An open challenge to advance probabilistic forecasting for dengue epidemics.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.

Investigation of Geometric Landscape and Structure-Property Relations for Colloidal Superstructures Using Genetic Algorithm.

Over the past two decades, colloidal particles with a variety of shapes, sizes, and compositions have been synthesized and characterized successfully. One of the most important applications for colloidal building blocks is to engineer functional structures as mechanical, electrical, and optical metamaterials. However, complex interaction dynamics between the building blocks as well as sophisticated structure-property relationships make it challenging to design structures with predictable target properties. In this paper, we implement an inverse material design framework using Genetic Algorithm (GA)-based techniques to streamline the design of colloidal structures based on target properties. We investigate spherical particles as well as colloidal molecules of different sizes and shapes and evaluate a Geometric Landscape Accessibility parameter that identifies the size of feasible domains within the geometric phase space of each structure. Considering target photonic properties, our GA-assisted framework is further utilized to identify sets of building blocks and structures that lead to various target values for the size of the photonic band gaps. The proposed framework in this study will provide new insight for predictive computational material design approaches and help establish more efficient ways of understanding structure-property relations in sub-micrometer-scale materials.

Enhancing epidemiological analysis of intercontinental dispersion of H5N1 viral strains by migratory waterfowl using phylogeography.

BACKGROUND: Intercontinental migratory waterfowl are the primary vectors for dispersion of H5N1 viruses and have been implicated in several zoonotic epidemics and pandemics. Recent investigations have established that with a single mutation, the virus gains the ability to transmit between humans. Consequently, there is a heightened urgency to identify innovative approaches to proactively mitigate emergent epidemics. Accordingly, a novel methodology combining temporo-geospatial epidemiology and phylogeographic analysis of viral strains is proposed to identify critical epicenters and epidemic pathways along with high risk candidate regions for increased surveillance. RESULTS: Epidemiological analysis was used to identify 91,245 candidate global infection transmission pathways between 22 high risk waterfowl species. Dominant infection pathways (25,625 and 54,500 in summering and wintering zones) were identified through annotation using phylogeographical data computed from the phylogram of 2417 H5N1 HA isolates (from GISAID EpiFlu database). Annotation of infection pathways in turn delineated 23 influential clades out of 130 clades in the phylogram. CONCLUSIONS: The phylogeographic analyses provides strong cross-validation of epidemic pathways and identifies the dominant pathways for use in other epidemiological and prophylactic studies. The temporo-geospatial characteristics of infection transmission provides corroborating, but novel evidence for rapid genesis of H5N1 lineages in S.E. Asia. The proposed method pinpoints several regions, particularly in the southern hemisphere, as candidates for increased surveillance.

Computational Evolutionary Methodology for Knowledge Discovery and Forecasting in Epidemiology and Medicine.

Humanity is facing an increasing number of highly virulent and communicable diseases such as avian influenza. Researchers believe that avian influenza has potential to evolve into one of the deadliest pandemics. Combating these diseases requires in-depth knowledge of their epidemiology. An effective methodology for discovering epidemiological knowledge is to utilize a descriptive, evolutionary, ecological model and use bio-simulations to study and analyze it. These types of bio-simulations fall under the category of computational evolutionary methods because the individual entities participating in the simulation are permitted to evolve in a natural manner by reacting to changes in the simulated ecosystem. This work describes the application of the aforementioned methodology to discover epidemiological knowledge about avian influenza using a novel eco-modeling and bio-simulation environment called SEARUMS. The mathematical principles underlying SEARUMS, its design, and the procedure for using SEARUMS are discussed. The bio-simulations and multi-faceted case studies conducted using SEARUMS elucidate its ability to pinpoint timelines, epicenters, and socio-economic impacts of avian influenza. This knowledge is invaluable for proactive deployment of countermeasures in order to minimize negative socioeconomic impacts, combat the disease, and avert a pandemic.