Predicting outbreaks of a climate-driven coral disease in the Great Barrier Reef

Links between anomalously high sea temperatures and outbreaks of coral diseases known as White Syndromes (WS) represent a threat to Indo-Pacific reefs that is expected to escalate in a changing climate. Further advances in understanding disease aetiologies, determining the relative importance of potential risk factors for outbreaks and in trialing management actions are hampered by not knowing where or when outbreaks will occur. Here, we develop a tool to target research and monitoring of WS outbreaks in the Great Barrier Reef (GBR). The tool is based on an empirical regression model and takes the form of user-friendly interactive ~1.5-km resolution maps. The maps denote locations where long-term monitoring suggests that coral cover exceeds 26% and summer temperature stress (measured by a temperature metric termed the mean positive summer anomaly) is equal to or exceeds that experienced at sites in 2002 where the only severe WS outbreaks documented on the GBR to date were observed. No WS outbreaks were subsequently documented at 45 routinely surveyed sites from 2003 to 2008, and model hindcasts for this period indicate that outbreak likelihood was never high. In 2009, the model indicated that outbreak likelihood was high at north-central GBR sites. The results of the regression model and targeted surveys in 2009 revealed that the threshold host density for an outbreak decreases as thermal stress increases, suggesting that bleaching could be a more important precursor to WS outbreaks than previously anticipated, given that bleaching was severe at outbreak sites in 2002 but not at any of the surveyed sites in 2009. The iterative approach used here has led to an improved understanding of disease causation, will facilitate management responses and can be applied to other coral diseases and/or other regions.     

Type A and type B botulism in the North: first reported cases due to toxin other than type E in Alaskan Inuit.

Botulism outbreaks shown to be due to type A and type B toxin occurred in Alaska, a region previously known for only type E botulism. The outbreak due to type A toxin involved three people, two of whom died. The outbreak due to type B toxin involved nine people, none of whom died. Both outbreaks were in Inuit villages, and native foods were incriminated. The occurrence of these outbreaks strongly suggests that Clostridium botulinum, types A and B are indigenous to Alaska. The outbreaks underscore the need for initial treatment of patients with antitoxin that is trivalent (ABE), even in Arctic regions.     

The impact of climate suitability,urbanisation, and connectivity on the expansion of dengue in 21st century Brazil

Dengue is hyperendemic in Brazil, with outbreaks affecting all regions. Previous studies identified geographical barriers to dengue transmission in Brazil, beyond which certain areas, such as South Brazil and the Amazon rainforest, were relatively protected from outbreaks. Recent data shows these barriers are being eroded. In this study, we explore the drivers of this expansion and identify the current limits to the dengue transmission zone. We used a spatio-temporal additive model to explore the associations between dengue outbreaks and temperature suitability, urbanisation, and connectivity to the Brazilian urban network. The model was applied to a binary outbreak indicator, assuming the official threshold value of 300 cases per 100,000 residents, for Brazil’s municipalities between 2001 and 2020. We found a nonlinear relationship between higher levels of connectivity to the Brazilian urban network and the odds of an outbreak, with lower odds in metropoles compared to regional capitals. The number of months per year with suitable temperature conditions for Aedes mosquitoes was positively associated with the dengue outbreak occurrence. Temperature suitability explained most interannual and spatial variation in South Brazil, confirming this geographical barrier is influenced by lower seasonal temperatures. Municipalities that had experienced an outbreak previously had double the odds of subsequent outbreaks. We identified geographical barriers to dengue transmission in South Brazil, western Amazon, and along the northern coast of Brazil. Although a southern barrier still exists, it has shifted south, and the Amazon no longer has a clear boundary. Few areas of Brazil remain protected from dengue outbreaks. Communities living on the edge of previous barriers are particularly susceptible to future outbreaks as they lack immunity. Control strategies should target regions at risk of future outbreaks as well as those currently within the dengue transmission zone.     

A hazards approach towards modelling pandora moth risk

Aim Pandora moth (Coloradia pandora Blake) is a phytophagous insect that produces a distinctive tree‐ring pattern in ponderosa pine (Pinus ponderosa Dougl. ex. Laws.) during outbreak cycles. This paper describes the spatial characteristics of the outbreak regions, determines whether the size of the 1989 outbreak was within the historical range of variability, and constructs a hazard map identifying the forests in Oregon that are susceptible to future pandora moth outbreaks. Location South‐central Oregon along the eastern flank of the Cascade mountain range in the High Lava Plains and Basin and Range Provinces. Methods We used dendrochronological records of 17 pandora moth outbreaks on 14 sites over 31,200 km² area spanning 433 years. Using the site locations, we calculated minimum bounding polygons of adjacent recording sites to determine the relative size of each outbreak. Published literature on past pandora moth outbreaks and the environmental conditions of locally known outbreaks were used to create an outbreak hazard map using a geographical information system (GIS) model. Vegetation, climate, and soil layers were used to determine the potential susceptibility of Oregon forests to pandora moth. Results We found the area affected by past pandora moth outbreaks ranged in size from 12.4 to 3,391.5 km². The 1989 outbreak covered 807.9 km², which was well within the historical range of variability. The vegetation and soil layers greatly restricted the area susceptible to pandora moth while the climate layer seemed to have little effect in restricting the susceptible area. Main conclusions Pandora moth outbreaks did not increase in size over the last century as we have seen with spruce budworm outbreaks in this same region. Analysis of the environmental variables that are known to affect pandora moth outbreaks enabled us to produce a hazard map that predicts the suitable habitat for pandora moth. Temperature at the landscape scale did not restrain the range of pandora moth. The GIS model enabled us to propose areas susceptible to future pandora moth outbreaks providing a predictive model that can now be tested and refined with further sampling.     

Phase transition from environmental to dynamic determinism in mountain pine beetle attack

Mountain pine beetle outbreaks are responsible for widespread tree mortality in pine forests throughout western North America. Intensive outbreaks result in significant economic loss to the timber industry and massive changes to the forest habitat. Because of the time and space scales involved in a beetle outbreak, mathematical models are needed to study the evolution of an outbreak. In this paper we present a partial differential equation model of the flight phase of the mountain pine beetle which includes chemotactic responses and tree defense. We present a numerical method for integrating this model and use this method to investigate the relationship between emergence rate, forest demographic and patterns of beetle attack. In particular we look at how emergence rate affects the beetles' ability to successfully attack strong trees, which may be an indicator of an epidemic outbreak.     

Advances in diagnostics,vaccines and therapeutics for Nipah virus

Nipah virus is an emerging zoonotic paramyxovirus that causes severe and often fatal respiratory and neurological disease in humans. The virus was first discovered after an outbreak of encephalitis in pig farmers in Malaysia and Singapore with subsequent outbreaks in Bangladesh or India occurring almost annually. Due to the highly pathogenic nature of NiV, its pandemic potential, and the lack of licensed vaccines or therapeutics, there is a requirement for research and development into highly sensitive and specific diagnostic tools as well as antivirals and vaccines to help prevent and control future outbreak situations.     

A Modeling Approach to Investigate Epizootic Outbreaks and Enzootic Maintenance of Rift Valley Fever Virus

We propose a mathematical model to investigate the transmission dynamics of Rift Valley fever (RVF) virus among ruminants. Our findings indicate that in endemic areas RVF virus maintains at a very low level among ruminants after outbreaks and subsequent outbreaks may occur when new susceptible ruminants are recruited into endemic areas or abundant numbers of mosquitoes emerge when herd immunity decreases. Many factors have been shown to have impacts on the severity of RVF outbreaks; a higher probability of death due to RVF among ruminants, a higher mosquito:ruminant ratio, or a shorter lifespan of animals can amplify the magnitude of the outbreaks; vaccination helps to reduce the magnitude of RVF outbreaks and the loss of animals efficiently, and the maximum vaccination effort (a high vaccination rate and a larger number of vaccinated animals) is recommended before the commencement of an outbreak but can be reduced later during the enzootic.     

Persistence of plague outbreaks among great gerbils in Kazakhstan: effects of host population dynamics

Outbreaks of plague (Yersinia pestis) among great gerbils (Rhombomys opimus) generally require a high host abundance to be initiated. The duration of an outbreak is expected to depend on the subsequent development of this abundance; however, prediction is nontrivial due to the complexity of the gerbil–plague system. The aim of this study was to investigate how the duration of outbreaks depends on different types of host population dynamics generated from: a cyclic model; an autoregressive model giving irregular fluctuations; and a simple model with uncorrelated fluctuations. For each model, outbreak duration was studied under various levels of mean and variability of host abundance. Its focus on the effect of different gerbil dynamics sets this study apart from the few published studies on diseases in dynamic host populations. Plague outbreaks were simulated in a cellular automaton model based on statistical analysis of archived records of plague and host abundance. Temporal autocorrelation was found to make outbreak duration less sensitive to changes in mean abundance than uncorrelated fluctuations. Cyclicity had little effect on the mean duration of outbreaks, but resulted in a multimodal distribution. For all three types of gerbil dynamics, increased variability in gerbil abundance reduced the duration of outbreaks when the mean abundance was high (paralleling results on the risk of species extinction in fluctuating environments), but increased their duration when the mean abundance was lower. Spatial heterogeneity was briefly tested and produced longer outbreaks than the homogenous case. The results are relevant to predicting plague activity in populations of great gerbils.     

Spatial pattern switching enables cyclic evolution in spatial epidemics

Infectious diseases often spread as spatial epidemic outbreak waves. A number of model studies have shown that such spatial pattern formation can have important consequences for the evolution of pathogens. Here, we show that such spatial patterns can cause cyclic evolutionary dynamics in selection for the length of the infectious period. The necessary reversal in the direction of selection is enabled by a qualitative change in the spatial pattern from epidemic waves to irregular local outbreaks. The spatial patterns are an emergent property of the epidemic system, and they are robust against changes in specific model assumptions. Our results indicate that emergent spatial patterns can act as a rich source for complexity in pathogen evolution.     

Can hyperparasitoids cause large‐scale outbreaks of insect herbivores?

Synchronous population fluctuations occur in many species and have large economic impacts, but remain poorly understood. Dispersal, climate and natural enemies have been hypothesized to cause synchronous population fluctuations across large areas. For example, insect herbivores cause extensive forest defoliation and have many natural enemies, such as parasitoids, that may cause landscape‐scale changes in density. Between outbreaks, parasitoid‐caused mortality of hosts/herbivores is high, but it drops substantially during outbreak episodes. Because of their essential role in regulating herbivore populations, we need to include parasitoids in spatial modelling approaches to more effectively manage insect defoliation. However, classic host‐parasitoid population models predict parasitoid density, and parasitoid density is difficult to relate to host‐level observations of parasitoid‐caused mortality. We constructed a novel model to study how parasitoids affect insect outbreaks at the landscape scale. The model represents metacommunity dynamics, in which herbivore regulation, colonisation and extinction are driven by interactions with the forest, primary parasitoids and hyperparasitoids. The model suggests that parasitoid spatial dynamics can produce landscape‐scale outbreaks. Our results propose the testable prediction that hyperparasitoid prevalence should increase just before the onset of an outbreak because of hyperparasitoid overexploitation. If verified empirically, hyperparasitoid distribution could provide a biotic indicator that an outbreak will occur.     

An IDEA for Short Term Outbreak Projection: Nearcasting Using the Basic Reproduction Number

Background

Communicable disease outbreaks of novel or existing pathogens threaten human health around the globe. It would be desirable to rapidly characterize such outbreaks and develop accurate projections of their duration and cumulative size even when limited preliminary data are available. Here we develop a mathematical model to aid public health authorities in tracking the expansion and contraction of outbreaks with explicit representation of factors (other than population immunity) that may slow epidemic growth.

Methodology

The Incidence Decay and Exponential Adjustment (IDEA) model is a parsimonious function that uses the basic reproduction number R₀, along with a discounting factor to project the growth of outbreaks using only basic epidemiological information (e.g., daily incidence counts).

Principal Findings

Compared to simulated data, IDEA provides highly accurate estimates of total size and duration for a given outbreak when R₀ is low or moderate, and also identifies turning points or new waves. When tested with an outbreak of pandemic influenza A (H1N1), the model generates estimated incidence at the i+1^th serial interval using data from the i^th serial interval within an average of 20% of actual incidence.

Conclusions and Significance

This model for communicable disease outbreaks provides rapid assessments of outbreak growth and public health interventions. Further evaluation in the context of real-world outbreaks will establish the utility of IDEA as a tool for front-line epidemiologists.     

What is the best control strategy for multiple infectious disease outbreaks?

Effective control of infectious disease outbreaks is an important public health goal. In a number of recent studies, it has been shown how different intervention measures like travel restrictions, school closures, treatment and prophylaxis might allow us to control outbreaks of diseases, such as SARS, pandemic influenza and others. In these studies, control of a single outbreak is considered. It is, however, not clear how one should handle a situation where multiple outbreaks are likely to occur. Here, we identify the best control strategy for such a situation. We further discuss ways in which such a strategy can be implemented to achieve additional public health objectives.     

我国甜菜夜蛾间歇性暴发频度的大尺度地理差异及其成因分析

【目的】为探索我国甜菜夜蛾Spodoptera exigua(Hübner)间歇性大暴发频度的大尺度地理差异及其成因,作者研究和分析了我国1956—2008年52年间共发生的121次甜菜夜蛾大暴发频次地理分布的行政区域特征、纬度特征和经度特征。【方法】首先查阅和整理我国历代以来有关甜菜夜蛾记载和研究的文献,并按时间序列方式进行归纳和总结,得出中国1956—2008年52年间共发生甜菜夜蛾大暴发频次为121年次,然后分别按中国省级行政区域、纬度分级区域和经度分级区域进行重新整理分析,在此基础上,进一步细分出中国不同省区及不同经纬度地区甜菜夜蛾有暴发记录的历年总频次数,然后进行统计分析。【结果】分析结果表明:按省级行政区域进行统计,中国有17省(直辖市)有甜菜夜蛾暴发的记录;按暴发频次多少可分为低频暴发区(记录1~4次的共有8省市,即天津1、北京1、海南1、湖南1、云南2、广西3、辽宁3、福建3)、中频暴发区(记录5~9次的共有5省市,即湖北5、安徽7、上海8、江西8、陕西8,占总频次的29.8%)和高频暴发区(记录10次以上的4省市,即山东10、河南13、浙江13、江苏34,占总频次的58%)。按经纬度值进行统计,中国甜菜夜蛾有暴发记录的纬度范围为21.44°~41.97°N,即从中国南部的福建莆田至北部的辽宁抚顺都有甜菜夜蛾暴发的记录,经度范围为107.09°~123.97°E,即从中国西部的陕西关中至东部的辽宁抚顺都有甜菜夜蛾暴发的记录。而暴发最频繁的纬度范围是28°~38°N,约占总频次的89.3%;暴发最频繁的经度范围是113°~121°E,约占总频次的81.8%。【结论】中国甜菜夜蛾间歇性暴发区以华北平原和长江中下游平原地区为主,暴发寄主以蔬菜和棉花为主,暴发季节以7—10月为主。甜菜夜蛾间歇性高频暴发区,其地理特征主要为低海拔地区,即平均海拔低于500 m(也有极少部分发生在平均海拔为1 000~2 000 m之间的盆地);其气候特征主要为暖温带3 400℃≤活动积温≤4 500℃及亚热带地区4 500℃≤活动积温≤8 000℃、湿润(年降水量≥800 mm)及半湿润地区(400≤年降水量≤800 mm)和亚热带及温带季风气候区(该两个气候区的共同特点是夏季高温多雨,冬季低温少雨或寒冷干燥)。     

Dynamics of a Tularemia Outbreak in a Closely Monitored Free-Roaming Population of Wild House Mice

Infectious disease outbreaks can be devastating because of their sudden occurrence, as well as the complexity of monitoring and controlling them. Outbreaks in wildlife are even more challenging to observe and describe, especially when small animals or secretive species are involved. Modeling such infectious disease events is relevant to investigating their dynamics and is critical for decision makers to accomplish outbreak management. Tularemia, caused by the bacterium Francisella tularensis, is a potentially lethal zoonosis. Of the few animal outbreaks that have been reported in the literature, only those affecting zoo animals have been closely monitored. Here, we report the first estimation of the basic reproduction number R ₀ of an outbreak in wildlife caused by F. tularensis using quantitative modeling based on a susceptible-infected-recovered framework. We applied that model to data collected during an extensive investigation of an outbreak of tularemia caused by F. tularensis subsp. holarctica (also designated as type B) in a closely monitored, free-roaming house mouse (Mus musculus domesticus) population in Switzerland. Based on our model and assumptions, the best estimated basic reproduction number R ₀ of the current outbreak is 1.33. Our results suggest that tularemia can cause severe outbreaks in small rodents. We also concluded that the outbreak self-exhausted in approximately three months without administrating antibiotics.     

Combinatorial decomposition of an outbreak signature

We use mathematically rigorous definitions of epidemiological concepts in order to derive a sequential combinatorial model of disease outbreak decomposition. We define the idea of a population specific 'disease signature' and use this in order to decompose and further understand outbreaks as incidents of spatial and temporal spread of disease exposure both in, and across, populations. This allows us to differentiate between different disease spread scenarios with a level of sensitivity that previous models were unable to provide. This perspective leads us to propose a new practical definition for 'outbreak'. In addition, we are able to use this model to understand, estimate, and, in some cases, correct for, the likely instances of reporting error inherent in disease surveillance. We demonstrate our model first with a hypothetical outbreak scenario and then in an analysis of suspected outbreaks of waterborne diseases in Massachusetts (MA) in 1995.     

Recurrent outbreaks of root mat in cucumber and tomato are associated with a monomorphic, cucumopine, Ri-plasmid harboured by various Alphaproteobacteria

Recurrent outbreaks of root mat have occurred in the UK and France in cucumber and tomato. Root mat is caused by bacterial strains harbouring a Ri-plasmid (pRi). Fifteen root mat-associated (RMA) cucumopine pRi were analysed by PCR-restriction fragment length polymorphism (RFLP) and Southern blotting. These pRi were harboured by Agrobacterium biovar 1 strains isolated during a 1970s outbreak of root mat in UK soil grown cucumber, and also by Agrobacterium biovar 1, Ochrobactrum, Rhizobium and Sinorhizobium isolated during outbreaks of root mat in cucumber and tomato grown hydroponically in the UK and France since 1993. PCR-RFLP analysis of the T-DNA and virD2 regions showed sequence homology between all cucumopine pRi, indicating that these pRi are monomorphic, and thus this pRi remained in the UK without inducing symptoms for some 15 years between outbreaks in the 1970s and 1990s. Cucumopine pRi were also shown to possess the virE2 substitute GALLS gene by Southern blotting. Two other pRi, harboured by Agrobacterium isolated from a recent root mat outbreak in one tomato crop, were also shown to possess the GALLS gene but were shown not to be cucumopine pRi by PCR-RFLP.     

The impact of spatial arrangements on epidemic disease dynamics and intervention strategies

The role of spatial arrangements on the spread and management strategies of a cholera epidemic is investigated. We consider the effect of human and pathogen movement on optimal vaccination strategies. A metapopulation model is used, incorporating a susceptible–infected–recovered system of differential equations coupled with an equation modelling the concentration of Vibrio cholerae in an aquatic reservoir. The model compared spatial arrangements and varying scenarios to draw conclusions on how to effectively manage outbreaks. The work is motivated by the 2010 cholera outbreak in Haiti. Results give guidance for vaccination strategies in response to an outbreak.     

There's no place like home: crown-of-thorns outbreaks in the central pacific are regionally derived and independent events

One of the most significant biological disturbances on a tropical coral reef is a population outbreak of the fecund, corallivorous crown-of-thorns sea star, Acanthaster planci. Although the factors that trigger an initial outbreak may vary, successive outbreaks within and across regions are assumed to spread via the planktonic larvae released from a primary outbreak. This secondary outbreak hypothesis is predominantly based on the high dispersal potential of A. planci and the assertion that outbreak populations (a rogue subset of the larger population) are genetically more similar to each other than they are to low-density non-outbreak populations. Here we use molecular techniques to evaluate the spatial scale at which A. planci outbreaks can propagate via larval dispersal in the central Pacific Ocean by inferring the location and severity of gene flow restrictions from the analysis of mtDNA control region sequence (656 specimens, 17 non-outbreak and six outbreak locations, six archipelagos, and three regions). Substantial regional, archipelagic, and subarchipelagic-scale genetic structuring of A. planci populations indicate that larvae rarely realize their dispersal potential and outbreaks in the central Pacific do not spread across the expanses of open ocean. On a finer scale, genetic partitioning was detected within two of three islands with multiple sampling sites. The finest spatial structure was detected at Pearl & Hermes Atoll, between the lagoon and forereef habitats (<10 km). Despite using a genetic marker capable of revealing subtle partitioning, we found no evidence that outbreaks were a rogue genetic subset of a greater population. Overall, outbreaks that occur at similar times across population partitions are genetically independent and likely due to nutrient inputs and similar climatic and ecological conditions that conspire to fuel plankton blooms.     

An epidemic model with infector and exposure dependent severity

A stochastic epidemic model allowing for both mildly and severely infectious individuals is defined, where an individual can become severely infectious directly upon infection or if additionally exposed to infection. It is shown that, assuming a large community, the initial phase of the epidemic may be approximated by a suitable branching process and that the main part of an epidemic that becomes established admits a law of large numbers and a central limit theorem, leading to a normal approximation for the final outcome of such an epidemic. Effects of vaccination prior to an outbreak are studied and the critical vaccination coverage, above which only small outbreaks can occur, is derived. The results are illustrated by simulations that demonstrate that the branching process and normal approximations work well for finite communities, and by numerical examples showing that the final outcome may be close to discontinuous in certain model parameters and that the fraction mildly infected may actually increase as an effect of vaccination.     

Susceptible-infected-recovered epidemics in dynamic contact networks

Contact patterns in populations fundamentally influence the spread of infectious diseases. Current mathematical methods for epidemiological forecasting on networks largely assume that contacts between individuals are fixed, at least for the duration of an outbreak. In reality, contact patterns may be quite fluid, with individuals frequently making and breaking social or sexual relationships. Here, we develop a mathematical approach to predicting disease transmission on dynamic networks in which each individual has a characteristic behaviour (typical contact number), but the identities of their contacts change in time. We show that dynamic contact patterns shape epidemiological dynamics in ways that cannot be adequately captured in static network models or mass-action models. Our new model interpolates smoothly between static network models and mass-action models using a mixing parameter, thereby providing a bridge between disparate classes of epidemiological models. Using epidemiological and sexual contact data from an Atlanta high school, we demonstrate the application of this method for forecasting and controlling sexually transmitted disease outbreaks.