Spatial methods for infectious disease outbreak investigations: systematic literature review

Small-scale spatiotemporal epidemiology of notifiable infectious diseases in China: a systematic review

Background

The prevalence of infectious diseases remains one of the major challenges faced by the Chinese health sector. Policymakers have a tremendous interest in investigating the spatiotemporal epidemiology of infectious diseases. We aimed to review the small-scale (city level, county level, or below) spatiotemporal epidemiology of notifiable infectious diseases in China through a systematic review, thus summarizing the evidence to facilitate more effective prevention and control of the diseases.

Methods

We searched four English language databases (PubMed, EMBASE, Cochrane Library, and Web of Science) and three Chinese databases (CNKI, WanFang, and SinoMed), for studies published between January 1, 2004 (the year in which China’s Internet-based disease reporting system was established) and December 31, 2021. Eligible works were small-scale spatial or spatiotemporal studies focusing on at least one notifiable infectious disease, with the entire territory of mainland China as the study area. Two independent reviewers completed the review process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Results

A total of 18,195 articles were identified, with 71 eligible for inclusion, focusing on 22 diseases. Thirty-one studies (43.66%) were analyzed using city-level data, 34 (47.89%) were analyzed using county-level data, and six (8.45%) used community or individual data. Approximately four-fifths (80.28%) of the studies visualized incidence using rate maps. Of these, 76.06% employed various spatial clustering methods to explore the spatial variations in the burden, with Moran’s I statistic being the most common. Of the studies, 40.85% explored risk factors, in which the geographically weighted regression model was the most commonly used method. Climate, socioeconomic factors, and population density were the three most considered factors.

Conclusions

Small-scale spatiotemporal epidemiology has been applied in studies on notifiable infectious diseases in China, involving spatiotemporal distribution and risk factors. Health authorities should improve prevention strategies and clarify the direction of future work in the field of infectious disease research in China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07669-9.

The epidemiology of diphtheria in Haiti, December 2014–June 2021: A spatial modeling analysis

Background

Haiti has been experiencing a resurgence of diphtheria since December 2014. Little is known about the factors contributing to the spread and persistence of the disease in the country. Geographic information systems (GIS) and spatial analysis were used to characterize the epidemiology of diphtheria in Haiti between December 2014 and June 2021.

Methods

Data for the study were collected from official and open-source databases. Choropleth maps were developed to understand spatial trends of diphtheria incidence in Haiti at the commune level, the third administrative division of the country. Spatial autocorrelation was assessed using the global Moran’s I. Local indicators of spatial association (LISA) were employed to detect areas with spatial dependence. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were built to identify factors associated with diphtheria incidence. The performance and fit of the models were compared using the adjusted r-squared (R²) and the corrected Akaike information criterion (AIC_c).

Results

From December 2014 to June 2021, the average annual incidence of confirmed diphtheria was 0.39 cases per 100,000 (range of annual incidence = 0.04–0.74 per 100,000). During the study period, diphtheria incidence presented weak but significant spatial autocorrelation (I = 0.18, p<0.001). Although diphtheria cases occurred throughout Haiti, nine communes were classified as disease hotspots. In the regression analyses, diphtheria incidence was positively associated with health facility density (number of facilities per 100,000 population) and degree of urbanization (proportion of urban population). Incidence was negatively associated with female literacy. The GWR model considerably improved model performance and fit compared to the OLS model, as indicated by the higher adjusted R² value (0.28 v 0.15) and lower AIC_c score (261.97 v 267.13).

Conclusion

This study demonstrates that GIS and spatial analysis can support the investigation of epidemiological patterns. Furthermore, it shows that diphtheria incidence exhibited spatial variability in Haiti. The disease hotspots and potential risk factors identified in this analysis could provide a basis for future public health interventions aimed at preventing and controlling diphtheria transmission.

Spatial Pattern and Land Surface Features Associated with Cloud-to-Ground Lightning in Bangladesh: An Exploratory Study

Severe weather events such as lightning appear to be a significant threat to humans and property in South Asia, an area known for intense convective activity directly related to the tropical climate of these areas. The current study was conducted in Bangladesh and examined the association between cloud-to-ground (CG) lightning and ground surface properties, with the aim of improving existing knowledge regarding this phenomenon. GLD360 data from 2015 to 2020 were used to describe the seasonal lightning climatology. Elevation, land use and land cover, vegetation and surface heat flux data were used to examine all land surface features possibly associated with CG lightning occurrence. Hot and cold spot spatial patterning was calculated using local indicators of spatial association. Results indicated a strong CG lightning seasonality. CG stroke density varied considerably across seasons with the pre-monsoon exhibiting the highest density. This was followed by occurrences in the monsoon season. The March-June period experienced 73% of the total observed. Elevation appeared to influence the post-monsoon CG stroke, however, its role in the other seasons was more difficult to define. The land cover/lightning index indicated that waterbodies and herbaceous wetlands had more influence than other land cover types, both during the day and at night, and it appeared that latent heat flux played a major role. The CG stroke hot and cold spot locations varied diurnally. The findings suggest that large-scale irrigation practices, especially during the pre-monsoon months, can influence the observed spatiotemporal pattern. The production of hotspot maps could be an initial step in the development of a reliable lightning monitoring system and play a part in increasing public awareness of this issue.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41748-022-00310-4.

Reactions to geographic data visualization of infectious disease outbreaks: an experiment on the effectiveness of data presentation format and past occurrence information

OBJECTIVES

This study intended to compare the effectiveness of thematic maps with that of tabular data in comprehension and memory of risk magnitudes, with Zika virus (ZIKV) disease outbreaks in the United States as the subject matter. The study also aimed to examine the effects of data presentation format and past occurrence information on risk perception and risk avoidance intention.

STUDY DESIGN

This study used an experiment.

METHODS

Each participant was randomly assigned to view ZIKV disease 2017 incidence data presented in one of the three formats: a choropleth map, a graduated-circle map, and a table, after which they answered questions about comprehension and memory of risk magnitudes. Each participant was then randomly assigned to view or not to view incidence data of the previous occurrence of ZIKV outbreaks in 2016, after which they answered questions about risk perception and risk avoidance intention.

RESULTS

The results revealed the effectiveness of thematic maps over tabular data in comprehension, risk perception, and risk avoidance intention. Compared to tabular data, the choropleth map led to a better comprehension of relative risk magnitudes, the graduated-circle map led to higher risk perception, and both thematic maps led to greater risk avoidance intention. In contrast, tabular data led to better recognition of absolute risk magnitudes than both thematic maps. In addition, past occurrence information enhanced risk perception and risk avoidance intention.

CONCLUSIONS

The findings reveal the importance of data presentation format in comprehension and memory of risk magnitudes. This can be attributed to the cognitive match between the information emphasized in the presentation and that required by the tasks. The findings also suggest that data presentation format and past occurrence information are important judgmental heuristics that help to form risk perception and risk avoidance intention.

Trends and Opportunities in Tick-Borne Disease Geography

Tick-borne diseases are a growing problem in many parts of the world, and their surveillance and control touch on challenging issues in medical entomology, agricultural health, veterinary medicine, and biosecurity. Spatial approaches can be used to synthesize the data generated by integrative One Health surveillance systems, and help stakeholders, managers, and medical geographers understand the current and future distribution of risk. Here, we performed a systematic review of over 8,000 studies and identified a total of 303 scientific publications that map tick-borne diseases using data on vectors, pathogens, and hosts (including wildlife, livestock, and human cases). We find that the field is growing rapidly, with the major Ixodes-borne diseases (Lyme disease and tick-borne encephalitis in particular) giving way to monitoring efforts that encompass a broader range of threats. We find a tremendous diversity of methods used to map tick-borne disease, but also find major gaps: data on the enzootic cycle of tick-borne pathogens is severely underutilized, and mapping efforts are mostly limited to Europe and North America. We suggest that future work can readily apply available methods to track the distributions of tick-borne diseases in Africa and Asia, following a One Health approach that combines medical and veterinary surveillance for maximum impact.

A Longitudinal, Clinical, and Spatial Epidemiologic Analysis of a Large COVID-19 Long-Term Care Home Outbreak

Objectives

COVID-19 has had devastating effects on long-term care homes across much of the world, and especially within Canada, with more than 50% of the mortality from COVID-19 in 2020 in these homes. Understanding the way in which the virus spreads within these homes is critical to preventing further outbreaks.

Design

Retrospective chart review.

Settings and Participants

Long-term care home residents and staff in Ontario, Canada.

Methods

We conducted a longitudinal study of a large long-term care home COVID-19 outbreak in Ontario, Canada, using electronic medical records, public health records, staff assignments, and resident room locations to spatially map the outbreak through the facility.

Results

By analyzing the outbreak longitudinally, we were able to draw 3 important conclusions: (1) 84.5% had typical COVID-19 symptoms and only 15.5% of residents had asymptomatic infection; (2) there was a high attack rate of 85.8%, which appeared to be explained by a high degree of interconnectedness within the home exacerbated by staffing shortages; and (3) clustering of infections within multibedded rooms was common.

Conclusion and Implications

Low rates of asymptomatic infection suggest that symptom-based screening in residents remains very important for detecting outbreaks, a high degree of interconnectedness explains the high attack rate, and there is a need for improved guidance for homes with multibedded rooms on optimizing resident room movement to mitigate spread of COVID-19 in long-term care homes.

Spatial patterns of lower respiratory tract infections and their association with fine particulate matter

This study aimed to identify the spatial patterns of lower respiratory tract infections (LRIs) and their association with fine particulate matter (PM_2.5). The disability-adjusted life year (DALY) database was used to represent the burden each country experiences as a result of LRIs. PM_2.5 data obtained from the Atmosphere Composition Analysis Group was assessed as the source for main exposure. Global Moran's I and Getis-Ord Gi* were applied to identify the spatial patterns and for hotspots analysis of LRIs. A generalized linear mixed model was coupled with a sensitivity test after controlling for covariates to estimate the association between LRIs and PM_2.5. Subgroup analyses were performed to determine whether LRIs and PM_2.5 are correlated for various ages and geographic regions. A significant spatial auto-correlated pattern was identified for global LRIs with Moran's Index 0.79, and the hotspots of LRIs were clustered in 35 African and 4 Eastern Mediterranean countries. A consistent significant positive association between LRIs and PM_2.5 with a coefficient of 0.21 (95% CI 0.06-0.36) was identified. Furthermore, subgroup analysis revealed a significant effect of PM_2.5 on LRI for children (0-14 years) and the elderly (≥ 70 years), and this effect was confirmed to be significant in all regions except for those comprised of Eastern Mediterranean countries.

Is Crowdsourcing a Reliable Method for Mass Data Acquisition? The Case of COVID-19 Spread in Greece During Spring 2020

We present a GIS-based crowdsourcing application that was launched soon after the first COVID-19 cases had been recorded in Greece, motivated by the need for fast, location-wise data acquisition regarding COVID-19 disease spread during spring 2020, due to limited testing. A single question was posted through a web App, to which the anonymous participants subjectively answered whether or not they had experienced any COVID-19 disease symptoms. Our main goal was to locate geographical areas with increased number of people feeling the symptoms and to determine any temporal changes in the statistics of the survey entries. It was found that the application was rapidly disseminated to the entire Greek territory via social media, having, thus, a great public reception. The higher percentages of participants experiencing symptoms coincided geographically with the highly populated urban areas, having also increased numbers of confirmed cases, while temporal variations were detected that accorded with the restrictions of activities. This application demonstrates that health systems can use crowdsourcing applications that assure anonymity, as an alternative to tracing apps, to identify possible hot spots and to reach and warn the public within a short time interval, increasing at the same time their situational awareness. However, a continuous reminder for participation should be scheduled.

How decision makers can use quantitative approaches to guide outbreak responses

Decision makers are responsible for directing staffing, logistics, selecting public health interventions, communicating to professionals and the public, planning future response needs, and establishing strategic and tactical priorities along with their funding requirements. Decision makers need to rapidly synthesize data from different experts across multiple disciplines, bridge data gaps and translate epidemiological analysis into an operational set of decisions for disease control. Analytic approaches can be defined for specific response phases: investigation, scale-up and control. These approaches include: improved applications of quantitative methods to generate insightful epidemiological descriptions of outbreaks; robust investigations of causal agents and risk factors; tools to assess response needs; identifying and monitoring optimal interventions or combinations of interventions; and forecasting for response planning. Data science and quantitative approaches can improve decision-making in outbreak response. To realize these benefits, we need to develop a structured approach that will improve the quality and timeliness of data collected during outbreaks, establish analytic teams within the response structure and define a research agenda for data analytics in outbreak response.

This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

Relationship between Coxiella burnetii (Q fever) antibody serology and time spent outdoors

BACKGROUND/AIM

From 2007 through 2010, the Netherlands experienced the largest recorded Q fever outbreak to date. People living closer to Coxiella burnetii infected goat farms were at increased risk for acute Q fever. Time spent outdoors near infected farms may have contributed to exposure to C. burnetii. The aim of this study was to retrospectively evaluate whether hours/week spent outdoors, in the vicinity of previously C. burnetii infected goat farms, was associated with presence of antibodies against C. burnetii in residents of a rural area in the Netherlands.

METHODS

Between 2014-2015, we collected C. burnetii antibody serology and self-reported data about habitual hours/week spent outdoors near the home from 2494 adults. From a subgroup we collected 941 GPS tracks, enabling analyses of active mobility in the outbreak region. Participants were categorised as exposed if they spent time within specified distances (500m, 1000m, 2000m, or 4000m) of C. burnetii infected goat farms. We evaluated whether time spent near these farms was associated with positive C. burnetii serology using spline analyses and logistic regression.

RESULTS

People that spent more hours/week outdoors near infected farms had a significantly increased risk for positive C. burnetii serology (time spent within 2000m of a C. burnetii abortion-wave positive farm, OR 3.6 (1.2-10.6)), compared to people spending less hours/week outdoors.

CONCLUSIONS

Outdoor exposure contributed to the risk of becoming C. burnetii serology positive. These associations were stronger if people spent more time near C. burnetii infected farms. Outdoor exposure should, if feasible, be included in outbreak investigations.

EpiExploreR: A Shiny Web Application for the Analysis of Animal Disease Data

Emerging and re-emerging infectious diseases are a significant public and animal health threat. In some zoonosis, the early detection of virus spread in animals is a crucial early warning for humans. The analyses of animal surveillance data are therefore of paramount importance for public health authorities to identify the appropriate control measure and intervention strategies in case of epidemics. The interaction among host, vectors, pathogen and environment require the analysis of more complex and diverse data coming from different sources. There is a wide range of spatiotemporal methods that can be applied as a surveillance tool for cluster detection, identification of risk areas and risk factors and disease transmission pattern evaluation. However, despite the growing effort, most of the recent integrated applications still lack of managing simultaneously different datasets and at the same time making available an analytical tool for a complete epidemiological assessment. In this paper, we present EpiExploreR, a user-friendly, flexible, R-Shiny web application. EpiExploreR provides tools integrating common approaches to analyze spatiotemporal data on animal diseases in Italy, including notified outbreaks, surveillance of vectors, animal movements data and remotely sensed data. Data exploration and analysis results are displayed through an interactive map, tables and graphs. EpiExploreR is addressed to scientists and researchers, including public and animal health professionals wishing to test hypotheses and explore data on surveillance activities.

Application of kernel smoothing to estimate the spatio-temporal variation in risk of STEC O157 in England

Identifying geographical areas with significantly higher or lower rates of infectious diseases can provide important aetiological clues to inform the development of public health policy and interventions designed to reduce morbidity. We applied kernel smoothing to estimate the spatial and spatio-temporal variation in risk of STEC O157 infection in England between 2009 and 2015, and to explore differences between the residential locations of cases reporting travel and those not reporting travel. We provide evidence that the distribution of STEC O157 infection in England is non-uniform with respect to the distribution of the at-risk population; that the spatial distribution of the three main genetic lineages infecting humans (I, II and I/II) differs significantly and that the spatio-temporal risk is highly dynamic. Our results also indicate that cases of STEC O157 reporting travel within or outside the UK are more likely to live in the south/south-east of the country, meaning that their residential location may not reflect the location of exposure that led to their infection. We suggest that the observed variation in risk reflects exposure to sources of STEC O157 that are geographically prescribed. These differences may be related to a combination of changes in the strains circulating in the ruminant reservoir, animal movements (livestock, birds or wildlife) or the behavior of individuals prior to infection. Further work to identify the importance of behaviours and exposures reported by cases relative to residential location is needed.

Interconnected clusters of invasive meningococcal disease due to Neisseria meningitidis serogroup C ST-11 (cc11), involving bisexuals and men who have sex with men, with discos and gay-venues hotspots of transmission, Tuscany, Italy, 2015 to 2016

In 2015 an increased incidence of invasive meningococcal disease due to serogroup-C (MenC) occurred in Tuscany, Italy. This led the Regional Health Authority of Tuscany to implement a reactive immunisation campaign and to launch an epidemiological field investigation aiming to address targeted immunisation interventions. In 2011–14, 10 MenC cases had been reported compared with 62 cases in 2015–16. The case fatality rate was 21% (n = 13) and 51 cases (82.3%) were confirmed as C:P1.5–1,10–8:F3–6:ST-11(cc11). Overall, 17 clusters were recognised. Six discos and four gay-venues were found to have a role as transmission-hotspots, having been attended by 20 and 14 cases in the 10 days before symptoms onset. Ten and three cases occurred, respectively, among men who have sex with men (MSM) and bisexual individuals, who were involved in 11 clusters. In addition, heterosexual cases (n = 5) attending gay-venues were also found. Secondary cases were not identified. Molecular typing indicated close relationship with MenC clusters recently described among gay, bisexual and other MSM in Europe and the United States, suggesting a possible international spread of the serogroup-C-variant P1.5–1,10–8:F3–6:ST-11(cc11) in this population-group; however, epidemiological links were not identified. In December 2016, a targeted vaccination campaign involving discos and lesbian, gay, bisexual, and transgender (LGBT) associations was implemented. During 2017, 10 cases of MenC occurred, compared with 32 and 30 cases reported in 2015 and 2016 respectively, suggesting the effectiveness of the reactive and targeted immunisation programmes.

Spatiotemporal Clustering of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Incidence in Saudi Arabia, 2012-2019

Middle East respiratory syndrome coronavirus (MERS-CoV) is a great public health concern globally. Although 83% of the globally confirmed cases have emerged in Saudi Arabia, the spatiotemporal clustering of MERS-CoV incidence has not been investigated. This study analysed the spatiotemporal patterns and clusters of laboratory-confirmed MERS-CoV cases reported in Saudi Arabia between June 2012 and March 2019. Temporal, seasonal, spatial and spatiotemporal cluster analyses were performed using Kulldorff’s spatial scan statistics to determine the time period and geographical areas with the highest MERS-CoV infection risk. A strongly significant temporal cluster for MERS-CoV infection risk was identified between April 5 and May 24, 2014. Most MERS-CoV infections occurred during the spring season (41.88%), with April and May showing significant seasonal clusters. Wadi Addawasir showed a high-risk spatial cluster for MERS-CoV infection. The most likely high-risk MERS-CoV annual spatiotemporal clusters were identified for a group of cities (n = 10) in Riyadh province between 2014 and 2016. A monthly spatiotemporal cluster included Jeddah, Makkah and Taif cities, with the most likely high-risk MERS-CoV infection cluster occurring between April and May 2014. Significant spatiotemporal clusters of MERS-CoV incidence were identified in Saudi Arabia. The findings are relevant to control the spread of the disease. This study provides preliminary risk assessments for the further investigation of the environmental risk factors associated with MERS-CoV clusters.

Spatial clustering of drug-resistant tuberculosis in Hlabisa subdistrict, KwaZulu-Natal, 2011-2015

SETTING:

Incidence rates of tuberculosis (TB) in South Africa are among the highest in the world, and drug resistance is a major concern. Understanding geographic variations in disease may guide targeted interventions.

OBJECTIVE:

To characterise the spatial distribution of drug-resistant TB (DR-TB) in a rural area of KwaZulu-Natal, South Africa, and to test for clustering.

DESIGN:

This was a cross-sectional analysis of DR-TB patients managed at a rural district hospital from 2011 to 2015. We mapped all patients in hospital data to local areas, and then linked to a population-based demographic surveillance system to map the patients to individual homesteads. We used kernel density estimation to visualise the distribution of disease and tested for clustering using spatial scan statistics.

RESULTS:

There were 489 patients with DR-TB in the subdistrict; 111 lived in the smaller demographic surveillance area. Spatial clustering analysis identified a high-risk cluster (relative risk of DR-TB inside vs. outside cluster 3.0, P <0.001) in the south-east, a region characterised by high population density and a high prevalence of human immunodeficiency virus infection.

CONCLUSION:

We have demonstrated evidence of a geographic high-risk cluster of DR-TB. This suggests that targeting interventions to spatial areas of highest risk, where transmission may be ongoing, could be effective.

Utility of Spatial Point-Pattern Analysis Using Residential and Workplace Geospatial Information to Localize Potential Outbreak Sources

Identifying the source of an outbreak facilitates its control. Spatial methods are not optimally used in outbreak investigation, due to a mix of the complexities involved (e.g., methods requiring additional parameter selection), imperfect performance, and lack of confidence in existing options. We simulated 30 mock outbreaks and compared 5 simple methods that do not require parameter selection but could select between mock cases’ residential and workplace addresses to localize the source. Each category of site had a unique spatial distribution; residential and workplace address were visually and statistically clustered around the residential neighborhood and city center sites respectively, suggesting that the value of workplace addresses is tied to the location where an outbreak might originate. A modification to centrographic statistics that we propose—the center of minimum geometric distance with address selection—was able to localize the mock outbreak source to within a 500 m radius in almost all instances when using workplace in combination with residential addresses. In the sensitivity analysis, when given sufficient workplace data, the method performed well in various scenarios with only 10 cases. It was also successful when applied to past outbreaks, except for a multisite outbreak from a common food supplier.

Use of systematic epidemiological methods in outbreak investigations from India, 2008-2016: A systematic review

Background

In the absence of a comprehensive review, we conducted a systematic review on the use of systematic approach in outbreak investigation using reports from India.

Objectives

The primary objective was to estimate the proportion of outbreak reports from India during 2008–16, that reported the steps in outbreak investigation. The secondary objectives were to (1) describe the outbreak reports by selected characteristics (source, investigating agency, disease, time, place and person) (2) estimate the proportion of outbreaks that conducted analytical and additional studies.

Methods

We searched eight electronic databases and grey literature for outbreak investigation reports among humans at community settings from India during 2008–2016. We developed a check-list based on the 10-steps approach used by Field Epidemiology Training Programme (FETP) of ICMR-National Institute of Epidemiology (ICMR-NIE), Chennai, India. This checklist was used to independently screen and extract data on general characteristics of the outbreak investigation reports and the steps completed. We adopted The Joanna Briggs Institute (JBI) check list for prevalence studies to examine the credibility and consistency. The protocol was registered at Prospero (CRD42017065038). We calculated proportion of reports that followed the steps in their investigation and descriptive statistics on selected characteristics.

Results

Of 10,657 articles screened, 136 articles were included for the review. Completion of the ten steps in the outbreak investigations was seen in 16% of reports. The highest level of completion was for drawing conclusion in outbreak investigation (98%) and the lowest completion (29%) was for developing a case definition by time, place and person followed by conducting an analytic study (24%).

Conclusions

Outbreak reports from India either lacked application of systematic steps for investigation or failed to report the actual procedures followed. We recommend improving systematic investigation of outbreaks through training and supervision of outbreak response teams and encouraging publications.

•

Systematic review of outbreak investigation reports from India (2008–16) for application of systematic approach documented poor application of use of methods used or reporting of recommended steps.

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Defining of a case by time, place and person and conducting an analytic study were the least reported steps.

•

There is a need to strengthen the quality and transparency of outbreak investigations while reporting.

Small-area spatial statistical analysis of malaria clusters and hotspots in Cameroon;2000-2015

Background

Malaria prevalence in Cameroon is a major public health problem both at the regional and urban-rural geographic scale. In 2016, an estimated 1.6 million confirmed cases, and 18,738 cases were reported in health facilities and communities respectively, with about 8000 estimated deaths. Several studies have estimated malaria prevalence in Cameroon using the analytical techniques at the regional scale. We aimed at identifying malaria clusters and hotspots at the urban-rural geographic scale from the Demographic and Health Survey (DHS) data for households between 2000 and 2015 using ArcGIS for intervention programs.

Methods

To identify malaria hotspots and analyze the pattern of distribution, we used the optimized hotspots toolset and spatial autocorrelation respectively in ArcGIS 10.3 for desktop. We also used Pearson’s Correlation analysis to identify associative environmental factors using the R-software 3.4.1.

Results

The spatial distribution of malaria showed statistically significant clustered pattern for the year 2000 and 2015 with Moran’s indexes 0.126 (P < 0.001) and 0.187 (P < 0.001) respectively. Meanwhile, the years 2005 and 2010 with Moran’s indexes 0.001 (P = 0.488) and 0.002 (P = 0.318) respectively, had a random malaria distribution pattern. There exist varying degrees of malaria clusters and statistically significant hotspots in the urban-rural areas of the 12 administrative regions. Malaria cases were associated with population density and some environmental covariates; rainfall, enhanced vegetation index and composite lights (P < 0.001).

Conclusion

This study identified urban-rural areas with high and low malaria clusters and hotspots. Our maps can be used as supportive tools for effective malaria control and elimination, and investments in malaria programs and research, malaria prevention, diagnosis and treatment, surveillance, should pay more attention to urban-rural geographic scale.

Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review

BACKGROUND

Tuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden.

METHODS

We conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017. The protocol for this systematic review was prospectively registered with PROSPERO ( CRD42016036655 ).

RESULTS

We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff's spatial scan statistic followed by local Moran's I and Getis and Ord's local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined.

CONCLUSIONS

A range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control.

Combining survey data, GIS and qualitative interviews in the analysis of health service access for persons with disabilities

Background

Equitable access to health services is a key ingredient in reaching health for persons with disabilities and other vulnerable groups. So far, research on access to health services in low- and middle-income countries has largely relied on self-reported survey data. Realizing that there may be substantial discrepancies between perceived and actual access, other methods are needed for more precise knowledge to guide health policy and planning. The objective of this article is to describe and discuss an innovative methodological triangulation where statistical and spatial analysis of perceived distance and objective measures of access is combined with qualitative evidence.

Methods

The data for the study was drawn from a large household and individual questionnaire based survey carried out in Namibia and Malawi. The survey data was combined with spatial data of respondents and health facilities, key informant interviews and focus group discussions. To analyse access and barriers to access, a model is developed that takes into account both measured and perceived access. The geo-referenced survey data is used to establish four outcome categories of perceived and measured access as either good or poor. Combined with analyses of the terrain and the actual distance from where the respondents live to the health facility they go to, the data allows for categorising areas and respondents according to the four outcome categories. The four groups are subsequently analysed with respect to variation in individual characteristics and vulnerability factors. The qualitative component includes participatory map drawing and is used to gain further insight into the mechanisms behind the different combinations of perceived and actual access.

Results

Preliminary results show that there are substantial discrepancies between perceived and actual access to health services and the qualitative study provides insight into mechanisms behind such divergences.

Conclusion

The novel combination of survey data, geographical data and qualitative data will generate a model on access to health services in poor contexts that will feed into efforts to improve access for the most vulnerable people in underserved areas.

Dot map cartograms for detection of infectious disease outbreaks: an application to Q fever, the Netherlands and pertussis, Germany

Geographical mapping of infectious diseases is an important tool for detecting and characterising outbreaks. Two common mapping methods, dot maps and incidence maps, have important shortcomings. The former does not represent population density and can compromise case privacy, and the latter relies on pre-defined administrative boundaries. We propose a method that overcomes these limitations: dot map cartograms. These create a point pattern of cases while reshaping spatial units, such that spatial area becomes proportional to population size. We compared these dot map cartograms with standard dot maps and incidence maps on four criteria, using two example datasets. Dot map cartograms were able to illustrate both incidence and absolute numbers of cases (criterion 1): they revealed potential source locations (Q fever, the Netherlands) and clusters with high incidence (pertussis, Germany). Unlike incidence maps, they were insensitive to choices regarding spatial scale (criterion 2). Dot map cartograms ensured the privacy of cases (criterion 3) by spatial distortion; however, this occurred at the expense of recognition of locations (criterion 4). We demonstrate that dot map cartograms are a valuable method for detection and visualisation of infectious disease outbreaks, which facilitates informed and appropriate actions by public health professionals, to investigate and control outbreaks.

Prediction of Local Transmission of Mycobacterium tuberculosis Isolates of a Predominantly Beijing Lineage by Use of a Variable-Number Tandem-Repeat Typing Method Incorporating a Consensus Set of Hypervariable Loci

Strain genotyping based on the variable-number tandem repeat (VNTR) is widely applied for identifying the transmission of Mycobacterium tuberculosis A consensus set of four hypervariable loci (1982, 3232, 3820, and 4120) has been proposed to improve the discrimination of Beijing lineage strains. Herein, we evaluated the utility of these four hypervariable loci for tracing local tuberculosis transmission in 981 cases over a 14-month period in Japan (2010 to 2011). We used six different VNTR systems, with or without the four hypervariable loci. Patient ages and weighted standard distances (a measure of the dispersion of genotype-clustered cases) were used as proxies for estimating local tuberculosis transmission. The highest levels of isolate discrimination were achieved with VNTR systems that incorporated the four hypervariable loci (i.e., the Japan Anti-Tuberculosis Association [JATA]18-VNTR, mycobacterial interspersed repetitive unit [MIRU]28-VNTR, and 24_Beijing-VNTR). The clustering rates by JATA12-VNTR, MIRU15-VNTR, JATA15-VNTR, JATA18-VNTR, MIRU28-VNTR, and 24_Beijing-VNTR systems were 52.2%, 51.0%, 39.0%, 24.1%, 23.1%, and 22.0%, respectively. As the discriminative power increased, the median weighted standard distances of the clusters tended to decrease (from 311 to 80 km, P < 0.001, Jonckheere-Terpstra trend test). Concurrently, the median ages of patients in the clusters tended to decrease (from 68 to 60 years, P < 0.001, Jonckheere-Terpstra trend test). These findings suggest that strain typing using the four hypervariable loci improves the prediction of active local tuberculosis transmission. The four-locus set can therefore contribute to the targeted control of tuberculosis in settings with high prevalence of Beijing lineage strains.

Communicating infectious disease prevalence through graphics: Results from an international survey

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The impact of graphics to inform the general public on health knowledge is unknown.

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Heat maps were evaluated as superior to dot maps or picto-trendlines.

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Heat maps are a viable option to widely disseminate information about infectious diseases.

Background

Graphics are increasingly used to represent the spread of infectious diseases (e.g., influenza, Zika, Ebola); however, the impact of using graphics to adequately inform the general population is unknown.

Objective

To examine whether three ways of visually presenting data (heat map, dot map, or picto-trendline)—all depicting the same information regarding the spread of a hypothetical outbreak of influenza—influence intent to vaccinate, risk perception, and knowledge.

Design

Survey with participants randomized to receive a simulated news article accompanied by one of the three graphics that communicated prevalence of influenza and number of influenza-related deaths.

Setting

International online survey.

Participants

16,510 adults living in 11 countries selected using stratified random sampling based on age and gender.

Measurements

After reading the article and viewing the presented graphic, participants completed a survey that measured interest in vaccination, perceived risk of contracting disease, knowledge gained, interest in additional information about the disease, and perception of the graphic.

Results

Heat maps and picto-trendlines were evaluated more positively than dot maps. Heat maps were more effective than picto-trendlines and no different from dot maps at increasing interest in vaccination, perceived risk of contracting disease, and interest in additional information about the disease. Heat maps and picto-trendlines were more successful at conveying knowledge than dot maps. Overall, heat maps were the only graphic to be superior in every outcome.

Limitations

Results are based on a hypothetical scenario.

Conclusion

Heat maps are a viable option to promote interest in and concern about infectious diseases.

A Simulation Study on Hypothetical Ebola Virus Transmission in India Using Spatiotemporal Epidemiological Modeler (STEM): A Way towards Precision Public Health

Background. Precision public health is a state-of-the-art concept in public health research and its application in health care. Application of information technology in field of epidemiology paves the way to its transformation to digital epidemiology. A geospatial epidemiological model was simulated to estimate the spread of Ebola virus disease after a hypothetical outbreak in India. Methods. It was a simulation study based on SEIR (Susceptible-Exposed-Infectious-Recovered) compartmental model. Simulation was done in Spatiotemporal Epidemiological Modeler (STEM). Epidemiological profile of Ebola virus, that transmitted throughout the Sierra Leon in 2014–2016, was fitted into the SEIR deterministic compartment model designed for India. Result. Spatiotemporal distribution of EVD exposed, infectious, and recovered population at 4-month interval represented by different figures. It is estimated that if no intervention is taken to stop the spread, within 2 years, almost half of the country will be effected by EVD and cumulative number of exposed individuals, infectious persons, and deaths will be 106947760, 30651674, and 18391005, respectively. Conclusion. Precision public health may play the key role to achieve the health related targets in the Sustainable Development Goals. Policy makers, public health specialists, and data scientists need to put their hands together to make precision public health a reality.

Multiple large clusters of tuberculosis in London: a cross-sectional analysis of molecular and spatial data

Large outbreaks of tuberculosis (TB) represent a particular threat to disease control because they reflect multiple instances of active transmission. The extent to which long chains of transmission contribute to high TB incidence in London is unknown. We aimed to estimate the contribution of large clusters to the burden of TB in London and identify risk factors.

We identified TB patients resident in London notified between 2010 and 2014, and used 24-locus mycobacterial interspersed repetitive units–variable number tandem repeat strain typing data to classify cases according to molecular cluster size. We used spatial scan statistics to test for spatial clustering and analysed risk factors through multinomial logistic regression.

TB isolates from 7458 patients were included in the analysis. There were 20 large molecular clusters (with n>20 cases), comprising 795 (11%) of all cases; 18 (90%) large clusters exhibited significant spatial clustering. Cases in large clusters were more likely to be UK born (adjusted odds ratio 2.93, 95% CI 2.28–3.77), of black-Caribbean ethnicity (adjusted odds ratio 3.64, 95% CI 2.23–5.94) and have multiple social risk factors (adjusted odds ratio 3.75, 95% CI 1.96–7.16).

Large clusters of cases contribute substantially to the burden of TB in London. Targeting interventions such as screening in deprived areas and social risk groups, including those of black ethnicities and born in the UK, should be a priority for reducing transmission.

Large clusters contribute substantially to the burden of tuberculosis in London, indicating ongoing transmissionhttp://ow.ly/3xk23068P6w

Outbreaks of syphilis among men who have sex with men attending STI clinics between 2007 and 2015 in the Netherlands: a space-time clustering study

OBJECTIVES

Infectious syphilis (syphilis) is diagnosed predominantly among men who have sex with men (MSM) in the Netherlands and is a strong indicator for sexual risk behaviour. Therefore, an increase in syphilis can be an early indicator of resurgence of other STIs, including HIV. National and worldwide outbreaks of syphilis, as well as potential changes in sexual networks were reason to explore syphilis trends and clusters in more depth.

METHODS

National STI/HIV surveillance data were used, containing epidemiological, behavioural and clinical data from STI clinics. We examined syphilis positivity rates stratified by HIV status and year. Additionally, we performed space-time cluster analysis on municipality level between 2007 and 2015, using SaTScan to evaluate whether or not there was a higher than expected syphilis incidence in a certain area and time period, using the maximum likelihood ratio test statistic.

RESULTS

Among HIV-positive MSM, the syphilis positivity rate decreased between 2007 (12.3%) and 2011 (4.5%), followed by an increasing trend (2015: 8.0%). Among HIV-negative MSM, the positivity rate decreased between 2007 (2.8%) and 2011 also (1.4%) and started to increase from 2013 onwards (2015: 1.8%). In addition, we identified three geospatial clusters. The first cluster consisted of MSM sex workers in the South of the Netherlands (July 2009-September 2010, n=10, p<0.001). The second cluster were mostly HIV-positive MSM (58.5%) (Amsterdam; July 2011-December 2015; n=1123, p<0.001), although the proportion of HIV-negative MSM increased over time. The third cluster was large in space (predominantly the city of Rotterdam; April-September 2015, n=72, p=0.014) and were mostly HIV-negative MSM (62.5%).

CONCLUSIONS

Using SaTScan analysis, we observed several not yet recognised outbreaks and a rapid resurgence of syphilis among known HIV-positive MSM first, but more recently, also among HIV-negative MSM. The three identified clusters revealed locations, periods and specific characteristics of the involved MSM that could be used when developing targeted interventions.

Near real-time space-time cluster analysis for detection of enteric disease outbreaks in a community setting

OBJECTIVES

To enhance timely surveillance of bacterial enteric pathogens, space-time cluster analysis was introduced in Israel in May 2013.

METHODS

Stool isolation data of Salmonella, Shigella, and Campylobacter from patients of a large Health Maintenance Organization were analyzed weekly by ArcGIS and SaTScan, and cluster results were sent promptly to local departments of health (LDOHs).

RESULTS

During eighteen months, we identified 52 Shigella sonnei clusters, two Salmonella clusters, and no Campylobacter clusters. S. sonnei clusters lasted from one to 33 days and included three to 30 individuals. Thirty-one (60%) of the S. sonnei clusters were known to LDOHs prior to cluster analysis. Clusters not previously known by the LDOHs prompted epidemiologic investigations. In 31 of the 37 (84%) confirmed clusters, educational institutes (nursery schools, kindergartens, and a primary school) were involved.

CONCLUSIONS

Cluster analysis demonstrated capability to complement enteric disease surveillance. Scaling up the system can further enhance timely detection and control of outbreaks.

DotMapper: an open source tool for creating interactive disease point maps

Background

Molecular strain typing of tuberculosis isolates has led to increased understanding of the epidemiological characteristics of the disease and improvements in its control, diagnosis and treatment. However, molecular cluster investigations, which aim to detect previously unidentified cases, remain challenging. Interactive dot mapping is a simple approach which could aid investigations by highlighting cases likely to share epidemiological links. Current tools generally require technical expertise or lack interactivity.

Results

We designed a flexible application for producing disease dot maps using Shiny, a web application framework for the statistical software, R. The application displays locations of cases on an interactive map colour coded according to levels of categorical variables such as demographics and risk factors. Cases can be filtered by selecting combinations of these characteristics and by notification date. It can be used to rapidly identify geographic patterns amongst cases in molecular clusters of tuberculosis in space and time; generate hypotheses about disease transmission; identify outliers, and guide targeted control measures.

Conclusions

DotMapper is a user-friendly application which enables rapid production of maps displaying locations of cases and their epidemiological characteristics without the need for specialist training in geographic information systems. Enhanced understanding of tuberculosis transmission using this application could facilitate improved detection of cases with epidemiological links and therefore lessen the public health impacts of the disease. It is a flexible system and also has broad international potential application to other investigations using geo-coded health information.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-1475-5) contains supplementary material, which is available to authorized users.