Use of a geographic information system to map cases of measles in real-time during an outbreak in Dublin, Ireland, 2011

Abstract

In 2011, there was a large measles outbreak in Dublin. Nationally 285 cases were notified to the end of December 2011, and 250 (88%) were located in the Dublin region. After the first case was notified in week 6, numbers gradually increased, with 25 notified in June and a peak of 53 cases in August. Following public health intervention including a measles-mumps-rubella (MMR) vaccination campaign, no cases were reported in the Dublin region in December 2011. Most cases (82%) were children aged between 6 months and 14 years, and 46 cases (18%) were under 12 months-old. This is the first outbreak in Dublin to utilise a geographic information system for plotting measles cases on a digital map in real time. This approach, in combination with the analysis of case notifications, assisted the department of public health in demonstrating the extent of the outbreak. The digital mapping documented the evolution of two distinct clusters of 87 (35%) cases. These measles cases were infected with genotype D4-Manchester recently associated with large outbreaks across Europe. The two clusters occurred in socio-economically disadvantaged areas and were attributable to inadequate measles vaccination coverage due in part to the interruption of a school-based MMR2 vaccination programme.

An ongoing measles outbreak linked to a suspected imported case, Ireland, April to June 2016

“…In Ireland, measles incidence has declined in the last decade, from 8.4 cases per 100,000 in 2004 to 0.7 cases per 100,000 in 2014 [1,2]. The most recent national measles outbreaks occurred over four years ago [3,4].…”

Mobile Technologies and Geographic Information Systems to Improve Health Care Systems: A Literature Review

“…In addition, GIS was used to discover the geographical variation of syphilis seeking clusters and hotspots [185]; plot measles cases on a digital map in real time [186]; visualize cancer risk patterns associated with incidence, mortality, and accessibility to care [187]; investigate factors associated with nosocomial transmission of resistant organisms [188]; and locate all out-of-hospital cardiac arrests (OHCAs) and identify clusters of OHCAs, as well as clusters of patients who did not receive bystander cardiopulmonary resuscitation [189]. In some studies, GIS was used to examine built environment characteristics and resident health behaviors as they relate to change in blood pressure [190]; quantify the effect of fish pond density on malaria occurrence [191]; identify geographic areas with elevated risk for the later development of amyotrophic lateral sclerosis among military personnel who served in the first Gulf War [192]; map the prevalence of malaria [193]; identify malaria hot spots [194]; create maps and charts displaying the geographic distribution of locations of injuries and their relationships with environmental and demographic parameters [195]; and examine sex-specific spatial patterns of overweight/obesity [196].…”

Spatial methods for infectious disease outbreak investigations: systematic literature review

“…Although the simple dot map was the most commonly used method, a wide range of techniques were applied, including more sophisticated data visualisations and analytic tools. Across the range of studies, there were examples of spatial tools being usefully applied throughout the course of an outbreak investigation; from initial confirmation of the outbreak to describing and analysing [11,18-28,30-36,38,40-50,55,56,58-60, 63,67-69,71-76,80,82-85,87-93,96-105] Thematic map 25 [18,19,[21][22][23]28,29,38,41,42,45,46,50,60,68,70,74,80,82,89,91,[93][94][95]103] Rate map 14 [20,30,44,45,49,50,57,63,70,76,79,81,90,101] Smoothed incidence map 13 [11,31,[48][49][50]73,…”

“…Use of dot mapping to support an outbreak in real time is described by Fitzpatrick and colleagues, who investigated a rise in measles cases in Dublin, Ireland [28]. Continuously updating their maps throughout the outbreak allowed them to identify clustering of cases as soon as it developed and ultimately assisted with targeting of control interventions.…”

“…Measures that aimed to control outbreaks included cleaning implicated cooling towers [40], issuing water boiling orders to areas served by contaminated supplies [41,42], vaccination catch-up campaigns [28], removal of breeding sites for mosquito larvae [43] and targeted information campaigns [36]. For example, Acheson and colleagues placed postcode-targeted information on social networks during an outbreak of heterosexually acquired syphilis in Teesside, UK [44].…”