over 3,900 rabbits sampled across a 7-year period at 12 Australian sites. Following the arrival of RHDV2, seasonal peaks in IgM and IgA seropositivity shifted forward one season, from winter to autumn and spring to winter, respectively. Contrary to predictions, we found only weak effects of rabbit age, seropositivity to non-pathogenic calicivirus RCV-A1 and population abundance on IgM/IgA seropositivity. Our results demonstrate that RHDV2 enters rabbit populations shortly after the commencement of annual breeding cycles. Upon entering, the population RHDV2 undergoes extensive replication in young rabbits, causing clinical disease, high virus shedding, mortality and the creation of virus-laden carcasses. This results in high virus contamination in the environment, furthering the transmission of RHDV2 and initiating outbreaks, whilst simultaneously removing the susceptible cohort required for the effective transmission of RHDV. Although RHDV may enter the population at the same time point, it is sub-clinical in young rabbits, causing minimal virus shedding and low environmental contamination. Our results demonstrate a major shift in epidemiological patterns in virus transmission, providing the first evidence that RHDV2's ability to clinically infect young rabbits is a key competitive advantage in the field.

Who acquires infection from whom? Estimating herpesvirus transmission rates between wild rodent host groups

To date, few studies of parasite epidemiology have investigated 'who acquires infection from whom' in wildlife populations. Nonetheless, identifying routes of disease transmission within a population, and determining the key groups of individuals that drive parasite transmission and maintenance, are fundamental to understanding disease dynamics. Gammaherpesviruses are a widespread group of DNA viruses that infect many vertebrate species, and murine gammaherpesviruses (i.e. MuHV-4) are a standard lab model for studying human herpesviruses, for which much about the pathology and immune response elicited to infection is well understood. However, despite this extensive research effort, primarily in the lab, the transmission route of murine gammaherpesviruses within their natural host populations is not well understood. Here, we aimed to understand wood mouse herpesvirus (WMHV) transmission, by fitting a series of population dynamic models to field data on wood mice naturally infected with WMHV and then estimating transmission parameters within and between demographic groups of the host population. Different models accounted for different combinations of host sex (male/female), age (subadult/adult) and transmission functions (density/frequency-dependent). We found that a density-dependent transmission model incorporating explicit sex groups fitted the data better than all other proposed models. Male-to-male transmission was the highest among all possible combinations of between- and within-sex transmission classes, suggesting that male behaviour is a key factor driving WMHV transmission. Our models also suggest that transmission between sexes, although important, wasn't symmetrical, with infected males playing a significant role in infecting naïve females but not vice versa. Overall this work shows the power of coupling population dynamic models with long-term field data to elucidate otherwise unobservable transmission processes in wild disease systems.

Mathematical model for COVID-19 management in crowded settlements and high-activity areas

This paper develops and analyses a habitat area size dependent mathematical model to study the transmission dynamics of COVID-19 in crowded settlements such as refugee camps, schools, markets and churches. The model quantifies the potential impact of physical/social distancing and population density on the disease burden. Results reveal that with no fatalities and no infected entrants, the reproduction numbers associated with asymptomatic and symptomatic cases are inversely proportional to; the habitat area size, and the efforts employed in tracing and hospitalising these cases. The critical habitat area below which the disease dies out is directly proportion to the time taken to identify and hospitalise infected individuals. Results also show that disease persistence in the community is guaranteed even with minimal admission of infected individuals. Our results further show that as the level of compliance to standard operating procedures (SOPs) increases, then the disease prevalence peaks are greatly reduced and delayed. Therefore, proper adherence to SOPs such as use of masks, physical distancing measures and effective contact tracing should be highly enforced in crowded settings if COVID-19 is to be mitigated.

Assessing the suitability for Aedes albopictus and dengue transmission risk in China with a delay differential equation model

Dengue is considered non-endemic to mainland China. However, travellers frequently import the virus from overseas and local mosquito species can then spread the disease in the population. As a consequence, mainland China still experiences large dengue outbreaks. Temperature plays a key role in these outbreaks: it affects the development and survival of the vector and the replication rate of the virus. To better understand its implication in the transmission risk of dengue, we developed a delay differential equation model that explicitly simulates temperature-dependent development periods and tested it with collected field data for the Asian tiger mosquito, Aedes albopictus. The model predicts mosquito occurrence locations with a high accuracy (Cohen's κ of 0.78) and realistically replicates mosquito population dynamics. Analysing the infection dynamics during the 2014 dengue outbreak that occurred in Guangzhou showed that the outbreak could have lasted for another four weeks if mosquito control interventions had not been undertaken. Finally, we analyse the dengue transmission risk in mainland China. We find that southern China, including Guangzhou, can have more than seven months of dengue transmission per year while even Beijing, in the temperate north, can have dengue transmission during hot summer months. The results demonstrate the importance of using detailed vector and infection ecology, especially when vector-borne disease transmission risk is modelled over a broad range of climatic zones.

Bidirectional interactions between host social behaviour and parasites arise through ecological and evolutionary processes

An animal's social behaviour both influences and changes in response to its parasites. Here we consider these bidirectional links between host social behaviours and parasite infection, both those that occur from ecological vs evolutionary processes. First, we review how social behaviours of individuals and groups influence ecological patterns of parasite transmission. We then discuss how parasite infection, in turn, can alter host social interactions by changing the behaviour of both infected and uninfected individuals. Together, these ecological feedbacks between social behaviour and parasite infection can result in important epidemiological consequences. Next, we consider the ways in which host social behaviours evolve in response to parasites, highlighting constraints that arise from the need for hosts to maintain benefits of sociality while minimizing fitness costs of parasites. Finally, we consider how host social behaviours shape the population genetic structure of parasites and the evolution of key parasite traits, such as virulence. Overall, these bidirectional relationships between host social behaviours and parasites are an important yet often underappreciated component of population-level disease dynamics and host-parasite coevolution.