Characterization of the Level and Type of Resistance of Potato Varieties to Late Blight (Phytophthora infestans).

Understanding the level and type of resistance in potato varieties is relevant for integrating varietal resistance into the management of potato late blight. Accordingly, 54 potato varieties were tested for their level and type of resistance to late blight in 2019 and 2020 in Denmark. Spreader rows were artificially inoculated to ensure an even inoculum distribution in the trial. Disease severity was assessed once or twice per week. Cluster analysis (CA) was done based on the weighted mean absolute rate (WMAR), the relative area under the disease progress curve, the onset of disease (Xo), the severity of disease in the middle of the season, the time to reach 1% disease severity, the time to reach final disease severity, and the maximum disease severity. The resistance types were determined by comparing the tested varieties to Bintje (susceptible reference) for Xo and WMAR. The CA ranked the varieties as susceptible, moderately resistant, resistant, and very resistant based on their level of resistance. Except for a few varieties, the expressed resistance levels varied between the years. Several varieties that were susceptible in 2019 were moderately resistant in 2020. Also, the types of resistance that the varieties exhibited varied from year to year. In 2020, most varieties exhibited race-specific resistance, while in 2019 they mostly showed susceptible characteristics. The variation between years for the level and types of resistance of the varieties highlights the importance of regularly monitoring varietal resistance across time and space.

What are the mental health impacts of epidemics on relatives of people affected, and relatives of healthcare workers: What interventions are available to support them? A systematic review and narrative synthesis.

BACKGROUND: Previous research has mainly focused on the impacts of epidemics on those people who are directly affected by the epidemic infection, or of healthcare workers caring for them. Less is known about the impact on mental health of their relatives, and potential interventions to support them. METHODS: Systematic review and narrative synthesis. OUTCOMES: 28 studies were identified, sixteen quantitative and twelve qualitative. One involved health workers' relatives, and the rest covered relatives of directly affected individuals. We found considerable burden of mental ill-health in both groups. Among relatives of healthcare workers, 29.4% reported symptoms consistent with probable anxiety disorder and 33.7% with probable depression. Prevalence rates for probable anxiety disorder ranged from 24-42% and probable depression 17-51% for the relatives of affected people. One study found a 2% prevalence of PTSD and another found odds of PTSS were significantly higher among relatives of affected individuals compared with the general population. Only two intervention studies were identified and both were descriptive in nature. INTERPRETATION: Available evidence suggests relatives of people affected by infective outbreaks report mental ill-health. Having a relative who died particularly increased risk. Good outcomes for relatives of affected individuals were promoted by practical and social support, public health guidance that recognises the caring role of relatives, and being supported to see the positives as well as negatives in their situation. Good outcomes for relatives of health workers were promoted by perceived effectiveness of protective equipment. High quality evidence on potential interventions to support relatives is lacking. FUNDING: No external funding sought.

Pangenomic Analysis of Dickeya dianthicola Strains Related to the Outbreak of Blackleg and Soft Rot of Potato in the United States.

Dickeya dianthicola has caused an outbreak of blackleg and soft rot of potato in the eastern half of the United States since 2015. To investigate genetic diversity of the pathogen, a comparative analysis was conducted on genomes of D. dianthicola strains. Whole genomes of 16 strains from the United States outbreak were assembled and compared with 16 previously sequenced genomes of D. dianthicola isolated from potato or carnation. Among the 32 strains, eight distinct clades were distinguished based on phylogenomic analysis. The outbreak strains were grouped into three clades, with the majority of the strains in clade I. Clade I strains were unique and homogeneous, suggesting a recent incursion of this strain into potato production from alternative hosts or environmental sources. The pangenome of the 32 strains contained 6,693 genes, 3,377 of which were core genes. By screening primary protein subunits associated with virulence from all U.S. strains, we found that many virulence-related gene clusters, such as plant cell wall degrading enzyme genes, flagellar and chemotaxis related genes, two-component regulatory genes, and type I/II/III secretion system genes, were highly conserved but that type IV and type VI secretion system genes varied. The clade I strains encoded two clusters of type IV secretion systems, whereas the clade II and III strains encoded only one cluster. Clade I and II strains encoded one more VgrG/PAAR spike protein than did clade III. Thus, we predicted that the presence of additional virulence-related genes may have enabled the unique clade I strain to become predominant in the U.S. outbreak.

Predicting plant disease epidemics using boosted regression trees.

Plant epidemics are often associated with weather-related variables. It is difficult to identify weather-related predictors for models predicting plant epidemics. In the article by Shah et al., to predict Fusarium head blight (FHB) epidemics of wheat, they explored a functional approach using scalar-on-function regression to model a binary outcome (FHB epidemic or non-epidemic) with respect to weather time series spanning 140 days relative to anthesis. The scalar-on-function models fit the data better than previously described logistic regression models. In this work, given the same dataset and models, we attempt to reproduce the article by Shah et al. using a different approach, boosted regression trees. After fitting, the classification accuracy and model statistics are surprisingly good.

Plant pathogenesis: Toward multidimensional understanding of the microbiome.

Single pathogen-targeted disease management measure has shown drawbacks in field efficacy under the scenario of global change. An in-depth understanding of plant pathogenesis will provide a promising solution but faces the challenges of the emerging paradigm involving the plant microbiome. While the beneficial impact of the plant microbiome is well characterized, their potential role in facilitating pathological processes has so far remained largely overlooked. To address these unsolved controversies and emerging challenges, we hereby highlight the pathobiome, the disease-assisting portion hidden in the plant microbiome, in the plant pathogenesis paradigm. We review the detrimental actions mediated by the pathobiome at multiple scales and further discuss how natural and human triggers result in the prevalence of the plant pathobiome, which would probably provide a clue to the mitigation of plant disease epidemics. Collectively, the article would advance the current insight into plant pathogenesis and also pave a new way to cope with the upward trends of plant disease by designing the pathobiome-targeted measure.

Epidemic spread on patch networks with community structure.

Predicting and preparing for the trajectory of disease epidemics relies on a knowledge of environmental and socioeconomic factors that affect transmission rates on local and global spatial scales. This article discusses the simulation of epidemic outbreaks on human metapopulation networks with community structure, such as cities within national boundaries, for which infection rates vary both within and between communities. We demonstrate mathematically, through next-generation matrices, that the structures of these communities, setting aside all other considerations such as disease virulence and human decision-making, have a profound effect on the reproduction rate of the disease throughout the network. In high modularity networks, with high levels of separation between neighboring communities, disease epidemics tend to spread rapidly in high-risk communities and very slowly in others, whereas in low modularity networks, the epidemic spreads throughout the entire network as a steady pace, with little regard for variations in infection rate. The correlation between network modularity and effective reproduction number is stronger in population with high rates of human movement. This implies that the community structure, human diffusion rate, and disease reproduction number are all intertwined, and the relationships between them can be affected by mitigation strategies such as restricting movement between and within high-risk communities. We then test through numerical simulation the effectiveness of movement restriction and vaccination strategies in reducing the peak prevalence and spread area of outbreaks. Our results show that the effectiveness of these strategies depends on the structure of the network and the properties of the disease. For example, vaccination strategies are most effective in networks with high rates of diffusion, whereas movement restriction strategies are most effective in networks with high modularity and high infection rates. Finally, we offer guidance to epidemic modelers as to the ideal spatial resolution to balance accuracy and data collection costs.

Genomic analysis, trajectory tracking, and field surveys reveal sources and long-distance dispersal routes of wheat stripe rust pathogen in China.

Identifying sources of phytopathogen inoculum and determining their contributions to disease outbreaks are essential for predicting disease development and establishing control strategies. Puccinia striiformis f. sp. tritici (Pst), the causal agent of wheat stripe rust, is an airborne fungal pathogen with rapid virulence variation that threatens wheat production through its long-distance migration. Because of wide variation in geographic features, climatic conditions, and wheat production systems, Pst sources and related dispersal routes in China are largely unclear. In the present study, we performed genomic analyses of 154 Pst isolates from all major wheat-growing regions in China to determine Pst population structure and diversity. Through trajectory tracking, historical migration studies, genetic introgression analyses, and field surveys, we investigated Pst sources and their contributions to wheat stripe rust epidemics. We identified Longnan, the Himalayan region, and the Guizhou Plateau, which contain the highest population genetic diversities, as the Pst sources in China. Pst from Longnan disseminates mainly to eastern Liupan Mountain, the Sichuan Basin, and eastern Qinghai; that from the Himalayan region spreads mainly to the Sichuan Basin and eastern Qinghai; and that from the Guizhou Plateau migrates mainly to the Sichuan Basin and the Central Plain. These findings improve our current understanding of wheat stripe rust epidemics in China and emphasize the need for managing stripe rust on a national scale.

Cucurbit Powdery Mildew: First Insights for the Identification of the Causal Agent and Screening for Resistance of Squash Genotypes (Cucurbita moschata (Duchesne ex Lam.) Duchesne ex Poir.) in Mendoza, Argentina.

The cucurbit powdery mildew (CPM) caused by different fungal species is a major concern for cucurbit crops around the world. In Argentina CPM constitutes the most common and damaging disease for cucurbits, especially for squash crops (Cucurbita moschata). The present study displays initial insights into the knowledge of the disease in western Argentina, including the determination of the prevalent species causing CPM, as well as the evaluation of the resistance of squash cultivars and breeding lines. Fungal colonies were isolated from samples collected in Mendoza province, Argentina. A field trial was also performed to assess the resistance of five squash accessions, including commercial cultivars and breeding lines. The severity of CPM was analyzed and epidemiological models were built based on empirical data. The morphological determinations and analysis with specific molecular markers confirmed Podosphaera xanthi as the prevalent causal agent of CPM in Mendoza. The results od the field trial showed differences in the resistance trait among the squash accessions. The advanced breeding line BL717/1 showed promising results as source of CPM resistance for the future development of open pollinated resistant cultivars, a crucial tool for an integrative control of the disease.

A non-parametric Hawkes model of the spread of Ebola in west Africa.

Recently developed methods for the non-parametric estimation of Hawkes point process models facilitate their application for describing and forecasting the spread of epidemic diseases. We use data from the 2014 Ebola outbreak in West Africa to evaluate how well a simple Hawkes point process model can forecast the spread of Ebola virus in Guinea, Sierra Leone, and Liberia. For comparison, SEIR models that fit previously to the same data are evaluated using identical metrics. To test the predictive power of each of the models, we simulate the ability to make near real-time predictions during an actual outbreak by using the first 75% of the data for estimation and the subsequent 25% of the data for evaluation. Forecasts generated from Hawkes models more accurately describe the spread of Ebola in each of the three countries investigated and result in a 38% reduction in RMSE for weekly case estimation across all countries when compared to SEIR models (total RMSE of 59.8 cases/week using SEIR compared to 37.1 for Hawkes). We demonstrate that the improved fit from Hawkes modeling cannot be attributed to overfitting and evaluate the advantages and disadvantages of Hawkes models in general for forecasting the spread of epidemic diseases.

Clinical evaluation of fever-screening thermography: impact of consensus guidelines and facial measurement location.

SIGNIFICANCE: Infrared thermographs (IRTs) have been used for fever screening during infectious disease epidemics, including severe acute respiratory syndrome, Ebola virus disease, and coronavirus disease 2019 (COVID-19). Although IRTs have significant potential for human body temperature measurement, the literature indicates inconsistent diagnostic performance, possibly due to wide variations in implemented methodology. A standardized method for IRT fever screening was recently published, but there is a lack of clinical data demonstrating its impact on IRT performance. AIM: Perform a clinical study to assess the diagnostic effectiveness of standardized IRT-based fever screening and evaluate the effect of facial measurement location. APPROACH: We performed a clinical study of 596 subjects. Temperatures from 17 facial locations were extracted from thermal images and compared with oral thermometry. Statistical analyses included calculation of receiver operating characteristic (ROC) curves and area under the curve (AUC) values for detection of febrile subjects. RESULTS: Pearson correlation coefficients for IRT-based and reference (oral) temperatures were found to vary strongly with measurement location. Approaches based on maximum temperatures in either inner canthi or full-face regions indicated stronger discrimination ability than maximum forehead temperature (AUC values of 0.95 to 0.97 versus 0.86 to 0.87, respectively) and other specific facial locations. These values are markedly better than the vast majority of results found in prior human studies of IRT-based fever screening. CONCLUSION: Our findings provide clinical confirmation of the utility of consensus approaches for fever screening, including the use of inner canthi temperatures, while also indicating that full-face maximum temperatures may provide an effective alternate approach.

Rainfall as a driver of epidemic cholera: Comparative model assessments of the effect of intra-seasonal precipitation events.

The correlation between cholera epidemics and climatic drivers, in particular seasonal tropical rainfall, has been studied in a variety of contexts owing to its documented relevance. Several mechanistic models of cholera transmission have included rainfall as a driver by focusing on two possible transmission pathways: either by increasing exposure to contaminated water (e.g. due to worsening sanitary conditions during water excess), or water contamination by freshly excreted bacteria (e.g. due to washout of open-air defecation sites or overflows). Our study assesses the explanatory power of these different modeling structures by formal model comparison using deterministic and stochastic models of the type susceptible-infected-recovered-bacteria (SIRB). The incorporation of rainfall effects is generalized using a nonlinear function that can increase or decrease the relative importance of the large precipitation events. Our modelling framework is tested against the daily epidemiological data collected during the 2015 cholera outbreak within the urban context of Juba, South Sudan. This epidemic is characterized by a particular intra-seasonal double peak on the incidence in apparent relation with particularly strong rainfall events. Our results show that rainfall-based models in both their deterministic and stochastic formulations outperform models that do not account for rainfall. In fact, classical SIRB models are not able to reproduce the second epidemiological peak, thus suggesting that it was rainfall-driven. Moreover we found stronger support across model types for rainfall acting on increased exposure rather than on exacerbated water contamination. Although these results are context-specific, they stress the importance of a systematic and comprehensive appraisal of transmission pathways and their environmental forcings when embarking in the modelling of epidemic cholera.

Salt Marsh Claviceps purpurea in Native and Invaded Spartina Marshes in Northern California.

The fungal pathogen Claviceps purpurea (subgroup G3) has a worldwide distribution on salt marsh Spartina spp. In Northern California (United States), native Spartina foliosa sustains high rates of infection by G3 C. purpurea in marshes north of the San Francisco Estuary. Invasive populations of S. alterniflora and S. alterniflora × foliosa hybrids are virtually disease free in the same estuary, although S. alterniflora is host to G3 C. purpurea in its native range (Atlantic Coast of the United States). Greenhouse inoculation experiments showed no differences in susceptibility among S. foliosa, S. alterniflora, and Spartina hybrids. Under field conditions, S. foliosa sustained a higher incidence of disease in coastal marshes than in marshes within the bay. This geographic effect may be attributable to environmental differences between the coast and the bay proper, with the former being more conducive to infection by C. purpurea. Seed set of S. foliosa spikelets was 40 to 70% lower on infected than on uninfected inflorescences, but seed germination was not affected. The C. purpurea epidemic on S. foliosa on the coast north of the San Francisco Estuary further reduces the meager competitive ability of this declining native plant species.

Wheat Rust Surveillance: Field Disease Scoring and Sample Collection for Phenotyping and Molecular Genotyping.

Long-distance migration capacity, emergence of invasive lineages, and variability in adaptation to a wide range of climatic conditions make wheat rusts the most important threat to wheat production worldwide. Efficient and coordinated efforts are required for surveillance of the pathogen population at different geographical levels to enable tracking of rust pathogen populations at local, regional, continental, and ultimately worldwide scale. Here we describe a standard procedure for rust surveillance to enable comparison across various research groups for a final compilation. The procedure described would enable tracking of disease severity, field level expression of host resistance, and collection of samples for further virulence phenotyping and molecular genotyping.

A non-persistently transmitted-virus induces a pull-push strategy in its aphid vector to optimize transmission and spread.

Plant viruses are known to modify the behaviour of their insect vectors, both directly and indirectly, generally adapting to each type of virus-vector relationship in a way that enhances transmission efficiency. Here, we report results of three different studies showing how a virus transmitted in a non-persistent (NP) manner (Cucumber mosaic virus; CMV, Cucumovirus) can induce changes in its host plant, cucumber (Cucumis sativus cv. Marumba) that modifies the behaviour of its aphid vector (Aphis gossypii Glover; Hemiptera: Aphididae) in a way that enhances virus transmission and spread non-viruliferous aphids changed their alighting, settling and probing behaviour activities over time when exposed to CMV-infected and mock-inoculated cucumber plants. Aphids exhibited no preference to migrate from CMV-infected to mock-inoculated plants at short time intervals (1, 10 and 30 min after release), but showed a clear shift in preference to migrate from CMV-infected to mock-inoculated plants 60 min after release. Our free-choice preference assays showed that A. gossypii alates preferred CMV-infected over mock-inoculated plants at an early stage (30 min), but this behaviour was reverted at a later stage and aphids preferred to settle and reproduce on mock-inoculated plants. The electrical penetration graph (EPG) technique revealed a sharp change in aphid probing behaviour over time when exposed to CMV-infected plants. At the beginning (first 15 min) aphid vectors dramatically increased the number of short superficial probes and intracellular punctures when exposed to CMV-infected plants. At a later stage (second hour of recording) aphids diminished their feeding on CMV-infected plants as indicated by much less time spent in phloem salivation and ingestion (E1 and E2). This particular probing behaviour including an early increase in the number of short superficial probes and intracellular punctures followed by a phloem feeding deterrence is known to enhance the transmission efficiency of viruses transmitted in a NP manner. We conclude that CMV induces specific changes in a plant host that modify the alighting, settling and probing behaviour of its main vector A. gossypii, leading to optimum transmission and spread of the virus. Our findings should be considered when modelling the spread of viruses transmitted in a NP manner.

Degree of host susceptibility in the initial disease outbreak influences subsequent epidemic spread.

Disease epidemics typically begin as an outbreak of a relatively small, spatially explicit population of infected individuals (focus), in which disease prevalence increases and rapidly spreads into the uninfected, at-risk population. Studies of epidemic spread typically address factors influencing disease spread through the at-risk population, but the initial outbreak may strongly influence spread of the subsequent epidemic.We initiated wheat stripe rust Puccinia striiformis f. sp. tritici epidemics to assess the influence of the focus on final disease prevalence when the degree of disease susceptibility differed between the at-risk and focus populations.When the focus/at-risk plantings consisted of partially genetic resistant and susceptible cultivars, final disease prevalence was statistically indistinguishable from epidemics produced by the focus cultivar in monoculture. In these experimental epidemics, disease prevalence was not influenced by the transition into an at-risk population that differed in disease susceptibility. Instead, the focus appeared to exert a dominant influence on the subsequent epidemic.Final disease prevalence was not consistently attributable to either the focus or the at-risk population when focus/at-risk populations were planted in a factorial set-up with a mixture (~28% susceptible and 72% resistant) and susceptible individuals. In these experimental epidemics, spatial heterogeneity in disease susceptibility within the at-risk population appeared to counter the dominant influence of the focus.Cessation of spore production from the focus (through fungicide/glyphosate application) after 1.3 generations of stripe rust spread did not reduce final disease prevalence, indicating that the focus influence on disease spread is established early in the epidemic.Synthesis and applications. Our experiments indicated that outbreak conditions can be highly influential on epidemic spread, even when disease resistance in the at-risk population is greater than that of the focus. Disease control treatments administered shortly after the initial outbreak within the focus may either prevent an epidemic from occurring or reduce its severity.