Infectious Disease

The mortality monitoring system (initiated in 2009 during the influenza A(H1N1) pandemic) is a collaboration between the Centre for Infectious Disease Control (CIb) and Statistics Netherlands. The system monitors nation-wide reported number of deaths (population size 2014: 16.8 million) from all causes, as cause of death information is not available real-time. Data is received from Statistics Netherlands by weekly emails. 

Objective

Weekly numbers of deaths are monitored to increase the capacity to deal with both expected and unusual (disease) events such as pandemic influenza, other infections and non-infectious incidents. The monitoring information can potentially be used to detect, track and estimate the impact of an outbreak or incident on all-cause mortality. 

The first travel-associated cases of Zika virus infection in New York City (NYC) were identified in January 2016. Local transmission of Zika virus from imported cases is possible due to presence of Aedes albopictus mosquitos. Timely detection of local Zika virus transmission could inform public health interventions and mitigate additional spread of illness. Daily emergency department (ED) visit surveillance to detect individual cases and spatio-temporal clusters of locally-acquired Zika virus disease was initiated in June 2016. 

Objective

Case and cluster identification of emergency department visits related to local transmission of Zika virus. 

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. 

Global targets for elimination of human rabies mediated by dogs have been set for 2030. In the Americas countries are progressing towards interruption of transmission and declaration of rabies freedom1. Guidance for managing elimination programmes to ensure continued progress during the endgame is critical, yet often limited and lacking in specific recommendations. Characteristic spatiotemporal incidence patterns are indicative of progress, and through their identification, tailored guidance can be provided. 

Objective

To provide surveillance tools to support policymakers and practitioners to identify epidemiological situations and inform the progressive implementation of rabies elimination programmes. 

Malaria is a preventable disease but 3.4 billion people at risk globally with 207 million cases and 627 deaths reported in 2013. Africa accounts for 80% of cases and 90% of all malaria deaths. Nigeria accounts for 25% of malaria burden in Africa. The goal of malaria control is to reduce malaria –related transmissions, cases and deaths to a level where it is no longer a public health concern.

Objective

We aim to assess the implementation of malaria prevention, diagnosis and treatment strategies, to assess implementation trends from 2011 to 2014 and if surveillance targets were met. 

Transparency of information on infectious disease epidemics is crucial for not only public health workers but also the residents in the communities. Traditionally, disease control departments created official websites for displaying disease maps or epi-curves with the confirmed case counts. The websites were usually very formal and static, without interaction, animation, or even the aid of spatial statistics. Therefore, we tried to take advantage of open data and use a lightweight programming language, JavaScript, to create an interactive website, named “Taiwan Infectious Disease Map (http://ide.geohealth.tw/)“. With the website, we expect to provide real-time incidence information and related epidemiological features using interactive maps and charts. 

Objective

To visualize the incidence of notifiable infectious diseases spatially and interactively, we aimed to provide a friendly interface to access local epidemic information based on open data for health professionals and the public. 

According to CDC, CRE is used to describe bacteria that are non- susceptible to one or more carbapenems; doripenem, meropenem or imipenem and resistant to third generation cephalosporins like ceftriaxone, cefotaxime and ceftazidime. These organisms cause infections that are associated with high mortality rates and they have the potential to spread widely. Antibiotic resistant bacteria cause more than 2 million illnesses and at least 23,000 deaths each year in United States. CREs are found in many health care settings like acute care hospitals, long term care facilities, nursing homes, rehabilitation facilities and other health care settings. Although CREs includes a number of species, reporting in State of Texas is limited to CRE- Klebsiella species and CRE-E.coli.

Objective

To examine demographic as well as clinical characteristics of the Carbapenam Resistant Enteriobacteriacae (CRE) Organisms cases in Houston, Texas, 2015-2016

Community influenza infection rates are highest among children. In children, influenza can cause severe illness and complications including, respiratory failure and death. Annual influenza vaccination is recommended for all persons aged ≥ 6 months. In 2004, influenza- associated deaths in children became a notifiable condition. 

Objective

To characterize and describe influenza-associated pediatric deaths in the United States over five influenza seasons, 2010–11 through 2014–15. 

Specific communicable diseases have to be reported by law within a specific time period. In Ohio, prior to 2001, most of these disease​ reports were on paper reports that were reported from providers to local health departments. In turn, the Communicable Disease Nurse mailed the hard copies to the Ohio Department of Health (ODH). In 2001 the Ohio Disease Reporting System (ODRS) was rolled out to all local public health agencies in Ohio. ODRS is Ohio’s portion of the National Electronic Disease Surveillance System. ODRS should not be confused with syndromic surveillance systems that are for detecting a disease outbreak before the disease itself is detected. Chronic disease surveillance system data has been evaluated for long-term trends and potential enhancements. However, the use of communicable disease reports vary greatly. However, the export data has not routinely been used for quality improvement purposes of the disease reporting process itself. In December 2014, Greene County Public Health (GCPH) began a project to improve reporting of communicable diseases and the response to disease outbreaks. 

Objective

Improve disease reporting and outbreak management.