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Amphibian chytrid fungus, Batrachochytrium dendrobatidis (Bd), is associated with declines in ~500 amphibian species globally. Decades after initial disease outbreaks, the trajectory of impacted species varies substantially; while some species continue to decline, there are instances of natural recovery, such as the whistling tree frog, Litoria verreauxii, in south‐eastern Australia. The decline and subsequent recovery of this species have been quantified through repeated surveys of historically occupied sites over the past 30 years; however, the underlying mechanisms driving this recovery remain unknown. In this study, we investigate the potential factors facilitating the recovery of L. verreauxii by examining Bd prevalence and intensity in both adults and tadpoles. Specifically, we addressed the following hypotheses: (1) Bd prevalence in tadpoles would be lower compared to adults at the same breeding sites, (2) Bd prevalence in tadpoles would decrease over the spring breeding season due to the increasing availability of warm water microhabitats where tadpoles could potentially avoid or clear Bd infections and (3) there would be a negative correlation between Bd prevalence in tadpoles and the abundance and diversity of microfauna, which may consume Bd zoospores. Our findings indicate that tadpole infection prevalence remained consistently low at 1.36% (95% CI: 0.6–2.47%) throughout our spring sampling period, across different developmental stages. Adults had moderate to high prevalence within the same ponds at 50.53% (95% CI: 43.19–57.84%). No effect of temperature or microfauna diversity and abundance was apparent. While the mechanisms driving the recovery of this species remain unknown, low infection prevalence in tadpoles is likely a key component to the species' recovery. Our results emphasize the need for comprehensive investigations in Bd dynamics across all life history stages within recovering and declining amphibian species. [ABSTRACT FROM AUTHOR]

The aim of the present study was to perform a comparative quantitative analysis of natural neurotropic autoantibodies (Nabs) in blood serum of patients with COVID-19 associated ischemic strokes (IS) in dynamics of disease. A total of 150 consecutive patients with acute primary IS were included, being divided in two groups: 100 patients with IS on the background of COVID-19 pneumonia (main group No. 1) and 50 patients with IS without COVID-19 symptoms and positive viral test (comparison group No. 2). The stroke severity and consciousness were measured by NIHSS and Glasgow coma scale. In blood serum of patients (n = 110), we have studied the levels of IgG Nabs to NF-200, GFAP, S100β, MBP, receptors to dopamine, serotonin, choline, glutamate, GABA by means of ELISA technique. The blood samples for analyses were taken at the 5th, 14th and 28th days of disease. In group 1 (n = 80), the Nabs levels have been increased as follows: antibodies to NF-200 (132.9±4.1 CU), by 1.09 and 1.8 times; for GFAP (118.9±3.9 CU), by 1.4 and 2 times; S100β antibodies (129.5±10.2 CU), by 1.05 and 1.6 times; MBP antibodies (97.3±4.5 CU) were 1.14 and 1.6 times higher; antibodies to dopamine receptors (77.9±4.4 CU) in 1.2 and 1.6 times; to serotonin receptors (81.96±3.25 CU) in 1.2 and 1.4 times; choline receptor antibodies (61.42±3.6 CU) were increased 1.4- and 1.8-fold; to glutamate (85.28±4.25 CU) by 1.19 and 1.4 times; to GABA (82.4±5.2 CU) the increase was 1.5- and 1.8-fold, respectively, when compared with group 2 and controls. The time-dependent monitoring of Nabs level in patients with COVID-19 associated ischemic stroke showed the highest increase in Nabs to the S-100, NF-200 and MBP proteins at the day +28 following the brain stroke. In patients with COVID-19 associated IS, more enhanced production of serum autoantibodies to neuroproteins and neurotransmitter receptors was detected, which accompanied a worse course of IS and can be considered as a predictor of unfavorable outcome of disease.

Societal Impact Statement Adapting agriculture to climate change requires an understanding of the long‐term relationship between climate, disease dynamics, and yield. While some countries have monitored major crop diseases for decades or centuries, comparable data is scarce or non‐existent for many countries that are most vulnerable to climate change. For this, a novel approach was developed to reconstruct climate‐mediated changes in disease dynamics and yield. Here, a case study on Arabica coffee in its area of origin demonstrates how to combine local knowledge, climate data, and spatial field surveys to reconstruct disease and yield time series and to postulate and test hypotheses for climate–disease–yield relationships. Summary While some countries have monitored crop diseases for several decades or centuries, other countries have very limited historical time series. In such areas, we lack data on long‐term patterns and drivers of disease dynamics, which is important for developing climate‐resilient disease management strategies. We adopted a novel approach, combining local knowledge, climate data, and spatial field surveys to understand long‐term climate‐mediated changes in disease dynamics in coffee agroforestry systems. For this, we worked with 58 smallholder farmers in southwestern Ethiopia, the area of origin of Arabica coffee. The majority of farmers perceived an increase in coffee leaf rust and a decrease in coffee berry disease, whereas perceptions of changes in coffee wilt disease and Armillaria root rot were highly variable among farmers. Climate data supported farmers' understanding of the climatic drivers (increased temperature, less rainy days) of these changes. Temporal disease‐climate relationships were matched by spatial disease‐climate relationships, as expected with space‐for‐time substitution. Understanding long‐term disease dynamics and yield is crucial to adapt disease management to climate change. Our study demonstrates how to combine local knowledge, climate data and spatial field surveys to reconstruct disease time series and postulate hypotheses for disease‐climate relationships in areas where few long‐term time series exist.

The paper is devoted to study the dynamics of the infestation of potatoes owing to the occurrence of late blight disease over two successive years (2014 and 2015) in the Terai region of West Bengal. Nonlinear models have been fitted on the potato late blight data (i.e. percent disease index data). The goodness-of-fit tests on different models have been performed by the application of the following criteria, namely coefficient of determination, mean absolute error, mean absolute percentage error, average relative predictive error, Akaike's information criterion and Bayesian information criterion, respectively. The validation of the models has been carried out by the Shapiro-Wilk test and running test for conforming to the assumptions of normality and independence of the errors, respectively. Based on the application of the goodness-of-fit tests on the models, it was found that for 2014 and 2015, cubic and Gompertz models provided the best-fitted models based on the above sets of data, respectively. Using the best-fitted models referred to above, the values of two important parameters, namely (1) maximum rate of growth of the disease and (2) maximum disease severity, were determined. For the year 2014, it was observed that the maximum rate of growth of the disease occurred at 31 days after planting (DAP) and the maximum disease severity occurred at 48 DAP; however, even after the attainment of the maximum rate of the growth of the disease, the severity of the disease may increase, so it can be concluded that an additional application of fungicidal spray is necessary (when the disease is located in the field). On the other hand, for 2015, we observed that the maximum rate of growth of the disease occurred at 43 DAP. It indicates that any protection measure should be adopted at this stage (here, 43 DAP). Importantly, in an unprotected field, the late blight disease grows indefinitely as time advances infinitely. [ABSTRACT FROM AUTHOR]

The critical community size (CCS) is the minimum closed population size in which a pathogen can persist indefinitely. Below this threshold, stochasticity eventually causes pathogen extinction. Here, we introduce a mechanism of behaviour-mediated persistence, by which the population response to the pathogen alters the CCS. We exemplify this with infection transmission and non-pharmaceutical interventions (NPIs) in a population where both individuals and government authorities restrict transmission more strongly when case numbers are higher. This results in a coupled social-ecological feedback between disease dynamics and population behaviour. In a parameter regime corresponding to a moderate population response, this feedback allows the pathogen to avoid extinction in epidemic troughs. The result is a very low CCS that allows long-term pathogen persistence. Hence, an incomplete population response represents a "sour spot" that not only ensures relatively high case incidence but also promotes long-term persistence of the pathogen by reducing the CCS. We illustrate this mechanism for parameters corresponding to severe coronavirus 2 (SARS-CoV-2). Given the worldwide prevalence of small, isolated populations, these results emphasize the need for incorporating behavioural feedback into CCS estimates. Regional elimination and global eradication programs for vaccine-preventable infections could also account for this effect. [ABSTRACT FROM AUTHOR]

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

Households play an important role in disease dynamics. Many infections happen there due to the close contact, while mitigation measures mainly target the transmission between households. Therefore, one can see households as boosting the transmission depending on household size. To study the effect of household size and size distribution, we differentiated within and between household reproduction rates. There are basically no preventive measures, and thus the close contacts can boost the spread. We explicitly incorporated that typically only a fraction of all household members are infected. Thus, viewing the infection of a household of a given size as a splitting process generating a new small fully infected subhousehold and a remaining still susceptible subhousehold, we derive a compartmental ODE model for the dynamics of the subhouseholds. In this setting, the basic reproduction number as well as prevalence and the peak of an infection wave in a population with given household size distribution can be computed analytically. We compare numerical simulation results of this novel household ODE model with results from an agent-based model using data for realistic household size distributions of different countries. We find good agreement of both models showing the catalytic effect of large households on the overall disease dynamics. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Adapting agriculture to climate change requires an understanding of the long‐term relationship between climate, disease dynamics, and yield. While some countries have monitored major crop diseases for decades or centuries, comparable data is scarce or non‐existent for many countries that are most vulnerable to climate change. For this, a novel approach was developed to reconstruct climate‐mediated changes in disease dynamics and yield. Here, a case study on Arabica coffee in its area of origin demonstrates how to combine local knowledge, climate data, and spatial field surveys to reconstruct disease and yield time series and to postulate and test hypotheses for climate–disease–yield relationships. Summary: While some countries have monitored crop diseases for several decades or centuries, other countries have very limited historical time series. In such areas, we lack data on long‐term patterns and drivers of disease dynamics, which is important for developing climate‐resilient disease management strategies.We adopted a novel approach, combining local knowledge, climate data, and spatial field surveys to understand long‐term climate‐mediated changes in disease dynamics in coffee agroforestry systems. For this, we worked with 58 smallholder farmers in southwestern Ethiopia, the area of origin of Arabica coffee.The majority of farmers perceived an increase in coffee leaf rust and a decrease in coffee berry disease, whereas perceptions of changes in coffee wilt disease and Armillaria root rot were highly variable among farmers. Climate data supported farmers' understanding of the climatic drivers (increased temperature, less rainy days) of these changes. Temporal disease‐climate relationships were matched by spatial disease‐climate relationships, as expected with space‐for‐time substitution.Understanding long‐term disease dynamics and yield is crucial to adapt disease management to climate change. Our study demonstrates how to combine local knowledge, climate data and spatial field surveys to reconstruct disease time series and postulate hypotheses for disease‐climate relationships in areas where few long‐term time series exist. [ABSTRACT FROM AUTHOR]

This study explores the asymptotic behavior of a stochastic epidemic model that accounts for the impact of media coverage. The initial focus lies on determining the conditions leading to the exponential extinction of the disease. Additionally, we investigate the weak convergence of the probability distribution of the stochastic process N(t), representing the total population, to a one-dimensional stochastic process with density calculated through the Fokker–Planck equation. Subsequently, we demonstrate the persistent nature of the disease and utilize Has’minskii theory to establish the presence of a unique ergodic stationary distribution for our stochastic epidemic model. Finally, numerical simulations are conducted to validate the theoretical findings.

Klebsiella pneumoniae is a Gram-negative bacterium known for causing infections such as pneumonia, urinary tract infections, and bloodstream infections in humans. Its increasing drug resistance poses a significant threat. Understanding the effects of climate change on this bacterium is crucial to managing its impacts effectively. In this study, we employed MaxEnt modeling to explore the current and future distribution of K. pneumoniae under different climate change scenarios for 2050 and 2070. Our models utilized 280 valid occurrence points and 19 bioclimatic variables, with Mean Annual Temperature and Mean Annual Precipitation being the most influential variables. The models demonstrated high accuracy, with an AUC of 0.889. Our findings revealed that K. pneumoniae has a cosmopolitan distribution, with the highest suitability observed in Southeastern Asia, Central Africa, and Southern Europe. Future predictions indicated an apparent expansion of its range across almost all continents, particularly in the RCP 8.5 2070 scenario. Results have crucial implications for epidemiology, as they enable the identification of current and potential future disease hotspots and geographical patterns. This knowledge can inform the development of effective infection control strategies. Furthermore, it provides valuable insights for decision-makers and policymakers in allocating resources efficiently for medical control and prevention strategies.

The dissemination of a disease within a homogeneous population can typically be modeled and managed in a uniform fashion. Conversely, in non-homogeneous populations, it is essential to account for variations among subpopulations to achieve more precise predictive modeling and efficacious intervention strategies. In this study, we introduce and examine the comprehensive behavior of a deterministic two-patch epidemic model alongside its stochastic counterpart to assess disease dynamics between two heterogeneous populations inhabiting distinct regions. First, utilizing a specific Lyapunov function, we demonstrate that the disease-free equilibrium of the deterministic model is globally asymptotically stable. For the stochastic model, we establish that it is well-posed, meaning it possesses a unique positive solution with probability one. Subsequently, we ascertain the conditions necessary to ensure the total extinction of the disease across both regions. Furthermore, we explicitly determine a threshold condition under which the disease persists in both areas. Additionally, we discuss a scenario wherein the disease persists in one region while simultaneously becoming extinct in the other. The article concludes with a series of numerical simulations that corroborate the theoretical findings.

Schistosomiasis, a neglected tropical disease caused by parasitic trematodes of the genus Schistosoma, affects millions of people in tropical and subtropical regions lacking access to clean water and proper hygiene. With its impact on health and well-being, the World Health Organization aspires to eliminate schistosomiasis by 2030. This work addresses the challenge of effective control in endemic areas by integrating diffusion in each sub-population using reaction-diffusion equations. The proposed model includes treated individuals who have undergone massive drug administration and a time-dependent function that models the change in human behavior. We present a Partial Differential Equation (PDE) model of schistosomiasis spread that incorporates population movement and human behavior change. Mathematical analysis explores the system’s dynamics according to the infection threshold R0, shedding light on the disease’s behavior. Sensitivity analysis is used to identify the key parameters affecting disease spread. Numerical simulations under different scenarios elucidate the impact of human behavior on disease dynamics. This research contributes to a deeper understanding of schistosomiasis transmission and provides insights into control strategies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Visceral Leishmaniasis remains a significant public health challenge in eastern Sudan despite extensive control efforts. This study employs MCMC Bayesian inference techniques to fit a mathematical model for studying visceral Leishmaniasis transmission dynamics with interventions. The research focuses on evaluation of control programs to adapt to the dynamic nature of visceral Leishmaniasis transmission. We analyze 22 years of cumulative visceral Leishmaniasis cases from eastern Sudan, an endemic region for visceral Leishmaniasis, and assessed the effectiveness of interventions implemented thus far. The results reveal that visceral Leishmaniasis is prevalent, with reported cases representing less than 20% of community infections. Improved surveillance and diagnostics are necessary for accurately estimating the disease burden. The effectiveness of intervention strategies for vector control for reducing VL transmission in the region is found to be limited. Furthermore, the model predicts visceral Leishmaniasis cases will continue to increase in the near future, underscoring the need adaptable initiatives to reduce the disease burden. [ABSTRACT FROM AUTHOR]

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

Forecasting the transmission patterns of infectious diseases is of paramount importance in gaining valuable insights into outbreak growth and optimizing the allocation of medical resources. In this paper, we conduct a comparative study between a physics-based model and a machine learning (ML) model for epidemic forecasting and countermeasures, considering criteria such as accuracy and practicality. We develop four ML models: back-propagation (BP), long short-term memory, support vector machine, and extreme learning machine. In addition, we propose a reaction-diffusion (R-D) model that incorporates factors such as susceptibility heterogeneity, lockdown measures, population movement, and dynamically dependent rates. The experimental results highlight the superior accuracy of the BP model for forecasting, while the R-D model provides comprehensive insights into disease dynamics, including stability and potential control strategies. [ABSTRACT FROM AUTHOR]

Theory predicts that susceptibility to disease in ectothermic hosts increases as temperatures depart from host’s thermal optima, because pathogens have functionally broader thermal tolerance ranges and acclimate faster than hosts to shifts in temperature. Hence, hosts adapted to cooler and warmer climates should be at greater risk of infection under abnormally warm and cool conditions, respectively. Batrachochytrium dendrobatidis (Bd) is a chytrid fungus that affects amphibians worldwide. In Brazil's Atlantic Forest, Bd outbreaks have been linked to numerous declines in amphibian populations, particularly in cooler high elevation areas. Thus, we hypothesize that years with abnormally warm temperatures could shift the balance in favor of the pathogen, thereby driving the historical declines. We also hypothesize that warm-adapted amphibians from lowland sites could experience elevated Bd infection risk during abnormally cold years. To test whether thermal mismatch (elevation vs. temperature anomaly) drove shifts in Bd prevalence through time we compiled a comprehensive database spanning 50 years, gathered across an elevational gradient within the Atlantic Forest. In agreement with our predictions, cool-adapted hosts had higher Bd prevalence when temperatures were higher than historical averages. In parallel, Bd prevalence in warm-adapted hosts was higher in colder-than-average years, although frogs from higher elevations exhibited an overall higher risk of disease due to disproportionally high infection prevalence. Our study links the thermal mismatch hypothesis with historical shifts in Bd prevalence in Brazilian frogs, indicating that Bd infections, modulated by climate change, may continue to have a negative impact on Neotropical amphibians.

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.

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. [ABSTRACT FROM AUTHOR]

In this research, we present the first steps toward developing a data-driven agent-based model (ABM) specifically designed for simulating infectious disease dynamics in Greece. Amidst the ongoing COVID-19 pandemic caused by SARS-CoV-2, this research holds significant importance as it can offer valuable insights into disease transmission patterns and assist in devising effective intervention strategies. To the best of our knowledge, no similar study has been conducted in Greece. We constructed a prototype ABM that utilizes publicly accessible data to accurately represent the complex interactions and dynamics of disease spread in the Greek population. By incorporating demographic information and behavioral patterns, our model captures the specific characteristics of Greece, enabling accurate and context-specific simulations. By using our proposed ABM, we aim to assist policymakers in making informed decisions regarding disease control and prevention. Through the use of simulations, policymakers have the opportunity to explore different scenarios and predict the possible results of various intervention measures. These may include strategies like testing approaches, contact tracing, vaccination campaigns, and social distancing measures. Through these simulations, policymakers can assess the effectiveness and feasibility of these interventions, leading to the development of well-informed strategies aimed at reducing the impact of infectious diseases on the Greek population. This study is an initial exploration toward understanding disease transmission patterns and a first step towards formulating effective intervention strategies for Greece. [ABSTRACT FROM AUTHOR]

In this paper, we propose a diffusive susceptible-infected-susceptible epidemic model in a spatiotemporally heterogeneous environment. We consider a saturated incidence function of the form S I m + S + I , where m is a nonnegative function. When there is a positive disease-induced mortality rate everywhere, we demonstrate that the disease will always become extinct and the susceptible population will stabilize at zero or a positive constant. In the case where the disease-induced mortality rate is negligible, we examine the model in a time-periodic and spatially heterogeneous environment and establish the threshold dynamics between disease extinction and persistence in terms of the basic reproduction number. Under certain conditions, we show the global attractivity of both the disease-free equilibrium and endemic equilibrium by constructing appropriate Lyapunov functions. Moreover, we determine the spatial distribution of the disease when the diffusion rate of the susceptible or infected population (or both) is sufficiently small. Our findings suggest that the presence of a saturation effect reduces the transmission risk, allows for the total population size to have a substantial impact on disease dynamics, and may significantly alter the spatial distribution of the disease under certain circumstances. [ABSTRACT FROM AUTHOR]

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

Cholera is an acute diarrhoeal illness that affects humanity globally, especially in areas where there is limited access to clean water and adequate sanitation. A Nigerian cholera outbreak from January 2021 to January 2022 resulted in many cases and deaths. A mathematical model that takes into consideration the challenges that affected effective implementation of control measures for this 2021 cholera outbreak is developed. Important epidemiological features of the model such as the basic reproduction number (R0), the disease-free equilibrium, and the endemic equilibrium are determined and analysed. The disease-free equilibrium is shown to be asymptotically stable provided R0 < 1. The model is shown to undergo forward bifurcation at R0 = 1 using the Centre Manifold Theorem. Sensitivity analysis is used to determine the parameters that have the highest influence on transmission. Fitting the model to data from the 2021 Nigerian cholera outbreak, important parameters of the model are estimated. The impact of control measures as well as challenges that affected the effective implementation of these control measures are considered.

Humans live in complex socio-ecological systems where we interact with parasites and pathogens that spend time in abiotic and biotic environmental reservoirs (e.g., water, air, soil, other vertebrate hosts, vectors, intermediate hosts). Through a synthesis of published literature, we reviewed the life cycles and environmental persistence of 150 parasites and pathogens tracked by the World Health Organization's Global Burden of Disease study. We used those data to derive the time spent in each component of a pathogen's life cycle, including total time spent in humans versus all environmental stages. We found that nearly all infectious organisms were "environmentally mediated" to some degree, meaning that they spend time in reservoirs and can be transmitted from those reservoirs to human hosts. Correspondingly, many infectious diseases were primarily controlled through environmental interventions (e.g., vector control, water sanitation), whereas few (14%) were primarily controlled by integrated methods (i.e., combining medical and environmental interventions). Data on critical life history attributes for most of the 150 parasites and pathogens were difficult to find and often uncertain, potentially hampering efforts to predict disease dynamics and model interactions between life cycle time scales and infection control strategies. We hope that this synthetic review and associated database serve as a resource for understanding both common patterns among parasites and pathogens and important variability and uncertainty regarding particular infectious diseases. These insights can be used to improve systems-based approaches for controlling environmentally mediated diseases of humans in an era where the environment is rapidly changing.

Abstract Background Migratory birds play an important part in the spread of parasites, with more or less impact on resident birds. Previous studies focus on the prevalence of parasites, but changes in infection intensity over time have rarely been studied. As infection intensity can be quantified by qPCR, we measured infection intensity during different seasons, which is important for our understanding of parasite transmission mechanisms. Methods Wild birds were captured at the Thousand Island Lake with mist nets and tested for avian hemosporidiosis infections using nested PCR. Parasites were identified using the MalAvi database. Then, we used qPCR to quantify the infection intensity. We analyzed the monthly trends of intensity for all species and for different migratory status, parasite genera and sexes. Results Of 1101 individuals, 407 were infected (37.0%) of which 95 were newly identified and mainly from the genus Leucocytozoon. The total intensity trend shows peaks at the start of summer, during the breeding season of hosts and during the over-winter season. Different parasite genera show different monthly trends. Plasmodium causes high prevalence and infection intensity of winter visitors. Female hosts show significant seasonal trends of infection intensity. Conclusions The seasonal changes of infection intensity is consistent with the prevalence. Peaks occur early and during the breeding season and then there is a downward trend. Spring relapses and avian immunity are possible reasons that could explain this phenomenon. In our study, winter visitors have a higher prevalence and infection intensity, but they rarely share parasites with resident birds. This shows that they were infected with Plasmodium during their departure or migration and rarely transmit the disease to resident birds. The different infection patterns of different parasite species may be due to vectors or other ecological properties. Graphical abstract

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.

This paper describes the model development process in detail, including the formulation of equations and parameters based on existing knowledge of hair cycle dynamics and immune system interactions. Various analyses are conducted to gain insights into the behavior of the model. Illustrative simulations are performed to observe the temporal dynamics of the disease progression under different conditions. Sensitivity analysis using eFAST (Extended Fourier Amplitude Sensitivity Test) is employed to identify the most influential parameters affecting the length of the anagen phase in hair follicles affected by alopecia areata. The findings of the study shed light on the complex dynamics of alopecia areata and contribute to a deeper understanding of the disease mechanisms. The model provides a valuable tool for studying autoimmune hair loss diseases and may have implications for the diagnosis and treatment of such conditions. By simulating the immune response and its effects on hair follicles, the model offers insights into potential treatment strategies that can target immune dysregulation. The temporal mathematical model presented in this dissertation provides a comprehensive framework for investigating alopecia areata and understanding its underlying dynamics. The integration of hair cycle dynamics and immune system interactions enhances our knowledge of the disease and opens avenues for future advancements in diagnosis and treatment approaches.

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

Context: Vector-borne diseases (VBD) are a worldwide public health problem for humans and wildlife. 'Global Change' such as habitat alteration and land-use intensification, biotic exchange, the introduction of invasive alien species, and climate change have all been linked to an increased occurrence of VBDs. Objectives: To evaluate the impact of land-use and land-cover (LULC) on the transmission of VBDs, we conducted a systematic review of the existing literature on the global effects of land use on VBDs. This was followed by a meta-analysis to test the relationship between LULC intensification and infection prevalence. Methods: Overall, 654 articles met our inclusion criteria for the systematic literature review, and 18 studies fulfilled the requirements for the meta-analysis. Results: The systematic literature review identified 162 articles with a total of 2541 data entries on the effect of LULC on VBDs. The majority of the studies were published after 2010, and the spatial distribution of data was biased towards North America and Europe. Overall, 193 different hosts and 144 different vector groups were identified. Avian and human malaria were the most frequently named diseases, with humans and Anopheles mosquitoes the most common host and vector, respectively. Our meta-analysis found that land-use intensity, as well as targets (host and vector), significantly impact the prevalence of VBDs. Tests for both residual heterogeneity and moderators were significant, where lower land-use intensity and vectors were linked to lower VBD prevalence, while medium land-use intensity was associated with higher prevalence. Analysis of the host sub-model supported these findings, with infection prevalence significantly lower in low land-use intensity. Conclusions: The systematic literature review revealed a temporal increase in publications on this topic, with a significant rise since 2007 and uneven distribution of data across countries, with the United States, Spain, and Brazil being the most prominent contributors and identified a wide range of pathogens and hosts involved in VBD systems, with human and avian malaria being the most commonly mentioned diseases. We also show, through a meta-analysis, that LULC intensification affects VBDs infection prevalence. Future studies should incorporate the effects of land-use intensity on vector-borne diseases in diverse ecosystems to inform management strategies and mitigate disease emergence with implications for human, livestock and wildlife health. [ABSTRACT FROM AUTHOR]

When a transmission hotspot for an environmentally persistent pathogen establishes in otherwise high-quality habitat, the disease may exert a strong impact on a host population. However, fluctuating environmental conditions lead to heterogeneity in habitat quality and animal habitat preference, which may interrupt the overlap between selected and risky habitats. We evaluated spatio-temporal patterns in anthrax mortalities in a plains zebra (Equus quagga) population in Etosha National Park, Namibia, incorporating remote-sensing and host telemetry data. A higher proportion of anthrax mortalities of herbivores was detected in open habitats than in other habitat types. Resource selection functions showed that the zebra population shifted habitat selection in response to changes in rainfall and vegetation productivity. Average to high rainfall years supported larger anthrax outbreaks, with animals congregating in preferred open habitats, while a severe drought forced animals into otherwise less preferred habitats, leading to few anthrax mortalities. Thus, the timing of anthrax outbreaks was congruent with preference for open plains habitats and a corresponding increase in pathogen exposure. Given shifts in habitat preference, the overlap in high-quality habitat and high-risk habitat is intermittent, reducing the adverse consequences for the population.

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.

For ectotherms, habitat temperature is one of the most fundamental factors responsible for disease dynamics. Therefore, temperature-dependent habitat selection of hosts could alter their susceptibility to pathogens. Here, we examined the effect of host behavior in the fluctuating thermal regime on disease dynamics, by a dynamical modeling with field surveys. Cyprinid herpesvirus 3 (CyHV-3) was used as a model disease, which is a mass mortality agent of common carp (Cyprinus carpio). Telemetry analysis revealed that carp shifted their location according to the temporal fluctuations of the thermal regime in the habitat, suggesting a preference for specific temperatures. Numerical simulation using a disease transmission model reproduced the characteristic bimodal seasonal trends of infection rate to CyHV-3. The simulation demonstrated that the temperature preference of individual carp was central in determining whether the temperature-dependent behavior ameliorates or exacerbates disease severity. Moreover, it also demonstrated that increases in the fraction of warmer coastal areas can mitigate the impact of CyHV-3 on the carp population by promoting the acquisition of immunity. Our findings suggest that the prevalence of infectious disease in poikilothermic animals can be regulated by the combined effects of the thermal regime of their habitat and the host's thermally induced behavior. [ABSTRACT FROM AUTHOR]

Background: Migratory birds play an important part in the spread of parasites, with more or less impact on resident birds. Previous studies focus on the prevalence of parasites, but changes in infection intensity over time have rarely been studied. As infection intensity can be quantified by qPCR, we measured infection intensity during different seasons, which is important for our understanding of parasite transmission mechanisms. Methods: Wild birds were captured at the Thousand Island Lake with mist nets and tested for avian hemosporidiosis infections using nested PCR. Parasites were identified using the MalAvi database. Then, we used qPCR to quantify the infection intensity. We analyzed the monthly trends of intensity for all species and for different migratory status, parasite genera and sexes. Results: Of 1101 individuals, 407 were infected (37.0%) of which 95 were newly identified and mainly from the genus Leucocytozoon. The total intensity trend shows peaks at the start of summer, during the breeding season of hosts and during the over-winter season. Different parasite genera show different monthly trends. Plasmodium causes high prevalence and infection intensity of winter visitors. Female hosts show significant seasonal trends of infection intensity. Conclusions: The seasonal changes of infection intensity is consistent with the prevalence. Peaks occur early and during the breeding season and then there is a downward trend. Spring relapses and avian immunity are possible reasons that could explain this phenomenon. In our study, winter visitors have a higher prevalence and infection intensity, but they rarely share parasites with resident birds. This shows that they were infected with Plasmodium during their departure or migration and rarely transmit the disease to resident birds. The different infection patterns of different parasite species may be due to vectors or other ecological properties. [ABSTRACT FROM AUTHOR]

Maize is an essential staple food crop and is vulnerable to diseases. Maize streak disease is one of the serious illnesses that affect maize, especially in sub-Saharan Africa where the disease is endemic. We developed a stochastic epidemic model with three control measure types (mechanical, chemical and preventative) that mitigate the disease. A dynamical system analysis of the deterministic version of the model is provided. The probability of maize streak disease extinction or persistence is determined using the theory of a multi-type branching process. Using these results, the degrees to which these control measures are effective in reducing maize streak disease are considered. It is shown that chemical and mechanical control measures, preferably together, are better than preventive controls in reducing disease prevalence. However, considering the possible negative effects of a chemical control, it is shown that sufficient mechanical control combined with a small degree of each of preventative and chemical control could be the most viable strategy to limit maize streak disease.

Plants modulate multitrophic ecological interactions, and variation in plant traits can affect these interactions. Pollinators are exposed to pathogens at flowers and acquire or transmit pathogens at different rates on different plant species, but the traits mediating those interactions are almost entirely unknown. We experimentally manipulated five plant traits that span scales including flower, inflorescence, and plant, to determine their effects on pathogen transmission between foraging bees. Specifically, we manipulated two morphological traits (corolla lip length and flower orientation within an inflorescence) and three resource distribution traits (inflorescence nectar, plant patch nectar, and plant aggregation) in tents to test how plant traits affect bee pathogen transmission. We also quantified foraging behavior and fecal deposition patterns as potential mechanisms driving differences in transmission, and assessed trait manipulation consequences for bee reproduction. We found that pathogen transmission was reduced when we trimmed the corolla lip, evenly dispersed nectar distribution within an inflorescence, or aggregated plants in space. Some traits also affected bee reproduction; tents with trimmed corollas had more larval production than control tents, and tents with evenly distributed nectar across plant patches had more larval production than tents with clumped resources. Thus, some trait manipulations both reduced transmission and increased bee microcolony reproduction, although our design does not allow us to discern whether these are related or separate effects. Taken together, our results demonstrate causal effects of several floral traits on pathogen transmission and pollinator reproduction, indicating the importance of intraspecific plant trait variation for pollinator health and population dynamics. [ABSTRACT FROM AUTHOR]

Researchers studying animal phenotypes often overlook the potential influence of parasites hiding inside their study organisms. Yet, most wild animals host parasites, which can alter individual phenotypes (e.g. morphology, physiology, behaviour).Infection‐induced phenotypes stem from several nonmutually exclusive mechanisms (i.e. adaptive host defences, infection‐induced debilitation and host manipulation by parasites), driven by parasites and/or hosts.Changes in host phenotype can impact all levels of biological organization, from cells to communities. The nature and magnitude of these effects can vary depending on the biotic and abiotic environmental factors experienced by hosts and parasites.This special feature highlights recent insights into the ways parasites alter host phenotypes across a range of systems. Here, we contextualize how each contribution expands our knowledge of the role of parasites in driving individual variation in animal phenotypes.Looking to the future, we need to better understand how infection‐induced phenotypes fluctuate with natural variation in infection (e.g. infection intensity, coinfection) and whether studies in laboratory‐based environments provide strong proxies for host–parasite interactions in the wild. The time is ripe to acknowledge, critique and discuss the implications of infection on host phenotypes across taxonomic boundaries and biological levels of organization. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

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

In this research, we present the first steps toward developing a data-driven agent-based model (ABM) specifically designed for simulating infectious disease dynamics in Greece. Amidst the ongoing COVID-19 pandemic caused by SARS-CoV-2, this research holds significant importance as it can offer valuable insights into disease transmission patterns and assist in devising effective intervention strategies. To the best of our knowledge, no similar study has been conducted in Greece. We constructed a prototype ABM that utilizes publicly accessible data to accurately represent the complex interactions and dynamics of disease spread in the Greek population. By incorporating demographic information and behavioral patterns, our model captures the specific characteristics of Greece, enabling accurate and context-specific simulations. By using our proposed ABM, we aim to assist policymakers in making informed decisions regarding disease control and prevention. Through the use of simulations, policymakers have the opportunity to explore different scenarios and predict the possible results of various intervention measures. These may include strategies like testing approaches, contact tracing, vaccination campaigns, and social distancing measures. Through these simulations, policymakers can assess the effectiveness and feasibility of these interventions, leading to the development of well-informed strategies aimed at reducing the impact of infectious diseases on the Greek population. This study is an initial exploration toward understanding disease transmission patterns and a first step towards formulating effective intervention strategies for Greece.

Abstract Background It remains elusive how the characteristics, the course of disease, the clinical management and the outcomes of critically ill COVID-19 patients admitted to intensive care units (ICU) worldwide have changed over the course of the pandemic. Methods Prospective, observational registry constituted by 90 ICUs across 22 countries worldwide including patients with a laboratory-confirmed, critical presentation of COVID-19 requiring advanced organ support. Hierarchical, generalized linear mixed-effect models accounting for hospital and country variability were employed to analyse the continuous evolution of the studied variables over the pandemic. Results Four thousand forty-one patients were included from March 2020 to September 2021. Over this period, the age of the admitted patients (62 [95% CI 60–63] years vs 64 [62–66] years, p

Optimization of control measures for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in high-risk institutional settings (e.g., prisons, nursing homes, or military bases) depends on how transmission dynamics in the broader community influence outbreak risk locally. We calibrated an individual-based transmission model of a military training camp to the number of RT-PCR positive trainees throughout 2020 and 2021. The predicted number of infected new arrivals closely followed adjusted national incidence and increased early outbreak risk after accounting for vaccination coverage, masking compliance, and virus variants. Outbreak size was strongly correlated with the predicted number of off-base infections among staff during training camp. In addition, off-base infections reduced the impact of arrival screening and masking, while the number of infectious trainees upon arrival reduced the impact of vaccination and staff testing. Our results highlight the importance of outside incidence patterns for modulating risk and the optimal mixture of control measures in institutional settings.

Lassa fever is a deadly viral illness that is endemic in some parts of West Africa, including Nigeria. A deterministic model in the form of a non-linear system of differential equations is developed to analyse the dynamics and possible control of the disease. The model is tested by fitting it to data from Nigeria's Lassa fever outbreak using a least-squares fitting routine and the model is shown to provide a reasonable fit to the data. Parameters representing various control measures in the model are estimated using the model fitting. Important epidemiological features of the model such as the basic reproduction number (R 0) , the disease-free equilibrium, and the endemic equilibrium are determined and analysed. The disease-free equilibrium is shown to be asymptotically stable when R 0 < 1 . A bifurcation about R 0 = 1 was determined using the Center Manifold Theorem. Using numerical simulations of the model future dynamics of Lassa fever disease in Nigeria are predicted and the impact of control measures on the disease determined. The use of approved rodenticides is shown to be the most effective control followed by reducing person-to-person and rodent-to-person contacts, respectively. Isolation and treatment of infected individuals are shown to be less effective when compared with the other control measures. [ABSTRACT FROM AUTHOR]