Announcements
Eurosurveillance remains in the updated list of the Directory of Open Access Journals (DOAJ). It was first added to the DOAJ on 9 September 2004. Eurosurveillance is also listed in the Securing a Hybrid Environment for Research Preservation and Access / Rights MEtadata for Open archiving (SHERPA/RoMEO) [2], a database which uses a colour‐coding scheme to classify publishers according to their self‐archiving policy and to show the copyright and open access self-archiving policies of academic journals. Eurosurveillance is listed there as a ‘green’ journal, which means that authors can archive pre-print (i.e. pre-refereeing), post-print (i.e. final draft post-refereeing) and archive the publisher's version/PDF.

ESCAIDE participants are invited to the fifth Eurosurveillance scientific seminar on 30 November 2016

Follow Eurosurveillance on Twitter: @Eurosurveillanc

Read our articles on Zika virus infection

Read our articles on mcr-1-mediated colistin resistance

Note of concern published for 'Epidemiological investigation of MERS-CoV spread in a single hospital in South Korea, May to June 2015', http://bit.ly/29QFXPp


In this issue


Home Eurosurveillance Edition  2015: Volume 20/ Issue 39 Article 2
Back to Table of Contents
Previous Download (pdf)
Next

Eurosurveillance, Volume 20, Issue 39, 01 October 2015
Systematic Review
Smith, Le Comber, Fry, Bull, Leach, and Hayward: Spatial methods for infectious disease outbreak investigations: systematic literature review

+ Author affiliations


Citation style for this article: Smith CM, Le Comber SC, Fry H, Bull M, Leach S, Hayward AC. Spatial methods for infectious disease outbreak investigations: systematic literature review. Euro Surveill. 2015;20(39):pii=30026. DOI: http://dx.doi.org/10.2807/1560-7917.ES.2015.20.39.30026

Received:19 February 2015; Accepted:02 September 2015


Introduction

Detecting and responding to outbreaks of infectious diseases is a key role of front-line public health organisations [1]. The primary reason for conducting an investigation into an outbreak is prevention of further cases through control measures, while other motivations include addressing public or political concerns, evaluating health programmes and advancing understanding of the disease [2]. Investigations are usually cross-agency exercises and conventionally involve examination of the outbreak in terms of person, time and place.

John Snow famously demonstrated the power of plotting the spatial locations of individuals affected in an outbreak [3]. His map of cholera cases in London in 1854 showed a clear pattern that implicated a water pump as the likely source of the illness. Today, guidelines for investigating outbreaks, including the European Centre for Disease Prevention and Control (ECDC) Outbreak Investigation Toolbox, invariably also recommend consideration of case locations [4-7]. Nevertheless, epidemiological investigations of outbreaks, and research into novel approaches for such investigations, have tended to focus more on analysis of person and time than of place [8]. Development of advanced molecular tools, for example, has allowed transmission of infectious agents among populations to be traced with ever increasing detail. Without also considering the spatial aspects of an outbreak, however, important relationships and therefore aetiological insights may be missed [8].

Geographic information systems (GIS) have increased the availability and range of tools that can be used to analyse outbreaks. A GIS is a database designed to handle geographically-referenced information complemented by software tools for the input, management, analysis and display of data [9]. GIS are used widely in epidemiology and the simplest application in an outbreak investigation is to create maps displaying the relative locations of cases, potential sources and/or risk factors. Maps are an engaging and easy-to-understand means of presenting data and can be used to describe patterns, identify outliers and communicate findings. Cases can be plotted using their point locations or aggregated into administrative areas and displayed as rates. Smoothed incidence maps are an alternative means of visualising point locations as continuous distributions of disease risk, generated by adjusting the density at each point according to the number of cases in adjacent areas [9]. Areas can also be demarcated according to locations of potential sources of infection. Examples of these different approaches to mapping, using Snow’s cholera data, are shown in Figure 1.

Figure 1

Maps of John Snow's cholera outbreak investigation in London in 1854

/images/dynamic/articles/21253/15-00102-f1

Spatial relationships not immediately apparent from maps can also be explored using GIS. Measuring distances from cases to potential sources, for example, can be informative if an infection is suspected to derive from an environmental point source. In outbreaks of Legionnaires’ disease, this method has been applied to identify cooling towers or other aerosol-producing devices proximal to the cases and therefore generate hypotheses about the likely source [10]. Integration of additional data in the GIS, such as wind direction, can further aid hypothesis generation, for example by identifying areas most likely to be exposed to air emitted from a suspected source during an outbreak of Q fever [11].

Identification and analysis of clusters, areas with higher than expected levels of disease risk, can trigger and be informative during outbreak investigations. Numerous geostatistical methods have been developed to detect clusters, including methods for point and aggregated data [9,12]. ‘Global’ tests evaluate the entire area for any evidence of clustering, without pinpointing specific clusters, while ‘local’ (or ‘cluster detection’) tests identify the positions of specific clusters. Cuzick and Edwards’ k-nearest neighbour test, for example, is a global method for assessing clustering in case–control point data [13]. It counts the number of nearest neighbours of cases that are also cases, and compares it to the number that would be expected under the null hypothesis that cases and controls were randomly distributed. Kulldorff’s spatial scan statistic is a method used to identify local clustering, usually in point data [14]. Observed numbers of cases within windows of various sizes are compared with numbers that would be expected under a random distribution. Circular or elliptical regions of elevated risk of disease are then located. Scan statistics and the k-nearest neighbour test have also been adapted to identify spatiotemporal clustering, testing the null hypothesis that cases geographically close to each other occur at random times [15,16].

Spatial relationships in outbreak data can also be analysed through modelling. A range of techniques can be used which, broadly, aim to create informative representations of features, events and processes in geographical space. Environmental risk mapping, for example, uses statistical methods to define relationships between spatially referenced variables and disease risk [9]. Air dispersion models, meanwhile, can be used to identify spatial locations likely to have been exposed to air-borne infections and infer potential release sites [10].

In this study, we explore through a systematic literature review how methods of spatial visualisation and analysis have been employed in infectious disease outbreak investigations. We aimed to use published reports of outbreak investigations (i) to describe the prevalence, utility and outcomes of applying spatial methods and (ii) to make recommendations for improving practice and identify opportunities for further development in this area.

Methods

Search strategy and selection criteria

The aim of our literature search was to identify published reports of infectious disease outbreak investigations that used spatial methods. We defined an outbreak as the occurrence of a series of cases of disease in excess of the number expected in a given time and place. We focused only on outbreaks with local or regional impact and excluded large national or multinational studies of epidemics or pandemics, such as pandemic influenza. Studies describing retrospective analyses of outbreaks that used spatial methods which could theoretically be applied in real-time investigations were included.

We employed a broad search strategy of multiple electronic databases with few restrictions in order to minimise the risk of bias: We searched Embase, Medline and Web of Science for items with terms relating to spatial analysis (‘spatial’, ‘cluster’, ‘geographic information systems’, ‘GIS’, ‘mapping’) and outbreaks (‘disease outbreak’, ‘outbreak’, ‘epidemic’). The search was run on 28 November 2013 and restricted to articles published after 1980 (Embase), 1946 (Medline) and 1900 (Web of Science). No exclusions were made on basis of language or location, and articles were not limited to human disease. Additional relevant articles known to the authors that were not retrieved from the database search were also added to the results.

After deduplication, titles and abstracts were reviewed to identify articles that met our inclusion criteria: Articles had to relate to an infectious disease, they had to describe an investigation of an outbreak (as defined above) and they had to involve application of spatial analysis or mapping. Abstracts that did not include clear information on the inclusion criteria were brought forward for full-text review. Full texts of articles were assessed with the same inclusion criteria.

We then ran a search of the same databases using only the outbreak investigation terms. We simulated the deduplication and screening process that would result from this search by excluding the same proportion of articles at each step as in the original search. This allowed us to obtain a crude estimate of the total number of published reports of infectious disease outbreak investigations and therefore the proportion that used spatial methods.

Data extraction

Each included study was reviewed and information about the spatial methods and outcomes of the studies extracted (Table 1). Descriptive details obtained were the location of the outbreak, date of publication, type of infection, context or suspected source, and whether the study was prospective or retrospective. Methodological details were the type of spatial methods used and the tools employed. Outcomes were results of the investigations that related specifically to the use of spatial methods and any comments on their advantages or limitations. We summarised reports according to the date of publication, type of infection, location and context of the outbreak. Spatial methods used were categorised into four broad classes: visualisation, cluster analysis, modelling and other spatial analyses.

Table 1

Details of included studies, systematic literature review on spatial methods in infectious disease outbreak investigations (n = 80)


Reference Infectious disease Location Publication year Context Prospective/ retrospective Spatial methods used Stage of investigationa Outcome summary
Acheson [44] Syphilis United Kingdom 2011 Sexual transmission P Dot map; rate map 4, 7 Some clusters found in high deprivation areas; adverts placed on social networks linked to users' postcodes
Boccia [67] Salmonellosis United Kingdom 2004 Food P Dot map; spatial case definition; source proximity 3, 4, 5 No significant difference between closest case and control to suspect outlets
Carr [59] Legionnaires' disease United Kingdom 2010 Environmental P Dot map; case movement map; spatial case definition 3, 4 Identified no hot spots; concluded pseudo-cluster
Hyland [47] Legionnaires' disease United Kingdom 2008 Environmental P Dot map; case movement map; spatial case definition 3, 4, 5, 6, 7, 8 Sullage tanks identified as source; review of national guidelines
Keramarou [26] Legionnaires' disease United Kingdom 2010 Environmental P Dot map; case movement map; spatial case definition 3, 4 Two distinct spatiotemporal clusters identified but no definitive source
Kirrage [40] Legionnaires' disease United Kingdom 2007 Environmental P Dot map; case movement map; spatial case definition; source proximity 3, 4, 5, 7 Identified cluster of cooling towers as likely source; closed and cleaned one of the towers
Neira-Munoz [68] Cryptosporidiosis United Kingdom 2007 Water P Dot map; thematic map; spatial case definition 3, 4, 6 Hypothesis that low level contamination of drinking water caused outbreak; potential change in water monitoring suggested
Sanson [69] Foot and mouth disease United Kingdom 2011 Farm R Dot map; spatial case definition; source proximity; case–case distance; air dispersion modelling 5, 6 Distance and direction from index farm significant predictors of infection status; minimum infective dose might be less than previously established
Wallensten [33] Q fever United Kingdom 2010 Farm P Dot map; spatial case definition; air dispersion modelling 3, 4, 5 Air from each of suspected farms may have exposed town, couldn’t rule any out as potential sources
Le Comber [34] Cholera & malaria United Kingdom & Egypt 2011 Vector/water R Dot map; spatial average; geographic profiling 4, 5 Identified most likely locations of sources of infection
Manfredi Selvaggi [70] Q fever Italy 1996 Farm P Rate map; thematic map; spatial case finding 3, 4 Infected individuals tended to live closer to sheep migration route
Orsi [71] Measles Italy 2010 Community P Dot map 4 Identified worst affected areas
Varani [72] Leishmaniasis Italy 2013 Vector P Dot map 3, 4 Most patients in hilly, rural areas
Norstrom [31] Acute respiratory disease Norway 1999 Farm R Dot map; smoothed incidence map; spatial case definition; space-time scan statistic; k-nearest neighbour test; Knox test 3, 4, 5 Described progression of outbreak; identified cluster; supports hypothesis of single common source of infection
Nygard [32] Legionnaires' disease Norway 2008 Environmental P Dot map; case movement map; spatial case definition; source proximity; air dispersion modelling 3, 4, 5, 6, 7 Identified industrial air scrubber as source of outbreak; scrubber closed, new routines for cleaning and national regulations implemented
Nygard [41] Giardiasis Norway 2006 Water P Dot map; thematic map; spatial case definition 3, 4, 5, 7 Higher attack rate in zone supplied by water supply A; boil water notice issued; flushed distribution system
Abellan [73] Legionnaires' disease Spain 2002 Environmental R Dot map; smoothed incidence map; k-function 4 ,5 Cases more aggregated than controls; confirmed environmental origin of outbreak
Garcia-Fulgueiras [57] Legionnaires' disease Spain 2003 Environmental P Rate map; spatial case definition; source proximity 3, 4, 5, 6 Zone of exposure around hospital associated with illness; replaced cooling tower; Legionella may be able to spread over larger distances from source than previously thought
Jansa [58] Legionnaires' disease Spain 2002 Environmental P Dot map; spatial case definition 3, 4 Cooling towers identified as source
Hackert [11] Q fever The Netherlands 2012 Farm R Dot map; smoothed incidence map; spatial case definition; source proximity 3, 4, 5, 6 Incidence increased with proximity to index farm; cases scattered in wedge shape area downwind of farm
Schimmer [74] Q fever The Netherlands 2010 Farm R Dot map; thematic map; spatial case definition; source proximity; spatial average 3, 4, 5 Gradual diminishing risk from certain farms, identified as probable sources
van der Hoek [50] Q fever The Netherlands 2012 Farm R Dot map; rate map; thematic map; smoothed incidence map; source proximity 4, 8 Identified 5 hot spots, all around infected dairy goat farms
Gubbels [42] Campylobacteriosis Denmark 2012 Water P Dot map; thematic map; spatial case definition 3, 4, 7 Cases lived across entire water supply area; concluded contamination of central water supply; implemented boiling order
Nguyen [63] Legionnaires' disease France 2006 Environmental P Dot map; rate map; case movement map; spatial case definition; spatial case finding; air dispersion modelling 3, 4 Dispersion of plumes from cooling tower correlated with geographical distribution of cases; spread over longer distance than previously thought possible
Kistemann [29] Salmonellosis Germany 2000 Hospital P Thematic maps; schematic map; spatial case definition 1, 3, 4, 7, 8 Identified functional relationship between cases; measures introduced to prevent future outbreaks
Fitzpatrick [28] Measles Ireland 2012 Community P Dot map; thematic map 4, 7 Identified emergence of cluster during outbreak in real time; intervention in high rate area - expediated MMR vaccine schedule/ catch-up campaign
Ulugtekin [18] Measles Turkey 2007 Community P Dot map; thematic maps 4 Identified high incidence areas
Lai [75] Influenza Hong Kong 2010 Community R Dot map; smoothed incidence map; standard deviation ellipse; Moran's I; Getis-Ord Gi* statistic 4, 5 Identified hot spots and directional trend
Lai [76] SARS Hong Kong 2004 Community R Dot map; rate map; smoothed incidence map; standard deviation ellipse; origin–destination plots; Moran's I; nearest neighbour analysis 4, 5 Clear clustering identified; directional bias and radius of spread of superspreading events demonstrated
Sze-To [77] Varicella Hong Kong 2011 Hospital R Schematic map; air dispersion modelling 4, 5 Model matches epidemiological distribution of cases
Wong [39] Influenza Hong Kong 2010 Hospital R Schematic map; spatial case definition; source proximity; air dispersion modelling 3, 4, 5, 6 Proximity to air purifier associated with infection; suggests possible role for aerosol transmission
Yu [78] SARS Hong Kong 2005 Hospital R Schematic map; spatial case definition; air dispersion modelling 3, 4, 5 Attack rates higher in bays closer to index patient; suggests airborne transmission played important role
Bali [27] Hepatitis E India 2008 Water P Dot map; spatial case finding; spatial case definition 3, 4 Cases mapped to water supply distribution area
Nisha [43] Dengue fever India 2005 Vector P Dot map; spatial case finding; scan statistic 3, 4, 7 Identified cluster; fogging and larval reduction; drawing up standard protocol for GIS in outbreaks
Saha [79] Shigellosis India 2009 Water P Rate map; spatial case definition 3, 4 Incidence higher downstream of damaged pipeline
Sarkar [46] Diarrhoea India 2007 Water P Dot map; thematic map; spatial case definition; source proximity; spatial case finding; spatial scan statistic 3, 4, 5, 7, 8 Showed dispersed nature of outbreak; no significant clustering; funds released to improve drainage network
Sowmyanarayanan [56] Hepatitis A India 2008 Water P Dot map; spatial scan statistic 4, 5 Cluster not significant; outbreak generalised across area
Fang [80] Influenza China 2013 Community R Dot map; thematic map; environmental risk prediction model 4 Identified high incidence areas; predicted areas with high risk to inform future control efforts
Liang [81] SARS China 2007 Community R Rate map 4 Rate increased with distance from city centre, supported spatial quarantining of city for future outbreaks
Ali [82] Dengue fever Bangladesh 2003 Vector R Dot map; thematic map; smoothed incidence map; source proximity; spatial case finding; kriging 3, 4, 5 Clusters identified, generally closer to major hospitals; spatial association between clusters and vector populations
Tenzin [83] Rabies Bhutan 2010 Community R Dot map; spatial average; standard deviation ellipse 4 Visualised spread of outbreak; seemed to follow road network that had many free-roaming dogs
Nishiguchi [84] Influenza Japan 2009 Farm R Dot map; scan statistic 3, 4, 5 Identified cluster and factors associated with farms inside cluster
Siddiqui [85] Cholera Pakistan 2006 Water R Dot map; spatial case definition; k-nearest neighbour test 3, 4, 5 Clustering in one of the outbreaks investigated; water reservoir identified as likely source
Miranda [86] Ebola Philippines 2002 Breeding facility R Schematic map 4 Documented progression of outbreak
Le [87] Porcine high fever disease Vietnam 2012 Farm R Dot map; smoothed incidence map; spatial and space–time scan statistic; k-nearest neighbour test; Knox test; space–time k function 4, 5 Little evidence for clustering; thought not to be important in this outbreak
Addiss [88] Legionnaires' disease United States 1989 Environmental P Dot maps; spatial case definition; source proximity 3, 4, 5 Rate decreased with distance from one cooling tower; implicated as probable source of outbreak
Blondin [89] Blastomycosis United States 2007 Environmental R Dot map; thematic map; source proximity 4, 5 No common source identified; infection likely to have been acquired close to homes
Brown [60] Legionnaires' disease United States 1999 Environmental P Dot map; thematic map; spatial case definition; source proximity 3, 4, 5 Transmission mostly in close proximity to cooling towers
Chung [90] West Nile fever United States 2013 Vector R Dot map; rate map; Getis-Ord Gi* statistic 4, 5 As outbreak progressed it became clustered and hot spot was identified
McKee [36] Shigellosis United States 2000 Water P Dot map; k-nearest neighbour test 4, 7 Space–time clustering found; identified communal wading pools as probable source; targeted information campaigns and education
Mongoh [91] Anthrax United States 2008 Farm R Dot map; thematic map 4 Displayed spatial distribution of premises with cases n study
Pfister [92] Blastomycosis United States 2011 Environmental P Dot map; spatial case definition; spatial average 3, 4 Centre of outbreak identified, north of river; yard waste disposal identified as likely source
Roy [25] Blastomycosis United States 2013 Environmental P Dot map; spatial case definition; scan statistic 1, 3, 4 Confirmed the presence of the outbreak
Bowie [93] Toxoplasmosis Canada 1997 Water P Dot map; thematic map 4, 5 Outbreak-related cases in area served by water distribution system
Epp [94] Anthrax Canada 2010 Farm R Thematic map; smoothed incidence map; velocity vector map; spatial case definition; space time scan statistic; k-nearest neighbour test; k-function; Oden's Ipop 4, 5 Three separate movements of spread identified; clusters located
Parkinson [22] Anthrax Canada 2003 Farm R Dot map; thematic maps 4 Described physical characteristics of outbreak and documented spatial descriptive patterns
Pasma [95] Influenza Canada 2008 Farm R Thematic map; spatial average; standard deviation ellipse; k-nearest neighbour test; spatial scan statistic; Knox test; nearest neighbour analysis; Mantel's test 4, 5, 6 Identified and located clusters; outbreak established in densely populated areas, moved to less densely populated areas; suggests focus for surveillance
Morrison [96] Dengue fever Puerto Rico 1998 Vector R Dot map; Knox test; k-function analysis; Barton–David test 3, 4 Significant case clustering within households over short periods of time; but in general, cases had pattern similar to population as a whole; control measures need to be applied to entire municipality
Chadee [97] Meningococcal meningitis Trinidad 2006 Community P Dot map; case–case distance 1, 4 Revealed two clusters
Chadee [55] Dengue fever West Indies 2005 Vector R Dot map; k-nearest neighbour test 4 Cases occurred in clusters when mosquito densities were high enough
Affolabi [24] Tuberculosis Benin 2009 Community R Dot map; case movement map 1, 4 Identified potential cluster
Bartels [98] Cholera Ethiopia 2010 Water P Dot map 4 Cases mapped along river; thought to be most likely source
Luquero [49] Cholera Guinea-Bissau 2011 Water P Dot map; rate map; smooth incidence maps; spatial case finding; scan statistic; k-function 3, 4, 5, 7, 8 Two clusters identified; improved sanitation systems and hygiene collection in affected area
Rivas [23] Influenza Nigeria 2010 Farm R Dot map; thematic maps; spatial case definition; case–case distance; risk factor proximity 3, 4, 5 Supports hypothesis that major highway network promoted epidemic spread
Roquet [20] Cholera Senegal 1998 Water R Dot map; rate map 4 Identified high incidence areas
Bessong [99] Diarrhoea South Africa 2009 Water P Dot map; spatial case finding 3, 4 Identified hot spots of the outbreak; two water extraction points implicated
Fevre [35] Trypanosomiasis Uganda 2001 Vector R Dot map; spatial case definition; source proximity; spatial scan statistic 3, 4, 5 Significant cluster detected; distance from market significant risk factor
Sasaki [30] Cholera Zambia 2008 Water R Dot map; rate map; Voronoi diagram; nearest neighbour analysis; Moran's I 4, 5 Significant clustering found in areas with lower coverage of latrines and effective drainage systems
Fernandez [45] Cholera Zimbabwe 2011 Water R Dot map; thematic map; rate map; empirical Bayes smoothing 4, 5, 7 Spatial pattern linked to historical social construction of city characterised by distinct regions of socioeconomic status
Angulo [100] Variola minor Brazil 1979 Community R Dot map; smoothed incidence map 4 Demonstrated importance of schools in epidemic spread
Barcellos [21] Leptospirosis Brazil 2000 Water R Dot map; thematic maps; risk factor proximity 4 Concentration of cases observed; identified characteristics of areas
Barreto [19] Schistosomiasis Brazil 1993 Vector P Dot map; thematic maps 4 Children with frequent water contact around open bodies of water, no sewage disposal, absence of water supply associated with infection
de Moura [101] Toxoplasmosis Brazil 2006 Water P Dot map; rate map 4, 6, 7 Cases more likely to be served by water reservoir A than B; closed reservoir.
Passos [102] Rabies Brazil 1998 Community R Dot map 4 Cases corresponded to parts of city with most slums and lower income populations
Rotela [48] Dengue fever Argentina 2007 Vector R Dot map; smoothed incidence map; Knox test; environmental risk prediction model 4, 5 Identified clusters and developed predictive risk model
Rivas [103] Foot and mouth disease Uruguay 2003 Farm R Dot map; thematic map; source proximity 4, 5 Generated hypothesis that early epidemic virus disseminated took advantage of road network, then spread outwards
Firestone [104] Influenza Australia 2011 Farm R Dot map; smoothed incidence map; spatial social network analysis; space-time scan statistic; kriging 4, 5, 6 Local spread through contact network to distance of 15 km; identified 5 significant clusters
Waldron [105] Cryptosporidiosis Australia 2011 Water R Dot map 4 Identified hot spots and movement of cluster over time
White [38] Legionnaires' disease New Zealand 2013 Environmental P Dot map; thematic map; scan statistic; Moran's I 4, 5, 6, 8 Identified clusters; case distribution consistent with plume effect from probable source
Turcios-Ruiz [106] Necrotising enterocolitis NA 2008 Hospital P Schematic map; spatial case definition; Grimson test 3, 4, 5 Clustering identified; suggested possible association with caregivers working in affected area

MMR: measles-mumps-rubella vaccine; NA: not available; P: prospective; R: retrospective; SARS: severe acute respiratory syndrome.

a Stages in outbreak investigations defined as: 1. Establishing existence of an outbreak; 2. Confirming diagnosis; 3. Defining and identifying outbreak cases; 4. Describing cases and developing hypotheses; 5. Evaluating hypotheses and drawing conclusions; 6. Comparing with established facts; 7. Executing prevention measures; 8. Communicating findings.

To demonstrate the utility of spatial methods during outbreak investigations, and therefore how they could be used in the future, we identified the stage(s) of the investigation to which they were applied. Outbreak investigations can be delineated into steps in various ways, and for the purpose of this review we used the following steps, adapted from the ECDC’s Field Epidemiology Manual [17]: 1. Establishing existence of an outbreak, 2. confirming diagnosis, 3. defining and identifying outbreak cases, 4. describing cases and developing hypotheses, 5. evaluating hypotheses and drawing conclusions, 6. comparing with established facts, 7. executing prevention measures, 8. communicating findings.

Results

Article screening and estimation of proportion with spatial methods

After excluding duplicates, we identified a total of 2,189 articles for abstract screening. Of these, 146 were selected for full text review and 80 of them were included in the analysis. Reasons for article exclusion are summarised in Figure 2A. Conducting the search without any terms specific to spatial analysis identified 487,495 articles. Assuming the same rate of article exclusion at each step in the review process, we estimated the total number of published articles relating to outbreak investigations of infectious diseases at ca 20,000 (Figure 2B). The overall proportion of published outbreak investigation reports that explicitly described spatial methods was therefore around 0.4%.

Figure 2

Study selection, systematic literature review on spatial methods in infectious disease outbreak investigations (n = 3,696)

/images/dynamic/articles/21253/15-00102-f2

Blue boxes are estimated numbers. Key details of all 80 included articles are described in Table 1.

Characteristics of studies included

Publication of outbreak investigations with spatial methods has increased markedly since 2000, with over half (n = 42) of the studies published since 2008 (Figure 3). Most articles (n = 66; 83%) concerned infections in human populations, of which the most frequently investigated infections were Legionnaires’ disease (n = 12), cholera (n = 7) and influenza (n = 7) (Table 2). Correspondingly, the most common transmission contexts for human infections were water/sanitation (n = 20), followed by environmental (n = 14) and community (n = 10) (Table 3).

Figure 3

Reports of outbreak investigations using spatial methods (n = 80)

/images/dynamic/articles/21253/15-00102-f3

Table 2

Infectious diseases investigated by category (n = 80 reports)


Infection category n Infection na References
Respiratory 23 Legionnaires’ disease 12 [26,32,38,40,47,57-60,63,73,88]
Influenza 7 [23,39,75,80,84,95,104]
SARS 3 [76,78,81]
Acute respiratory disease 1 [31]
Intestinal 18 Cholera 7 [20,30,34,45,49,85,98]
Cryptosporidiosis 2 [68,105]
Diarrhoea 2 [46,99]
Salmonellosis 2 [29,67]
Shigellosis 2 [36,79]
Campylobacteriosis 1 [42]
Giardiasis 1 [41]
Necrotising enterocolitis 1 [106]
Viral haemorrhagic fever 8 Dengue fever 5 [43,48,55,82,96]
Ebola 1 [86]
Porcine high fever disease 1 [87]
West Nile fever 1 [90]
Viral skin infections 7 Measles 3 [18,28,71]
Foot and mouth disease 2 [69,103]
Varicella 1 [77]
Variola minor 1 [100]
Protozoal 5 Toxoplasmosis 2 [93,101]
Leishmaniasis 1 [72]
Malaria 1 [34]
Trypanosomiasis 1 [35]
Rickettsioses 5 Q fever 5 [11,33,50,70,74]
Bacterial zoonotic 4 Anthrax 3 [22,91,94]
Leptospirosis 1 [21]
Mycoses 3 Blastomycosis 3 [25,89,92]
Viral CNS infections 2 Rabies 2 [83,102]
Viral hepatitis 2 Hepatitis A 1 [56]
Hepatitis E 1 [27]
Helminthiases 1 Schistosomiasis 1 [19]
Other bacterial 1 Meningococcal meningitis 1 [97]
Sexually transmitted 1 Syphilis 1 [44]
Tuberculosis 1 Tuberculosis 1 [24]

CNS: central nervous system; SARS: severe acute respiratory syndrome.

a The total is 81 because one study reported two investigations.

Table 3

Contexts of outbreak investigations of human and animal diseases (n = 80 reports)


Context Humana Animal
n References n References
Water/sanitation 20 [20,21,27,30,34,36,41,42,45,46, 49,56,68,79,85,93,98,99,101,105] 0
Environmental 14 [25,26,32,38,40,47,57-60,63,73,88,92] 1 [89]
Community 10 [18,24,28,71,75,76,80,81,97,100] 2 [83,102]
Vector-borne 10 [19,34,35,43,48,55,72,82,90,96] 0
Farm/breeding facility 5 [11,33,50,70,74] 12 [22,23,31,69,84,86,87, 91,94,95,103,104]
Healthcare-associated 5 [29,39,77,78,106] 0
Food 1 [67] 0
Sexually transmitted 1 [44] 0
Total b 66 15

a Includes outbreaks affecting humans that had animal origin.

b The total is 81 because one article reported two investigations.

Healthcare-associated infections were reported in five of the articles while food-borne and sexually transmitted infections were reported once apiece. Veterinary infections were almost exclusively linked to farms or other breeding facilities (n = 12) and influenza was the most frequently investigated infection affecting animals (n = 4). Prospective outbreak investigations comprised around half (n = 39) of the articles included, with the remainder describing retrospective analyses of outbreak data.

Figure 4 displays the outbreaks by country, with the most reports in the United Kingdom (UK) (n = 10) or the United States (US) (n = 8), and by continent, with a third of reports in Europe (n = 27) and fewer in Africa (n = 10).

Figure 4

Locations of outbreak investigations using spatial methods by country and continent (n = 80)

/images/dynamic/articles/21253/15-00102-f4

Spatial methods

Spatial methods used are listed and classified according to type in Table 4.

Table 4

Spatial methods used in outbreak investigations (n = 80)


Method category n (prospective, retrospective) Method n References
Visualisation 80 (39, 41) Dot map 68 [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 [18,19,21-23,28,29,38,41,42,45,46,50,
60,68,70,74,80,82,89,91,93-95,103]
Rate map 14 [20,30,44,45,49,50,57,63,70,76,79,81,
90,101]
Smoothed incidence map 13 [11,31,48-50,73,75,76,82,87,94,100,104]
Case movement map 7 [24,26,32,40,47,59,63]
Schematic map 6 [29,39,77,78,86,106]
Standard deviation ellipse 4 [75,76,83,95]
Origin–destination plot 1 [76]
Velocity vector map 1 [94]
Voronoi diagram 1 [30]
Spatial exploration 47 (28, 19) Spatial case definition 32 [11,23,25-27,29,31-33,35,39-42,46,47,
57-60,63,67-69,74,78,79,85,88,92,94,106]
Source proximity 16 [11,32,35,39,40,46,50,57,60,67,69,74,
82,88,89,103]
Spatial case finding 8 [27,43,46,49,63,70,82,99]
Spatial average 5 [34,74,83,92,95]
Case–case distance 3 [23,69,97]
Risk factor proximity 2 [21,23]
Spatial social network analysis 1 [104]
Cluster 24 (8, 16) Kulldorff’s spatial/ spatiotemporal scan statistic 13 [25,31,35,38,43,46,49,56,84,87,94,95,104]
Cuzick–Edwards k-nearest neighbour test/Jacquez’s k-nearest neighbours for space time interaction 7 [31,36,55,85,87,94,95]
Knox test 5 [31,48,87,95,96]
k-function/space–time k-function 5 [49,73,87,94,96]
Moran’s I 4 [30,38,75,76]
Nearest neighbour analysis 3 [30,76,95]
Getis Ord Gi(d) statistic 2 [75,90]
Barton–David’s test 1 [96]
Grimson test 1 [106]
Oden’s Ipop 1 [94]
Mantel’s test 1 [95]
Spatial modelling 13 (3, 10) Air dispersion modelling 7 [32,33,39,63,69,77,78]
Environmental risk prediction model 2 [48,80]
Kriging 2 [82,104]
Empirical Bayes smoothing 1 [45]
Geographic profiling 1 [34]

All articles presented or referred to at least one method of visualising case distributions to describe outbreaks in space. Plotting cases as dots on a map is the simplest form of visualisation and was used in the majority (n = 68; 85%) of studies. Dot maps were either presented using case locations only, or were enhanced with further information such as their vaccination status [18], migratory status [19] or date of disease onset [20]. Thematic maps provide context to case locations by displaying the spatial distributions of other variables. Such maps were used in 25 studies and variables plotted included socioeconomic status [21], soil type [22] and road density [23]. Maps of disease rates were used in 14 studies, with data usually aggregated according to administrative boundaries. Smoothed incidence maps were used in 13 studies. Other methods for visualising outbreaks that were used in fewer studies included standard deviation ellipses and velocity vector maps. Both use the locations of cases to describe the direction of spread of outbreaks.

Cluster analyses were used in 24 studies (30%), and spatial scan statistics were the most frequently used (n = 13 studies). k-nearest neighbour tests, k-function analyses and the Knox test were also used frequently (n = 7, 5 and 5 studies, respectively). Modelling approaches were used in 13 studies, including seven which used air dispersion models to identify areas that may have been exposed to air from suspected contaminated environmental sources.

A range of other spatial methods based on geographic attributes of cases were also identified. These included methods for defining (n = 31 studies) and identifying (n = 8 studies) cases, summarising the average locations of cases (n = 5 studies) and assessing proximity to potential sources (n = 16 studies).

Analytic methods were used less frequently in prospective than retrospective articles: Cluster methods were used in 16 (39%) retrospective compared with eight (21%) prospective studies, and modelling in 10 (24%) and three (8%) retrospective and prospective analyses, respectively.

The most frequently cited GIS software was ArcGIS/ArcView, used in 30 studies, with MapInfo the other commonly used programme (n = 7). Various other packages including R, ClusterSeer, GeoDa and SaTScan were used for specific analyses.

Application of spatial methods in outbreak investigations

Applications of spatial methods to different stages during outbreak investigations are described below (see also Table 1).

1. Establishing existence of an outbreak

Few studies (n = 4) used spatial methods to assist with establishing the existence of an outbreak. Methods that were used aimed to identify unusual patterns of cases, either visually or through formal statistical tests of clustering.

For example, Affolabi and colleagues described complementary use of molecular and geographic methods to identify an outbreak of tuberculosis in Benin [24]. Among a series of 194 M. tuberculosis isolates, 17 belonged to the Beijing genotype and exhibited an identical 12-loci subtype. Mapping of patients’ residences, workplaces and movements revealed a corresponding spatial cluster, confirming that the cases were likely to be linked. In another study, Roy and colleagues plotted the locations of cases of blastomycosis in Wisconsin after noting an increase in the number of reports [25]. They visually identified clustering within five neighbourhoods and used the spatiotemporal scan statistic to confirm that this was statistically significant.

2. Confirming diagnosis

Although knowledge of the endemicity of diseases in the geographic regions in which outbreaks arise is useful in developing plausible preliminary diagnostic hypotheses, spatial methods alone are not able to confirm a diagnosis and were therefore not used for this purpose in any of the studies.

3. Defining and identifying outbreak cases

Geographic boundaries in which outbreak cases were defined were stated explicitly in over a third (n = 31) of the studies. For instance, Keramarou and colleagues’ investigation of an outbreak of Legionnaires’ disease included only cases that lived or worked in the outbreak area, defined as a 12 km corridor on either side of a major road [26].

Spatial methods were also used to assist with active case finding in eight studies. Bali and colleagues describe a search for cases of hepatitis E prompted by identification of three cases in a small town in northern India [27]. A house-to-house survey in this region identified 3,170 cases of jaundice with an attack rate of 5.2%.

4. Describing outbreak cases and developing hypotheses

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.

Simple maps were also used to develop hypotheses about the origins of outbreaks. For example, Kistemann and colleagues plotted cases by date of onset in an investigation of a nosocomial Salmonella outbreak [29]. Their schematic map revealed the central kitchen as the only functional relationship linking the cases, which they therefore hypothesised to be the source of the infection.

Sasaki and colleagues created a Voronoi diagram to demarcate their study area using locations of water taps [30]. Plotting incidence rates in the different areas defined by these water tap boundaries helped to visualise clear spatial clustering of cholera cases associated with poor water and sanitation facilities. Smoothed incidence maps were used in an investigation by Norström and colleagues into acute respiratory disease in Norwegian cattle herds. They used smoothing based on kernel density estimation to describe the progression of the outbreak, which was shown to spread locally before jumping to new areas [31].

A common method to develop hypotheses about sources of infections was to construct concentric circles of varying radii around potential sources and compare the attack rates in each. Nygard and colleagues used this technique in an investigation of Legionnaires’ disease in Norway [32]. They calculated attack rates in five rings of increasing distance around eight potential sources and observed a trend of decreasing rate ratios with increasing distance from an air scrubber. Other metrics used to describe cases included calculating their average location and proximity to risk factors.

Possible air-borne spread of Q fever from farms near Cheltenham, UK was investigated by Wallensten and colleagues using the Numerical Atmospheric-dispersion Modelling Environment (NAME) model [33]. Plotting the modelled distribution showed that air from each of the suspected farms may have exposed the town. Geographic profiling is another modelling technique used to generate hypotheses about the locations of sources of infections. Le Comber and colleagues used this method to identify most likely locations of mosquito breeding sites using residential locations of a series of cases of malaria in Cairo, Egypt [34].

5. Evaluating hypotheses and drawing conclusions

More than half of the studies (n = 42) used statistical tests, such as cluster and regression analyses, to conduct formal evaluations of hypotheses arising from observations of case distributions. Fevre and colleagues, for example, assessed clustering of cases of trypanosomiasis under the hypothesis that a cattle market was the source of the outbreak [35]. A significant cluster encompassing the location of the market was detected using the spatiotemporal scan statistic, supporting this theory.

In an investigation on a military installation in North Carolina, McKee and colleagues used the k-nearest neighbour method to identify significant spatiotemporal clustering of shigellosis [36]. They used dot maps to locate the area with intense transmission and targeted it with educational interventions to bring the outbreak under control.

Combinations of multiple tests for clustering were used in some studies, such as Norström and colleagues’ investigation of acute respiratory disease in Norwegian cattle herds [31]. They combined the Knox test [37], a global test for space–time clustering, with the k-nearest neighbour test and space–time scan statistic. These tests allowed them, respectively, to define the smallest distance and time frame in which the events had been clustered, to determine whether cases tended to be close to other cases and to locate the most significant clusters. All methods indicated presence of space–time clustering, adding weight to the conclusion that a common contagious source was responsible for the outbreak.

The hypothesis that risk of infection decreased with increasing distance from a suspected source was tested in several studies through regression analysis. Hackert and colleagues, for example, used linear regression of log-transformed attack rates to assess a cluster of human cases of Q fever in the Netherlands [11]. Incidence increased by a statistically significant exposure–response gradient with proximity to a dairy goat farm, which they concluded was likely to be the primary and sole source.

6. Comparing results with established facts

Results from spatial analyses in some cases provided updates to knowledge about the dynamics of the infectious agents concerned, such as their minimum infective dose and mode of transmission. For example, in an outbreak of Legionnaires’ disease in Christchurch, New Zealand, cases were identified at a distance of 12 km from the implicated cooling tower [38]. White and colleagues therefore proposed updates to World Health Organization guidelines which at the time placed the area at risk from such sources at 3.2 km.

Wong and colleagues used a computational fluid dynamics model to study the spread of an influenza outbreak in a hospital setting [39]. Concentrations of hypothetical virus-laden particles from modelled air distributions correlated closely with locations of infected patients. This suggested a possible role for aerosol transmission of influenza, which is predominantly associated with transmission by droplets and direct contact.

7. Executing prevention measures

Spatially targeted interventions to control the outbreak or prevent future cases were described in many studies. 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].

Attempts to prevent future outbreaks included improvement of infrastructure [45,46], change of policy [29,32,47] and generation of risk maps [48]. Luquero and colleagues, for instance, used results of their analysis to recommend specific regions in which to focus preparedness activities to avoid future cholera outbreaks in Guinea-Bissau [49].

8. Communicating findings

All studies in this review had, by definition, used their spatial analyses in communication of findings through reports in peer-reviewed publications. Several studies also highlighted the usefulness of maps in reports or presentations to communicate results to health officials [29,47], policymakers [49,50] and the public [38]. Sarkar and colleagues, for example, presented dot maps of cases of acute diarrhoeal disease in a village in southern India to the local community and health authorities [46]. Their maps visualised the proximity of cases to a contaminated water supply, and the presentation resulted in release of funds to improve sanitation in the area.

Discussion

In this review, we have identified 80 published articles of infectious disease outbreak investigations that used spatial methods, less than half a per cent of our estimated total of 20,000 outbreak reports. 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 cases and communicating findings. Spatial techniques often provided valuable insights that supplemented traditional epidemiological analyses of person and time and led to public health actions.

Outbreak investigations of infectious diseases occurring in any context were included in this study. Thus, we extended the scope of two previous reviews that focused, respectively, on use of spatial methods in outbreaks of Legionnaires’ disease [10] and on spatiotemporal methods to investigate transmission of infections in hospital settings [51]. In doing so, we have highlighted imbalances in application of spatial methods in different types of investigations. For example, it was notable that only one study reported an outbreak of food-borne illness. Annual summary statistics from 2013 report a total of 5,196 food- and waterborne outbreaks in the European Union (EU) [52] and 831 reports of food-borne outbreaks in 2012 in the US [53]. Although only a small proportion of these are likely to have been published in academic journals, this still indicates a substantial shortfall in use of spatial methods in this context.

Our review also allowed assessment of the extent to which spatial methods have been used in Europe. Although there was a large number of reports from Europe compared with other parts of the world, those reports derived from only 10 counties. These were predominantly in western Europe, with one report from Turkey the only investigation in eastern areas. Sharing expertise through the European Centre of Disease Prevention and Control could help to reduce this gap and strengthen outbreak investigation capacity across the continent. Expanding the use of these tools is also important in other parts of the world. Only 10 reports described outbreaks in Africa, the same number as in the UK alone, which clearly does not correlate with the distribution of the global burden of infectious diseases.

There are several limitations of spatial methods, and barriers to their use, which may account for the unequal and under-use of these tools as identified here. Firstly, reliable spatial analyses can only be conducted with accurate location data. This can be a particular challenge in developing countries in which good quality maps of residential areas are often not available [54]. Several investigations of outbreaks in such settings conducted field surveys and used Global Positioning Systems (GPS) to accurately record patient residence or risk factor locations [30,35,43,46,55,56]. However, this is a time- and cost-intensive approach and will not always be feasible. In settings in which good quality maps of residential data are available, quality of case location data is still not assured: Errors can arise from incomplete or mistranscribed addresses, out of date GIS databases or incomplete information on potential source locations. During outbreaks of Legionnaires’ disease, for example, some investigators had to conduct visual searches or make public enquiries to ascertain the locations of aerosol-producing devices because there was no central registry [26,32,40,57-60].

Simplification of case locations to static points, usually residential locations, also impacts the utility of location data. In reality, individuals can become exposed to infectious agents at any place where they spend time and, similarly, traditional census population denominators that record night-time populations are not necessarily reflective of population distributions during the day [61,62]. Although a number of studies made attempts to record case movements [24,26,32,40,47,59,63], none accounted for diurnal fluctuations in populations. Ideally, this spatial uncertainty should be accounted for in data collection, analysis and visualisation stages to improve reliability of estimates of spatial risk, and new analytic methods may be required to achieve this.

Secondly, even if reliable location data are available, presentation of information on maps can be open to misinterpretation. Dot maps, for instance, were used widely but do not take into account the geographic distribution of the underlying population and can therefore mask important trends. Similarly, patterns in aggregated data are sensitive to changes in the boundaries into which they are grouped, a phenomenon known as the modifiable aerial unit problem [9]. Presentation of data on maps fails to highlight these limitations, and relatively few prospective investigations used statistical methods to formally confirm observations identified from visual displays of data.

Thirdly, researchers may be deterred from using spatial analytic methods because they involve selection of parameter values, often with an element of subjectivity. Methods that display or identify clustering require specification of the degree to which distant points may be considered part of the same neighbourhood. For the spatial scan statistic, the user must define the maximum spatial extent of clusters in terms of the percentage of the population that can be included, in k-nearest neighbour analysis, the number of neighbours included must be specified, and equivalent parameters must be selected for other spatial cluster and modelling analyses [9]. Altering these parameters can have a profound influence on the results, and a trial and error approach is often required to arrive at an appropriate value. This can raise issues of multiple hypothesis testing, although some methods, including the spatial scan statistic and Tango’s maximised excess events test [64], are able to adjust for this while evaluating clustering at multiple scales. Results of spatial analyses can also suffer from lack of specificity. For example, in several studies of Legionnaires’ disease, spatial methods identified areas most likely to be the source of the infection, but could not discriminate between potential sources that were close together [40,57,58].

Another barrier to the effective use of spatial methods that is often cited is the expense of specialised GIS software and the need for trained personnel to operate it. Although some GIS programmes are available free of charge, the most commonly used was a commercial package, ArcGIS. However, it is also noteworthy that spatial scan statistics were the most frequently adopted analytic methods. Scan statistics can be implemented with relatively little training through SaTScan, a programme free to download from the Internet. This suggests a possible model for wider adoption of other more advanced techniques.

The results of our study point to a number of recommendations for improved practice and opportunities for further development of spatial methods. Given the potential utility of existing tools demonstrated here, under-use of these methods has doubtless resulted in missed opportunities for more effective real-time outbreak investigations. Public health officials must be supported to address this issue, and a useful first step would be development of protocols describing the application of appropriate analyses. Table 5, which relates spatial methods to specific stages in outbreak investigations, provides a framework for this. Provision of training, for example through short courses, and interdisciplinary working with specialists in geographic analysis would also be beneficial to improve the skills base of the workforce.

Table 5

Application of spatial methods to steps in outbreak investigation


1. Establish the existence of an outbreak ◦ Visualise case distribution (e.g. dot map)
◦ Identify and confirm clustering (e.g. spatial scan statistic)
2. Confirm diagnosis ◦ Spatial methods alone cannot confirm diagnoses. Consider spatial epidemiology of infection to develop preliminary diagnostic hypotheses.
3. Define and identify outbreak cases ◦ Set geographic limits in which cases are considered part of the outbreak (e.g. postcode area hospital ward)
◦ Select controls in case–control study based on same geographic limits
◦ Use maps to assist with active case finding
4. Describe cases and develop hypotheses ◦ Visualise distribution of cases in relation to known risk factors or potential sources (e.g. rate map, thematic maps)
◦ Describe progression of outbreak (e.g. dot maps at different stages, standard deviation ellipse)
◦ Identify centre of outbreak (e.g. spatial mean)
◦ Identify high-risk areas (e.g. attack rates in zones at different distances from potential sources)
◦ Assess likelihood of potential sources (e.g. geographic profiling)
5. Evaluate hypotheses and draw conclusions ◦ Test for overall clustering (e.g. k-nearest neighbour test)
◦ Locate significant clusters (e.g. spatial scan statistic)
◦ Identify significant trends in attack rates with distance from potential sources (e.g. linear regression of log-transformed attack rates)
6. Compare with established facts ◦ Calculate maximum dispersal distance from probable source to cases
◦ Model concentrations of infected particles to understand transmission dynamics (e.g. computational fluid dynamics, NAME atmospheric modelling)
7. Execute prevention measures ◦ Spatial targeting of interventions to control outbreak (e.g. order to boil water in area served by contaminated reservoir)
◦ Spatial targeting of health promotion campaigns (e.g. using postcodes on social networks)
◦ Identify geographic areas at risk of future outbreaks (e.g. risk mapping)
8. Communicate findings ◦ Use maps to communicate results to health officials/policymakers, to the public and in scientific journals

The majority of studies identified in this review that used analytic methods described retrospective analysis of data collected during outbreaks. These reports demonstrated the potential utility of analytic methods, but will be of greater public health benefit when used in real time. Assembly of GIS databases in advance is essential to allow spatial analyses during prospective outbreak investigations. Improving data accessibility will save time during investigations, improve accuracy of analyses and prevent duplication of effort. Reports of analyses using spatial methods would also benefit from some degree of standardisation. For example, reporting of the sources and level of precision of spatial data would enable more accurate interpretation of the results by researchers not familiar with the study site. This could be achieved, for example, through extension of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement with items specific to spatial data [65].

Finally, there is scope for development of new tools for analysis and visualisation of spatial data. A move towards web-based applications with user-friendly interfaces would be a natural progression, provided that these platforms included adequate training materials and data governance infrastructure. This would make spatial analyses more accessible to non-experts and could facilitate wider use of interactive displays of data and animations. The quantity and detail of geo-located data available to researchers is also increasing. GPS-enabled mobile devices and applications for self-reported or crowd-sourced information (for example sickweather [66], based on reports on social networks) have the potential to provide near real-time data including information on individuals’ movements. Development of new analytic techniques will be needed to ensure that these data are effectively exploited and potential benefits are met. In the context of outbreak investigations, possible applications include contact tracing and improved estimation of exposure to environmental risk factors.

The primary limitation of this study was the challenge of designing the database search strategy. Although we employed a broad search which identified a large number of abstracts for screening, the number of studies identified here will inevitably be an underestimate of the outbreak investigations that used spatial methods. Our search will not have captured studies that used spatial methods but did not refer to them explicitly in the title, abstract, subject headings or MeSH terms. Restricting the search to articles published in academic journals also excluded reports in the grey literature. Inclusion of such reports would increase the number of investigations using spatial methods, but would be unlikely to reveal novel approaches or tools not identified here. Articles published since the database search was run at the end of 2013 are also not included in this study. Recent years have seen an increase in reports using spatial methods, probably due to increased availability of GIS software. This trend is likely to have continued, and recent publications will focus on current public health issues, for example the recent Ebola outbreak in West Africa.

There was also a possible publication bias in this study: spatial analyses may have been more likely to be presented in published reports if they were found to be useful. Concerns of breaching patient confidentiality could have further limited the number of studies that published maps. Nevertheless, the proportion of studies using spatial methods was very small, and even if our estimate is an order of magnitude too low, it would still represent less than 5% of the estimated total number of investigations published.

Conclusion

Investigations of outbreaks of infectious diseases require synthesis of information and expertise from a range of fields. Spatial analyses can make many valuable contributions, with simple maps alone providing fundamental insights about the distribution of cases. However, advancements in GIS technology and increasing availability of good quality spatial data provide an opportunity for development and implementation of more sophisticated tools. Adoption of these new techniques, and wider use of existing methods, has the potential to support more effective investigations and therefore limit the public health impacts of infectious disease outbreaks.


Conflict of interest

None declared.

Authors’ contributions

CS and ACH designed the study. CS did the literature search, analyses and wrote the first draft. ACH, SLC, HF, MB and SL revised and edited the final report.


References

  1. Centers for Disease Control and Prevention (CDC). Mission, role and pledge. Atlanta; CDC. [Accessed: 25 Sep 2014]. Available from: http://www.cdc.gov/about/organization/mission.htm

  2. Goodman RA, Buehler JW, Koplan JP. The epidemiologic field investigation: science and judgment in public health practice.Am J Epidemiol. 1990;132(1):9-16.PMID: 2356818

  3. Snow J. On the Mode of Communication of Cholera. London: John Churchill; 1855.

  4. World Health Organization (WHO). Foodborne disease outbreaks: guidelines for investigation and control. Geneva: WHO; 2008. Available from: http://www.who.int/foodsafety/publications/foodborne_disease/outbreak_guidelines.pdf

  5. Public Health England (PHE). Communicable disease outbreak management: operational guidance. London: PHE; 2014. Available from: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/343723/12_8_2014_CD_Outbreak_Guidance_REandCT_2__2_.pdf

  6. European Centre for Disease Prevention and Control (ECDC). Toolkit for investigation and response to food and waterborne disease outbreaks with an EU dimension. Stockholm: ECDC. [Accessed: 30 Sep 2014]. Available from: http://www.ecdc.europa.eu/en/healthtopics/food_and_waterborne_disease/toolkit/Pages/index.aspx

  7. Centers for Disease Control and Prevention (CDC). Multistate and nationwide foodborne outbreak investigations: a step-by-step guide: Atlanta: CDC. [Accessed: 30 Sep 2014]. Available from: http://www.cdc.gov/foodsafety/outbreaks/investigating-outbreaks/investigations/index.html

  8. Moore DA, Carpenter TE. Spatial analytical methods and geographic information systems: use in health research and epidemiology.Epidemiol Rev. 1999;21(2):143-61. DOI: 10.1093/oxfordjournals.epirev.a017993 PMID: 10682254

  9. Pfeiffer DU, Robinson T, Stevenson M, Stevens KB, Rogers D, Clements AC. Spatial analysis in epidemiology. Oxford: Oxford University Press; 2008.

  10. Bull M, Hall IM, Leach S, Robesyn E. The application of geographic information systems and spatial data during Legionnaires disease outbreak responses. Euro Surveill. 2012;17(49):pii=20331.

  11. Hackert VH, van der Hoek W, Dukers-Muijrers N, de Bruin A, Al Dahouk S, Neubauer H,  et al.  Q fever: single-point source outbreak with high attack rates and massive numbers of undetected infections across an entire region. Clin Infect Dis. 2012;55(12):1591-9. DOI: 10.1093/cid/cis734 PMID: 22918992

  12. Guidelines for Investigating Clusters of Health Events. MMWR Recomm Rep. 1990;39(RR-11):1-23. PMID:2117247

  13. Cuzick J, Edwards R. Spatial clustering for inhomogeneous populations.J R Stat Soc.1990;52:73-104.

  14. Kulldorff M. A spatial scan statistic.Commun Stat Theory Methods. 1997;26(6):1481-96. DOI: 10.1080/03610929708831995

  15. Kulldorff M, Heffernan R, Hartman J, Assunção RM, Mostashari F. A space-time permutation scan statistic for the early detection of disease outbreaks.PLoS Med. 2005;2(3):e59. DOI: 10.1371/journal.pmed.0020059

  16. Jacquez GM. Disease cluster statistics for space-time interaction. Stat Med. 1996;15(7-9):873-85. DOI: 10.1002/(SICI)1097-0258(19960415)15:7/9<873::AID-SIM256>3.0.CO;2-U PMID: 8861156

  17. European Centre for Disease Prevention and Control (ECDC). Outbreak investigations. Stockholm: ECDC. [Accessed: 15 Oct 2014]. Available from: https://wiki.ecdc.europa.eu/fem/w/wiki/outbreak-investigations.aspx

  18. Ulugtekin N, Alkoy S, Seker DZ. Use of a geographic information system in an epidemiological study of measles in Istanbul.J Int Med Res. 2007;35(1):150-4. DOI: 10.1177/147323000703500117 PMID: 17408067

  19. Barreto ML. The dot map as an epidemiological tool: a case study of Schistosoma mansoni infection in an urban setting.Int J Epidemiol. 1993;22(4):731-41. DOI: 10.1093/ije/22.4.731 PMID: 8225750

  20. Roquet D, Diallo A, Kodio B, Daff BM, Fenech C, Etard JF. [Cholera epidemic in Senegal in 1995-1996: an example of geographic approach to health]. Sante. 1998;8(6):421-8. French.

  21. Barcellos C, Sabroza PC. Socio-environmental determinants of the leptospirosis outbreak of 1996 in western Rio de Janeiro: a geographical approach.Int J Environ Health Res. 2000;10(4):301-13. DOI: 10.1080/0960312002001500 PMID: 11260779

  22. Parkinson R, Rajic A, Jenson C. Investigation of an anthrax outbreak in Alberta in 1999 using a geographic information system.Can Vet J. 2003;44(4):315-8.PMID: 12715984

  23. Rivas AL, Chowell G, Schwager SJ, Fasina FO, Hoogesteijn AL, Smith SD,  et al.  Lessons from Nigeria: the role of roads in the geo-temporal progression of avian influenza (H5N1) virus. Epidemiol Infect. 2010;138(2):192-8. DOI: 10.1017/S0950268809990495 PMID: 19653927

  24. Affolabi D, Faïhun F, Sanoussi N, Anyo G, Shamputa IC, Rigouts L,  et al.  Possible outbreak of streptomycin-resistant Mycobacterium tuberculosis Beijing in Benin. Emerg Infect Dis. 2009;15(7):1123-5. DOI: 10.3201/eid1507.080697 PMID: 19624936

  25. Roy M, Benedict K, Deak E, Kirby MA, McNiel JT, Sickler CJ,  et al.  A large community outbreak of blastomycosis in Wisconsin with geographic and ethnic clustering. Clin Infect Dis. 2013;57(5):655-62. DOI: 10.1093/cid/cit366 PMID: 23735332

  26. Keramarou M, Evans MR,, South Wales Legionnaires Disease Outbreak Control Team. A community outbreak of Legionnaires’ disease in South Wales, August-September 2010.Euro Surveill. 2010;15(42):pii=19691.PMID: 21034723

  27. Bali S, Kar SS, Kumar S, Ratho RK, Dhiman RK, Kumar R. Hepatitis E epidemic with bimodal peak in a town of north India. Indian J Public Health. 2008;52(4):189-93, 99.

  28. Fitzpatrick G, Ward M, Ennis O, Johnson H, Cotter S, Carr MJ,  et al.  Use of a geographic information system to map cases of measles in real-time during an outbreak in Dublin, Ireland, 2011. Euro Surveill. 2012;17(49):19-29.PMID: 23231894

  29. Kistemann T, Dangendorf F, Krizek L, Sahl HG, Engelhart S, Exner M. GIS-supported investigation of a nosocomial Salmonella outbreak.Int J Hyg Environ Health. 2000;203(2):117-26. DOI: 10.1078/S1438-4639(04)70016-4 PMID: 11109563

  30. Sasaki S, Suzuki H, Igarashi K, Tambatamba B, Mulenga P. Spatial analysis of risk factor of cholera outbreak for 2003-2004 in a peri-urban area of Lusaka, Zambia.Am J Trop Med Hyg. 2008;79(3):414-21.PMID: 18784235

  31. Norström M, Pfeiffer DU, Jarp J. A space-time cluster investigation of an outbreak of acute respiratory disease in Norwegian cattle herds.Prev Vet Med. 1999;47(1-2):107-19. DOI: 10.1016/S0167-5877(00)00159-8 PMID: 11018738

  32. Nygård K, Werner-Johansen Ø, Rønsen S, Caugant DA, Simonsen Ø, Kanestrøm A,  et al.  An outbreak of legionnaires disease caused by long-distance spread from an industrial air scrubber in Sarpsborg, Norway. Clin Infect Dis. 2008;46(1):61-9. DOI: 10.1086/524016 PMID: 18171215

  33. Wallensten A, Moore P, Webster H, Johnson C, van der Burgt G, Pritchard G,  et al.  Q fever outbreak in Cheltenham, United Kingdom, in 2007 and the use of dispersion modelling to investigate the possibility of airborne spread. Euro Surveill. 2010;15(12):pii=19521.PMID: 20350497

  34. Le Comber SC, Rossmo DK, Hassan AN, Fuller DO, Beier JC. Geographic profiling as a novel spatial tool for targeting infectious disease control.Int J Health Geogr. 2011;10(1):35. DOI: 10.1186/1476-072X-10-35 PMID: 21592339

  35. Fèvre EM, Coleman PG, Odiit M, Magona JW, Welburn SC, Woolhouse MEJ. The origins of a new Trypanosoma brucei rhodesiense sleeping sickness outbreak in eastern Uganda.Lancet. 2001;358(9282):625-8. DOI: 10.1016/S0140-6736(01)05778-6 PMID: 11530149

  36. McKee KT, Shields TM, Jenkins PR, Zenilman JM, Glass GE. Application of a geographic information system to the tracking and control of an outbreak of shigellosis.Clin Infect Dis. 2000;31(3):728-33. DOI: 10.1086/314050 PMID: 11017823

  37. Knox EG. The detection of space-time interactions.J Appl Stat. 1964;13:24-30.

  38. White PS, Graham FF, Harte DJG, Baker MG, Ambrose CD, Humphrey ARG. Epidemiological investigation of a Legionnaires’ disease outbreak in Christchurch, New Zealand: the value of spatial methods for practical public health.Epidemiol Infect. 2013;141(4):789-99. DOI: 10.1017/S0950268812000994 PMID: 22697112

  39. Wong BCK, Lee N, Li Y, Chan PKS, Qiu H, Luo Z,  et al.  Possible role of aerosol transmission in a hospital outbreak of influenza. Clin Infect Dis. 2010;51(10):1176-83. DOI: 10.1086/656743 PMID: 20942655

  40. Kirrage D, Reynolds G, Smith GE, Olowokure B, Hereford Legionnaires Outbreak Control Team. Investigation of an outbreak of Legionnaires’ disease: Hereford, UK 2003.Respir Med. 2007;101(8):1639-44. DOI: 10.1016/j.rmed.2006.11.026 PMID: 17513103

  41. Nygård K, Schimmer B, Søbstad Ø, Walde A, Tveit I, Langeland N,  et al.  A large community outbreak of waterborne giardiasis-delayed detection in a non-endemic urban area. BMC Public Health. 2006;6(1):141. DOI: 10.1186/1471-2458-6-141 PMID: 16725025

  42. Gubbels S-M, Kuhn KG, Larsson JT, Adelhardt M, Engberg J, Ingildsen P,  et al.  A waterborne outbreak with a single clone of Campylobacter jejuni in the Danish town of Køge in May 2010. Scand J Infect Dis. 2012;44(8):586-94. DOI: 10.3109/00365548.2012.655773 PMID: 22385125

  43. Nisha V, Gad SS, Selvapandian D, Suganya V, Rajagopal V, Suganti P,  et al.  Geographical information system (GIS) in investigation of an outbreak. J Commun Dis. 2005;37(1):39-43.PMID: 16637399

  44. Acheson P, McGivern M, Frank P, Kunonga E, Simms I, Tayal S,  et al.  An ongoing outbreak of heterosexually-acquired syphilis across Teesside, UK. Int J STD AIDS. 2011;22(9):514-6. DOI: 10.1258/ijsa.2011.011008 PMID: 21890548

  45. Luque Fernández , Mason PR, Gray H, Bauernfeind A, Fesselet JF, Maes P. Descriptive spatial analysis of the cholera epidemic 2008-2009 in Harare, Zimbabwe: a secondary data analysis.Trans R Soc Trop Med Hyg. 2011;105(1):38-45. DOI: 10.1016/j.trstmh.2010.10.001 PMID: 21075411

  46. Sarkar R, Prabhakar AT, Manickam S, Selvapandian D, Raghava MV, Kang G,  et al.  Epidemiological investigation of an outbreak of acute diarrhoeal disease using geographic information systems. Trans R Soc Trop Med Hyg. 2007;101(6):587-93. DOI: 10.1016/j.trstmh.2006.11.005 PMID: 17267000

  47. Hyland JM, Hamlet N, Saunders C, Coppola J, Watt J. Outbreak of Legionnaires’ disease in West Fife: review of environmental guidelines needed.Public Health. 2008;122(1):79-83. DOI: 10.1016/j.puhe.2007.05.005 PMID: 17663011

  48. Rotela C, Fouque F, Lamfri M, Sabatier P, Introini V, Zaidenberg M,  et al.  Space-time analysis of the dengue spreading dynamics in the 2004 Tartagal outbreak, Northern Argentina. Acta Trop. 2007;103(1):1-13. DOI: 10.1016/j.actatropica.2007.05.003 PMID: 17603989

  49. Luquero FJ, Banga CN, Remartínez D, Palma PP, Baron E, Grais RF. Cholera epidemic in Guinea-Bissau (2008): the importance of "place".PLoS ONE. 2011;6(5):e19005. DOI: 10.1371/journal.pone.0019005 PMID: 21572530

  50. van der Hoek W, van de Kassteele J, Bom B, de Bruin A, Dijkstra F, Schimmer B,  et al.  Smooth incidence maps give valuable insight into Q fever outbreaks in The Netherlands. Geospat Health. 2012;7(1):127-34. DOI: 10.4081/gh.2012.111 PMID: 23242690

  51. Davis GS, Sevdalis N, Drumright LN. Spatial and temporal analyses to investigate infectious disease transmission within healthcare settings.J Hosp Infect. 2014;86(4):227-43. DOI: 10.1016/j.jhin.2014.01.010 PMID: 24650720

  52. European Food Safety Authority (EFSA), European Centre for Disease Prevention and Control (ECDC). The European Union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2013. EFSA Journal. 2015;13(1):3991. Available from: http://www.efsa.europa.eu/en/efsajournal/pub/3991

  53. Centers for Disease Control and Prevention (CDC). Surveillance for foodborne disease outbreaks, United States, 2012: Annual report. Atlanta: CDC; 2014. Available from: http://www.cdc.gov/foodsafety/pdfs/foodborne-disease-outbreaks-annual-report-2012-508c.pdf

  54. Tanser FC, Le Sueur D. The application of geographical information systems to important public health problems in Africa.Int J Health Geogr. 2002;1:4. DOI: 10.1186/1476-072X-1-4 PMID: 12537589

  55. Chadee DD, Williams FLR, Kitron UD. Impact of vector control on a dengue fever outbreak in Trinidad, West Indies, in 1998.Trop Med Int Health. 2005;10(8):748-54. DOI: 10.1111/j.1365-3156.2005.01449.x PMID: 16045461

  56. Sowmyanarayanan TV, Mukhopadhya A, Gladstone BP, Sarkar R, Kang G. Investigation of a hepatitis A outbreak in children in an urban slum in Vellore, Tamil Nadu, using geographic information systems.Indian J Med Res. 2008;128(1):32-7.PMID: 18820356

  57. García-Fulgueiras A, Navarro C, Fenoll D, García J, González-Diego P, Jiménez-Buñuales T,  et al.  Legionnaires’ disease outbreak in Murcia, Spain. Emerg Infect Dis. 2003;9(8):915-21. DOI: 10.3201/eid0908.030337 PMID: 12967487

  58. Jansà JM, Caylà JA, Ferrer D, Gracia J, Pelaz C, Salvador M,  et al.  An outbreak of Legionnaires’ disease in an inner city district: importance of the first 24 hours in the investigation. Int J Tuberc Lung Dis. 2002;6(9):831-8.PMID: 12234140

  59. Carr R, Warren R, Towers L, Bartholomew A, Duggal HV, Rehman Y,  et al.  Investigating a cluster of Legionnaires’ cases: public health implications. Public Health. 2010;124(6):326-31. DOI: 10.1016/j.puhe.2010.03.001 PMID: 20483439

  60. Brown CM, Nuorti PJ, Breiman RF, Hathcock AL, Fields BS, Lipman HB,  et al.  A community outbreak of Legionnaires’ disease linked to hospital cooling towers: an epidemiological method to calculate dose of exposure. Int J Epidemiol. 1999;28(2):353-9. DOI: 10.1093/ije/28.2.353 PMID: 10342703

  61. Bhaduri B. Encyclopedia of GIS. New York: Springer; 2008.

  62. Martin D. Directions in Population GIS.Geogr Compass. 2011;5(9):655-65. DOI: 10.1111/j.1749-8198.2011.00440.x

  63. Nguyen TM, Ilef D, Jarraud S, Rouil L, Campese C, Che D,  et al.  A community-wide outbreak of legionnaires disease linked to industrial cooling towers--how far can contaminated aerosols spread? J Infect Dis. 2006;193(1):102-11. DOI: 10.1086/498575 PMID: 16323138

  64. Tango T. A test for spatial disease clustering adjusted for multiple testing.Stat Med. 2000;19(2):191-204. DOI: 10.1002/(SICI)1097-0258(20000130)19:2<191::AID-SIM281>3.0.CO;2-Q PMID: 10641024

  65. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP,  et al.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Prev Med. 2007;45(4):247-51. DOI: 10.1016/j.ypmed.2007.08.012 PMID: 17950122

  66. Sickweather. Baltimore: Sickweather Inc. [Accessed: 15 Oct 2014]. Available from: http://www.sickweather.com/

  67. Boccia D, Oliver CI, Charlett A, Bennett S, Orr H, Sarangi J,  et al.  Outbreak of a new Salmonella phage type in South West England: alternative epidemiological investigations are needed. Commun Dis Public Health. 2004;7(4):339-43.PMID: 15779803

  68. Neira-Munoz E, Okoro C, McCarthy ND. Outbreak of waterborne cryptosporidiosis associated with low oocyst concentrations.Epidemiol Infect. 2007;135(7):1159-64. DOI: 10.1017/S0950268807008503 PMID: 17445321

  69. Sanson RL, Gloster J, Burgin L. Reanalysis of the start of the UK 1967 to 1968 foot-and-mouth disease epidemic to calculate airborne transmission probabilities.Vet Rec. 2011;169(13):336. DOI: 10.1136/vr.d4401 PMID: 21846685

  70. Manfredi Selvaggi T, Rezza G, Scagnelli M, Rigoli R, Rassu M, De Lalla F,  et al.  Investigation of a Q-fever outbreak in northern Italy. Eur J Epidemiol. 1996;12(4):403-8. DOI: 10.1007/BF00145305 PMID: 8891546

  71. Orsi A, Alicino C, Patria AG, Parodi V, Carloni R, Turello V,  et al.  Epidemiological and molecular approaches for management of a measles outbreak in Liguria, Italy. J Prev Med Hyg. 2010;51(2):67-72.PMID: 21155408

  72. Varani S, Cagarelli R, Melchionda F, Attard L, Salvadori C, Finarelli AC,  et al.  Ongoing outbreak of visceral leishmaniasis in Bologna Province, Italy, November 2012 to May 2013. Euro Surveill. 2013;18(29):20530. DOI: 10.2807/1560-7917.ES2013.18.29.20530 PMID: 23929116

  73. Abellán JJ, Martínez-Beneito MA, Zurriaga O, Jorques G, Ferrándiz J, López-Quílez A. [Point processes as a tool for analyzing possible sources of contamination]. Gac Sanit. 2002;16(5):445-9. Spanish.PMID: 12372192

  74. Schimmer B, Ter Schegget R, Wegdam M, Züchner L, de Bruin A, Schneeberger PM,  et al.  The use of a geographic information system to identify a dairy goat farm as the most likely source of an urban Q-fever outbreak. BMC Infect Dis. 2010;10(1):69. DOI: 10.1186/1471-2334-10-69 PMID: 20230650

  75. Lai P-c, Kwong K-h. Spatial Analysis of the 2008 Influenza Outbreak of Hong Kong. Computational Science and Its Applications - Iccsa 2010, Pt 1, Proceedings. Lecture Notes in Computer Science. 60162010. p. 374-88.

  76. Lai PC, Wong CM, Hedley AJ, Lo SV, Leung PY, Kong J,  et al.  Understanding the spatial clustering of severe acute respiratory syndrome (SARS) in Hong Kong. Environ Health Perspect. 2004;112(15):1550-6. DOI: 10.1289/ehp.7117 PMID: 15531441

  77. Sze-To GN, Chao CYH. Use of risk assessment and likelihood estimation to analyze spatial distribution pattern of respiratory infection cases.Risk Anal. 2011;31(3):351-69. DOI: 10.1111/j.1539-6924.2010.01525.x PMID: 21039710

  78. Yu ITS, Wong TW, Chiu YL, Lee N, Li Y. Temporal-spatial analysis of severe acute respiratory syndrome among hospital inpatients.Clin Infect Dis. 2005;40(9):1237-43. DOI: 10.1086/428735 PMID: 15825024

  79. Saha T, Murhekar M, Hutin YJ, Ramamurthy T. An urban, water-borne outbreak of diarrhoea and shigellosis in a district town in eastern India.Natl Med J India. 2009;22(5):237-9.PMID: 20334044

  80. Fang L-Q, Li X-L, Liu K, Li Y-J, Yao H-W, Liang S,  et al.  Mapping spread and risk of avian influenza A (H7N9) in China. Sci Rep. 2013;3:2722. DOI: 10.1038/srep02722 PMID: 24072008

  81. Liang W, McLaws ML, Liu M, Mi J, Chan DKY. Hindsight: a re-analysis of the severe acute respiratory syndrome outbreak in Beijing.Public Health. 2007;121(10):725-33. DOI: 10.1016/j.puhe.2007.02.023 PMID: 17555781

  82. Ali M, Wagatsuma Y, Emch M, Breiman RF. Use of a geographic information system for defining spatial risk for dengue transmission in Bangladesh: role for Aedes albopictus in an urban outbreak.Am J Trop Med Hyg. 2003;69(6):634-40.PMID: 14740881

  83. Tenzin SB, Sharma B, Dhand NK, Timsina N, Ward MP. Reemergence of rabies in Chhukha district, Bhutan, 2008.Emerg Infect Dis. 2010;16(12):1925-30. DOI: 10.3201/eid1612.100958 PMID: 21122223

  84. Nishiguchi A, Kobayashi S, Ouchi Y, Yamamoto T, Hayama Y, Tsutsui T. Spatial analysis of low pathogenic H5N2 avian influenza outbreaks in Japan in 2005.J Vet Med Sci. 2009;71(7):979-82. DOI: 10.1292/jvms.71.979 PMID: 19652489

  85. Siddiqui FJ, Bhutto NS, von Seidlein L, Khurram I, Rasool S, Ali M,  et al.  Consecutive outbreaks of Vibrio cholerae O139 and V. cholerae O1 cholera in a fishing village near Karachi, Pakistan. Trans R Soc Trop Med Hyg. 2006;100(5):476-82. DOI: 10.1016/j.trstmh.2005.07.019 PMID: 16443247

  86. Miranda ME, Yoshikawa Y, Manalo DL, Calaor AB, Miranda NL, Cho F, et al. Chronological and spatial analysis of the 1996 Ebola Reston virus outbreak in a monkey breeding facility in the Philippines. Exp Anim. 2002;51(2):173-9.

  87. Le H, Poljak Z, Deardon R, Dewey CE. Clustering of and risk factors for the porcine high fever disease in a region of Vietnam.Transbound Emerg Dis. 2012;59(1):49-61. DOI: 10.1111/j.1865-1682.2011.01239.x PMID: 21722329

  88. Addiss DG, Davis JP, LaVenture M, Wand PJ, Hutchinson MA, McKinney RM. Community-acquired Legionnaires’ disease associated with a cooling tower: evidence for longer-distance transport of Legionella pneumophila.Am J Epidemiol. 1989;130(3):557-68.PMID: 2764000

  89. Blondin N, Baumgardner DJ, Moore GE, Glickman LT. Blastomycosis in indoor cats: suburban Chicago, Illinois, USA.Mycopathologia. 2007;163(2):59-66. DOI: 10.1007/s11046-006-0090-1 PMID: 17262169

  90. Chung WM, Buseman CM, Joyner SN, Hughes SM, Fomby TB, Luby JP,  et al.  The 2012 West Nile encephalitis epidemic in Dallas, Texas. JAMA. 2013;310(3):297-307. DOI: 10.1001/jama.2013.8267 PMID: 23860988

  91. Mongoh MN, Dyer NW, Stoltenow CL, Khaitsa ML. Risk factors associated with anthrax outbreak in animals in North Dakota, 2005: a retrospective case-control study.Public Health Rep. 2008;123(3):352-9.PMID: 19006977

  92. Pfister JR, Archer JR, Hersil S, Boers T, Reed KD, Meece JK,  et al.  Non-rural point source blastomycosis outbreak near a yard waste collection site. Clin Med Res. 2011;9(2):57-65. DOI: 10.3121/cmr.2010.958 PMID: 20974888

  93. Bowie WR, King AS, Werker DH, Isaac-Renton JL, Bell A, Eng SB,  et al.  Outbreak of toxoplasmosis associated with municipal drinking water. Lancet. 1997;350(9072):173-7. DOI: 10.1016/S0140-6736(96)11105-3 PMID: 9250185

  94. Epp T, Argue C, Waldner C, Berke O. Spatial analysis of an anthrax outbreak in Saskatchewan, 2006.Can Vet J. 2010;51(7):743-8.PMID: 20885827

  95. Pasma T. Spatial epidemiology of an H3N2 swine influenza outbreak.Can Vet J. 2008;49(2):167-76.PMID: 18309747

  96. Morrison AC, Getis A, Santiago M, Rigau-Perez JG, Reiter P. Exploratory space-time analysis of reported dengue cases during an outbreak in Florida, Puerto Rico, 1991-1992.Am J Trop Med Hyg. 1998;58(3):287-98.PMID: 9546405

  97. Chadee DD, Lee R, Ferdinand A, Prabhakar P, Clarke D, Jacob B. Meningococcal meningitis outbreak in Trinidad, 1998.European Journal of General Medicine.2006;3(2):49-53.

  98. Bartels SA, Greenough PG, Tamar M, VanRooyen MJ. Investigation of a cholera outbreak in Ethiopia's Oromiya Region. Disaster med. 2010;4(4):312-7.

  99. Bessong PO, Odiyo JO, Musekene JN, Tessema A. Spatial distribution of diarrhoea and microbial quality of domestic water during an outbreak of diarrhoea in the Tshikuwi community in Venda, South Africa.J Health Popul Nutr. 2009;27(5):652-9. DOI: 10.3329/jhpn.v27i5.3642 PMID: 19902801

  100. Angulo JJ, Pederneiras CA, Sakuma ME, Takiguti CK, Megale P. Contour mapping of the temporal-spatial progression of a contagious disease.Bull Soc Pathol Exot Filiales. 1979;72(4):374-85.PMID: 535118

  101. de Moura L, Bahia-Oliveira LMG, Wada MY, Jones JL, Tuboi SH, Carmo EH,  et al.  Waterborne toxoplasmosis, Brazil, from field to gene. Emerg Infect Dis. 2006;12(2):326-9. DOI: 10.3201/eid1202.041115 PMID: 16494765

  102. Passos AD, Castro e Silva AA, Ferreira AH, Maria e Silva J, Monteiro ME, Santiago RC. [Rabies epizootic in the urban area of Ribeirão Preto, São Paulo, Brazil]. Cad Saude Publica. 1998;14(4):735-40. Portuguese. DOI: 10.1590/S0102-311X1998000400015 PMID: 9878906

  103. Rivas AL, Smith SD, Sullivan PJ, Gardner B, Aparicio JP, Hoogesteijn AL,  et al.  Identification of geographic factors associated with early spread of foot-and-mouth disease. Am J Vet Res. 2003;64(12):1519-27. DOI: 10.2460/ajvr.2003.64.1519 PMID: 14672431

  104. Firestone SM, Ward MP, Christley RM, Dhand NK. The importance of location in contact networks: Describing early epidemic spread using spatial social network analysis.Prev Vet Med. 2011;102(3):185-95. DOI: 10.1016/j.prevetmed.2011.07.006 PMID: 21852007

  105. Waldron LS, Ferrari BC, Cheung-Kwok-Sang C, Beggs PJ, Stephens N, Power ML. Molecular epidemiology and spatial distribution of a waterborne cryptosporidiosis outbreak in Australia.Appl Environ Microbiol. 2011;77(21):7766-71. DOI: 10.1128/AEM.00616-11 PMID: 21908623

  106. Turcios-Ruiz RM, Axelrod P, St John K, Bullitt E, Donahue J, Robinson N,  et al.  Outbreak of necrotizing enterocolitis caused by norovirus in a neonatal intensive care unit. J Pediatr. 2008;153(3):339-44. DOI: 10.1016/j.jpeds.2008.04.015 PMID: 18534621



Back to Table of Contents
Previous Download (pdf)
Next

The publisher’s policy on data collection and use of cookies.

Disclaimer: The opinions expressed by authors contributing to Eurosurveillance do not necessarily reflect the opinions of the European Centre for Disease Prevention and Control (ECDC) or the editorial team or the institutions with which the authors are affiliated. Neither ECDC nor any person acting on behalf of ECDC is responsible for the use that might be made of the information in this journal. The information provided on the Eurosurveillance site is designed to support, not replace, the relationship that exists between a patient/site visitor and his/her physician. Our website does not host any form of commercial advertisement. Except where otherwise stated, all manuscripts published after 1 January 2016 will be published under the Creative Commons Attribution (CC BY) licence. You are free to share and adapt the material, but you must give appropriate credit, provide a link to the licence, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

Eurosurveillance [ISSN 1560-7917] - ©2007-2016. All rights reserved.
 

This website is certified by Health On the Net Foundation. Click to verify. This site complies with the HONcode standard for trustworthy health information:
verify here.