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Forecasting

Dengue is a mosquito-borne viral disease, and there is considerable evidence that case numbers are rising and geographical distribution of the disease is widening within the United States, and around the world. 

The accuracy and reporting frequency of dengue morbidity and mortality information varies geographically, and often is an underestimation of the actual number of dengue infections. As traditional methods of disease surveillance may not accurately capture the true impact of this disease, it is important to gather professional observations and opinions from individuals in the public health, medical, and vector control fields of practice. Prediction markets are one way of supplementing traditional surveillance and quantifying the observations and predictions of professionals in the field. 

Prediction markets have been successfully used to forecast future events, including future influenza activity. For these markets, we divided the possible outcomes for each question into multiple mutually exclusive contracts to forecast dengue-related events. This differed from many previous prediction markets that offered single sets of yes-no questions and used ‘real’ money in the form of educational grants. However, with more detailed contracts, we were able to generate more refined predictions of dengue activity.

 

Objective

The objective of this project is to use prediction markets to forecast the spread of dengue.

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Submitted by hparton on

Influenza peaks around June and December in Singapore every year. Facing an ageing population, hospitals in Singapore have been constantly reaching maximum bed occupancy. The ability to be able to make early decisions during peak periods is important. Tan Tock Seng Hospital is the second largest adult acute care general hospital in Singapore. Pneumonia-related emergency department (ED) admissions are a huge burden to the hospital's resources. The number of cases vary year on year as it depends on seasonal vaccine effectiveness and the population's immunity to the circulating strain. While many pneumonia cases are of unknown origin, they tend to mirror the influenza seasons very closely.

Objective: Using the information that we have available, our primary objective is to explore if there was any cross-correlation between pneumonia admissions and hospital influenza positivity. We then aim to develop a data driven approach to forecast pneumonia admissions using data from our hospital's weekly surveillance. We also attempted using external sources of information such as national infectious diseases notifications and climate data to see if they were useful for our model.

Submitted by elamb on

Influenza viral infection is contentious, has a short incubation period, yet preventable if multiple barriers are employed. At some extend school holidays and travel restrictions serve as a socially accepted control measure. A study of a spatiotemporal spread of influenza among school-aged children in Belgium illustrated that changes in mixing patterns are responsible for altering disease seasonality3. Stochastic numerical simulations suggested that weekends and holidays can delay disease seasonal peaks, mitigate the spread of infection, and slow down the epidemic by periodically dampening transmission. While Christmas holidays had the largest impact on transmission, other school breaks may also help in reducing an epidemic size. Contrary to events reducing social mixing, sporting events and mass gatherings facilitate the spread of infections. A study on county-level vital statistics of the US from 1974-2009 showed that Super Bowl social mixing affects influenza dissemination by decreasing mortality rates in older adults in Bowl-participating counties. The effect is most pronounced for highly virulent influenza strains and when the Super Bowl occurs closer to the influenza seasonal peak. Simulation studies exploring how social mixing affects influenza spread demonstrated that impact of the public gathering on prevalence of influenza depends on time proximity to epidemic peak. While the effects of holidays and social events on seasonal influenza have been explored in surveillance time series and agent-based modeling studies, the understanding of the differential effects across age groups is incomplete.

Objective: In the presented study, we examined the impact of school holidays (Autumn, Winter, Summer, and Spring Breaks) and social events (Super Bowl, NBA Finals, World Series, and Black Friday) for five age groups (<4, 5-24, 25-44, 45-64, >65 years) on four health outcomes of influenza (total tested, all influenza positives, positives for influenza A, and B) in Milwaukee, WI, in 2004-2009 using routine surveillance.

Submitted by elamb on

As the electronic medical record (EMR) market matures, long-term time series of EMR-based surveillance data are becoming available. In this work, we hypothesized that statistical aberrancy-detection methods that incorporate seasonality and other long-term data trends reduce the time required to discover an influenza outbreak compared with methods that only consider the most recent past.

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Submitted by teresa.hamby@d… on

It has been suggested that changes in various socioeconomic, environmental and biological factors have been drivers of emerging and reemerging infectious disease, although few have assessed these relationships on a global scale. Understanding these associations could help build better forecasting models, and therefore identify high-priority regions for public health and surveillance implementation. Although infectious disease surveillance and research have tended to be concentrated in wealthier, developed countries in North America, Europe and Australia, it is developing countries that have been predicted to be the next hotspots for emerging infectious diseases.

Objective

To evaluate the association between socioeconomic factors and infectious disease outbreaks, to develop a prediction model for where future outbreaks would most likely to occur worldwide and identify priority countries for surveillance capacity building.

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Submitted by teresa.hamby@d… on

Predictionmarkets have been successfully used to forecast future events in other fields. We adapted this method to provide estimates of the likelihood of H5N1 influenza related events.

 

Objective

The purpose of this study is to compare the results of an H5N1 influenza prediction market model with a standard statistical model.

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Submitted by hparton on

In 1911, Christophers developed an early-warning system for malaria epidemics in Punjab based on rainfall, fever-related deaths and wheat prices. Since that initial system, researchers and practitioners have continued to search for determinants of spatial and temporal variability of malaria to improve systems for forecasting disease burden. Malaria thrives in poor tropical and subtropical countries where resources are limited. Accurate disease prediction and early warning of increased disease burden can provide public health and clinical health services with the information needed to implement targeted approaches for malaria control and prevention that make effective use of limited resources. Malaria forecasting models do not typically consider clinical predictors, such as type of antimalarial treatment, in the forecasting models. 

Objective

The objective of the research was to identify the most accurate models for forecasting malaria at six different sentinel sites in Uganda, using environmental and clinical data sources.

Submitted by elamb on

The time series of syphilis cases has been studied at the country and state level at the yearly basis, and it has been found that syphilis has a periodicity of approximately 10 years. However, to inform prevention efforts, it is important to understand the short term dynamics of disease activity.

 

Objective

(i) To forecast syphilis cases per state in the US to support early containment of outbreaks. (ii) For each state, to determine which states are most correlated, to find "bellwether" states to inform surveillance efforts. (iii) To determine a small collection of states whose syphilis incidence patterns are most closely correlated with all the states.

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Submitted by elamb on

Interactive tools for visualization of disease outbreaks has been improving markedly in the past few years. With the flagships Google Flutrends1 and HealthMap2 providing prime examples. These tools provide interactive access to the general public concerning the current state-of-affairs for disease outbreaks generally and specifically for influenza. For example, while browsing HealthMap I learned of a case of tuberculosis on my campus, Iowa State University. While extremely sophisticated, these tools do not utilize modern statistical algorithms for detection or forecasting. In addition, the development cost and perhaps the maintenance cost is not trivial. We aim to build a similar visualization tool that incorporates modern algorithms for detection and forecasting but has low development and maintenance cost. Due to the low cost this tool is appropriate for quick deployment in developing countries for emerging outbreaks as well as public health agencies with declining operating budgets.

Objective

To build a zero-cost tool for disease outbreak visualization, detection, and forecasting incorporating modern tools.

Submitted by knowledge_repo… on

Researchers have developed varied methods for forecasting influenza activity using surveillance data with predictive models, but real-world applications in public health programs are rare. To inform consideration of whether and how public health practice should incorporate influenza forecasting, we conducted a systematic review of these methods.

Objective

To assess studies of epidemiological forecasting models for human influenza activity.

Submitted by knowledge_repo… on
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