Judith V. Douglas

The effect of antibody-dependent enhancement, cross immunity, and vector population on the dynamics of dengue fever

Dengue is a major international public health concern and impacts one-third of the worlds population. No specific vaccine and treatment are available for this vector-borne disease. There are four similar but distinct serotypes of dengue viruses (DENV). Infection with one serotype affords life-long immunity to that serotype but only temporary partial immunity, or cross immunity (CI), to others. This increases the risk of developing lethal complications upon re-infection, mainly because of the effect of antibody-dependent enhancement (ADE). There have been multiple studies of the dynamic behavior created by the interplay of ADE and CI using mathematical models. However, models in the literature seldom capture the vector population, which we consider important because combating the mosquito vector is the only way to contain dengue transmission in the absence of vaccines. We therefore propose two differential-equation models of dengue fever (DF) with different levels of complexity and details. Our results support the need for ADE to explain the complexity of the epidemiological data.

The Cost of Simplifying Air Travel When Modeling Disease Spread

Background Air travel plays a key role in the spread of many pathogens. Modeling the long distance spread of infectious disease in these cases requires an air travel model. Highly detailed air transportation models can be over determined and computationally problematic. We compared the predictions of a simplified air transport model with those of a model of all routes and assessed the impact of differences on models of infectious disease. Methodology/Principal Findings Using U.S. ticket data from 2007, we compared a simplified “pipe” model, in which individuals flow in and out of the air transport system based on the number of arrivals and departures from a given airport, to a fully saturated model where all routes are modeled individually. We also compared the pipe model to a “gravity” model where the probability of travel is scaled by physical distance; the gravity model did not differ significantly from the pipe model. The pipe model roughly approximated actual air travel, but tended to overestimate the number of trips between small airports and underestimate travel between major east and west coast airports. For most routes, the maximum number of false (or missed) introductions of disease is small (<1 per day) but for a few routes this rate is greatly underestimated by the pipe model. Conclusions/Significance If our interest is in large scale regional and national effects of disease, the simplified pipe model may be adequate. If we are interested in specific effects of interventions on particular air routes or the time for the disease to reach a particular location, a more complex point-to-point model will be more accurate. For many problems a hybrid model that independently models some frequently traveled routes may be the best choice. Regardless of the model used, the effect of simplifications and sensitivity to errors in parameter estimation should be analyzed.

A global model of malaria climate sensitivity: comparing malaria response to historic climate data based on simulation and officially reported malaria incidence.

BackgroundThe role of the Anopheles vector in malaria transmission and the effect of climate on Anopheles populations are well established. Models of the impact of climate change on the global malaria burden now have access to high-resolution climate data, but malaria surveillance data tends to be less precise, making model calibration problematic. Measurement of malaria response to fluctuations in climate variables offers a way to address these difficulties. Given the demonstrated sensitivity of malaria transmission to vector capacity, this work tests response functions to fluctuations in land surface temperature and precipitation.MethodsThis study of regional sensitivity of malaria incidence to year-to-year climate variations used an extended Macdonald Ross compartmental disease model (to compute malaria incidence) built on top of a global Anopheles vector capacity model (based on 10 years of satellite climate data). The predicted incidence was compared with estimates from the World Health Organization and the Malaria Atlas. The models and denominator data used are freely available through the Eclipse Foundation’s Spatiotemporal Epidemiological Modeller (STEM).ResultsAlthough the absolute scale factor relating reported malaria to absolute incidence is uncertain, there is a positive correlation between predicted and reported year-to-year variation in malaria burden with an averaged root mean square (RMS) error of 25% comparing normalized incidence across 86 countries. Based on this, the proposed measure of sensitivity of malaria to variations in climate variables indicates locations where malaria is most likely to increase or decrease in response to specific climate factors. Bootstrapping measures the increased uncertainty in predicting malaria sensitivity when reporting is restricted to national level and an annual basis. Results indicate a potential 20x improvement in accuracy if data were available at the level ISO 3166–2 national subdivisions and with monthly time sampling.ConclusionsThe high spatial resolution possible with state-of-the-art numerical models can identify regions most likely to require intervention due to climate changes. Higher-resolution surveillance data can provide a better understanding of how climate fluctuations affect malaria incidence and improve predictions. An open-source modelling framework, such as STEM, can be a valuable tool for the scientific community and provide a collaborative platform for developing such models.

Comparing three basic models for seasonal influenza

In this paper we report the use of the open source Spatiotemporal Epidemiological Modeler (STEM, www.eclipse.org/stem) to compare three basic models for seasonal influenza transmission. The models are designed to test for possible differences between the seasonal transmission of influenza A and B. Model 1 assumes that the seasonality and magnitude of transmission do not vary between influenza A and B. Model 2 assumes that the magnitude of seasonal forcing (i.e., the maximum transmissibility), but not the background transmission or flu season length, differs between influenza A and B. Model 3 assumes that the magnitude of seasonal forcing, the background transmission, and flu season length all differ between strains. The models are all optimized using 10 years of surveillance data from 49 of 50 administrative divisions in Israel. Using a cross-validation technique, we compare the relative accuracy of the models and discuss the potential for prediction. We find that accounting for variation in transmission amplitude increases the predictive ability compared to the base. However, little improvement is obtained by allowing for further variation in the shape of the seasonal forcing function.

Infectious Disease Modeling: Creating a Community to Respond to Biological Threats

The rise of global economies in the 21st century, the rapid national and international movement of people, and the increased reliance of developed countries on global trade, all greatly increase the potential and possible magnitude of a worldwide pandemic. New epidemics may be the result of global climate change, vector-borne diseases, food-borne illness, new naturally occurring pathogens, or bio-terrorist attacks. The threat is most severe for highly communicable diseases. When rapidly spreading microparasitic infections coincide with the rapid transportation, propagation, and dissemination of the pathogens and vectors for infection, the risks associated with emerging infectious disease increase. We discuss the use of publicly-available technologies in assisting public health officials and scientists in protecting populations from emerging disease or in implementing improved response measures. We illustrate possibilities using the SpatioTemporal Epidemiological Modeler (STEM) that was developed to run on the Open Health Framework (OHF) created by the Eclipse Foundation in 2004. An illustration regarding the spread of the influenza H1N1 virus from Mexico to the United States via air travel in Spring 2009 is briefly discussed.

A Generic Open-Source Software Framework Supporting Scenario Simulations in Bioterrorist Crises

Since the 2001 anthrax attack in the United States, awareness of threats originating from bioterrorism has grown. This led internationally to increased research efforts to improve knowledge of and approaches to protecting human and animal populations against the threat from such attacks. A collaborative effort in this context is the extension of the open-source Spatiotemporal Epidemiological Modeler (STEM) simulation and modeling software for agro- or bioterrorist crisis scenarios. STEM, originally designed to enable community-driven public health disease models and simulations, was extended with new features that enable integration of proprietary data as well as visualization of agent spread along supply and production chains. STEM now provides a fully developed open-source software infrastructure supporting critical modeling tasks such as ad hoc model generation, parameter estimation, simulation of scenario evolution, estimation of effects of mitigation or management measures, and documentation. This open-source software resource can be used free of charge. Additionally, STEM provides critical features like built-in worldwide data on administrative boundaries, transportation networks, or environmental conditions (eg, rainfall, temperature, elevation, vegetation). Users can easily combine their own confidential data with built-in public data to create customized models of desired resolution. STEM also supports collaborative and joint efforts in crisis situations by extended import and export functionalities. In this article we demonstrate specifically those new software features implemented to accomplish STEM application in agro- or bioterrorist crisis scenarios.

Insular Microbiogeography: Three Pathogens as Exemplars

Traditional taxonomy in biology assumes that life is organized in a simple tree. Attempts to classify microorganisms in this way in the genomics era led microbiologists to look for finite sets of ‘core’ genes that uniquely group taxa as clades in the tree. However, the diversity revealed by large-scale whole genome sequencing is calling into question the long-held model of a hierarchical tree of life, which leads to questioning of the definition of a species. Large-scale studies of microbial genome diversity reveal that the cumulative number of new genes discovered increases with the number of genomes studied as a power law and subsequently leads to the lack of evidence for a unique core genome within closely related organisms. Sampling ‘enough’ new genomes leads to the discovery of a replacement or alternative to any gene. This power law behaviour points to an underlying self-organizing critical process that may be guided by mutation and niche selection. Microbes in any particular niche exist within a local web of organism interdependence known as the microbiome. The same mechanism that underpins the macro-ecological scaling first observed by MacArthur and Wilson also applies to microbial communities. Recent metagenomic studies of a food microbiome demonstrate the diverse distribution of community members, but also genotypes for a single species within a more complex community. Collectively, these results suggest that traditional taxonomic classification of bacteria could be replaced with a quasispecies model. This model is commonly accepted in virology and better describes the caister.com/cimb 89 Curr. Issues Mol. Biol. Vol. 36

Modeling the dynamics of dengue fever

Dengue is a major international public health concern that impacts one-third of the worlds population. There are four serotypes of the dengue virus (DENV). Infection with one serotype affords life-long immunity to that serotype but only temporary cross immunity (CI) to other serotypes. The risk of lethal complications is elevated upon re-infection, possibly because of the effect of antibody-dependent enhancement (ADE). In this paper we propose a system dynamics model that captures both host and vector populations, latency, and four dengue serotypes. This model allows one to study both CI and ADE. Modeling the Aedes vector adds complexity, but we consider this to be important because combating the mosquito vector may be the most practical intervention in the absence of an effective vaccine. Our results support the need to model the vector population and ADE to explain the observed epidemiological data.