This paper describes an integrated modeling, sensing, and information system for forecasting atmospheric dust episodes and monitoring PM2.5 and PM10 concentrations that aggravate respiratory diseases in the American southwest.
The Hajj is considered to be the largest mass gathering to date, attracting an estimated 2.5 million Muslims from more than 160 countries annually. The H1N1 Influenza A pandemic of 2009 generated a global wave of concern among public health departments that resulted in the institution of preventive measures to limit transmission of the disease. Meanwhile, the pandemic amplified an urgent need for more innovative disease surveillance tools to combat disease outbreaks. A collaborative effort between the KSA Ministry of Health (MOH) and the U.S. Centers for Disease Control and Prevention (CDC) was initiated to implement and deploy an informatics-based mobile solution to provide early detection and reporting of disease outbreaks during the 2009 Hajj. The mobile-based tool aimed to improve the efficiency of disease case reporting, recognize potential outbreaks, and enhance the MOH’s operational effectiveness in deploying resources.
Objective
To develop and implement a mobile-based disease surveillance system in the Kingdom of Saudi Arabia (KSA) for the 2009 Hajj; to strengthen public health preparedness for the H1N1 Influenza A pandemic.
Spatial methods are an important component of epidemiological research motivated by a strong correlation between disease spread and ecological factors. Our case studies examine the relationship between environmental conditions, such as climate and location, and vector distribution and abundance. Therefore, GIS can be used as a platform for integrating local environmental and meteorological variables into the analysis of disease spread, which would help in surveillance and decision making.
Objective
Use GIS to illustrate and understand the association between environmental factors and spread of infectious diseases.
Uncertainty introduced by the selective identification of cases must be recognized and corrected for in order to accurately map the distribution of risk. Consider the problem of identifying geographic areas with increased risk of DRTB. Most countries with a high TB burden only offer drug sensitivity testing (DST) to those cases at highest risk for drug-resistance. As a result, the spatial distribution of confirmed DRTB cases under-represents the actual number of drug-resistant cases. Also, using the locations of confirmed DRTB cases to identify regions of increased risk of drug-resistance may bias results towards areas of increased testing. Since testing is neither done on all incident cases nor on a representative sample of cases, current mapping methods do not allow standard inference from programmatic data about potential locations of DRTB transmission.
Objective
Uncertainty regarding the location of disease acquisition, as well as selective identification of cases, may bias maps of risk. We propose an extension to a distance-based mapping method (DBM) that incorporates weighted locations to adjust for these biases. We demonstrate this method by mapping potential drug-resistant tuberculosis (DRTB) transmission hotspots using programmatic data collected in Lima, Peru.
The NYC Department of Health and Mental Hygiene (DOHMH) ED syndromic surveillance system receives data from 95% of all ED visits in NYC totaling 4 million visits each year. The data include residential ZIP code as reported by the patient. ZIP code information has been used by the DOHMH to separate visits into NYC and nonNYC for analysis; and, a closer examination of non-NYC visits may further inform disease surveillance.
Objective
To classify visits to NYC emergency departments (ED) into NYC residential, NYC PO Box or commercial building, commuters to NYC, and out-of-town visitors. To describe patterns in each group, to evaluate how they differ, and to consider how the differences can affect syndromic surveillance analyses and results.
Dengue Fever (DF) is a vector-borne disease of the flavivirus family carried by the Aedes aegypti mosquito, and one of the leading causes of illness and death in tropical regions of the world. Nearly 400 million people become infected each year, while roughly one-third of the worldâs population live in areas of risk. Dengue fever has been endemic to Colombia since the late 1970s and is a serious health problem for the country with over 36 million people at risk. We used the Magdalena watershed of central Colombia as the site for this study due to its natural separation from other geographical regions in the country, its wide range of climatic conditions, the fact that it includes the main urban centers in Colombia, and houses 80% of the countryâs population. Advances in the quality and types of remote sensing (RS) satellite imagery has made it possible to enhance or replace the field collection of environmental data such as precipitation, temperature, and land use, especially in remote areas of the world such as the mountainous areas of Colombia. We modeled the cases of DF by municipality with the environmental factors derived from the satellite data using boosted regression tree analysis. Boosted regression tree analysis (BRT), has proven useful in a wide range of studies, from predicting forest productivity to other vector-borne diseases such as Leishmaniosis, and Crimean-Congo hemorrhagic fever. Using this framework, we set out to determine what are the differences between using presence/absence and case counts of DF in this type of analysis?
Objective:
In this paper we used Boosted Regression Tree analysis coupled with environmental factors gathered from satellite data, such as temperature, elevation, and precipitation, to model the niche of Dengue Fever (DF) in Colombia.
In the 2015 dengue outbreak in Taiwan, 43,784 people were infected and 228 died, making it the nationâs largest outbreak ever. Facing the increasing threat of dengue, the integration of health information for prevention and control of outbreaks becomes very important. Based on past epidemics, the areas with higher incidence of dengue fever are located in southern Taiwan. Without a smart and integrated surveillance system, the information on case distribution, high risk areas, mosquito surveillance, flooding areas and so on is fragmented. The first-line public health workers need to check all this information through different systems manually. When outbreaks occurred, paper-based outbreak investigation forms had to be prepared and filled in by public health workers. Then, they needed to enter part of this information into Taiwan CDCâs system. Duplicated work occurred and cost lots of labor time during the epidemic period. Therefore, we choose one rural county, Pingtung County, with scarce financial resources, to set up a new dengue surveillance system.
Objective:
In this paper we designed one cross-platform surveillance system to assist dengue fever surveillance, outbreak investigation and risk management of dengue fever.
Epidemiological surveillance is used to monitor time trends in diseases and the distribution of the diseases in the population. To streamline the process of identifying outbreaks, and notification of disease, syndromic surveillance has emerged as a method to report and analyze health data. Rather than report data by disease status (ie disease/no disease), clinical symptoms are used to detect outbreaks as early as possible.
Currently, only data collected via active surveillance (notifiable disease investigations) are usable for identifying communities that require attention. Therefore, any interventions performed using said data is reactive in nature. Syndromic surveillance systems must be disaggregated to enable proactive health promotion, and responses.
Furthermore, a common method must be established to assess the overall impact of syndromes. Diseases are not equal; some have a greater impact on health, and life. To address this issue, the World Health Organization (WHO) has created disability weights to be used in calculating disability adjusted life years (DALY). DALYs are effective in calculating the overall impact of disease in a community. DALYs estimate the burden of disease, not syndromes; therefore, it is reactive tool. To create a more effective syndromic surveillance system, syndromes must be associated with an overall impact weight.
Objective
The justification for address based syndromic surveillance systems, and building syndrome weighting mechanisms.
Michael A. Horst, PhD, MPHS, MS, joined the April 2010 ISDS Literature Review to present his recent publication, "Observing the Spread of Common Illnesses Through a Community: Using Geographic Information Systems (GIS) for Surveillance," from the Journal of the American Board of Family Medicine.The Literature Review Subgroup found this article particularly important becase it represents an initiative to link health risk mapping with cluster detection methods that many health monitors employ.
Mapping ILI surveillance data can be useful in identifying the direction and speed of an outbreak and for focusing control measures for an efficient public health response. The Centers for Disease Control and Prevention’s (CDC) ILINet currently displays weekly ILI geographic data at a national/regional/state level, but this visual data could also be useful at the local level.
Objective
To create a local geographic influenza-like illness (ILI) activity report.