HealthMap is an established global leader in utilizing online informal sources for disease outbreak monitoring and real-time surveillance of emerging public health threats. The freely available Web site 'healthmap.org' and mobile app 'Outbreaks Near Me' deliver real-time intelligence on a broad range of emerging infectious diseases for a diverse audience including libraries, local health departments, governments, and international travelers. 

This references and resources finding aid from the outbreak response team of the Centers for Disease Control and Prevention (CDC) offers a variety of resources including relevant government links. The main resources is the Foodborne Diseases Surveillance and Outbreak Investigation Toolkit that includes guidelines, case forms, and a variety of questionnaires to assist users. 

Mathematical models that estimate the proportion of foodborne illnesses attributable to food commodities at specific points in the food chain may be useful to risk managers and policy makers to formulate public health goals, prioritize interventions, and document the effectiveness of mitigations aimed at reducing illness. Using human surveillance data on laboratory-confirmed Salmonella infections from the Centers for Disease Control and Prevention and Salmonella testing data from U.S. Department of Agriculture Food Safety and Inspection Service's regulatory programs, the researchers developed a point-of-processing foodborne illness attribution model by adapting the Hald Salmonella Bayesian source attribution model. Key model outputs include estimates of the relative proportions of domestically acquired sporadic human Salmonella infections resulting from contamination of raw meat, poultry, and egg products processed in the United States from 1998 through 2003. 

The Centers for Disease Control and Prevention (CDC) has created multiple outbreak response toolkits and other resources to assist public health professionals during their outbreak response to save time on creating tools so more time can be spent on the investigation. This finding aid directs the user to many such tools. 

The Enteric Disease Outbreak Investigation Model provides guidelines for enteric disease outbreak investigation to local health departments. The model covers many parts of outbreak investigation, including epidemiologic, environmental, nursing, legal, and public information.

OpenEpi is free and open source software for epidemiologic statistics. It can be run from a web server or downloaded and run without a web connection. A server is not required. OpenEpi provides statistics for counts and measurements in descriptive and analytic studies, stratified analysis with exact confidence limits, matched pair and person-time analysis, sample size and power calculations, random numbers, sensitivity, specificity and other evaluation statistics, R x C tables, chi-square for dose-response, and links to other useful sites.

Foodborne illness surveillance based on consumer complaints detects outbreaks by finding common exposures among callers, but this process is often difficult. Laboratory testing of ill callers could also help identify potential outbreaks. However, collection of stool samples from all callers is not feasible. Methods to help screen calls for etiology are needed to increase the efficiency of complaint surveillance systems and increase the likelihood of detecting foodborne outbreaks caused by Salmonella. Data from the Minnesota Department of Health foodborne illness surveillance database (2000 to 2008) were analyzed. Complaints with identified etiologies were examined to create a predictive model for Salmonella. Bootstrap methods were used to internally validate the model. 

Quantitative risk assessments are now required to support many regulatory decisions involving infectious diseases of animals. Current methods, however, do not consider the relative values of historical and recent data. A Markov-chain model can use specific disease characteristics to estimate the present value of disease information collected in the past. Uncertainty about the disease characteristics and variability among animals and herds can be accounted for with Monte Carlo simulation modeling. This results in a transparent method of valuing historical testing information for use in risk assessments. The researchers constructed such a model to value historical testing information in a more-transparent and -reproducible manner. Applications for this method include trade, food safety, and domestic animal-health regulations.

Prevalence and counts of Salmonella Typhimurium in fresh pork sausage packs at the point of retail were modeled by using Irish and United Kingdom retail surveys' data. A methodology for modeling a second-order distribution for the initial Salmonella concentration (λ₀) in pork sausage at retail was presented considering the uncertainty originated from the most probable-number (MPN) serial dilutions.

The BRFSS, the world’s largest telephone survey, tracks health risks in the United States. Information from the survey is used to improve the health of the American people. Here, Cross Tabulation Analysis and Logistic Analysis are available. 

In this paper, the authors summarize the application of extensions to the Hald, Dutch, and Island models for source attribution to the isolates from a sentinel collection site over a 4 year period, focusing on the temporal variation in the attribution through time.

Outbreaks from 2002 through 2006 recorded in a European norovirus surveillance database were investigated for virologic and epidemiologic indicators of food relatedness. The selection tool enabled prospectively focused follow-up. Use of this tool is likely to improve data quality and strain typing in current surveillance systems, which is necessary for identification of potential international foodborne outbreaks (a spreadsheet of this tool is also provided with this reference).

Here, the authors adapted and applied the Hald model to L. monocytogenes to determine whether such an approach may provide further insight and quantify the relative importance of food sources to listeriosis cases, including risk population groups such as the elderly.

In this study, the authors propose a class of hierarchical models for the purpose of estimating the herd-level prevalence distribution and the accuracies of two tests in the absence of a gold standard when several exchangeable populations with differing disease prevalence are available for sampling, relaxing the assumption of conditional independence between tests. The models are used to estimate the prevalence of bovine brucellosis in Mexican cow herds and to estimate the error rates of two tests for the detection of swine pneumonia.

These case studies are interactive exercises developed to teach epidemiologic principles and practices. They are based on real-life outbreaks and public health problems and were developed in collaboration with the original investigators and experts from the Centers for Disease Control and Prevention. The case studies require students to apply their epidemiologic knowledge and skills to problems confronted by public health practitioners at the local, state, and national level every day. Two types of epidemiologic case studies are available. The computer-based case studies can be used as self-study and in the classroom setting. The classroom case studies are primarily for use in a group setting with a knowledgeable instructor.

BetaBuster is a GUI that can be used to obtain specific Beta prior distributions for Se, Sp and Prevalence. BetaBuster was designed by for the purpose of obtaining specific Beta prior distributions based on scientific input for quantities like the sensitivity or speciticity of a diagnostic test.

The following modules contain software, program code, illustrated examples, and associated publications. The purpose of the program is to make available to a larger audience some of the methods developed in addressing issues of analyzing data derived from imperfect diagnostic tests. These programs have mainly been written for use with WinBUGS, to facilitate their use by data analysts.

The TAGS software can be used to estimate the sensitivity of two or more diagnostic tests in the absence of a gold standard, provided two or more populations with differing prevalences can be cross-classified based on diagnostic test results. The algorithm in the TAGS software follows the frequentist paradigm and utilizes Newton-Raphson and EM algorithms to generate maximum likelihood estimates.

A Bayesian approach was developed by Hald et al. to estimate the contribution of different food sources to the burden of human salmonellosis in Denmark. This article describes the development of several modifications that can be used to adapt the model to different countries and pathogens.

This Toolbox is an area on NACCHO's Web site where you can find useful public-health-related tools. Toolbox is a free service available for public use, intended to promote and advance public health objectives. Enter a keyword or select a toolkit to find search-able information.