A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data.

BACKGROUND: Dengue is the most common arboviral disease of humans, with more than one third of the world's population at risk. Accurate prediction of dengue outbreaks may lead to public health interventions that mitigate the effect of the disease. Predicting infectious disease outbreaks is a challenging task; truly predictive methods are still in their infancy. METHODS: We describe a novel prediction method utilizing Fuzzy Association Rule Mining to extract relationships between clinical, meteorological, climatic, and socio-political data from Peru. These relationships are in the form of rules. The best set of rules is automatically chosen and forms a classifier. That classifier is then used to predict future dengue incidence as either HIGH (outbreak) or LOW (no outbreak), where these values are defined as being above and below the mean previous dengue incidence plus two standard deviations, respectively. RESULTS: Our automated method built three different fuzzy association rule models. Using the first two weekly models, we predicted dengue incidence three and four weeks in advance, respectively. The third prediction encompassed a four-week period, specifically four to seven weeks from time of prediction. Using previously unused test data for the period 4-7 weeks from time of prediction yielded a positive predictive value of 0.686, a negative predictive value of 0.976, a sensitivity of 0.615, and a specificity of 0.982. CONCLUSIONS: We have developed a novel approach for dengue outbreak prediction. The method is general, could be extended for use in any geographical region, and has the potential to be extended to other environmentally influenced infections. The variables used in our method are widely available for most, if not all countries, enhancing the generalizability of our method.

The pandemic influenza policy model: a planning tool for military public health officials.

The Pandemic Influenza Policy Model (PIPM) is a collaborative computer modeling effort between the U.S. Department of Defense (DoD) and the Johns Hopkins University Applied Physics Laboratory. Many helpful computer simulations exist for examining the propagation of pandemic influenza in civilian populations. We believe the mission-oriented nature and structured social composition of military installations may result in pandemic influenza intervention strategies that differ from those recommended for civilian populations. Intervention strategies may differ between military bases because of differences in mission, location, or composition of the population at risk. The PIPM is a web-accessible, user-configurable, installation-specific disease model allowing military planners to evaluate various intervention strategies. Innovations in the PIPM include expanding on the mathematics of prior stochastic models, using military-specific social network epidemiology, utilization of DoD personnel databases to more accurately characterize the population at risk, and the incorporation of possible interventions, e.g., pneumococcal vaccine, not examined in previous models.

Meningococcal disease among United States military service members in relation to routine uses of vaccines with different serogroup-specific components, 1964-1998.

Historically, military recruits have been at high risk of acquiring meningococcal disease. Beginning in the 1940s, the US military relied on mass treatment with sulfadiazine to control outbreaks in training camps. In the 1960s, a vaccine was developed in response to the emergence of sulfadiazine-resistant strains. Since 1971, all new recruits in the US military have been immunized against Neisseria meningitidis during their first days of service. Serogroups represented in vaccines given to service members have changed over time: the quadrivalent (A, C, Y, W135) vaccine has been given since 1982. In the US military, meningococcal disease rates decreased by approximately 94% from 1964 to 1998. After initiating routine immunization in 1971, crude rates decreased sharply and have remained low; in addition, there have been few cases of disease caused by serogroups represented in contemporaneously administered vaccines. In the US military, immunizations have been effective for the prevention of disease caused by vaccine-homologous serogroups of N. meningitidis.

Large epidemic of adenovirus type 4 infection among military trainees: epidemiological, clinical, and laboratory studies.

Outbreaks of adenovirus type 4 (Ad4) acute respiratory disease (ARD) have reemerged among US military personnel during the past decade. A prospective epidemiological investigation of 678 military recruits was conducted at Fort Jackson, South Carolina, in the fall of 1998; 115 (17%) of the recruits were hospitalized for febrile ARD. Adenovirus types 4, 3, and 21 were recovered from the cultures of 70 (72%), 7 (7%), and 2 (2%) of 97 recruits, respectively. In addition, 69 (83%) of the 83 hospitalized and 82 (49%) of the 166 nonhospitalized unit contacts had seroconversion to Ad4, which indicates the very high susceptibility and communicability of Ad4 among military recruits. Young age (<20 years) and male sex increased the risk for anti-Ad4 seroconversion. Recruits from tropical areas had higher preexisting immunity than did recruits from temperate regions. Military recruits are highly susceptible to Ad4 infections. Prompt reinstitution of an adenovirus vaccination program in this high-risk population is urgently needed.

SAGES: a suite of freely-available software tools for electronic disease surveillance in resource-limited settings.

Public health surveillance is undergoing a revolution driven by advances in the field of information technology. Many countries have experienced vast improvements in the collection, ingestion, analysis, visualization, and dissemination of public health data. Resource-limited countries have lagged behind due to challenges in information technology infrastructure, public health resources, and the costs of proprietary software. The Suite for Automated Global Electronic bioSurveillance (SAGES) is a collection of modular, flexible, freely-available software tools for electronic disease surveillance in resource-limited settings. One or more SAGES tools may be used in concert with existing surveillance applications or the SAGES tools may be used en masse for an end-to-end biosurveillance capability. This flexibility allows for the development of an inexpensive, customized, and sustainable disease surveillance system. The ability to rapidly assess anomalous disease activity may lead to more efficient use of limited resources and better compliance with World Health Organization International Health Regulations.