Plethodontid salamanders show variable disease dynamics in response to Batrachochytrium salamandrivorans chytridiomycosis

AbstractLeaf curl, a whitefly-borne begomovirus disease, is the cause of frequent epidemic in chili. In the present study, transmission parameters involved in tripartite interaction are estimated to simulate disease dynamics in a population dynamics model framework. Epidemic is characterized by a rapid conversion rate of healthy host population into infectious type. Infection rate as basic reproduction number, R0 = 13.54, has indicated a high rate of virus transmission. Equilibrium population of infectious host and viruliferous vector are observed to be sensitive to the immigration parameter. A small increase in immigration rate of viruliferous vector increased the population of both infectious host and viruliferous vector. Migrant viruliferous vectors, acquisition, and transmission rates as major parameters in the model indicate leaf curl epidemic is predominantly a vector -mediated process. Based on underlying principles of temperature influence on vector population abundance and transmission parameters, spatio-temporal pattern of disease risk predicted is noted to correspond with leaf curl distribution pattern in India. Temperature in the range of 15–35 °C plays an important role in epidemic as both vector population and virus transmission are influenced by temperature. Assessment of leaf curl dynamics would be a useful guide to crop planning and evolution of efficient management strategies.

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PDE limits of stochastic SIS epidemics on networks

Journal of Complex Networks ◽

10.1093/comnet/cnaa043 ◽

2020 ◽

Vol 8 (4) ◽

Author(s):

F Di Lauro ◽

J-C Croix ◽

L Berthouze ◽

I Z Kiss

Keyword(s):

Network Inference ◽

Mean Field ◽

Population Level ◽

Death Process ◽

Disease Dynamics ◽

Epidemic Outbreak ◽

Numerical Tests ◽

Mean Field Models ◽

Stochastic Epidemic ◽

Fokker Planck Equations

Abstract Stochastic epidemic models on networks are inherently high-dimensional and the resulting exact models are intractable numerically even for modest network sizes. Mean-field models provide an alternative but can only capture average quantities, thus offering little or no information about variability in the outcome of the exact process. In this article, we conjecture and numerically demonstrate that it is possible to construct partial differential equation (PDE)-limits of the exact stochastic susceptible-infected-susceptible epidemics on Regular, Erdős–Rényi, Barabási–Albert networks and lattices. To do this, we first approximate the exact stochastic process at population level by a Birth-and-Death process (BD) (with a state space of $O(N)$ rather than $O(2^N)$) whose coefficients are determined numerically from Gillespie simulations of the exact epidemic on explicit networks. We numerically demonstrate that the coefficients of the resulting BD process are density-dependent, a crucial condition for the existence of a PDE limit. Extensive numerical tests for Regular, Erdős–Rényi, Barabási–Albert networks and lattices show excellent agreement between the outcome of simulations and the numerical solution of the Fokker–Planck equations. Apart from a significant reduction in dimensionality, the PDE also provides the means to derive the epidemic outbreak threshold linking network and disease dynamics parameters, albeit in an implicit way. Perhaps more importantly, it enables the formulation and numerical evaluation of likelihoods for epidemic and network inference as illustrated in a fully worked out example.

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Extinction and persistence of a stochastic SIRV epidemic model with nonlinear incidence rate

Advances in Difference Equations ◽

10.1186/s13662-021-03347-3 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Ramziya Rifhat ◽

Zhidong Teng ◽

Chunxia Wang

Keyword(s):

Epidemic Model ◽

Control Strategies ◽

Random Perturbations ◽

Disease Dynamics ◽

Nonlinear Incidence Rate ◽

Nonlinear Incidence ◽

Analysis Theory ◽

Lyapunov Function Method ◽

Random Fluctuations ◽

Theoretical Results

AbstractIn this paper, a stochastic SIRV epidemic model with general nonlinear incidence and vaccination is investigated. The value of our study lies in two aspects. Mathematically, with the help of Lyapunov function method and stochastic analysis theory, we obtain a stochastic threshold of the model that completely determines the extinction and persistence of the epidemic. Epidemiologically, we find that random fluctuations can suppress disease outbreak, which can provide us some useful control strategies to regulate disease dynamics. In other words, neglecting random perturbations overestimates the ability of the disease to spread. The numerical simulations are given to illustrate the main theoretical results.

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Innate and Adaptive Immune Genes Associated with MERS-CoV Infection in Dromedaries

Cells ◽

10.3390/cells10061291 ◽

2021 ◽

Vol 10 (6) ◽

pp. 1291

Author(s):

Sara Lado ◽

Jean P. Elbers ◽

Martin Plasil ◽

Tom Loney ◽

Pia Weidinger ◽

...

Keyword(s):

Immune Response ◽

United Arab Emirates ◽

Innate Immune ◽

Disease Dynamics ◽

Genetic Pathways ◽

Immune Genes ◽

Adaptive Immune ◽

Potential Association ◽

Host Genetic ◽

Human Infections

The recent SARS-CoV-2 pandemic has refocused attention to the betacoronaviruses, only eight years after the emergence of another zoonotic betacoronavirus, the Middle East respiratory syndrome coronavirus (MERS-CoV). While the wild source of SARS-CoV-2 may be disputed, for MERS-CoV, dromedaries are considered as source of zoonotic human infections. Testing 100 immune-response genes in 121 dromedaries from United Arab Emirates (UAE) for potential association with present MERS-CoV infection, we identified candidate genes with important functions in the adaptive, MHC-class I (HLA-A-24-like) and II (HLA-DPB1-like), and innate immune response (PTPN4, MAGOHB), and in cilia coating the respiratory tract (DNAH7). Some of these genes previously have been associated with viral replication in SARS-CoV-1/-2 in humans, others have an important role in the movement of bronchial cilia. These results suggest similar host genetic pathways associated with these betacoronaviruses, although further work is required to better understand the MERS-CoV disease dynamics in both dromedaries and humans.

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Haplotype Explorer: an infection cluster visualization tool for spatiotemporal dissection of the COVID-19 pandemic

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab126 ◽

2021 ◽

Author(s):

Tetsuro Kawano-Sugaya ◽

Koji Yatsu ◽

Tsuyoshi Sekizuka ◽

Kentaro Itokawa ◽

Masanori Hashino ◽

...

Keyword(s):

Functional Limitations ◽

Time Dependent ◽

Network Visualization ◽

Disease Dynamics ◽

Accession Number ◽

Haplotype Networks ◽

Virus Serotype ◽

Serotype 1 ◽

Cluster Visualization ◽

Infectious Disease Dynamics

Abstract Summary Many of software for network visualization are available, but existing software have not been optimized to infection cluster visualization, especially the current worldwide invasion of COVID-19 since 2019. To reach the spatiotemporal understanding of epidemics, we have developed Haplotype Explorer. In Haplotype Explorer, users can explore the network interactively with metadata like accession number, locations, and collection dates. Time dependent transition of the network can be exported as continuous sections for making a movie. Here, we introduce features and products of Haplotype Explorer, demonstrating time-dependent snapshots and a movie of haplotype networks inferred from total of 4,282 SARS-CoV-2 genomes. Abstract The worldwide eruption of COVID-19 that began in Wuhan, China in late 2019 reached 10 million cases by late June 2020. In order to understand the epidemiological landscape of the COVID-19 pandemic, many studies have attempted to elucidate phylogenetic relationships between collected viral genome sequences using haplotype networks. However, currently available applications for network visualization are not suited to understand the COVID-19 epidemic spatiotemporally due to functional limitations, that motivated us to develop Haplotype Explorer, an intuitive tool for visualizing and exploring haplotype networks. Haplotype Explorer enables to dissect epidemiological consequences via interactive node filters and provides the perspective on infectious disease dynamics depend on regions and time, such as introduction, outbreak, expansion, and containment. Here, we demonstrate the effectiveness of Haplotype Explorer by showing features and an example of visualization. The demo using SARS-CoV-2 genomes are available at https://github.com/TKSjp/HaplotypeExplorer/blob/master/Example/. There are several examples using SARS-CoV-2 genomes and Dengue virus serotype 1 E-genes sequence.

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Landscape epidemiology of Batrachochytrium salamandrivorans : reconciling data limitations and conservation urgency

Ecological Applications ◽

10.1002/eap.2342 ◽

2021 ◽

Author(s):

Wouter Beukema ◽

Jesse Erens ◽

Vanessa Schulz ◽

Gwij Stegen ◽

Annemarieke Spitzen‐van der Sluijs ◽

...

Keyword(s):

Landscape Epidemiology ◽

Batrachochytrium Salamandrivorans ◽

Data Limitations

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