Infectious Disease

Salmonella Enteritidis is dangerous for human due the reason of toxicoinfaction. These pathogen demonstrate high virulence for small children and people with chronic pathologies and can causes people die. The main source of infection to humans is birds (poultry and wild).

Wild birds represent the natural reservoir of same bacterial pathogens. It is known that Salmonella can occupy an intestinal tract of birds. This colonization in general is constant, sometimes proceeds with an alternating fever, and usually, without clinical signs. Infected birds can transmit pathogens to other isolates in close contact. This usually occurs on the nesting during seasonal migrations. In the southern region of Ukraine are several points of intersection of migration routes of wild birds on the way from Europe to Africa and Asia (National Park “Askania Nova”and others).

Objective

The aim of our study was to identify possible natural reservoirs of Salmonella Enteritidis among wild birds.

Information about disease severity could help with both detection and situational awareness during outbreaks of acute respiratory infections (ARI). In this work, we use data from the EMR to identify patients with pneumonia, a key landmark of ARI severity. We asked if computerized analysis of the free-text of clinical notes or imaging reports could complement structured EMR data to uncover pneumonia cases.

Objective

To improve the surveillance for pneumonia using the free-text of electronic medical records (EMR).

In a 2007 survey of public health officials in the United States, International Society for Disease Surveillance found that only 7% used pharmacy prescription sales data for surveillance (1). There have been many reports suggesting effective use of prescription sales data in syndromic surveillance (2, 3, 4, 5). Community pharmacies can provide a valuable supplementary tool for syndromic surveillance of infectious diseases.

Objective

To examine if the prescription sales data from a large retail pharmacy chain in the US were comparable to Google Flu trends and CDC’s US ILI Network data as flu activity indicator.

Influenza is viral illness that affects mainly the nose, throat, bronchi and occasionally, the lungs. Influenza viruses have been an under-appreciated contributor to morbidity and mortality in Nigeria. They are a substantial contributor to respiratory disease burden in Nigeria and other developing countries. Nigeria started influenza sentinel surveillance in 2008 to inform disease control and prevention efforts.

Objective

To analyze Influenza surveillance data from 2009 to 2010 the Northern, Southern, and Western zones in Nigeria and determined co-morbidity factors associated with influenza in Nigeria.

A sudden surge in hospital admissions in public hospital during influenza peak season has been a challenge to healthcare and manpower planning. In Hong Kong, the timing of influenza peak seasons are variable and early short-term indication of possible surge may facilitate preparedness which could be translated into strategies such as early discharge or reallocation of extra hospital beds. In this study we explore the potential use of multiple routinely collected syndromic data in the forecast of hospital admissions.

Objective

This paper describes the potential use of multiple influenza surveillance data to forecast hospital admissions for respiratory diseases.

The Infectious Disease Society of America’s Emerging Infections Network (EIN) is a sentinel network of over 1,200 practicing infectious disease physicians, supported by the Centers for Disease Control and Prevention (CDC). In January 2012, the EIN listserv fielded a member inquiry about treatment recommendations for a complicated polymicrobial wound infection in a traveler returning to the United States from India. The posting led to a member-to-member communication that resulted in shipment of clinical microbiology isolates from one member’s hospital to another’s research laboratory. Molecular evaluation of the clinical isolates uncovered previously undetected carriage of the emerging NDM-1 enzyme in 2 of the Enterobacteriaceae species. Based on this interaction, we built a flexible online surveillance registry (CaseFinder) for infectious disease physicians to report cases of CRE.

Objective

To create a flexible online surveillance system for infectious disease experts to report cases of emerging infectious diseases.

Introduction

Measles is a vaccine preventable disease that has been successfully eliminated in some parts of the world. It causes high morbidity and mortality with the potential of large outbreaks. About a third of reported measles cases involve one or more complications including diarrhea, pneumonia, otitis media, blindness, post infections encephalitis and subacute sclerosing panencephalitis. It is however, one of the leading causes of childhood morbidity and mortality in Nigeria despite availability of safe and effective vaccines.

Objective

The objective of this study was to describe the performance of the measles surveillance in Lagos, characterize the epidemiologic pattern of measles infection and determine the measles vaccine efficacy.

The past decade has witnessed rapid development and implementation of numerous syndromic and other advanced surveillance systems to supplement traditional laboratory testing to identify the presence of novel influenza strains and track the impact on local populations. While much of the development and widespread implementation of these systems had been supported by public health preparedness funding, the loss of these monies has greatly constrained the ability of public health agencies to staff and maintain these systems. The periodic appearance of novel flu viruses, such as H3N2v, requires agencies to carefully choose which systems will provide the most cost-effective data to support their public health practice.

Objective

This project was organized to facilitate discussions on whether successful novel flu surveillance can be conducted by jurisdictions with limited resources. The discussions will focus on gathering opinions regarding the best combination of surveillance systems to quickly and efficiently identify the presence of influenza A (H3N2)v and other novel influenza viruses in circulation.

Over the past five years, efforts to control malaria have been intensified in Uganda (1). With the intensification of these efforts, accurate and timely data are needed to monitor impact of the interventions and guide malaria control program planning (2, 3). We present data on trends in malaria burden over four years from six outpatient health facilities located in regions of varying malaria endemicity in Uganda.

Objective:

To estimate trends in malaria morbidity at six sentinel sites in Uganda.

 

Infectious diseases, though initially tend to be limited geographically to a reservoir; a subsequent spatial variation in disease prevalence (including spread & intensity) arises from the underlying differences in physical-biological conditions that support pathogen, its vectors & reservoirs. Different factors like spatial proximity, physical & social connectivity, & local environmental conditions which add to its susceptibility influence the occurrence[2]. In Disease management, analysis of historical data over various aspects of geography, epidemiology, social structures & network dynamics need to be accounted for. Large amounts of data raise issues of data processing, storage, pattern identification, etc. In addition, identifying the source of disease occurrence & its pattern can be of immense value. ST-DM of disease data can be an effective tool for endemic preparedness[3], as it extracts implicit knowledge, spatial & temporal relationships, or other patterns inherent in such databases. Here, Core Region is defined as a set of spatial entities(eg.counties) aggregated over time, which occur frequently at places having high values in a defined region (considering areas of influence around them)[1].

Objective:

This work leverages spatio-temporal data mining (ST-DM), the MiSTIC (Mining Spatio-Temporally Invariant Cores)[1,6] method for infectious disease surveillance, by identifying a) Extent of spatial spread of disease core regions across populations-scale of disease prevalence b) Possible causes of the observed patterns-for better prediction, detection & management of infectious disease & its outbreaks.