Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks.

We assess how presymptomatic infection affects predictability of infectious disease epidemics. We focus on whether or not a major outbreak (i.e. an epidemic that will go on to infect a large number of individuals) can be predicted reliably soon after initial cases of disease have appeared within a population. For emerging epidemics, significant time and effort is spent recording symptomatic cases. Scientific attention has often focused on improving statistical methodologies to estimate disease transmission parameters from these data. Here we show that, even if symptomatic cases are recorded perfectly, and disease spread parameters are estimated exactly, it is impossible to estimate the probability of a major outbreak without ambiguity. Our results therefore provide an upper bound on the accuracy of forecasts of major outbreaks that are constructed using data on symptomatic cases alone. Accurate prediction of whether or not an epidemic will occur requires records of symptomatic individuals to be supplemented with data concerning the true infection status of apparently uninfected individuals. To forecast likely future behavior in the earliest stages of an emerging outbreak, it is therefore vital to develop and deploy accurate diagnostic tests that can determine whether asymptomatic individuals are actually uninfected, or instead are infected but just do not yet show detectable symptoms.This is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pcbi.100483

When Does Spatial Diversification Usefully Maximize the Durability of Crop Disease Resistance?

Maximizing the durability of crop disease resistance genes in the face of pathogen evolution is a major challenge in modern agricultural epidemiology. Spatial diversification in the deployment of resistance genes, where susceptible and resistant fields are more closely intermixed, is predicted to drive lower epidemic intensities over evolutionary timescales. This is due to an increase in the strength of dilution effects, caused by pathogen inoculum challenging host tissue to which it is not well-specialized. The factors that interact with and determine the magnitude of this spatial suppressive effect are not currently well understood, however, leading to uncertainty over the pathosystems where such a strategy is most likely to be cost-effective. We model the effect on landscape scale disease dynamics of spatial heterogeneity in the arrangement of fields planted with either susceptible or resistant cultivars, and the way in which this effect depends on the parameters governing the pathosystem of interest. Our multiseason semidiscrete epidemiological model tracks spatial spread of wild-type and resistance-breaking pathogen strains, and incorporates a localized reservoir of inoculum, as well as the effects of within and between field transmission. The pathogen dispersal characteristics, any fitness cost(s) of the resistance-breaking trait, the efficacy of host resistance, and the length of the timeframe of interest all influence the strength of the spatial diversification effect. A key result is that spatial diversification has the strongest beneficial effect at intermediate fitness costs of the resistance-breaking trait, an effect driven by a complex set of nonlinear interactions. On the other hand, however, if the resistance-breaking strain is not fit enough to invade the landscape, then a partially effective resistance gene can result in spatial diversification actually worsening the epidemic. These results allow us to make general predictions of the types of system for which spatial diversification is most likely to be cost-effective, paving the way for potential economic modeling and pathosystem specific evaluation. These results highlight the importance of studying the effect of genetics on landscape scale spatial dynamics within host-pathogen disease systems.[Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license

Follow-up of X-ray transients detected by SWIFT with COLORES using the BOOTES network

The Burst Observer and Optical Transient Exploring System (BOOTES) is a network of telescopes that allows the continuous monitoring of transient astrophysical sources. It was originally devoted to the study of the optical emission from gamma-ray bursts (GRBs) that occur in the Universe. In this paper we show the initial results obtained using the spectrograph COLORES (mounted on BOOTES-2), when observing compact objects of diverse nature.Comment: 6 pages, 2 figues, to appear in "Swift: 10 years of discovery", Proceedings of Scienc

INITIAL FOLLOW-UP OF OPTICAL TRANSIENTS WITH COLORES USING THE BOOTES NETWORK

The Burst Observer and Optical Transient Exploring System (BOOTES) is a network of telescopes that allows the continuous monitoring of transient astrophysical sources. It was originally devoted to the study of the optical emissions from gamma-raybursts (GRBs) that occur in the Universe. In this paper we show the initial results obtained using the spectrograph COLORES (mounted on BOOTES-2), when observing optical transients (OTs) of a diverse nature

Control fast or control smart: When should invading pathogens be controlled?

The intuitive response to an invading pathogen is to start disease management as rapidly as possible, since this would be expected to minimise the future impacts of disease. However, since more spread data become available as an outbreak unfolds, processes underpinning pathogen transmission can almost always be characterised more precisely later in epidemics. This allows the future progression of any outbreak to be forecast more accurately, and so enables control interventions to be targeted more precisely. There is also the chance that the outbreak might die out without any intervention whatsoever, making prophylactic control unnecessary. Optimal decision-making involves continuously balancing these potential benefits of waiting against the possible costs of further spread. We introduce a generic, extensible data-driven algorithm based on parameter estimation and outbreak simulation for making decisions in real-time concerning when and how to control an invading pathogen. The Control Smart Algorithm (CSA) resolves the trade-off between the competing advantages of controlling as soon as possible and controlling later when more information has become available. We show-using a generic mathematical model representing the transmission of a pathogen of agricultural animals or plants through a population of farms or fields-how the CSA allows the timing and level of deployment of vaccination or chemical control to be optimised. In particular, the algorithm outperforms simpler strategies such as intervening when the outbreak size reaches a pre-specified threshold, or controlling when the outbreak has persisted for a threshold length of time. This remains the case even if the simpler methods are fully optimised in advance. Our work highlights the potential benefits of giving careful consideration to the question of when to start disease management during emerging outbreaks, and provides a concrete framework to allow policy-makers to make this decision

Cucumber mosaic virus and its 2b protein alter emission of host volatile organic compounds but not aphid vector settling in tobacco

Aphids, including the generalist herbivore Myzus persicae, transmit cucumber mosaic virus (CMV). CMV (strain Fny) infection affects M. persicae feeding behavior and performance on tobacco (Nicotiana tabacum), Arabidopsis thaliana and cucurbits in varying ways. In Arabidopsis and cucurbits, CMV decreases host quality and inhibits prolonged feeding by aphids, which may enhance virus transmission rates. CMV-infected cucurbits also emit deceptive, aphid-attracting volatiles, which may favor virus acquisition. In contrast, aphids on CMV-infected tobacco (cv. Xanthi) exhibit increased survival and reproduction. This may not increase transmission but might increase virus and vector persistence within plant communities. The CMV 2b counter-defense protein diminishes resistance to aphid infestation in CMV-infected tobacco plants. We hypothesised that in tobacco CMV and its 2b protein might also alter the emission of volatile organic compounds that would influence aphid behavior

Modelling Vector Transmission and Epidemiology of Co-Infecting Plant Viruses.

Co-infection of plant hosts by two or more viruses is common in agricultural crops and natural plant communities. A variety of models have been used to investigate the dynamics of co-infection which track only the disease status of infected and co-infected plants, and which do not explicitly track the density of inoculative vectors. Much less attention has been paid to the role of vector transmission in co-infection, that is, acquisition and inoculation and their synergistic and antagonistic interactions. In this investigation, a general epidemiological model is formulated for one vector species and one plant species with potential co-infection in the host plant by two viruses. The basic reproduction number provides conditions for successful invasion of a single virus. We derive a new invasion threshold which provides conditions for successful invasion of a second virus. These two thresholds highlight some key epidemiological parameters important in vector transmission. To illustrate the flexibility of our model, we examine numerically two special cases of viral invasion. In the first case, one virus species depends on an autonomous virus for its successful transmission and in the second case, both viruses are unable to invade alone but can co-infect the host plant when prevalence is high

Spatiotemporal dynamics and modelling support the case for area-wide management of citrus greasy spot in a Brazilian smallholder farming region

Citrus greasy spot (CGS), caused by Zasmidium citri, induces premature defoliation and yield loss in Citrus spp. The epidemiology of CGS is well understood in high humidity areas, but remains unaddressed in Brazil, despite differing climatic conditions and disease management practices. The spatiotemporal dynamics of CGS was characterized in the Recôncavo of Bahia (Brazil) at four hierarchical levels (quadrant, plant, grove and region). A survey conducted in 19 municipalities found the disease to be present throughout the region with an incidence of 100% in groves and plants, and higher than 70% on leaves. Index of dispersion (D) values suggest the spatial pattern of symptomatic units lies between random and regular. This was confirmed by the parameters of the binary power law for plants and their quadrants (log(A)<0 and b<1). No consistent differences were observed in the disease incidence at different plant heights. We introduce a compartmental model synthesizing CGS epidemiology. The collected data allow such a model to be parameterised, albeit with some ambiguity over the proportion of new infections that result from inoculum produced within the grove vs. external sources of infection. By extending the model to include two populations of growers – those who control and those who do not – coupled by the airborne inoculum, we investigate likely performance of cultural controls accessible to citrus growers in Northeastern Brazil. The results show that control via removal of fallen leaves can be very effective. However, successful control is likely to require area-wide strategies, in which a large proportion of growers actively manage disease