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The article presents a study examining how environmental fluctuations release zebra from a potential pathogen-triggered ecological trap. It reports how in a transmission hotspot for an environmentally persistent pathogen establishes a high-quality habitat, the disease may exert a strong impact on a host population.

Lameness in sheep is a global health, welfare and economic concern. White line disease (WLD), also known as shelly hoof, is a prevalent, non-infectious cause of lameness, characterised by the breakdown of the white line. Little is known about the predisposing factors, nor the individual disease dynamics over time. Our exploratory study aimed to investigate the prevalence and temporal dynamics of WLD, and the associated risk factors. Feet of 400 ewes from four UK commercial sheep farms were inspected for WLD at four time points across 12 months. The change in WLD state at foot-level (develop or recover) was calculated for three transition periods. We present WLD to be widespread, affecting 46.8% of foot-level and 76.6% of sheep-level observations. States in WLD changed over time, with feet readily developing and recovering from WLD within the study period. The presence of WLD at foot-level, the number of feet affected at sheep-level and dynamics in development and recovery were driven by a variety of foot-, sheep- and farm-level factors. We provide key insight into the multifaceted aetiology of WLD and corroborate previous studies demonstrating its multifactorial nature. Our study highlights an opportunity to reduce WLD prevalence and informs hypotheses for future prospective studies. [ABSTRACT FROM AUTHOR]

In recent years, our understanding of the importance of microorganisms on and within our bodies has been revolutionized by the ability to characterize entire microbial communities. No more so is this true than in cases of disease. Community studies have revealed strong associations between microbial populations and disease states where such concomitance was previously absent from aetiology: including in cancers. The study of viruses, in particular, has benefited from the development of new community profiling techniques and we are now realising that their prominence within our physiology is nearly as broad as the diversity of the organisms themselves. Here, we examine the relationship between viruses and colorectal cancer (CRC), the leading cause of gastrointestinal cancer-related death worldwide. In CRC, viruses have been suggested to be involved in oncogenesis both directly, through infection of our cells, and indirectly, through modulating the composition of bacterial communities. Interestingly though, these characteristics have also led to their examination from another perspective—as options for treatment. Advances in our understanding of molecular and viral biology have caused many to look at viruses as potential modular biotherapeutics, where deleterious characteristics can be tamed and desirable characteristics exploited. In this article, we will explore both of these perspectives, covering how viral infections and involvement in microbiome dynamics may contribute to CRC, and examine ways in which viruses themselves could be harnessed to treat the very condition their contemporaries may have had a hand in creating.

Computer-based disease spread models are frequently used in veterinary science to simulate disease spread. They are used to predict the impacts of the disease, plan and assess surveillance, or control strategies, and provide insights about disease causation by comparing model outputs with real life data. There are many types of disease spread models, and here we present and describe the implementation of a particular type: individual-based models. Our aim is to provide a practical introduction to building individual-based disease spread models. We also introduce code examples with the goal to make these techniques more accessible to those who are new to the field. We describe the important steps in building such models before, during and after the programming stage, including model verification (to ensure that the model does what was intended), validation (to investigate whether the model results reflect the modeled system), and convergence analysis (to ensure models of endemic diseases are stable before outputs are collected). We also describe how sensitivity analysis can be used to assess the potential impact of uncertainty about model parameters. Finally, we provide an overview of some interesting recent developments in the field of disease spread models.

The study of the microbiome has changed our overall perspective on health and disease. Although studies of the lung microbiome have lagged behind those on the gastrointestinal microbiome, there is now evidence that the lung microbiome is a rich, dynamic ecosystem. Tuberculosis is one of the oldest human diseases, it is primarily a respiratory infectious disease caused by strains from the Mycobacterium tuberculosis Complex. Even today, during the COVID-19 pandemic, it remains one of the principal causes of morbidity and mortality worldwide. Tuberculosis disease manifests itself as a dynamic spectrum that ranges from asymptomatic latent infection to life-threatening active disease. The review aims to provide an overview of the microbiome in the tuberculosis setting, both in patients’ and animal models. We discuss the relevance of the microbiome and its dysbiosis, and how, probably through its interaction with the immune system, it is a significant factor in tuberculosis’s susceptibility, establishment, and severity.

In symbiotic interactions, spatiotemporal variation in the distribution or population dynamics of one species represents spatial and temporal heterogeneity of the landscape for the other. Such interdependent demographic dynamics result in situations where the relative importance of biotic and abiotic factors in determining ecological processes is complicated to decipher. Using a detailed survey of three metapopulations of the succulent plant Cakile maritima and the necrotrophic fungus Alternaria brassicicola located along the southeastern Australian coast, we developed a series of statistical analyses—namely, synchrony analysis, patch occupancy dynamics, and a spatially explicit metapopulation model—to understand how habitat quality, weather conditions, dispersal, and spatial structure determine metapopulation dynamics. Climatic conditions are important drivers, likely explaining the high synchrony among populations. Host availability, landscape features facilitating dispersal, and habitat conditions also impact the occurrence and spread of disease. Overall, we show that the collection of extensive data on host and pathogen population dynamics, in combination with spatially explicit epidemiological modeling, makes it possible to accurately predict disease dynamics—even when there is extreme variability in host population dynamics. Finally, we discuss the importance of genetic information for predicting demographic dynamics in this pathosystem. [ABSTRACT FROM AUTHOR]

In recent years, our understanding of the importance of microorganisms on and within our bodies has been revolutionized by the ability to characterize entire microbial communities. No more so is this true than in cases of disease. Community studies have revealed strong associations between microbial populations and disease states where such concomitance was previously absent from aetiology: including in cancers. The study of viruses, in particular, has benefited from the development of new community profiling techniques and we are now realising that their prominence within our physiology is nearly as broad as the diversity of the organisms themselves. Here, we examine the relationship between viruses and colorectal cancer (CRC), the leading cause of gastrointestinal cancer-related death worldwide. In CRC, viruses have been suggested to be involved in oncogenesis both directly, through infection of our cells, and indirectly, through modulating the composition of bacterial communities. Interestingly though, these characteristics have also led to their examination from another perspective—as options for treatment. Advances in our understanding of molecular and viral biology have caused many to look at viruses as potential modular biotherapeutics, where deleterious characteristics can be tamed and desirable characteristics exploited. In this article, we will explore both of these perspectives, covering how viral infections and involvement in microbiome dynamics may contribute to CRC, and examine ways in which viruses themselves could be harnessed to treat the very condition their contemporaries may have had a hand in creating. [ABSTRACT FROM AUTHOR]

Waterborne diseases are present major health problems to humanity especially in rural communities where many individuals belong to the lower socioeconomic classes (SECs). The impacts of introducing waterborne disease control measures for such communities are investigated by considering a waterborne disease model. The model is extended by introducing treatment of infected individuals and water purification as control measures. The possible benefits of considering these control measures for the various SECs are investigated. Further analyses show how different degrees of control impact the rate at which waterborne diseases are spread across SECs. The disease control model is validated by using it to study the cholera outbreak in Haiti. [ABSTRACT FROM AUTHOR]

In recent history, COVID-19 is one of the worst infectious disease outbreaks currently affecting humanity globally. Using real data on the COVID-19 outbreak from 22 January 2020 to 30 March 2020, we developed a mathematical model to investigate the impact of control measures in reducing the spread of the disease. Analyses of the model were carried out to determine the dynamics. The results of the analyses reveal that, using the data from China, implementing all possible control measures best reduced the rate of secondary infections. However, quarantine (isolation) of infectious individuals is shown to have the most dominant effect. This possibility emphasizes the need for extensive testing due to the possible prevalence of asymptomatic COVID-19 cases.

The World Health Organization declared the coronavirus disease 2019 a pandemic on March 11th, pointing to the over 118,000 cases in over 110 countries and territories around the world at that time. At the time of writing this manuscript, the number of confirmed cases has been surging rapidly past the half-million mark, emphasizing the sustained risk of further global spread. Governments around the world are imposing various containment measures while the healthcare system is bracing itself for tsunamis of infected individuals that will seek treatment. It is therefore important to know what to expect in terms of the growth of the number of cases, and to understand what is needed to arrest the very worrying trends. To that effect, we here show forecasts obtained with a simple iteration method that needs only the daily values of confirmed cases as input. The method takes into account expected recoveries and deaths, and it determines maximally allowed daily growth rates that lead away from exponential increase toward stable and declining numbers. Forecasts show that daily growth rates should be kept at least below 5% if we wish to see plateaus any time soon—unfortunately far from reality in most countries to date. We provide an executable as well as the source code for a straightforward application of the method on data from other countries.

Landscape effects might impede or increase spore dispersal and disease risk for crops, as trees and hedges buffer winds and can behave as spore traps, therefore limiting diffusion of fungi, or, on the contrary, behave as disease relay once vegetation is infected and become inoculum sources. In this study, we investigated weekly prevalence of the pathogenic fungus Colletotrichum gloeosporioides on guava tree leaves, differentiating impacts of leaf height on tree, age, and location within leaf. We first estimated differences in prevalence for each covariate, and then related infection rates to weather effects during the year. Our results highlighted a great variance of prevalence among individual trees, and a lower contamination of tree tops, as well as a tendency for greater odds of infection in tips of young leaves compared to older ones. Last, we show evidence that individual tree contaminations are associated with different disease dynamics: early and dispersal-based, late and growth-based, as well as with intermediate dynamic ranges. Pathogen infection dynamics will thus be greatly impacted by cover characteristics at local scale, and tree cover should not be perceived as homogeneously driving disease levels.

In this paper we introduce an agent-based epidemiological model that generalizes the classical SIR model by Kermack and McKendrick. We further provide a multiscale approach to the derivation of a macroscopic counterpart via the mean-field limit. The chain of equations acquired via the multiscale approach is investigated, analytically as well as numerically. The outcome of these results provides strong evidence of the models’ robustness and justifies their practicality in describing disease dynamics, in particularly when mobility is involved. The numerical results provide further insights into the applicability of the different scaling limits.

Abstract Billions of users of mobile phones, social media platforms, and other technologies generate an increasingly large volume of data that has the potential to be leveraged towards solving public health challenges. These and other big data resources tend to be most successful in epidemiological applications when utilized within an appropriate conceptual framework. Here, we demonstrate the importance of assumptions about host mobility in a framework for dynamic modeling of infectious disease spread among districts within a large urban area. Our analysis focused on spatial and temporal variation in the transmission of dengue virus (DENV) during a series of large seasonal epidemics in Lahore, Pakistan during 2011–2014. Similar to many directly transmitted diseases, DENV transmission occurs primarily where people spend time during daytime hours, given that DENV is transmitted by a day-biting mosquito. We inferred spatiotemporal variation in DENV transmission under five different assumptions about mobility patterns among ten districts of Lahore: no movement among districts, movement following patterns of geo-located tweets, movement proportional to district population size, and movement following the commonly used gravity and radiation models. Overall, we found that inferences about spatiotemporal variation in DENV transmission were highly sensitive to this range of assumptions about intra-urban human mobility patterns, although the three assumptions that allowed for a modest degree of intra-urban mobility all performed similarly in key respects. Differing inferences about transmission patterns based on our analysis are significant from an epidemiological perspective, as they have different implications for where control efforts should be targeted and whether conditions for transmission became more or less favorable over time.

Many demographic and ecological processes generate seasonal and other periodicities. Seasonality in infectious disease transmission can result from climatic forces such as temperature and humidity; variation in contact rates as a result of migration or school calendar; or temporary surges in birth rates. Seasonal drivers of acute immunizing infections can also drive longer‐term fluctuations.Tensor decomposition has been used in many disciplines to uncover dominant trends in multi‐dimensional data. We introduce tensors as a novel method for decomposing oscillatory infectious disease time series.We illustrate the reliability of the method by applying it to simulated data. We then present decompositions of measles data from England and Wales. This paper leverages simulations as well as much‐studied data to illustrate the power of tensor decomposition to uncover dominant epidemic signals as well as variation in space and time. We then use tensor decomposition to uncover new findings and demonstrate the potential power of the method for disease incidence data. In particular, we are able to distinguish between annual and biennial signals across locations and shifts in these signals over time.Tensor decomposition is able to isolate variation in disease seasonality as a result of variation in demographic rates. The method allows us to discern variation in the strength of such signals by space and population size. Tensors provide an opportunity for a concise approach to uncovering heterogeneity in disease transmission across space and time in large datasets. [ABSTRACT FROM AUTHOR]

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

We consider a mixture-theoretic continuum model of the spread of COVID-19 in Texas. The model consists of multiple coupled partial differential reaction–diffusion equations governing the evolution of susceptible, exposed, infectious, recovered, and deceased fractions of the total population in a given region. We consider the problem of model calibration, validation, and prediction following a Bayesian learning approach implemented in OPAL (the Occam Plausibility Algorithm). Our goal is to incorporate COVID-19 data to calibrate the model in real-time and make meaningful predictions and specify the confidence level in the prediction by quantifying the uncertainty in key quantities of interests. Our results show smaller mortality rates in Texas than what is reported in the literature. We predict 7003 deceased cases by September 1, 2020 in Texas with 95 % CI 6802–7204. The model is validated for the total deceased cases, however, is found to be invalid for the total infected cases. We discuss possible improvements of the model. [ABSTRACT FROM AUTHOR]

Social distancing as one of the main non-pharmaceutical interventions can help slow down the spread of diseases, like in the COVID-19 pandemic. Effective social distancing, unless enforced as drastic lockdowns and mandatory cordon sanitaire, requires consistent strict collective adherence. However, it remains unknown what the determinants for the resultant compliance of social distancing and their impact on disease mitigation are. Here, we incorporate into the epidemiological process with an evolutionary game theory model that governs the evolution of social distancing behaviour. In our model, we assume an individual acts in their best interest and their decisions are driven by adaptive social learning of the real-time risk of infection in comparison with the cost of social distancing. We find interesting oscillatory dynamics of social distancing accompanied with waves of infection. Moreover, the oscillatory dynamics are dampened with a non-trivial dependence on model parameters governing decision-makings and gradually cease when the cumulative infections exceed the herd immunity. Compared to the scenario without social distancing, we quantify the degree to which social distancing mitigates the epidemic and its dependence on individuals' responsiveness and rationality in their behaviour changes. Our work offers new insights into leveraging human behaviour in support of pandemic response. [ABSTRACT FROM AUTHOR]

Purpose: In mCRC, disease dynamics may play a critical role in the understanding of long-term outcome. We evaluated depth of response (DpR), time to DpR, and post-DpR survival as relevant endpoints. Methods: We analyzed DpR by central review of computer tomography images (change from baseline to smallest tumor diameter), early tumor shrinkage (≥ 20% reduction in tumor diameter at first reassessment), time to DpR (study randomization to DpR-image), post-DpR progression-free survival (pPFS = DpR-image to tumor progression or death), and post-DpR overall survival (pOS = DpR-image to death) with special focus on BRAF status in 66 patients and primary tumor site in 86 patients treated within the VOLFI-trial, respectively. Results: BRAF wild-type (BRAF-WT) compared to BRAF mutant (BRAF-MT) patients had greater DpR (− 57.6% vs. − 40.8%, p = 0.013) with a comparable time to DpR [4.0 (95% CI 3.1–4.4) vs. 3.9 (95% CI 2.5–5.5) months; p = 0.8852]. pPFS was 6.5 (95% CI 4.9–8.0) versus 2.6 (95% CI 1.2–4.0) months in favor of BRAF-WT patients (HR 0.24 (95% CI 0.11–0.53); p < 0.001). This transferred into a significant difference in pOS [33.6 (95% CI 26.0–41.3) vs. 5.4 (95% CI 5.0–5.9) months; HR 0.27 (95% CI 0.13–0.55); p < 0.001]. Similar observations were made for patients stratified for primary tumor site. Conclusions: BRAF-MT patients derive a less profound treatment response compared to BRAF-WT patients. The difference in outcome according to BRAF status is evident after achievement of DpR with BRAF-MT patients hardly deriving any further disease control beyond DpR. Our observations hint towards an aggressive tumor evolution in BRAF-MT tumors, which may already be molecularly detectable at the time of DpR. [ABSTRACT FROM AUTHOR]

In recent history, COVID-19 is one of the worst infectious disease outbreaks currently affecting humanity globally. Using real data on the COVID-19 outbreak from 22 January 2020 to 30 March 2020, we developed a mathematical model to investigate the impact of control measures in reducing the spread of the disease. Analyses of the model were carried out to determine the dynamics. The results of the analyses reveal that, using the data from China, implementing all possible control measures best reduced the rate of secondary infections. However, quarantine (isolation) of infectious individuals is shown to have the most dominant effect. This possibility emphasizes the need for extensive testing due to the possible prevalence of asymptomatic COVID-19 cases. [ABSTRACT FROM AUTHOR]

Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent‐borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining‐modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans. [ABSTRACT FROM AUTHOR]

Since the end of 2019 an outbreak of a new strain of coronavirus, called SARS-CoV-2, is reported from China and later other parts of the world. Since January 21, World Health Organization (WHO) reports daily data on confirmed cases and deaths from both China and other countries (www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports). The Johns Hopkins University (github.com/CSSEGISandData/COVID-19/blob/master/csse_COVID_19_data/csse_COVID_19_time_series/time_series_COVID19_confirmed_global.csv) collects those data from various sources worldwide on a daily basis. For Germany, the Robert-Koch-Institute (RKI) also issues daily reports on the current number of infections and infection related fatal cases (www.rki.de/DE/Content/InfAZ/N/Neuartiges%5fCoronavirus/Situationsberichte/Gesamt.html). However, due to delays in the data collection, the data from RKI always lags behind those reported by Johns Hopkins. In this work we present an extended SEIRD-model to describe the disease dynamics in Germany. The parameter values are identified by matching the model output to the officially reported cases. An additional parameter to capture the influence of unidentified cases is also included in the model. [ABSTRACT FROM AUTHOR]

Background: Characterizing breast cancer progression and aggressiveness relies on categorical descriptions of tumor stage and grade. Interpreting these categorical descriptions is challenging because stage convolutes the size and spread of the tumor and no consensus exists to define high/low grade tumors. Methods: We address this challenge of heterogeneity in patient‐specific cancer samples by adapting and applying several tools originally created for understanding heterogeneity and phenotype development in single cells (specifically, single‐cell topological data analysis and Wanderlust) to create a continuous metric describing breast cancer progression using bulk RNA‐seq samples from individual patient tumors. We also created a linear regression‐based method to predict tumor aggressiveness in vivo from bulk RNA‐seq data. Results: We found that breast cancer proceeds along three convergent phenotype trajectories: luminal, HER2‐enriched, and basal‐like. Furthermore, 31 296 genes (for luminal cancers), 17 827 genes (for HER2‐enriched), and 18 505 genes (for basal‐like) are dynamically differentially expressed during breast cancer progression. Across progression trajectories, our results show that expression of genes related to ADP‐ribosylation decreased as tumors progressed (while PARP1 and PARP2 increased or remained stable), suggesting the potential for a differential response to PARP inhibitors based on cancer progression. Additionally, we developed a 132‐gene expression regression equation to predict mitotic index and a 23‐gene expression regression equation to predict growth rate from a single breast cancer biopsy. Conclusion: Our results suggest that breast cancer dynamically changes during disease progression, and growth rate of the cancer cells is associated with distinct transcriptional profiles. [ABSTRACT FROM AUTHOR]

Multi‐strain, host‐disease dynamics describe a system where multiple strains of a pathogen compete for susceptible individuals of a single host. The theoretical properties of these systems have been well studied, but there are few empirical studies in wildlife hosts.We examined the impacts of two novel strains of rabbit haemorrhagic disease virus (RHDV) recently introduced into Australia, one inadvertently (RHDV2) and one deliberately for rabbit biocontrol (RHDV‐K5), by analysing long‐term monitoring data for introduced European rabbits Oryctolagus cuniculus from 18 sites throughout Australia. We examined population‐level impacts using rabbit spotlight counts pre‐ and post‐arrival of the two strains. We also analysed serological data to determine potential interactions among the introduced and existing field strains of RHDV, as well as a pre‐existing benign strain of calicivirus (RCV‐A1).Serological analyses suggested that RHDV2 arrived in Australia during spring 2014 and spread rapidly through the Australian rabbit population within 2 years. Following the establishment of RHDV2, rabbit abundance was reduced by an average of 60%, with impacts most pronounced in South and Western Australia. In contrast, the deliberate release of RHDV‐K5 had little impact on rabbit populations.Although RHDV2 has spread rapidly throughout Australia, our serological analyses do not support the observation that RHDV2 is rapidly replacing existing field strains of RHDV, as was previously reported in Australia and Europe. Nevertheless, RHDV2 has negatively impacted the ability of RHDV and RCV‐A1 to spread within rabbit populations, most likely due to its ability to infect juvenile rabbits, thereby removing them from the pool of susceptible individuals available to be infected by competing strains.Synthesis and applications. The impact of the release of a novel strain of rabbit haemorrhagic disease virus (RHDV‐K5) for rabbit biocontrol in Australia has been suppressed by the emergence of a competing strain, RHDV2. Hence, the success of further releases of similar RHDV strains for rabbit biocontrol appears doubtful. Despite this, RHDV2 has suppressed rabbit abundances by an average of 60%, with impacts most pronounced in South and Western Australia. Whether the incursion of RHDV2 leads to the competitive exclusion of other endemic RHDV strains remains to be resolved. However, the existence of partial cross‐immunity could allow some level of coexistence between RHDV2 and RHDV strains, at least in the medium term. [ABSTRACT FROM AUTHOR]

Antibiotic-resistant tuberculosis (TB) strains pose a major challenge to TB eradication. Existing US epidemiological models have not fully incorporated the impact of antibiotic-resistance. To develop a more realistic model of US TB dynamics, we formulated a compartmental model integrating single- and multi-drug resistance. We fit twenty-seven parameters to twenty-two years of historical data using a genetic algorithm to minimize a non-differentiable error function. Since counts for several compartments are not available, many parameter combinations achieve very low error. We demonstrate that a crowd of near-best fits can provide compelling new evidence about the ranges of key parameters. While available data is sparse and insufficient to produce point estimates, our crowd of near-best fits computes remarkably consistent predictions about TB prevalence. We believe that our crowd-based approach is applicable to a common problem in mathematical biological research, namely situations where data are sparse and reliable point estimates cannot be directly obtained. [ABSTRACT FROM AUTHOR]

The emerging pathogen, Batrachochytrium dendrobatidis (Bd), causes a fatal amphibian disease called chytridiomycosis, and has impacted amphibian biodiversity. Amphibian declines were first noticed in the 1970s and 1980s, decades before Bd was discovered (1998). In the Sierra Nevada mountains (California, USA), mass die-offs of Yosemite toads (Anaxyrus canorus) occurred in the late 1970s. In this study, we investigated the historical and contemporary Bd presence on A. canorus. We used a retrospective analysis to determine Bd presence/absence on 719 museum specimens collected between 1915 and 2005, and on an additional 1678 samples collected from live animals in the wild (2004–2012). For the 1678 contemporary dataset, we developed a statistical model to identify biotic and abiotic drivers of Bd infection likelihood. We found that: 1) Bd emerged coincident with historical declines; 2) Bd is currently widely distributed throughout the species range; 3) life stage, elevation, and precipitation regime were associated with Bd infection likelihood; and, 4) the juvenile life stage was the most highly infected, with some having Bd infection loads surpassing a mortality threshold identified in other species. In combination, our results suggest that Bd may have played a significant yet unrecognized role in the decline of A. canorus. Additionally, we find evidence that Bd may continue to affect survival and recruitment, depressing population growth and increasing extinction risk in remaining populations. These represent key insights for efforts to recover this species across its historical range and may also be important in hundreds of other species that declined before Bd was discovered. [ABSTRACT FROM AUTHOR]

Transient decreases in the proportion of individuals newly infected with an HIV-resistant virus (primary resistance) are documented for several cities of North America, including San Francisco. Using a staged SI deterministic model, we identified three potential causes consistent with the history of the epidemic: (1) increase in risky behaviour, (2) reduction in the proportion of HIV-acutely infected individuals undergoing treatment, and (3) replacement of mono- and dual-drug therapies with triple-drug therapies. Although observed patterns resemble scenario 1 most closely, these explanations are not mutually exclusive and may have contributed synergistically to the decline. Under scenario I the counterintuitive situation arises where, although the proportion of primary resistance cases decreases transiently, the epidemic worsens because the actual numbers of infected individuals and of drug resistance carriers increases. Our results call for improved efforts to control the epidemic in developed nations, and highlight the usefulness of drug resistant strains as epidemiological markers. © 2004 Society for Mathematical Biology. Published by Elsevier Ltd. All rights reserved.

Abstract Background Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically devastating infectious diseases for the swine industry. A better understanding of the disease dynamics and the transmission pathways under diverse epidemiological scenarios is a key for the successful PRRS control and elimination in endemic settings. In this paper we used a two step parameter-driven (PD) Bayesian approach to model the spatio-temporal dynamics of PRRS and predict the PRRS status on farm in subsequent time periods in an endemic setting in the US. For such purpose we used information from a production system with 124 pig sites that reported 237 PRRS cases from 2012 to 2015 and from which the pig trade network and geographical location of farms (i.e., distance was used as a proxy of airborne transmission) was available. We estimated five PD models with different weights namely: (i) geographical distance weight which contains the inverse distance between each pair of farms in kilometers, (ii) pig trade weight (PT ji ) which contains the absolute number of pig movements between each pair of farms, (iii) the product between the distance weight and the standardized relative pig trade weight, (iv) the product between the standardized distance weight and the standardized relative pig trade weight, and (v) the product of the distance weight and the pig trade weight. Results The model that included the pig trade weight matrix provided the best fit to model the dynamics of PRRS cases on a 6-month basis from 2012 to 2015 and was able to predict PRRS outbreaks in the subsequent time period with an area under the ROC curve (AUC) of 0.88 and the accuracy of 85% (105/124). Conclusion The result of this study reinforces the importance of pig trade in PRRS transmission in the US. Methods and results of this study may be easily adapted to any production system to characterize the PRRS dynamics under diverse epidemic settings to more timely support decision-making.

The Zika arbovirus transmitted by the Aedes aegypti mosquitoes has been shown to be capable of infecting humans via two routes: the bites of infected vectors and through sexual contacts involving infected and non-infected persons. There is no treatment and current prevention or mitigating efforts rely on the use of the Centers for Disease Control and Prevention recommendations including the use of insecticide-treated bed nets (ITN) and indoor residual spraying (IRS). In this work, we investigate via a mathematical model, the role of ITN and IRS as methods for limiting the impact of Zika transmission. We introduce a model that builds on classical SEIR epidemiological single outbreak models. We compute the basic and control reproduction numbers and the final epidemic size in the presence of control measures ITN and IRS. We derive a gross estimate for the rate of sexual transmission, during the initial stages of the outbreak, in terms of prior estimates of the basic reproduction number from related albeit not sexually transmitted arboviral diseases.

This paper presents the results of a retrospective analysis of congenital anomalies (CA) in theRepublic of Moldova (RM) over a period of 11 years (2004-2014). The spread of CA in Moldovaduring the analyzed period is characterized by a stable level. In the infant mortality structure,congenital anomalies ranked second. There is a significant increase in CA, caused bychromosomal and cardiovascular system anomalies, with an average annual rate of 0.1 and 0.5%,respectively. At the same time, the CA of the musculoskeletal system has a reduction trend with aaverage annual rate of -2.7%. Stabilization of CA spread is the result of the implementation of thenational program for the prevention and reduction of mortality and morbidity in children withcongenital anomalies and hereditary diseases for the years 2013-2017. Some identified trendsdemonstrate the existence of gaps in the primary health care work concerning family planning.

Emerging infectious diseases are an increasingly important threat to wildlife conservation, with amphibian chytridiomycosis, caused by Batrachochytrium dendrobatidis, the disease most commonly associated with species declines and extinctions. However, some amphibians can be infected with B. dendrobatidis in the absence of disease and can act as reservoirs of the pathogen. We surveyed robber frogs (Eleutherodactylus spp.), potential B. dendrobatidis reservoir species, at three sites on Montserrat, 2011–2013, and on Dominica in 2014, to identify seasonal patterns in B. dendrobatidis infection prevalence and load (B. dendrobatidis genomic equivalents). On Montserrat there was significant seasonality in B. dendrobatidis prevalence and B. dendrobatidis load, both of which were correlated with temperature but not rainfall. B. dendrobatidis prevalence reached 35% in the cooler, drier months but was repeatedly undetectable during the warmer, wetter months. Also, B. dendrobatidis prevalence significantly decreased from 53.2% when the pathogen emerged on Montserrat in 2009 to a maximum 34.8% by 2011, after which it remained stable. On Dominica, where B. dendrobatidis emerged seven years prior to Montserrat, the same seasonal pattern was recorded but at lower prevalence, possibly indicating long-term decline. Understanding the dynamics of disease threats such as chytridiomycosis is key to planning conservation measures. For example, reintroductions of chytridiomycosis-threatened species could be timed to coincide with periods of low B. dendrobatidis infection risk, increasing potential for reintroduction success.

The front-wave velocity of African swine fever (ASF) virus spread is depicted through a retrospective spatial and temporal analyses of wild boar outbreaks from Jan. 2014 to Jan. 2022 in Estonia, Latvia, Lithuania and Eastern Poland-regions responsible for more than 50% of all wild boar cases in the EU. The study uses empirical semivariograms in a universal kriging model to assess spatial autocorrelation in notification dates and identifies a discernable large-scale spatial trend. The critical parameter of ASF front-wave velocity was identified (Mean = 66.33 km/month, SD = 163.24) in the whole study area, and explored the variations across countries, wild boar habitat suitability, seasons, and the study period. Statistical differences in front-wave velocity values among countries and temporal clusters are explored, shedding light on potential factors influencing ASF transmission dynamics. The implications of these findings for surveillance and control strategies are discussed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Martínez Avilés, Montes, Sacristán, de la Torre and Iglesias.)

In infectious disease models, it is known that mechanisms such as births, seasonality in transmission and pathogen evolution can generate oscillations in infection numbers. We show how waning immunity is also a mechanism that is sufficient on its own to enable sustained oscillations. When previously infected or vaccinated individuals lose full protective immunity, they become partially susceptible to reinfections. This partial immunity subsequently wanes over time, making individuals more susceptible to reinfections and potentially more infectious if infected. Losses of full and partial immunity lead to a surge in infections, which is the precursor of oscillations. We present a discrete-time Susceptible-Infectious-Immune-Waned-Infectious (SIRWY) model that features the waning of fully immune individuals (as a distribution of time at which individuals lose fully immunity) and the gradual loss of partial immunity (as increases in susceptibility and potential infectiousness over time). A special case of SIRWY is the discrete-time SIRS model with geometric distributions for waning and recovery. Its continuous-time analogue is the classic SIRS with exponential distributions, which does not produce sustained oscillations for any choice of parameters. We show that the discrete-time version can produce sustained oscillations and that the oscillatory regime disappears as discrete-time tends to continuous-time. A different special case of SIRWY is one with fixed times for waning and recovery. We show that this simpler model can also produce sustained oscillations. In conclusion, under certain feature and parameter choices relating to how exactly immunity wanes, fluctuations in infection numbers can be sustained without the need for any additional mechanisms.

Pathogen circulation among reservoir hosts is a precondition for zoonotic spillover. Unlike the acute, high morbidity infections typical in spillover hosts, infected reservoir hosts often exhibit low morbidity and mortality. Although it has been proposed that reservoir host infections may be persistent with recurrent episodes of shedding, direct evidence is often lacking. We construct a generalized SEIR (susceptible, exposed, infectious, recovered) framework encompassing 46 sub-models representing the full range of possible transitions among those four states of infection and immunity. We then use likelihood-based methods to fit these models to nine years of longitudinal data on henipavirus serology from a captive colony of Eidolon helvum bats in Ghana. We find that reinfection is necessary to explain observed dynamics; that acute infectious periods may be very short (hours to days); that immunity, if present, lasts about 1–2 years; and that recurring latent infection is likely. Although quantitative inference is sensitive to assumptions about serology, qualitative predictions are robust. Our novel approach helps clarify mechanisms of viral persistence and circulation in wild bats, including estimated ranges for key parameters such as the basic reproduction number and the duration of the infectious period. Our results inform how future field-based and experimental work could differentiate the processes of viral recurrence and reinfection in reservoir hosts. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'. [ABSTRACT FROM AUTHOR]

• Hatching in Marshallagia marshalli occurs primarily as L1s in an advanced stage of development. • After hatching, free-living larvae of M. marshalli do not feed nor grow as they mature from L1 to infective L3. • Marshallagia can also hatch as L3s, with this phenomenon being significantly more common at higher temperatures. Despite the economic, social and ecological importance of the ostertagiine abomasal nematode Marshallagia marshalli , little is known about its life history traits and its adaptations to cope with environmental extremes. Conserved species-specific traits can act as exaptations that may enhance parasite fitness in changing environments. Using a series of experiments, we revealed several unique adaptations of the free-living stages of M. marshalli that differ from other ostertagiines. Eggs were isolated from the feces of bighorn sheep (Ovis canadensis) from the Canadian Rocky Mountains and were cultured at different temperatures and with different media. Hatching occurred primarily as L1s in an advanced stage of development, morphologically very similar to a L2. When cultured at 20 °C, however, 2.86% of eggs hatched as L3, with this phenomenon being significantly more common at higher temperatures, peaking at 30 °C with 28.95% of eggs hatching as L3s. After hatching, free-living larvae of M. marshalli did not feed nor grow as they matured from L1 to infective L3. These life history traits seem to be adaptations to cope with the extreme environmental conditions that Marshallagia faces across its extensive latitudinal distribution in North America and Eurasia. In order to refine the predictions of parasite dynamics under scenarios of a changing climate, basic life history traits and temperature-dependent phenotypic behaviour should be incorporated into models for parasite biology. [ABSTRACT FROM AUTHOR]

Predicting risk of zoonotic diseases, i.e., diseases shared by humans and animals, is often complicated by the population ecology of wildlife host(s). We here demonstrate how ecological knowledge of a disease system can be used for early prediction of human risk using Puumala hantavirus (PUUV) in bank voles (Myodes glareolus), which causes Nephropathia epidemica (NE) in humans, as a model system. Bank vole populations at northern latitudes exhibit multiannual fluctuations in density and spatial distribution, a phenomenon that has been studied extensively. Nevertheless, existing studies predict NE incidence only a few months before an outbreak. We used a time series on cyclic bank vole population density (1972–2013), their PUUV infection rates (1979–1986; 2003–2013), and NE incidence in Sweden (1990–2013). Depending on the relationship between vole density and infection prevalence (proportion of infected animals), either overall density of bank voles or the density of infected bank voles may be used to predict seasonal NE incidence. The density and spatial distribution of voles at density minima of a population cycle contribute to the early warning of NE risk later at its cyclic peak. When bank voles remain relatively widespread in the landscape during cyclic minima, PUUV can spread from a high baseline during a cycle, culminating in high prevalence in bank voles and potentially high NE risk during peak densities. [ABSTRACT FROM AUTHOR]

The dynamics of infectious diseases are greatly influenced by the movement of both susceptible and infected hosts. To accurately represent disease dynamics among a mobile host population, detailed movement models have been coupled with disease transmission models. However, a number of different host movement models have been proposed, each with their own set of assumptions and results that differ from the other models. Here, we compare two movement models coupled to the same disease transmission model using network analyses. This application of network analysis allows us to evaluate the fit and accuracy of the movement model in a multilevel modeling framework with more detail than established statistical modeling fitting methods. We used data that detailed mobile pastoralists’ movements as input for 100 stochastic simulations of a Spatio-Temporal Movement (STM) model and 100 stochastic simulations of an Individual Movement Model (IMM). Both models represent dynamic movement and subsequent contacts. We generated networks in which nodes represent camps and edges represent the distance between camps. We simulated pathogen transmission over these networks and tested five network metrics–strength, betweenness centrality, three-step reach, density, and transitivity–to determine which could predict disease simulation outcomes and thereby be used to correlate model simulation results with disease transmission simulations. We found that strength, network density, and three-step reach of movement model results correlated with the final epidemic size of outbreak simulations. Betweenness centrality only weakly correlated for the IMM model. Transitivity only weakly correlated for the STM model and time-varying IMM model metrics. We conclude that movement models coupled with disease transmission models can affect disease transmission results and should be carefully considered and vetted when modeling pathogen spread in mobile host populations. Strength, network density, and three-step reach can be used to evaluate movement models before disease simulations to predict final outbreak sizes. These findings can contribute to the analysis of multilevel models across systems. [ABSTRACT FROM AUTHOR]

Canine distemper virus (CDV) was reported in wild tigers from Russia and recently from India. Very few studies, however, have been carried out to gain an insight into the prevalence of the disease in India, particularly in the wild. CDV is the etiological agent of one of the most infectious diseases of domestic dogs. With the aim of exploring the threat CDV poses for tigers, a preliminary assessment was carried out to determine its prevalence from villages near Ranthambhore National Park in Rajasthan, India. Free-roaming dog populations within a 4-km-radius of the park's periphery were tested for antibodies against CDV. The seroprevalence of CDV antibodies in the sampled dogs was 86% (95% CI 78-91 %), indicating the probability of the dogs acting as a reservoir and having been exposed to CDV in the past. The seroprevalence of CAV antibodies was 44.23% (95% CI 35-54 %) and CPV antibodies was 95.19% (95% CI 91-99 %). This could threaten the tiger populations in the park, considering the close proximity of dogs to tigers. It is, therefore, crucial to assess disease threats at the domestic-wildlife interface and to establish management strategies for more effective conservation practices in the landscape. [ABSTRACT FROM AUTHOR]

Invasion of new territories by alien organisms is of primary concern for environmental and health agencies and has been a core topic in mathematical modeling, in particular in the intents of reconstructing the past dynamics of the alien organisms and predicting their future spatial extents. Partial differential equations offer a rich and flexible modeling framework that has been applied to a large number of invasions. In this article, we are specifically interested in dating and localizing the introduction that led to an invasion using mathematical modeling, post-introduction data and an adequate statistical inference procedure. We adopt a mechanistic-statistical approach grounded on a coupled reaction–diffusion–absorption model representing the dynamics of an organism in an heterogeneous domain with respect to growth. Initial conditions (including the date and site of the introduction) and model parameters related to diffusion, reproduction and mortality are jointly estimated in the Bayesian framework by using an adaptive importance sampling algorithm. This framework is applied to the invasion of Xylella fastidiosa, a phytopathogenic bacterium detected in South Corsica in 2015, France. [ABSTRACT FROM AUTHOR]

Natural populations can vary considerably in their genotypic and/or phenotypic diversity. Differences in this intraspecific diversity can have important consequences for contemporary ecological dynamics, but the direction and magnitude of these effects appear inconsistent across studies and systems. Here we proposed and tested the hypothesis that context-dependent ecological effects of altering phenotypic variance are predictable and arise from the relationship between a population's mean phenotype and the local environmental optimum. By factorially manipulating the mean and variance of a key host trait in environments with and without a lethal parasite, we demonstrate that increasing phenotypic variance can have beneficial effects for host populations (e.g., smaller disease epidemics) but only when the population's initial phenotype was poorly matched to the local environment. When phenotypes were initially well suited to environmental conditions, in contrast, greater phenotypic variance led to larger disease epidemics. Significant reductions in individual susceptibility occurred in both contexts over time, but the mechanisms leading to those reductions differed; strong selection was caused by either a suboptimal trait mean and insufficient trait variance or a near-optimal trait mean and too much trait variance. Increasing intraspecific variation is clearly not always beneficial for populations, instead producing predictable ecological and evolutionary effects that depend on environmental context and biological interactions. [ABSTRACT FROM AUTHOR]

Despite the ubiquity of disease in nature, the role that disease dynamics play in the compensatory growth response to harvesting has been ignored. We use a mathematical approach to show that harvesting can lead to compensatory growth due to a release from disease-induced mortality. Our findings imply that culling in systems that harbor virulent parasites can reduce disease prevalence and increase population density. Our models predict that this compensation occurs for a broad range of infectious disease characteristics unless the disease induces long-lasting immunity in hosts. Our key insight is that a population can be regulated at a similar density by disease or at reduced prevalence by a combination of culling and disease. We illustrate our predictions with a system-specific model representing wild boar tuberculosis infection, parameterized for central Spain, and find significant compensation to culling. Given that few wildlife diseases are likely to induce long-lived immunity, populations with virulent diseases may often be resilient to harvesting. [ABSTRACT FROM AUTHOR]

Understanding the distribution of pathogens across landscapes and their prevalence within host populations is a common aim of wildlife managers. Despite the need for unbiased estimates of pathogen occurrence and prevalence for planning effective management interventions, many researchers fail to account for imperfect pathogen detection. Instead raw data are often reported, which may lead to ineffective, or even detrimental, management actions. We illustrate the utility of occupancy models for generating unbiased estimates of disease parameters by 1) providing a written tutorial describing how to fit these models in Program PRESENCE and 2) presenting a case study with the pathogen ranavirus. We analyzed ranavirus detection data from a wildlife refuge (Maryland, US) using occupancy modeling, which yields unbiased estimates of pathogen occurrence and prevalence. We found ranavirus prevalence was underestimated by up to 30% if imperfect pathogen detection was ignored. The unbiased estimate of ranavirus prevalence in larval wood frog (Lithobates sylvaticus; 0.73) populations was higher than in larval spotted salamander (Ambystoma maculatum; 0.56) populations. In addition, the odds of detecting ranavirus in tail samples were 6.7 times higher than detecting ranavirus in liver samples. Therefore, tail samples presented a nonlethal sampling method for ranavirus that may be able to detect early (nonsystemic) infections. [ABSTRACT FROM AUTHOR]

• An agent-based modelling framework GEOFIL was developed for modelling lymphatic filariasis transmission. • GEOFIL predicted continual increase in microfilaraemia prevalence in the absence of further intervention. • Evidence from seroprevalence and transmission assessment surveys conducted from 2010 to 2016 indicated a resurgence of LF in American Samoa, corroborating GEOFIL's predictions. • Mosquito biting rates were found to be a critical determinant of infection risk. In this study, a spatially-explicit agent-based modelling framework GEOFIL was developed to predict lymphatic filariasis (LF) transmission dynamics in American Samoa. GEOFIL included individual-level information on age, gender, disease status, household location, household members, workplace/school location and colleagues/schoolmates at each time step during the simulation. In American Samoa, annual mass drug administration from 2000 to 2006 successfully reduced LF prevalence dramatically. However, GEOFIL predicted continual increase in microfilaraemia prevalence in the absence of further intervention. Evidence from seroprevalence and transmission assessment surveys conducted from 2010 to 2016 indicated a resurgence of LF in American Samoa, corroborating GEOFIL's predictions. The microfilaraemia and antigenaemia prevalence in 6-7-yo children were much lower than in the overall population. Mosquito biting rates were found to be a critical determinant of infection risk. Transmission hotspots are likely to disappear with lower biting rates. GEOFIL highlights current knowledge gaps, such as data on mosquito abundance, biting rates and within-host parasite dynamics, which are important for improving the accuracy of model predictions. [ABSTRACT FROM AUTHOR]

The world is continuously urbanising, resulting in clusters of densely populated urban areas and more sparsely populated rural areas. We propose a method for generating spatial fields with controllable levels of clustering of the population. We build a synthetic country, and use this method to generate versions of the country with different clustering levels. Combined with a metapopulation model for infectious disease spread, this allows us to in silico explore how urbanisation affects infectious disease spread. In a baseline scenario with no interventions, the underlying population clustering seems to have little effect on the final size and timing of the epidemic. Under within-country restrictions on non-commuting travel, the final size decreases with increased population clustering. The effect of travel restrictions on reducing the final size is larger with higher clustering. The reduction is larger in the more rural areas. Within-country travel restrictions delay the epidemic, and the delay is largest for lower clustering levels. We implemented three different vaccination strategies—uniform vaccination (in space), preferentially vaccinating urban locations and preferentially vaccinating rural locations. The urban and uniform vaccination strategies were most effective in reducing the final size, while the rural vaccination strategy was clearly inferior. Visual inspection of some European countries shows that many countries already have high population clustering. In the future, they will likely become even more clustered. Hence, according to our model, within-country travel restrictions are likely to be less and less effective in delaying epidemics, while they will be more effective in decreasing final sizes. In addition, to minimise final sizes, it is important not to neglect urban locations when distributing vaccines. To our knowledge, this is the first study to systematically investigate the effect of urbanisation on infectious disease spread and in particular, to examine effectiveness of prevention measures as a function of urbanisation. [ABSTRACT FROM AUTHOR]

Hantaviruses can cause hantavirus pulmonary syndrome (HPS) in the Americas and hemorrhagic fever with renal syndrome (HFRS) in Eurasia. In recent decades, repeated outbreaks of hantavirus disease have led to public concern and have created a global public health burden. Hantavirus spillover from natural hosts into human populations could be considered an ecological process, in which environmental forces, behavioral determinants of exposure, and dynamics at the human–animal interface affect human susceptibility and the epidemiology of the disease. In this review, we summarize the progress made in understanding hantavirus epidemiology and rodent reservoir population biology. We mainly focus on three species of rodent hosts with longitudinal studies of sufficient scale: the striped field mouse (Apodemus agrarius, the main reservoir host for Hantaan virus [HTNV], which causes HFRS) in Asia, the deer mouse (Peromyscus maniculatus, the main reservoir host for Sin Nombre virus [SNV], which causes HPS) in North America, and the bank vole (Myodes glareolus, the main reservoir host for Puumala virus [PUUV], which causes HFRS) in Europe. Moreover, we discuss the influence of ecological factors on human hantavirus disease outbreaks and provide an overview of research perspectives. [ABSTRACT FROM AUTHOR]