Infectious Disease

Sequence-informed surveillance is now recognized as an important extension to the monitoring of rapidly evolving pathogens [2]. This includes phylogeography, a field that studies the geographical lineages of species including viruses [3] by using sequence data (and relevant metadata such as sampling location). This work relies on bioinformatics knowledge. For example, the user first needs to find a relevant sequence database, navigate through it, and use proper search parameters to obtain the desired data. They also must ensure that there is sufficient metadata such as collection date and sampling location. They then need to align the sequences and integrate everything into specific software for phylogeography. For example, BEAST [4] is a popular tool for discrete phylogeography. For proper use, the software requires knowledge of phylogenetics and utilization of BEAUti, its XML processing software. The user then needs to use other software, like TreeAnnotator [4], to produce a single (representative) maximum clade credibility (MCC) tree. Even then, the evolutionary spread of the virus can be difficult to interpret via a simple tree viewer. There is software (such as SpreaD3 [5]) for visualizing a tree within a geographic context, yet for novice users, it might not be easy to use. Currently, there are only a few systems designed to automate these types of tasks for virus surveillance and phylogeography.

Objective: We will describe the ZooPhy system for virus phylogeography and public health surveillance [1]. ZooPhy is designed for public health personnel that do not have expertise in bioinformatics or phylogeography. We will show its functionality by performing case studies of different viruses of public health concern including influenza and rabies virus. We will also provide its URL for user feedback by ISDS delegates.

Syndromic surveillance data is typically used for the monitoring of symptom combinations in patient chief complaints (i.e. syndromes) or health indicators within a population to inform public health actions. The Tennessee Department of Health collects emergency department (ED) data from more than 80 hospitals across Tennessee to support statewide situational awareness. Most hospitals in Tennessee provide data within 48 hours of the patient being seen in the emergency department. The timeliness of syndromic surveillance data allow for rapid estimates of impact in emergency department populations. Tennessee has successfully used these data to monitor influenza, heat related illnesses, and emergency department impacts from disaster evacuations. In addition to assessing impact and trends, syndromic surveillance can also provide early warnings for conditions of public health concern and increase the lead time public health has to initiate a response. In Tennessee, routine syndromic surveillance for mumps, hepatitis A, and other conditions has been successfully conducted statewide. Three successes from these surveillance efforts include detecting a clinically diagnosed but unreported case of mumps, early identification of hepatitis A cases during Tennessee's ongoing 2018 hepatitis A outbreak, and the detection of an epidemiologically unlikely clinical diagnosis of mumps associated with an exposure at a recreational center.

Objective: To demonstrate the utility of syndromic surveillance data in aiding public health actions and response across multiple investigations in Tennessee.

Monitoring of long-term infectious disease mortality trends is of great value to national public health systems both in estimation of the efficacy of preventive programs, and in development of the new strategies of preventive measures. In the developed countries, there are a number of studies with long-term time series of infectious disease mortality analysis in epidemiological and historical aspects. Our research was based on the work by Armstrong GL, Conn LA and Pinner RW, 1999. Literature review revealed that such analysis has been never carried out in Ukraine up to now.

Objective: The aim of our work is to determine the main trends and structure in infectious disease mortality in Ukraine over the last 50 years.