Modeling the Impact of White-Plague Coral Disease in Climate Change Scenarios

Coral reefs are in global decline, with coral diseases increasing both in prevalence and in space, a situation that is expected only to worsen as future thermal stressors increase. Through intense surveillance, we have collected a unique and highly resolved dataset from the coral reef of Eilat (Israel, Red Sea), that documents the spatiotemporal dynamics of a White Plague Disease (WPD) outbreak over the course of a full season. Based on modern statistical methodologies, we develop a novel spatial epidemiological model that uses a maximum-likelihood procedure to fit the data and assess the transmission pattern of WPD. We link the model to sea surface temperature (SST) and test the possible effect of increasing temperatures on disease dynamics. Our results reveal that the likelihood of a susceptible coral to become infected is governed both by SST and by its spatial location relative to nearby infected corals. The model shows that the magnitude of WPD epidemics strongly depends on demographic circumstances; under one extreme, when recruitment is free-space regulated and coral density remains relatively constant, even an increase of only 0.5°C in SST can cause epidemics to double in magnitude. In reality, however, the spatial nature of transmission can effectively protect the community, restricting the magnitude of annual epidemics. This is because the probability of susceptible corals to become infected is negatively associated with coral density. Based on our findings, we expect that infectious diseases having a significant spatial component, such as Red-Sea WPD, will never lead to a complete destruction of the coral community under increased thermal stress. However, this also implies that signs of recovery of local coral communities may be misleading; indicative more of spatial dynamics than true rehabilitation of these communities. In contrast to earlier generic models, our approach captures dynamics of WPD both in space and time, accounting for the highly seasonal nature of annual WPD outbreaks.     

Effect of Climate Change on Lyme Disease Risk in North America

An understanding of the influence of climate change on Ixodes scapularis, the main vector of Lyme disease in North America, is a fundamental component in assessing changes in the spatial distribution of human risk for the disease. We used a climate suitability model of I. scapularis to examine the potential effects of global climate change on future Lyme disease risk in North America. A climate-based logistic model was first used to explain the current distribution of I. scapularis in North America. Climate-change scenarios were then applied to extrapolate the model in time and to forecast vector establishment. The spatially modeled relationship between I. scapularis presence and large-scale environmental data generated the current pattern of I. scapularis across North America with an accuracy of 89% (P < 0.0001). Extrapolation of the model revealed a significant expansion of I. scapularis north into Canada with an increase in suitable habitat of 213% by the 2080s. Climate change will also result in a retraction of the vector from the southern U.S. and movement into the central U.S. This report predicts the effect of climate change on Lyme disease risk and specifically forecasts the emergence of a tickborne infectious disease in Canada. Our modeling approach could thus be used to outline where future control strategies and prevention efforts need to be applied.     

Aeroallergens, Allergic Disease, and Climate Change: Impacts and Adaptation

Recent research has shown that there are many effects of climate change on aeroallergens and thus allergic diseases in humans. Increased atmospheric carbon dioxide concentration acts as a fertilizer for plant growth. The fertilizing effects of carbon dioxide, as well as increased temperatures from climate change, increase pollen production and the allergen content of pollen grains. In addition, higher temperatures are changing the timing and duration of the pollen season. As regional climates change, plants can move into new areas and changes in atmospheric circulation can blow pollen- and spore-containing dust to new areas, thus introducing people to allergens to which they have not been exposed previously. Climate change also influences the concentrations of airborne pollutants, which alone, and in conjunction with aeroallergens, can exacerbate asthma or other respiratory illnesses. The few epidemiological analyses of meteorological factors, aeroallergens, and allergic diseases demonstrate the pathways through which climate can exert its influence on aeroallergens and allergic diseases. In addition to the need for more research, there is the imperative to take preventive and adaptive actions to address the onset and exacerbation of allergic diseases associated with climate variability and change.     

Asymmetry in the Presence of Migration Stabilizes Multistrain Disease Outbreaks

We study the effect of migration between coupled populations, or patches, on the stability properties of multistrain disease dynamics. The epidemic model used in this work displays a Hopf bifurcation to oscillations in a single, well-mixed population. It is shown numerically that migration between two non-identical patches stabilizes the endemic steady state, delaying the onset of large amplitude outbreaks and reducing the total number of infections. This result is motivated by analyzing generic Hopf bifurcations with different frequencies and with diffusive coupling between them. Stabilization of the steady state is again seen, indicating that our observation in the full multistrain model is based on qualitative characteristics of the dynamics rather than on details of the disease model.     

Sustainable Approaches to the Management of Plant-parasitic Nematodes and Disease Complexes

Physical, chemical, and biological factors of soil may reduce damage caused by plant-parasitic nematodes. Suppression of plant-parasitic nematodes is particularly challenging in soils in which there are short crop sequences, sequential susceptible host crops, or infestations of multiple nematode species. In southern Indiana, a watermelon production system involving rotations with soybean and corn does not suppress Meloidogyne incognita, but several aspects of such systems can be modified to reduce nematode damage in an integrated management approach. Cash crops with resistance to M. incognita can be used to reduce population densities of M. incognita. Small grains as cover crops can be replaced by cover crops with resistance to M. incognita or by crops with biofumigation potential. Mycorrhizal fungal inoculations of potting mixes during transplanting production of watermelon seedlings may improve early crop establishment. Other approaches to nematode management utilize soil suppressiveness. One-year rotations of soybean with corn neither reduced the soil-borne complex of sudden death syndrome (SDS) nor improved soybean root health over that in soybean monoculture. Reduced tillage combined with crop rotation may reduce the activity of soil-borne pathogens in some soils. For example in a long-term trial, numbers of Heterodera glycines and severity of foliar SDS symptoms were reduced under minimum tillage. Thus, sustainable management strategies require holistic approaches that consider entire production systems rather than focus on a single crop in its year of production.     

Relating Phylogenetic Trees to Transmission Trees of Infectious Disease Outbreaks

Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available. The genealogy estimated from genetic sequences of sampled pathogens is another rich source of information on transmission history. Optimal inference of transmission events calls for the combination of genetic data and epidemiological data into one joint analysis. A key difficulty is that the transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral relationships between pathogens sampled from these hosts. The trees differ both in timing of the internal nodes and in topology. These differences become more pronounced when a higher fraction of infected hosts is sampled. We show how the phylogenetic tree of sampled pathogens is related to the transmission tree of an outbreak of an infectious disease, by the within-host dynamics of pathogens. We provide a statistical framework to infer key epidemiological and mutational parameters by simultaneously estimating the phylogenetic tree and the transmission tree. We test the approach using simulations and illustrate its use on an outbreak of foot-and-mouth disease. The approach unifies existing methods in the emerging field of phylodynamics with transmission tree reconstruction methods that are used in infectious disease epidemiology.     

Molecular Epidemiology of Human Oral Chagas Disease Outbreaks in Colombia

Background

Trypanosoma cruzi, the causative agent of Chagas disease, displays significant genetic variability revealed by six Discrete Typing Units (TcI-TcVI). In this pathology, oral transmission represents an emerging epidemiological scenario where different outbreaks associated to food/beverages consumption have been reported in Argentina, Bolivia, Brazil, Ecuador and Venezuela. In Colombia, six human oral outbreaks have been reported corroborating the importance of this transmission route. Molecular epidemiology of oral outbreaks is barely known observing the incrimination of TcI, TcII, TcIV and TcV genotypes.

Methodology and Principal Findings

High-throughput molecular characterization was conducted performing MLMT (Multilocus Microsatellite Typing) and mtMLST (mitochondrial Multilocus Sequence Typing) strategies on 50 clones from ten isolates. Results allowed observing the occurrence of TcI, TcIV and mixed infection of distinct TcI genotypes. Thus, a majority of specific mitochondrial haplotypes and allelic multilocus genotypes associated to the sylvatic cycle of transmission were detected in the dataset with the foreseen presence of mitochondrial haplotypes and allelic multilocus genotypes associated to the domestic cycle of transmission.

Conclusions

These findings suggest the incrimination of sylvatic genotypes in the oral outbreaks occurred in Colombia. We observed patterns of super-infection and/or co-infection with a tailored association with the severe forms of myocarditis in the acute phase of the disease. The transmission dynamics of this infection route based on molecular epidemiology evidence was unraveled and the clinical and biological implications are discussed.     

Cost-Effective Control of Infectious Disease Outbreaks Accounting for Societal Reaction

Background

Studies of cost-effective disease prevention have typically focused on the tradeoff between the cost of disease transmission and the cost of applying control measures. We present a novel approach that also accounts for the cost of social disruptions resulting from the spread of disease. These disruptions, which we call social response, can include heightened anxiety, strain on healthcare infrastructure, economic losses, or violence.

Methodology

The spread of disease and social response are simulated under several different intervention strategies. The modeled social response depends upon the perceived risk of the disease, the extent of disease spread, and the media involvement. Using Monte Carlo simulation, we estimate the total number of infections and total social response for each strategy. We then identify the strategy that minimizes the expected total cost of the disease, which includes the cost of the disease itself, the cost of control measures, and the cost of social response.

Conclusions

The model-based simulations suggest that the least-cost disease control strategy depends upon the perceived risk of the disease, as well as media intervention. The most cost-effective solution for diseases with low perceived risk was to implement moderate control measures. For diseases with higher perceived severity, such as SARS or Ebola, the most cost-effective strategy shifted toward intervening earlier in the outbreak, with greater resources. When intervention elicited increased media involvement, it remained important to control high severity diseases quickly. For moderate severity diseases, however, it became most cost-effective to implement no intervention and allow the disease to run its course. Our simulation results imply that, when diseases are perceived as severe, the costs of social response have a significant influence on selecting the most cost-effective strategy.     

Global Climate Change and the Precautionary Principle

The precautionary principle is promoted as a common sense approach that avoids unreasonable delays in taking action. A weak form of the precautionary principle, that action should not wait until all uncertainties are resolved, is indeed common sense and consistent with even the most elementary application of the methods of decision making under uncertainty to the climate change problem. The standard tools of decision analysis imply conclusions consistent with a weak precau tionary principle of taking some action before all the evidence is in. Decision theory also reveals what the basis is for stronger recommendations from the precautionary principle, to the effect that action should be based on the most pessimistic possible interpretation of the future. This conclusion is only possible if prior beliefs are so pessimistic and so strong that they would outweigh any possible new scientific evidence.     

Sentinel Nematodes of Land-Use Change and Restoration in Tallgrass Prairie

Changes in land use and the associated changes in land cover are recognized as the most important component of human-induced global change. Much attention has been focused on deforestation, but grasslands are among the most endangered ecosystems on Earth. The North American tallgrass prairie is a dramatic example, exhibiting a greater than 95% decline in historical area. Renewed interest in prairie conservation and restoration has highlighted the need for ecological indicators of disturbance and recovery in native systems, including the belowground component. The tallgrass prairie differs from the agricultural systems that have replaced it in having greater diversity and heterogeneity of resources, less physical soil disturbance (although other disturbances, such as fire and grazing, are prominent), and greater nitrogen limitation. Understanding the responses of nematode taxa to these characteristic differences is crucial to the development and improvement of community indices, but while knowledge of disturbance responses by individual taxa is accumulating, the level of necessary taxonomic resolution remains in question. Although nematode communities generally are better described for temperate grasslands than for other natural ecosystems, identification of sentinel taxa is further confounded by high levels of diversity, and both spatial and temporal heterogeneity.     

Effects of Climate Change on Birds

2010, Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK

344 pages, hardcover or paperback, 75 black-and-white illustrations, and a four-page color plate section

ISBN 978-0-19-956974-8 (hardcover), 978-0-19-956975-5 (softcover). Price R575     

Implication of Lateral Genetic Transfer in the Emergence of Aeromonas hydrophila Isolates of Epidemic Outbreaks in Channel Catfish

To investigate the molecular basis of the emergence of Aeromonas hydrophila responsible for an epidemic outbreak of motile aeromonad septicemia of catfish in the Southeastern United States, we sequenced 11 A. hydrophila isolates that includes five reference and six recent epidemic isolates. Comparative genomics revealed that recent epidemic A. hydrophila isolates are highly clonal, whereas reference isolates are greatly diverse. We identified 55 epidemic-associated genetic regions with 313 predicted genes that are present in epidemic isolates but absent from reference isolates and 35% of these regions are located within genomic islands, suggesting their acquisition through lateral gene transfer. The epidemic-associated regions encode predicted prophage elements, pathogenicity islands, metabolic islands, fitness islands and genes of unknown functions, and 34 of the genes encoded in these regions were predicted as virulence factors. We found two pilus biogenesis gene clusters encoded within predicted pathogenicity islands. A functional metabolic island that encodes a complete pathway for myo-inositol catabolism was evident by the ability of epidemic A. hydrophila isolates to use myo-inositol as a sole carbon source. Testing of A. hydrophila field isolates found a consistent correlation between myo-inositol utilization as a sole carbon source and the presence of an epidemic-specific genetic marker. All epidemic isolates and one reference isolate shared a novel O-antigen cluster. Altogether we identified four different O-antigen biosynthesis gene clusters within the 11 sequenced A. hydrophila genomes. Our study reveals new insights into the evolutionary changes that have resulted in the emergence of recent epidemic A. hydrophila strains.     

Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data

Research is needed to create early warnings of dengue outbreaks to inform stakeholders and control the disease. This analysis composes of a comparative set of prediction models including only meteorological variables; only lag variables of disease surveillance; as well as combinations of meteorological and lag disease surveillance variables. Generalized linear regression models were used to fit relationships between the predictor variables and the dengue surveillance data as outcome variable on the basis of data from 2001 to 2010. Data from 2011 to 2013 were used for external validation purposed of prediction accuracy of the model. Model fit were evaluated based on prediction performance in terms of detecting epidemics, and for number of predicted cases according to RMSE and SRMSE, as well as AIC. An optimal combination of meteorology and autoregressive lag terms of dengue counts in the past were identified best in predicting dengue incidence and the occurrence of dengue epidemics. Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods. A combination of surveillance and meteorological data including lag patterns up to a few years in the past showed most predictive of dengue incidence and occurrence in Yogyakarta, Indonesia. The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead. Prior studies support the fact that past meteorology and surveillance data can be predictive of dengue. However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population.     

Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data

Recent years have seen progress in the development of statistically rigorous frameworks to infer outbreak transmission trees (“who infected whom”) from epidemiological and genetic data. Making use of pathogen genome sequences in such analyses remains a challenge, however, with a variety of heuristic approaches having been explored to date. We introduce a statistical method exploiting both pathogen sequences and collection dates to unravel the dynamics of densely sampled outbreaks. Our approach identifies likely transmission events and infers dates of infections, unobserved cases and separate introductions of the disease. It also proves useful for inferring numbers of secondary infections and identifying heterogeneous infectivity and super-spreaders. After testing our approach using simulations, we illustrate the method with the analysis of the beginning of the 2003 Singaporean outbreak of Severe Acute Respiratory Syndrome (SARS), providing new insights into the early stage of this epidemic. Our approach is the first tool for disease outbreak reconstruction from genetic data widely available as free software, the R package outbreaker. It is applicable to various densely sampled epidemics, and improves previous approaches by detecting unobserved and imported cases, as well as allowing multiple introductions of the pathogen. Because of its generality, we believe this method will become a tool of choice for the analysis of densely sampled disease outbreaks, and will form a rigorous framework for subsequent methodological developments.     

Climate Change and the Geographic Distribution of Infectious Diseases

Our ability to predict the effects of climate change on the spread of infectious diseases is in its infancy. Numerous, and in some cases conflicting, predictions have been developed, principally based on models of biological processes or mapping of current and historical disease statistics. Current debates on whether climate change, relative to socioeconomic determinants, will be a major influence on human disease distributions are useful to help identify research needs but are probably artificially polarized. We have at least identified many of the critical geophysical constraints, transport opportunities, biotic requirements for some disease systems, and some of the socioeconomic factors that govern the process of migration and establishment of parasites and pathogens. Furthermore, we are beginning to develop a mechanistic understanding of many of these variables at specific sites. Better predictive understanding will emerge in the coming years from analyses regarding how these variables interact with each other.