Geospatial analysis applied to epidemiological studies of dengue: a systematic 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.

Spatial Analysis of Mosquito-Borne Diseases in Europe: A Scoping Review

Mosquito-borne infections are increasing in endemic areas and previously unaffected regions. In 2020, the notification rate for Dengue was 0.5 cases per 100,000 population, and for Chikungunya <0.1/100,000. In 2019, the rate for Malaria was 1.3/100,000, and for West Nile Virus, 0.1/100,000. Spatial analysis is increasingly used in surveillance and epidemiological investigation, but reviews about their use in this research topic are scarce. We identify and describe the methodological approaches used to investigate the distribution and ecological determinants of mosquito-borne infections in Europe. Relevant literature was extracted from PubMed, Scopus, and Web of Science from inception until October 2021 and analysed according to PRISMA-ScR protocol. We identified 110 studies. Most used geographical correlation analysis (n = 50), mainly applying generalised linear models, and the remaining used spatial cluster detection (n = 30) and disease mapping (n = 30), mainly conducted using frequentist approaches. The most studied infections were Dengue (n = 32), Malaria (n = 26), Chikungunya (n = 26), and West Nile Virus (n = 24), and the most studied ecological determinants were temperature (n = 39), precipitation (n = 24), water bodies (n = 14), and vegetation (n = 11). Results from this review may support public health programs for mosquito-borne disease prevention and may help guide future research, as we recommended various good practices for spatial epidemiological studies.

Geographical heterogeneity and socio-ecological risk profiles of dengue in Jakarta, Indonesia

The aim of this study was to assess the role of climate variability on the incidence of dengue fever (DF), an endemic arboviral infection existing in Jakarta, Indonesia. The work carried out included analysis of the spatial distribution of confirmed DF cases from January 2007 to December 2018 characterising the sociodemographical and ecological factors in DF high-risk areas. Spearman's rank correlation was used to examine the relationship between DF incidence and climatic factors. Spatial clustering and hotspots of DF were examined using global Moran's I statistic and the local indicator for spatial association analysis. Classification and regression tree (CART) analysis was performed to compare and identify demographical and socio-ecological characteristics of the identified hotspots and low-risk clusters. The seasonality of DF incidence was correlated with precipitation (r=0.254, P<0.01), humidity (r=0.340, P<0.01), dipole mode index (r= -0.459, P<0.01) and Tmin (r= -0.181, P<0.05). DF incidence was spatially clustered at the village level (I=0.294, P<0.001) and 22 hotspots were identified with a concentration in the central and eastern parts of Jakarta. CART analysis showed that age and occupation were the most important factors explaining DF clustering. Areaspecific and population-targeted interventions are needed to improve the situation among those living in the identified DF high-risk areas in Jakarta.

Spatiotemporal Tools for Emerging and Endemic Disease Hotspots in Small Areas: An Analysis of Dengue and Chikungunya in Barbados, 2013-2016

Dengue fever and other febrile mosquito-borne diseases place considerable health and economic burdens on small island nations in the Caribbean. Here, we used two methods of cluster detection to find potential hotspots of transmission of dengue and chikungunya in Barbados, and to assess the impact of input surveillance data and methodology on observed patterns of risk. Using Moran's I and spatial scan statistics, we analyzed the geospatial and temporal distribution of disease cases and rates across Barbados for dengue fever in 2013-2016, and a chikungunya outbreak in 2014. During years with high numbers of dengue cases, hotspots for cases were found with Moran's I in the south and central regions in 2013 and 2016, respectively. Using smoothed disease rates, clustering was detected in all years for dengue. Hotspots suggesting higher rates were not detected via spatial scan statistics, but coldspots suggesting lower than expected rates of disease activity were found in southwestern Barbados during high case years of dengue. No significant spatiotemporal structure was found in cases during the chikungunya outbreak. Spatial analysis of surveillance data is useful in identifying outbreak hotspots, potentially complementing existing early warning systems. We caution that these methods should be used in a manner appropriate to available data and reflecting explicit public health goals-managing for overall case numbers or targeting anomalous rates for further investigation.

A Mapping Review on Urban Landscape Factors of Dengue Retrieved from Earth Observation Data, GIS Techniques, and Survey Questionnaires

To date, there is no effective treatment to cure dengue fever, a mosquito-borne disease which has a major impact on human populations in tropical and sub-tropical regions. Although the characteristics of dengue infection are well known, factors associated with landscape are highly scale dependent in time and space, and therefore difficult to monitor. We propose here a mapping review based on 78 articles that study the relationships between landscape factors and urban dengue cases considering household, neighborhood and administrative levels. Landscape factors were retrieved from survey questionnaires, Geographic Information Systems (GIS), and remote sensing (RS) techniques. We structured these into groups composed of land cover, land use, and housing type and characteristics, as well as subgroups referring to construction material, urban typology, and infrastructure level. We mapped the co-occurrence networks associated with these factors, and analyzed their relevance according to a three-valued interpretation (positive, negative, non significant). From a methodological perspective, coupling RS and GIS techniques with field surveys including entomological observations should be systematically considered, as none digital land use or land cover variables appears to be an univocal determinant of dengue occurrences. Remote sensing urban mapping is however of interest to provide a geographical frame to distribute human population and movement in relation to their activities in the city, and as spatialized input variables for epidemiological and entomological models.

Suicide mortality among youth in southern Brazil: a spatiotemporal evaluation of socioeconomic vulnerability

Objective:

To conduct a geospatial analysis of suicide deaths among young people in the state of Paraná, southern Brazil, and evaluate their association with socioeconomic and spatial determinants.

Methods:

Data were obtained from the Mortality Information System and the Brazilian Institute of Geography and Statistics. Data on suicide mortality rates (SMR) were extracted for three age groups (15-19, 20-24, and 25-29 years) from two 5-year periods (1998-2002 and 2008-2012). Geospatial data were analyzed through exploratory spatial data analysis. We applied Bayesian networks algorithms to explore the network structure of the socioeconomic predictors of SMR.

Results:

We observed spatial dependency in SMR in both periods, revealing geospatial clusters of high SMR. Our results show that socioeconomic deprivation at the municipality level was an important determinant of suicide in the youth population in Paraná, and significantly influenced the formation of high-risk SMR clusters.

Conclusion:

While youth suicide is multifactorial, there are predictable geospatial and sociodemographic factors associated with high SMR among municipalities in Paraná. Suicide among youth aged 15-29 occurs in geographic clusters which are associated with socioeconomic deprivation. Rural settings with poor infrastructure and development also correlate with increased SMR clusters.

Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

Dengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

The Social and Spatial Ecology of Dengue Presence and Burden during an Outbreak in Guayaquil, Ecuador, 2012

Dengue fever, a mosquito-borne arbovirus, is a major public health concern in Ecuador. In this study, we aimed to describe the spatial distribution of dengue risk and identify local social-ecological factors associated with an outbreak of dengue fever in the city of Guayaquil, Ecuador. We examined georeferenced dengue cases (n = 4248) and block-level census data variables to identify social-ecological risk factors associated with the presence/absence and burden of dengue in Guayaquil in 2012. Local Indicators of Spatial Association (LISA), specifically Anselin’s Local Moran’s I, and Moran’s I tests were used to locate hotspots of dengue transmission, and multimodel selection was used to identify covariates associated with dengue presence and burden at the census block level. We identified significant dengue transmission hotspots near the North Central and Southern portions of Guayaquil. Significant risk factors for presence of dengue included poor housing conditions, access to paved roads, and receipt of remittances. Counterintuitive positive correlations with dengue presence were observed with several municipal services such as garbage collection and access to piped water. Risk factors for increased burden of dengue included poor housing conditions, garbage collection, receipt of remittances, and sharing a property with more than one household. Social factors such as education and household demographics were negatively correlated with increased dengue burden. These findings elucidate underlying differences with dengue presence versus burden, and suggest that vulnerability and risk maps could be developed to inform dengue prevention and control; this is information that is also relevant for emerging epidemics of chikungunya and Zika viruses.

Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010

Background

Dengue fever, a mosquito-borne viral disease, is a rapidly emerging public health problem in Ecuador and throughout the tropics. However, we have a limited understanding of the disease transmission dynamics in these regions. Previous studies in southern coastal Ecuador have demonstrated the potential to develop a dengue early warning system (EWS) that incorporates climate and non-climate information. The objective of this study was to characterize the spatiotemporal dynamics and climatic and social-ecological risk factors associated with the largest dengue epidemic to date in Machala, Ecuador, to inform the development of a dengue EWS.

Methods

The following data from Machala were included in analyses: neighborhood-level georeferenced dengue cases, national census data, and entomological surveillance data from 2010; and time series of weekly dengue cases (aggregated to the city-level) and meteorological data from 2003 to 2012. We applied LISA and Moran’s I to analyze the spatial distribution of the 2010 dengue cases, and developed multivariate logistic regression models through a multi-model selection process to identify census variables and entomological covariates associated with the presence of dengue at the neighborhood level. Using data aggregated at the city-level, we conducted a time-series (wavelet) analysis of weekly climate and dengue incidence (2003-2012) to identify significant time periods (e.g., annual, biannual) when climate co-varied with dengue, and to describe the climate conditions associated with the 2010 outbreak.

Results

We found significant hotspots of dengue transmission near the center of Machala. The best-fit model to predict the presence of dengue included older age and female gender of the head of the household, greater access to piped water in the home, poor housing condition, and less distance to the central hospital. Wavelet analyses revealed that dengue transmission co-varied with rainfall and minimum temperature at annual and biannual cycles, and we found that anomalously high rainfall and temperatures were associated with the 2010 outbreak.

Conclusions

Our findings highlight the importance of geospatial information in dengue surveillance and the potential to develop a climate-driven spatiotemporal prediction model to inform disease prevention and control interventions. This study provides an operational methodological framework that can be applied to understand the drivers of local dengue risk.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-014-0610-4) contains supplementary material, which is available to authorized users.