Recent results on the spatiotemporal modelling and comparative analysis of Black Death and bubonic plague epidemics.

BACKGROUND: This work demonstrates the importance of spatiotemporal stochastic modelling in constructing maps of major epidemics from fragmentary information, assessing population impacts, searching for possible etiologies, and performing comparative analysis of epidemics. METHODS: Based on the theory previously published by the authors and incorporating new knowledge bases, informative maps of the composite space-time distributions were generated for important characteristics of two major epidemics: Black Death (14th century Western Europe) and bubonic plague (19th-20th century Indian subcontinent). RESULTS: The comparative spatiotemporal analysis of the epidemics led to a number of interesting findings: (1) the two epidemics exhibited certain differences in their spatiotemporal characteristics (correlation structures, trends, occurrence patterns and propagation speeds) that need to be explained by means of an interdisciplinary effort; (2) geographical epidemic indicators confirmed in a rigorous quantitative manner the partial findings of isolated reports and time series that Black Death mortality was two orders of magnitude higher than that of bubonic plague; (3) modern bubonic plague is a rural disease hitting harder the small villages in the countryside whereas Black Death was a devastating epidemic that indiscriminately attacked large urban centres and the countryside, and while the epidemic in India lasted uninterruptedly for five decades, in Western Europe it lasted three and a half years; (4) the epidemics had reverse areal extension features in response to annual seasonal variations. Temperature increase at the end of winter led to an expansion of infected geographical area for Black Death and a reduction for bubonic plague, reaching a climax at the end of spring when the infected area in Western Europe was always larger than in India. Conversely, without exception, the infected area during winter was larger for the Indian bubonic plague; (5) during the Indian epidemic, the disease disappeared and reappeared several times at most locations; in Western Europe, once the disease entered a place, it lasted a time proportional to the population and then disappeared for several years (this on-and-off situation lasted more than three centuries); and (6) on average, Black Death moved much faster than bubonic plague to reach virgin territories, despite the fact that India is only slightly larger in area than Western Europe and had a railroad network almost instantly moving infected rats, fleas, and people from one end of the subcontinent to the other. CONCLUSIONS: These findings throw new light on the spatiotemporal characteristics of the epidemics and need to be taken into consideration in the scientific discussion concerning the two devastating diseases and the lessons learned from them.

El Niño effects on influenza mortality risks in the state of California.

Recent El Niño events have emphasized the need to develop modelling techniques to assess climate-related health events. Experts agree that climate changes affect the spread of infectious diseases and that the geographic range of infectious diseases may expand as a result of these changes. Nevertheless, the world health modelling community cannot yet predict, with reasonable accuracy, when or where exactly these effects will occur or how large the threat of these diseases will be to particular populations. This study compared the spatiotemporal patterns of influenza mortality risk in the state of California during El Niño vs normal weather periods. By applying a stochastic methodology to county-specific mortality data, various sources of uncertainty were accounted for, and informative influenza mortality maps and profiles were generated. This methodology enabled the detection of significant effects of climate change on the influenza risk distributions. Geographical maps of risk variation during El Niño differed from those during normal weather, the corresponding covariances exhibited distinct space-time dependence features, and the temporal mean mortality profiles were considerably higher during normal weather than during El Niño. These rather unexpected results of spatiotemporal analysis are worth further investigation that seeks substantive and biologically plausible explanations. The findings of this study can offer a methodological framework to evaluate public health management strategies.

Spatial analysis and mapping of sexually transmitted diseases to optimise intervention and prevention strategies.

OBJECTIVE: We analysed and mapped the distribution of four reportable sexually transmitted diseases, chlamydial infection/non-gonococcal urethritis (chlamydial infection), gonorrhoea, primary and secondary syphilis (syphilis), and HIV infection, for Wake County, North Carolina, to optimise an intervention. METHODS: We used STD surveillance data reported to Wake County, for the year 2000 to analyse and map STD rates. STD rates were mathematically represented as a spatial random field. We analysed spatial variability by calculating and modelling covariance functions of random field theory. Covariances are useful in assessing spatial patterns of disease locally and at a distance. We combined observed STD rates and appropriate covariance models using a geostatistical method called kriging, to predict STD rates and associated prediction errors for a grid covering Wake County. Final disease estimates were interpolated using a spline with tension and mapped to generate a continuous surface of infection. RESULTS: Lower incidence STDs exhibited larger spatial variability and smaller neighbourhoods of influence than higher incidence STDs. Each reported STD had a clustered spatial distribution with one primary core area of infection. Core areas overlapped for all four STDs. CONCLUSIONS: Spatial heterogeneity within STD suggests that STD specific prevention strategies should not be targeted uniformly across Wake County, but rather to core areas. Overlap of core areas among STDs suggests that intervention and prevention strategies can be combined to target multiple STDs effectively. Geostatistical techniques are objective, population level approaches to spatial analysis and mapping that can be used to visualise disease patterns and identify emerging outbreaks.

Critical conceptualism in environmental modeling and prediction.

Many important problems in environmental science and engineering are of a conceptual nature. Research and development, however, often becomes so preoccupied with technical issues, which are themselves fascinating, that it neglects essential methodological elements of conceptual reasoning and theoretical inquiry. This work suggests that valuable insight into environmental modeling can be gained by means of critical conceptualism which focuses on the software of human reason and, in practical terms, leads to a powerful methodological framework of space-time modeling and prediction. A knowledge synthesis system develops the rational means for the epistemic integration of various physical knowledge bases relevant to the natural system of interest in order to obtain a realistic representation of the system, provide a rigorous assessment of the uncertainty sources, generate meaningful predictions of environmental processes in space-time, and produce science-based decisions. No restriction is imposed on the shape of the distribution model or the form of the predictor (non-Gaussian distributions, multiple-point statistics, and nonlinear models are automatically incorporated). The scientific reasoning structure underlying knowledge synthesis involves teleologic criteria and stochastic logic principles which have important advantages over the reasoning method of conventional space-time techniques. Insight is gained in terms of real world applications, including the following: the study of global ozone patterns in the atmosphere using data sets generated by instruments on board the Nimbus 7 satellite and secondary information in terms of total ozone-tropopause pressure models; the mapping of arsenic concentrations in the Bangladesh drinking water by assimilating hard and soft data from an extensive network of monitoring wells; and the dynamic imaging of probability distributions of pollutants across the Kalamazoo river.

An application of the holistochastic human exposure methodology to naturally occurring arsenic in Bangladesh drinking water.

The occurrence of arsenic in drinking water is an issue of considerable interest. In the case of Bangladesh, arsenic concentrations have been closely monitored since the early 1990s through an extensive sampling network. The focus of the present work is methodological. In particular, we propose the application of a holistochastic framework of human exposure to study lifetime population damage due to arsenic exposure across Bangladesh. The Bayesian Maximum Entropy theory is an important component of this framework, which possesses solid theoretical foundations and offers powerful tools to assimilate a variety of knowledge bases (physical, epidemiologic, toxicokinetic, demographic, etc.) and uncertainty sources (soft data, measurement errors, etc.). The holistochastic exposure approach leads to physically meaningful and informative spatial maps of arsenic distribution in Bangladesh drinking water. Global indicators of the adverse health effects on the population are generated, and valuable insight is gained by blending information from different scientific disciplines. The numerical results indicate an increased lifetime bladder cancer probability for the Bangladesh population due to arsenic. The health effect estimates obtained and the associated uncertainty assessments are valuable tools for a broad spectrum of end-users.

Spatiotemporal analysis of environmental exposure-health effect associations.

The goal of this work is to discuss a general methodology for studying associations between environmental exposures and health effect by means of the spatiotemporal random field theory. This theory is the tool of choice for rigorously accounting for important spatiotemporal variations and uncertainties related to exposures and effect. Within the framework of the random field theory, the Bayesian maximum entropy model neatly synthesizes various sources of physical and epidemiological knowledge into spatiotemporal analysis. Therefore, unlike technical statistics, this approach relies on the blending of substantive physical knowledge with powerful mathematical techniques and a coherent rationale. Given the well-founded fact that certain health effects may be caused by environmental exposures, the significance of these exposures is assessed in terms of a criterion that is based on the joint stochastic representation of exposure and health-effect distributions in space/time. In view of this criterion, the strength and consistency of the exposure-effect association are evaluated on the basis of the health-effect predictions that the combined physico-epidemiologic analysis generates in space/time. The main features of the approach are demonstrated by a simulation example and a real case study involving mortality and cold temperatures in North Carolina. The studies demonstrated the practical usefulness of the stochastic human exposure analysis in assessing the exposure-effect association. The results reported here emphasize the links between spatiotemporal models of physical systems and population health-effect distributions, thus suggesting directions for improving the current understanding of quantitative "exposure-health effect" functions.

A study of the spatiotemporal health impacts of ozone exposure.

Exposure analysis and mapping of spatiotemporal pollutants in relation to their health effects are important challenges facing environmental health scientists and integrated assessment modellers. In this work, a methodological framework is discussed to study the impact of spatiotemporal ozone (O3) exposure distributions on the health of human populations. The framework, however, is very general and can be used to study various other pollutants. The spatiotemporal analysis starts with exposure distributions producing the input to pollutokinetic (or toxicokinetic) laws which are linked to effect models which, in turn, are integrated with relationships that describe how effects are distributed across populations. Important characteristics of the environmental health framework are holisticity and stochasticity. Holisticity emphasizes the functional relationships between composite space/time O3 maps, pollutokinetic models of burden on target organs and tissues, and health effects. These relationships offer a meaningful physical interpretation of the exposure and biological processes that affect human exposure. Stochasticity involves the rigorous representation of natural uncertainties and biological variations in terms of spatiotemporal random fields. The stochastic perspective introduces a deeper epistemological understanding in the development of improved models of spatiotemporal human exposure analysis and mapping. Also, it explicitly determines the knowledge bases available and develops logically plausible rules and standards for data processing and human exposure map construction. The proposed approach allows the horizontal integration among sciences related to the human exposure problem that leads to accurate and informative spatiotemporal maps of O3 exposure and effect distributions and an integrative analysis of the whole risk case. By processing a variety of knowledge bases, the spatiotemporal analysis can bring together several sciences which are all relevant to the aspect of human exposure reality that is examined.

A new sterile bicarbonate dialysis solution for use during cardiopulmonary bypass.

Adding a dialysis filter to the perfusion circuit at the end of cardiopulmonary bypass (CPB) has become an accepted means of reducing potassium rapidly and safely. Rapid removal of solute requires a dialysate for diffusion, and peritoneal dialysis solutions have been the standard because of availability, although occasionally normal saline or bicarb/ saline mixtures are used. Cardioplegia solution is high in glucose as well as potassium and, with many diabetic patients undergoing CPB, it is desirable to minimize glucose loads. In this prospective cohort study, six patients received a commercially available sterile bicarbonate dialysate prepared in a point of care fashion. From the cardiovascular data base, four control patients (receiving lactate based dialysis solution during CPB) were matched for age, surgery type, body surface area (BSA), and pump duration for each of the six patients receiving bicarbonate dialysate. All of the control patients were dialysed against lactate buffered peritoneal dialysis solution. Plasma levels of potassium, glucose, and bicarb were measured before and after dialysis for each dialysate. Plasma potassium, glucose, and bicarb were not significantly different at start of dialysis. The lactate dialysate (LD) group received a mean of 17.4+/-7.7 L of lactate containing dialysate versus 14.6+/-4.7 L of bicarbonate dialysate (BD) (p = 0.41). After dialysis, potassium had been reduced to a similar degree in both groups, but plasma glucose levels had increased during LD while they fell during BD, and bicarbonate levels fell during LD while they rose during BD. Use of a commercially available sterile bicarbonate dialysate can safely help to lower plasma potassium during CPB and preserve more physiologic levels of glucose and bicarbonate.

A novel method for studying population health impacts of spatiotemporal ozone distribution.

This work is concerned with the development of a method to study the impact of ozone exposure on human health. The analysis is based on random field representations of exposure variation and health damage uncertainty in a composite space/time continuum, which previous studies did not allow. Ozone exposure-health damage is considered as a spatiotemporal holistic system, by looking at the whole picture, not just certain isolated parts. Ozone maps over the eastern United States provide the basic Framework for studying exposure-health relationships, that focus on a community wise basis. Composite space/time maps of health damage indicators associated with the ozone exposure levels are obtained. These maps constitute an important part of many health studies, offering a valuable description of the data and an important basis for further analysis. Health damage maps can identify, for example, regions over the eastern United States that will respond in certain ways to variation of ozone levels or the application of health management practices. Application areas are identified for future study.

A study of the breast cancer dynamics in North Carolina.

This work is concerned with the study of breast cancer incidence in the State of North Carolina. Methodologically, the current analysis illustrates the importance of spatiotemporal random field modelling and introduces a mode of reasoning that is based on a combination of inductive and deductive processes. The composite space/time analysis utilizes the variability characteristics of incidence and the mathematical features of the random field model to fit it to the data. The analysis is significantly general and can efficiently represent non-homogeneous and non-stationary characteristics of breast cancer variation. Incidence predictions are produced using data at the same time period as well as data from other time periods and disease registries. The random field provides a rigorous and systematic method for generating detailed maps, which offer a quantitative description of the incidence variation from place to place and from time to time, together with a measure of the accuracy of the incidence maps. Spatiotemporal mapping accounts for the geographical locations and the time instants of the incidence observations, which is not usually the case with most empirical Bayes methods. It is also more accurate than purely spatial statistics methods, and can offer valuable information about the breast cancer risk and dynamics in North Carolina. Field studies could be initialized in high-rate areas identified by the maps in an effort to uncover environmental or life-style factors that might be responsible for the high risk rates. Also, the incidence maps can help elucidate causal mechanisms, explain disease occurrences at a certain scale, and offer guidance in health management and administration.