Modeling

One of ASTHO’s key goals is to help its jurisdictions meet member needs for technical assistance, including making informed decisions about their syndromic surveillance options. To help them make such decisions, ASTHO worked with Booz Allen to create a decision analysis model, which factors in both a Value of Information (VOI) model and a Return on Investment (ROI) model. The model provides a dashboard of its outputs, which is a simple, easy-to-understand comparative view of multiple syndromic surveillance investment scenarios.

Objective

Provide a demonstration of the recently developed prototype decision analysis model for syndromic surveillance investments. The roundtable will be used to discuss the model, obtain feedback on its usefulness, and brainstorm future uses and improvements.

Currently Centers for Disease Control and Prevention (CDC) employ threshold rules to declare epidemic outbreaks, such as influenza, separately in each population. However each year influenza starts in one population and spreads population-to-population throughout the country. Therefore there is a need for an algorithm to declare the epidemic that uses information from multiple populations.

Objective

Detect epidemics over multiple Populations using computational methods

Real-time syndromic surveillance requires daily surveillance of a range of health data sources. Most real-time data sources from health care systems exhibit large day of the week fluctuations as service provision and patient behaviour varies by day of the week. Regular day of the week effects are further complicated by the occurrence of public holidays (usually 8 per year in England), which can limit the availability of certain services and affect patient behaviour. Simple seven day moving averages fail to provide a smoothed trend around public holidays and can lead to false alarms or potentially delays in detection of outbreaks.

Objective

To develop smoothing techniques for daily syndromic surveillance data that allow for the easier identification of trends and unusual activity independent of day of the week and holiday effects.

The basic reproduction number represents the number of secondary infections expected to be caused by an infectious individual introduced into an entirely susceptible population. It is a fundamental measure used to characterize infectious disease outbreaks and is essential in developing mathematical models to determine appropriate interventions. Much work has been done to investigate methods for estimating the basic reproduction number during the early stages of infectious disease outbreaks. However, these methods often require data that may not be readily available at the beginning of an outbreak. An approach developed by Becker has been widely used to estimate the basic reproduction number using only the final case count and size of the at-risk population. A modification to this approach is proposed that allows estimates to be obtained earlier in an outbreak using only the current case count, number currently ill, and the size of the at-risk population.

Objective

To present a modification to an established approach to estimating the basic reproduction number to allow estimates to be obtained at any point during an outbreak using only the current case count, number currently ill, and the size of the at-risk population.

Social media messages are often short, informal, and ungrammatical. They frequently involve text, images, audio, or video, which makes the identification of useful information difficult. This complexity reduces the efficacy of standard information extraction techniques1. However, recent advances in NLP, especially methods tailored to social media2, have shown promise in improving real-time PH surveillance and emergency response3. Surveillance data derived from semantic analysis combined with traditional surveillance processes has potential to improve event detection and characterization. The CDC Office of Public Health Preparedness and Response (OPHPR), Division of Emergency Operations (DEO) and the Georgia Tech Research Institute have collaborated on the advancement of PH SA through development of new approaches in using semantic analysis for social media.

Objective

The objective of this analysis is to leverage recent advances in natural language processing (NLP) to develop new methods and system capabilities for processing social media (Twitter messages) for situational awareness (SA), syndromic surveillance (SS), and event-based surveillance (EBS). Specifically, we evaluated the use of human-in-the-loop semantic analysis to assist public health (PH) SA stakeholders in SS and EBS using massive amounts of publicly available social media data.

Assigning causes of deaths to seasonal infectious diseases is difficult in part due to laboratory testing prior to death being uncommon. Since influenza (and other common respiratory pathogens) are therefore notoriously underreported as a (contributing) cause of death in deathcause statistics modeling studies are commonly used to estimate the impact of influenza on mortality.

Objective

To estimate mortality attributable to influenza adjusted for other common respiratory pathogens, baseline seasonal trends and extreme temperatures.

The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In order to achieve this goal, the primary foundation is using those big surveillance data for understanding and controlling the spatiotemporal variability of disease through populations. Typically, public health’s surveillance system would generate data with the big data characteristics of high volume, velocity, and variety. One common question of big data analysis is most of the data have the multilevel or hierarchy structure, in other word the big data are non-independent. Traditional multilevel or hierarchical model can only deal with 2 or 3 hierarchical data structure, which bound health big data further research for modeling, forecast and early-warning in the public health surveillance, in particular involving complex spatial and temporal variability of Infectious Diseases in the reality. 

Objective

The purpose of this article was to quantitative analyses the spatial variability and temporal variability of influenza like illness (ILI) by a three-level Poisson model, which means to explain the spatial and temporal level effects by introducing the random effects. 

An increasing number of geo-coded information streams are available with possible use in disease surveillance applications. In this setting, multivariate modeling of health and non-health data allows assessment of concurrent patterns among data streams and conditioning on one another. Therefore it is appropriate to consider the analysis of their spatial distributions together. Specifically for vector-borne diseases, knowledge of spatial and temporal patterns of vector distribution could inform incidence in humans. Tularemia is an infectious disease endemic in North America and parts of Europe. In Finland tularemia is typically mosquito-transmitted with rodents serving as a host; however, a country-wide understanding of the relationship between rodents and the disease in humans is still lacking. We propose a methodology to help understand the association between human tularemia incidence and rodent population levels. 

Objective

We seek to integrate multiple streams of geo-coded information with the aim to improve public health surveillance accuracy and efficiency. Specifically for vector-borne diseases, knowledge of spatial and temporal patterns of vector distribution can help early prediction of human incidence. To this end, we develop joint modeling approaches to evaluate the contribution of vector or reservoir information on early prediction of human cases. A case study of spatiotemporal modeling of tularemia human incidence and rodent population data from Finnish health care districts during the period 1995-2013 is provided. Results suggest that spatial and temporal information of rodent abundance is useful in predicting human cases. 

Epidemiological modeling for infectious disease is useful for disease management and routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. We offer this framework and an associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model

description and facilitating the use of epidemiological models. Such a framework could help the understanding of diverse models by various stakeholders with different preconceptions, backgrounds, expertise, and needs, and can foster greater use of epidemiological models as tools in infectious disease surveillance.

Objectives

1. To develop a comprehensive model characterization framework to describe epidemiological models in an operational context.

2. To apply the framework to characterize “operational” models for specific infectious diseases and provide a web-based directory, the biosurveillance analytics resource directory (BARD) to the global infectious disease surveillance community.

Every year nearly 12 million new cases of syphilis in the world are registered. Currently, in many countries of the world the stabilization or even reduction of the incidence of syphilis is marked, but this does not apply to Ukraine. The current stage of development of the syphilis problem in Ukraine is characterized by not only high morbidity, but also the fact that in the overwhelming number of cases, we are talking about the latent forms and atypical manifestations of the disease and resistance to therapy [1]. Preventive and prophylactic measures are important in maintaining the public health. Predicting the dynamics of disease spreading allows developing appropriate countermeasures and ensuring rational use of human and material resources. Qualitative forecast of syphilis spreading is possible to implement by means of mathematical modeling.