Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes

Space-time clusters and co-occurrence of Plasmodium vivax and Plasmodium falciparum malaria in West Bengal, India

BACKGROUND

Malaria, a prominent vector borne disease causing over a million annual cases worldwide, predominantly affects vulnerable populations in the least developed regions. Despite their preventable and treatable nature, malaria remains a global public health concern. In the last decade, India has faced a significant decline in malaria morbidity and mortality. As India pledged to eliminate malaria by 2030, this study examined a decade of surveillance data to uncover space-time clustering and seasonal trends of Plasmodium vivax and Plasmodium falciparum malaria cases in West Bengal.

METHODS

Seasonal and trend decomposition using Loess (STL) was applied to detect seasonal trend and anomaly of the time series. Univariate and multivariate space-time cluster analysis of both malaria cases were performed at block level using Kulldorff's space-time scan statistics from April 2011 to March 2021 to detect statistically significant space-time clusters.

RESULTS

From the time series decomposition, a clear seasonal pattern is visible for both malaria cases. Statistical analysis indicated considerable high-risk P. vivax clusters, particularly in the northern, central, and lower Gangetic areas. Whereas, P. falciparum was concentrated in the western region with a significant recent transmission towards the lower Gangetic plain. From the multivariate space-time scan statistics, the co-occurrence of both cases were detected with four significant clusters, which signifies the regions experiencing a greater burden of malaria cases.

CONCLUSIONS

Seasonal trends from the time series decomposition analysis show a gradual decline for both P. vivax and P. falciparum cases in West Bengal. The space-time scan statistics identified high-risk blocks for P. vivax and P. falciparum malaria and its co-occurrence. Both malaria types exhibit significant spatiotemporal variations over the study area. Identifying emerging high-risk areas of P. falciparum malaria over the Gangetic belt indicates the need for more research for its spatial shifting. Addressing the drivers of malaria transmission in these diverse clusters demands regional cooperation and strategic strategies, crucial steps towards overcoming the final obstacles in malaria eradication.

Spatial variation and clustering of anaemia prevalence in school-aged children in Western Kenya

Anaemia surveillance has overlooked school-aged children (SAC), hence information on this age group is scarce. This study examined the spatial variation of anaemia prevalence among SAC (5-14 years) in western Kenya, a region associated with high malaria infection rates. A total of 8051 SAC were examined from 82 schools across eight counties in Western Kenya in February 2022. Haemoglobin (Hb) concentrations were assessed at the school and village level and anaemia defined as Hb<11.5g/dl for age 5-11yrs and Hb <12.0g/dl for 12-14yrs after adjusting for altitude. Moran's I analysis was used to measure spatial autocorrelation, and local clusters of anaemia were mapped using spatial scan statistics and local indices of spatial association (LISA). The prevalence of anaemia among SAC was 27.8%. The spatial variation of anaemia was non-random, with Global Moran's I 0.2 (p-value < 0.002). Two significant anaemia cluster windows were identified: Cluster 1 (LLR = 38.9, RR = 1.4, prevalence = 32.0%) and cluster 2 (LLR = 23.6, RR = 1.6, prevalence = 45.5%) at schools and cluster 1 (LLR = 41.3, RR = 1.4, prevalence = 33.3%) and cluster 2 (LLR = 24.5, RR = 1.6, prevalence = 36.8%) at villages. Additionally, LISA analysis identified ten school catchments as anaemia hotspots corresponding geographically to SatScan clusters. Anaemia in the SAC is a public health problem in the Western region of Kenya with some localised areas presenting greater risk relative to others. Increasing coverage of interventions, geographically targeting the prevention of anaemia in the SAC, including malaria, is required to alleviate the burden among children attending school in Western Kenya.

Geographic Variation in Late-Stage Cervical Cancer Diagnosis

Importance

There are stark disparities in cervical cancer burden in the United States, notably by race and ethnicity and geography. Late-stage diagnosis is an indicator of inadequate access to and utilization of screening.

Objective

To identify geospatial clusters of late-stage cervical cancer at time of diagnosis in Texas.

Design, Setting, and Participants

This population-based cross-sectional study used incident cervical cancer data from the Texas Cancer Registry from 2014 to 2018 of female patients aged 18 years or older. Late-stage cervical cancer cases were geocoded at the census tract level (n = 5265) using their residential coordinates (latitude and longitude) at the time of diagnosis. Statistical analysis was performed from April to September 2023.

Exposures

Census tract of residence at diagnosis.

Main Outcome and Measures

Late-stage cervical cancer diagnosis (ie, cases classified by the National Cancer Institute Surveillance, Epidemiology and End Results summary stages 2 to 4 [regional spread] or 7 [distant metastasis]). A Poisson probability-based model of the SaTScan purely spatial scan statistics was applied at the census tract-level to identify geographic clusters of higher (hot spots) or lower (cold spots) proportions than expected of late-stage cervical cancer diagnosis and adjusted for age.

Results

Among a total of 6484 female patients with incident cervical cancer cases (mean [SD] age, 48.7 [14.7] years), 2300 (35.5%) were Hispanic, 798 (12.3%) were non-Hispanic Black, 3090 (47.6%) were non-Hispanic White, and 296 (4.6%) were other race or ethnicity. Of the 6484 patients, 2892 with late-stage diagnosis (mean [SD] age, 51.8 [14.4] years were analyzed. Among patients with late-stage diagnosis, 1069 (37.0%) were Hispanic, 417 (14.4%) were non-Hispanic Black, 1307 (45.2%) were non-Hispanic White, and 99 (3.4%) were other race or ethnicity. SaTScan spatial analysis identified 7 statistically significant clusters of late-stage cervical cancer diagnosis in Texas, of which 4 were hot spots and 3 were cold spots. Hot spots included 1128 census tracts, predominantly in the South Texas Plains, Gulf Coast, and Prairies and Lakes (North Texas) regions. Of the 2892 patients with late-stage cervical cancer, 880 (30.4%) were observed within hot spots. Census tract-level comparison of characteristics of clusters suggested that hot spots differed significantly from cold spots and the rest of Texas by proportions of racial and ethnic groups, non-US born persons, and socioeconomic status.

Conclusions and Relevance

In this cross-sectional study examining geospatial clusters of late-stage cervical cancer diagnosis, place-based disparities were found in late-stage cervical cancer diagnosis in Texas. These findings suggest that these communities may benefit from aggressive cervical cancer interventions.

Spatio-Temporal Cluster Detection of Dengue, Chikungunya, and Zika Viruses' Infection in Rio de Janeiro State from 2010 to 2019

Dengue (DENV), chikungunya (CHIKV), and Zika (ZIKV) virus infections are widespread throughout the Rio de Janeiro state. The co-circulation of these emergent arboviruses constitutes a serious public health problem, resulting in outbreaks that can spatially and temporally overlap. Environmental conditions favor the presence, maintenance, and expansion of Aedes aegypti, the primary vector of these urban arboviruses. This study assessed the detection of clusters of urban arboviruses in the Rio de Janeiro state from 2010 to 2019. Notified cases of dengue, chikungunya, and Zika were grouped by year according to the onset of symptoms and their municipality of residence. The study period recorded the highest number of dengue epidemics in the state along with the simultaneous circulation of chikungunya and Zika viruses. The analyzes showed that the central municipalities of the metropolitan regions were associated with higher risk areas. Central municipalities in metropolitan regions were the first most likely clusters for dengue and Zika, and the second most likely cluster for chikungunya. Furthermore, the northwest and north regions were comprised clusters with the highest relative risk for the three arboviruses, underscoring the impact of these arboviruses in less densely populated regions of Brazil. The identification of high-risk areas over time highlights the need for effective control measures, targeted prevention and control interventions for these urban arboviral diseases.

The utility of Zip4 codes in spatial epidemiological analysis

There are many public health situations within the United States that require fine geographical scale data to effectively inform response and intervention strategies. However, a condition for accessing and analyzing such data, especially when multiple institutions are involved, is being able to preserve a degree of spatial privacy and confidentiality. Hospitals and state health departments, who are generally the custodians of these fine-scale health data, are sometimes understandably hesitant to collaborate with each other due to these concerns. This paper looks at the utility and pitfalls of using Zip4 codes, a data layer often included as it is believed to be "safe", as a source for sharing fine-scale spatial health data that enables privacy preservation while maintaining a suitable precision for spatial analysis. While the Zip4 is widely supplied, researchers seldom utilize it. Nor is its spatial characteristics known by data guardians. To address this gap, we use the context of a near-real time spatial response to an emerging health threat to show how the Zip4 aggregation preserves an underlying spatial structure making it potentially suitable dataset for analysis. Our results suggest that based on the density of urbanization, Zip4 centroids are within 150 meters of the real location almost 99% of the time. Spatial analysis experiments performed on these Zip4 data suggest a far more insightful geographic output than if using more commonly used aggregation units such as street lines and census block groups. However, this improvement in analytical output comes at a spatial privy cost as Zip4 centroids have a higher potential of compromising spatial anonymity with 73% of addresses having a spatial k anonymity value less than 5 when compared to other aggregations. We conclude that while offers an exciting opportunity to share data between organizations, researchers and analysts need to be made aware of the potential for serious confidentiality violations.

Spatial distribution and determinants of stunting, wasting and underweight in children under-five in Ethiopia

BACKGROUND

The burden of malnutrition in children under five remains an important child health issue where significant regional variations are observed in Ethiopia. The main aim of the current study was to explore the spatial distribution of stunting, wasting, and underweight in children under five in Ethiopia.

METHODS

The secondary data from EDHS, 2016, was employed, and a total of 7960 under-five children were included in the analysis. The general spatial analysis was performed to explore the spatial distribution of malnutrition among under-five within and between the regions of Ethiopia. The Spatial Lag and Spatial Error models were used to examine the spatial dependence between stunting, wasting, and being underweight. The Geographically weighted regression analysis was performed to estimate the types and strength of relationships between malnutrition and associated determinant factors across the regions and within the clusters or Zones of each region.

RESULTS

Among the under-five children included in the study, 36.6% were stunted, 12.2% were wasted and 25.2% were underweight. The Global Moran Index's value indicates (Stunting, I = 0.3135, p-value < 0.00001, Wasting, I = 0.1948, p-value < 0.0001 and Underweight, I = 0.5291, p-value < 0.0001) that there was a significant spatial variation of malnutrition across the regions and Zones of Ethiopia. The significant source of spatial variation of malnutrition in children under five was associated with the mother's education level, drinking water facility, toilet facilities, number of children under-five in the household, household's wealth index, breastfeeding duration of the child, child size at birth, Body Mass Index of Mothers (BMI), region, and place of residence.

CONCLUSIONS

The spatial association and spatial patterns of stunting, wasting, and being underweight were clustered within and between the regions of Ethiopia. The hotspot areas of stunting, wasting, and being underweight were detected in the regions where there were poor health facilities and limited socioeconomic indicators.

The spatial signature of Plasmodium vivax and Plasmodium falciparum infections: quantifying the clustering of infections in cross-sectional surveys and cohort studies

BACKGROUND

Over the last decades, enormous successes have been achieved in reducing malaria burden globally. In Latin America, South East Asia, and the Western Pacific, many countries now pursue the goal of malaria elimination by 2030. It is widely acknowledged that Plasmodium spp. infections cluster spatially so that interventions need to be spatially informed, e.g. spatially targeted reactive case detection strategies. Here, the spatial signature method is introduced as a tool to quantify the distance around an index infection within which other infections significantly cluster.

METHODS

Data were considered from cross-sectional surveys from Brazil, Thailand, Cambodia, and Solomon Islands, conducted between 2012 and 2018. Household locations were recorded by GPS and finger-prick blood samples from participants were tested for Plasmodium infection by PCR. Cohort studies from Brazil and Thailand with monthly sampling over a year from 2013 until 2014 were also included. The prevalence of PCR-confirmed infections was calculated at increasing distance around index infections (and growing time intervals in the cohort studies). Statistical significance was defined as prevalence outside of a 95%-quantile interval of a bootstrap null distribution after random re-allocation of locations of infections.

RESULTS

Prevalence of Plasmodium vivax and Plasmodium falciparum infections was elevated in close proximity around index infections and decreased with distance in most study sites, e.g. from 21.3% at 0 km to the global study prevalence of 6.4% for P. vivax in the Cambodian survey. In the cohort studies, the clustering decreased with longer time windows. The distance from index infections to a 50% reduction of prevalence ranged from 25 m to 3175 m, tending to shorter distances at lower global study prevalence.

CONCLUSIONS

The spatial signatures of P. vivax and P. falciparum infections demonstrate spatial clustering across a diverse set of study sites, quantifying the distance within which the clustering occurs. The method offers a novel tool in malaria epidemiology, potentially informing reactive intervention strategies regarding radius choices of operations around detected infections and thus strengthening malaria elimination endeavours.

Spatiotemporal variation of malaria incidence in parasite clearance interventions and non-intervention areas in the Amhara Regional State, Ethiopia

Background

In Ethiopia, malaria remains a major public health problem. To eliminate malaria, parasite clearance interventions were implemented in six kebeles (the lowest administrative unit) in the Amhara region. Understanding the spatiotemporal distribution of malaria is essential for targeting appropriate parasite clearance interventions to achieve the elimination goal. However, little is known about the spatiotemporal distribution of malaria incidence in the intervention and non-intervention areas. This study aimed to investigate the spatiotemporal distribution of community-based malaria in the intervention and non-intervention kebeles between 2013 and 2018 in the Amhara Regional State, Ethiopia.

Methods

Malaria data from 212 kebeles in eight districts were downloaded from the District Health Information System2 (DHIS2) database. We used Autoregressive integrated moving average (ARIMA) model to investigate seasonal variations; Anselin Local Moran’s I statistical analysis to detect hotspot and cold spot clusters of malaria cases; and a discrete Poisson model using Kulldorff scan statistics to identify statistically significant clusters of malaria cases.

Results

The result showed that the reduction in the trend of malaria incidence was higher in the intervention areas compared to the non-intervention areas during the study period with a slope of -0.044 (-0.064, -0.023) and -0.038 (-0.051, -0.024), respectively. However, the difference was not statistically significant. The Global Moran’s I statistics detected the presence of malaria clusters (z-score = 12.05; p<0.001); the Anselin Local Moran’s I statistics identified hotspot malaria clusters at 21 locations in Gendawuha and Metema districts. A statistically significant spatial, temporal, and space-time cluster of malaria cases were detected. Most likely type of spatial clusters of malaria cases (LLR = 195501.5; p <0.001) were detected in all kebeles of Gendawuha and Metema districts. The temporal scan statistic identified three peak periods between September 2013 and November 2015 (LLR = 8727.5; p<0.001). Statistically significant most-likely type of space-time clusters of malaria cases (LLR = 97494.3; p<0.001) were detected at 22 locations from June 2014 to November 2016 in Metema district.

Conclusion

There was a significant decline in malaria incidence in the intervention areas. There were statistically significant spatiotemporal variations of malaria in the study areas. Applying appropriate parasite clearance interventions is highly recommended for the better achievement of the elimination goal. A more rigorous evaluation of the impact of parasite clearance interventions is recommended.

Spatio-temporal variation on syphilis from 2005 to 2018 in Zhejiang Province, China

Background

Syphilis has spread throughout China, especially in Zhejiang Province which endangers the health and lives of people. However, the spatial and temporal epidemiological studies of syphilis in Zhejiang are not thorough enough. The temporal and spatial variation and the relevant factors of syphilis incidence should be analyzed for more effective prevention and control in Zhejiang, China.

Methods

Data on confirmed cases of syphilis in Zhejiang Province from 2005 to 2018 was used and the spatio-temporal distributions were described. The spatial autocorrelation analysis and SaTScan analysis were performed to identify spatio-temporal clusters. A Bayesian spatial Conditional Autoregression (CAR) model was constructed to explore the relationships between syphilis incidence and common social and natural indicators.

Results

474,980 confirmed cases of syphilis were reported between 2005 and 2018 with a large peak in 2010. Farmers and unemployed people accounted for the largest proportion of confirmed cases. And the significant spatial clusters of syphilis were concentrated in the north of Zhejiang Province, especially in more economically developed regions. Seven spatio-temporal clusters were identified and the main three high-risk areas were located in Hangzhou (RR = 1.62, P < 0.05), Zhoushan and Ningbo (RR = 1.99, P < 0.05), and Lishui (RR = 1.68, P < 0.05). The findings showed that the morbidity of syphilis was positively correlated with the Gross Domestic Product (GDP) per capita, the number of health technicians per 10,000 people, the proportion of the elderly and air temperature were negatively correlated with the proportion of the urban population, the proportion of men and precipitation.

Conclusions

The spatio-temporal analysis revealed that the prevalence of syphilis was still serious in Zhejiang Province. Syphilis high-risk areas were mainly located in the more developed coastal regions where more targeted intervention measures were required to be implemented. The study highlighted the need to strengthen Sexually Transmitted Diseases (STD) screening and health education for high-risk groups and improve the coverage of syphilis testing to reduce hidden syphilis cases.

Mathematical modeling in perspective of vector-borne viral infections: a review

Background

Viral diseases are highly widespread infections caused by viruses. These viruses are passing from one human to other humans through a certain medium. The medium might be mosquito, animal, reservoir and food, etc. Here, the population of both human and mosquito vectors are important.

Main body of the abstract

The main objectives are here to introduce the historical perspective of mathematical modeling, enable the mathematical modeler to understand the basic mathematical theory behind this and present a systematic review on mathematical modeling for four vector-borne viral diseases using the deterministic approach. Furthermore, we also introduced other mathematical techniques to deal with vector-borne diseases. Mathematical models could help forecast the infectious population of humans and vectors during the outbreak.

Short conclusion

This study will be helpful for mathematical modelers in vector-borne diseases and ready-made material in the review for future advancement in the subject. This study will not only benefit vector-borne conditions but will enable ideas for other illnesses.

How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission

Both directly and indirectly transmitted infectious diseases in humans are spatial-related. Spatial dimensions include: distances between susceptible humans and the environments shared by people, contaminated materials, and infectious animal species. Therefore, spatial concepts in managing and understanding emerging infectious diseases are crucial. Recently, due to the improvements in computing performance and statistical approaches, there are new possibilities regarding the visualization and analysis of disease spatial data. This review provides commonly used spatial or spatial-temporal approaches in managing infectious diseases. It covers four sections, namely: visualization, overall clustering, hot spot detection, and risk factor identification. The first three sections provide methods and epidemiological applications for both point data (i.e., individual data) and aggregate data (i.e., summaries of individual points). The last section focuses on the spatial regression methods adjusted for neighbour effects or spatial heterogeneity and their implementation. Understanding spatial-temporal variations in the spread of infectious diseases have three positive impacts on the management of diseases. These are: surveillance system improvements, the generation of hypotheses and approvals, and the establishment of prevention and control strategies. Notably, ethics and data quality have to be considered before applying spatial-temporal methods. Developing differential global positioning system methods and optimizing Bayesian estimations are future directions.

Simulation and Prediction of Evolution of Specialized Villages Agglomeration Based on System Dynamics

Rural recession is a worldwide problem, and rural revitalization will increasingly become an important global issue. The development of specialized villages is the engine of rural revitalization, so the research on it is of great significance for guiding the sustainable development of the rural economy. In this study, by analyzing the evolutionary mechanism of the specialized villages agglomeration (SVA) from the perspective of system theory, we simulated and predicted the evolution of specialized villages agglomeration at the foot of the Taihang Mountains in Henan Province, China. We based this work on the general process of establishing a system dynamics model, and made the following conclusions: (1) According to the boundary suitability, dimensional consistency, structural evaluation, and sensitivity tests, we considered that the model could simulate the evolution of SVA at the foot of the Taihang Mountains and we explored the corresponding evolutionary mechanism; (2) The SVA effect was an “S-shaped” curve, which we divided into three periods: the formation period (0–8 years), the growth period (9–20 years), and the maturation period (21–30 years); (3) The contribution of capital to the agglomeration effect was most significant in the formation period; during the growth period, the contribution of laborers and technologies to the agglomeration effect increased rapidly; and in the maturation period, the contribution of laborers to the agglomeration effect gradually declined, whereas the contribution of technology and capital to the agglomeration effect still increased. The research results provide a theoretical reference for the sustainable development of specialized villages.

Epidemiological features and sociodemographic factors associated with mumps in mainland China from 2004 to 2018

BACKGROUND

Mumps is an acute infectious disease that spreads widely around the world. The aim of this study was to investigate the epidemiological features and sociodemographic factors associated with mumps in mainland China from 2004 to 2018.

METHODS

Incidence data for mumps during the period 2004-2018 were collected from the Public Health Sciences Data Center of China. Joinpoint regression analysis was performed to explore the trends of mumps. Space-time clustering analysis was conducted to spatial and temporal aggregation areas of mumps. A generalized linear model was used to explore sociodemographic factors associated with the incidence of mumps.

RESULTS

Average annual incidence of mumps was 21.44/100,000 in mainland China. It was increased dramatically during 2004-2012 (APC = 7.51, 95%CI: 2.28 to 13.00). After 2012, it remained stable, however, significantly increased in intermediately developed regions from 2015 to 2018 (APC = 25.84, 95%CI: 3.59 to 52.86). The first-level spatial and temporal aggregation areas were distributed in Xinjiang, Gansu, Qinghai, Ningxia and Shaanxi, Tibet, Sichuan, Yunnan, Chongqing, Guizhou, Guangxi, with gathering times from Jan 1, 2006 to Dec 31, 2012 (RR = 1.87, P < 0.001). The percentage of the population aged 0-14 years, number of health workers per capital, and number of passengers were found to be positively associated with the incidence of mumps.

CONCLUSIONS

Overall, after 2012, the incidence of mumps in mainland China remained stable. High-risk periods, clusters of regions and sociodemographic factors for mumps were identified, which will help the government develop disease- and location-specific interventive measures. This article is protected by copyright. All rights reserved.

Detecting space-time patterns of disease risk under dynamic background population

We are able to collect vast quantities of spatiotemporal data due to recent technological advances. Exploratory space-time data analysis approaches can facilitate the detection of patterns and formation of hypotheses about their driving processes. However, geographic patterns of social phenomena like crime or disease are driven by the underlying population. This research aims for incorporating temporal population dynamics into spatial analysis, a key omission of previous methods. As population data are becoming available at finer spatial and temporal granularity, we are increasingly able to capture the dynamic patterns of human activity. In this paper, we modify the space-time kernel density estimation method by accounting for spatially and temporally dynamic background populations (ST-DB), assess the benefits of considering the temporal dimension and finally, compare ST-DB to its purely spatial counterpart. We delineate clusters and compare them, as well as their significance, across multiple parameter configurations. We apply ST-DB to an outbreak of dengue fever in Cali, Colombia during 2010-2011. Our results show that incorporating the temporal dimension improves our ability to delineate significant clusters. This study addresses an urgent need in the spatiotemporal analysis literature by using population data at high spatial and temporal resolutions.

Detection of temporal, spatial and spatiotemporal clustering of malaria incidence in northwest Ethiopia, 2012–2020

Malaria is one of Ethiopia's most targeted communicable diseases for elimination. Malaria transmission varies significantly across space and time; and Ethiopia had space–time disparity in its transmission intensities. Considering heterogeneity and transmission intensity at the district level could play a crucial role in malaria prevention and elimination. This study aimed to explore temporal, spatial, and spatiotemporal clusters of malaria incidence in northwest Ethiopia. The analysis is based on monthly malaria surveillance data of districts and collected from the Amhara public health institute. The Kulldorff's retrospective space–time scan statistics using a discrete Poisson model were used to detect temporal, spatial, and space–time clusters of malaria incidence with and without adjusting the altitude + LLIN arm. Monthly malaria incidence had seasonal variations, and higher seasonal indices occurred in October and November. The temporal cluster occurred in the higher transmission season between September and December annually. The higher malaria incidence risk occurred between July 2012 and December 2013 (LLR = 414,013.41, RR = 2.54, P < 0.05). The purely spatial clustering result revealed that the most likely cluster occurred in the north and northwest parts of the region while secondary clusters varied in years. The space–time clusters were detected with and without considering altitude + LLIN arm. The most likely space–time cluster was concentrated in northwestern and western parts of the region with a high-risk period between July 2012 and December 2013 (LLR = 880,088.3, RR = 5.5, P < 0.001). We found eight significant space–time clusters using the altitude + LLIN arm. The most likely space–time cluster occurred in the western and northwestern parts of the region in July 2012–December 2013 (LLR = 886,097.7, RR = 5.55, P < 0.05). However, secondary clusters were located in eastern, northwestern, western parts of regions, which had different cases and relative risks in each cluster. Malaria transmission had temporal, spatial, and space–time variation in the region at the district level. Hence, considering these variations and factors contributing to malaria stratification would play an indispensable role in preventing and controlling practices that ultimately leads to malaria eliminations.

A spatiotemporal data mining study to identify high-risk neighborhoods for out-of-hospital cardiac arrest (OHCA) incidents

Out-of-hospital cardiac arrest (OHCA) is a worldwide health problem. The aim of the study is to utilize the territorial-wide OHCA data of Hong Kong in 2012-2015 to examine its spatiotemporal pattern and high-risk neighborhoods. Three techniques for spatiotemporal data mining (SaTScan's spatial scan statistic, Local Moran's I, and Getis Ord Gi*) were used to extract high-risk neighborhoods of OHCA occurrence and identify local clusters/hotspots. By capitalizing on the strengths of these methods, the results were then triangulated to reveal "truly" high-risk OHCA clusters. The final clusters for all ages and the elderly 65+ groups exhibited relatively similar patterns. All ages groups were mainly distributed in the urbanized neighborhoods throughout Kowloon. More diverse distribution primarily in less accessible areas was observed among the elderly group. All outcomes were further converted into an index for easy interpretation by the general public. Noticing the spatial mismatches between hospitals and ambulance depots (representing supplies) and high-risk neighborhoods (representing demands), this setback should be addressed along with public education and strategic ambulance deployment plan to shorten response time and improve OHCA survival rate. This study offers policymakers and EMS providers essential spatial evidence to assist with emergency healthcare planning and informed decision-making.

An exploration of factors characterising unusual spatial clusters of COVID-19 cases in the East Midlands region, UK: A geospatial analysis of ambulance 999 data

Complex interactions between physical landscapes and social factors increase vulnerability to emerging infections and their sequelae. Relative vulnerability to severe illness and/or death (VSID) depends on risk and extent of exposure to a virus and underlying health susceptibility. Identifying vulnerable communities and the regions they inhabit in real time is essential for effective rapid response to a new pandemic, such as COVID-19. In the period between first confirmed cases and the introduction of widespread community testing, ambulance records of suspected severe illness from COVID-19 could be used to identify vulnerable communities and regions and rapidly appraise factors that may explain VSID. We analyse the spatial distribution of more than 10,000 suspected severe COVID-19 cases using records of provisional diagnoses made by trained paramedics attending medical emergencies. We identify 13 clusters of severe illness likely related to COVID-19 occurring in the East Midlands of the UK and present an in-depth analysis of those clusters, including urban and rural dynamics, the physical characteristics of landscapes, and socio-economic conditions. Our findings suggest that the dynamics of VSID vary depending on wider geographic location. Vulnerable communities and regions occur in more deprived urban centres as well as more affluent peri-urban and rural areas. This methodology could contribute to the development of a rapid national response to support vulnerable communities during emerging pandemics in real time to save lives.

Geographical variation and predictors of zero utilization for a standard maternal continuum of care among women in Ethiopia: a spatial and geographically weighted regression analysis

BACKGROUND

Maintaining and effectively utilizing maternal continuum of care could save an estimated 860,000 additional mothers and newborn lives each year. In Ethiopia, the number of maternal and neonatal deaths occurred during pregnancy, childbirth, and the postpartum period was very high. It is indisputable that area-based heterogeneity of zero utilization for a standard maternal continuum of care is critical to improve maternal and child health interventions. However, none of the previous studies explored the spatial distribution of zero utilization for maternal continuum of care. Hence, this study was aimed to explore geographical variation and predictors of zero utilization for a standard maternal continuum of care among women in Ethiopia.

METHODS

A total of 4178 women who gave birth five years preceding the 2016 Ethiopian demographic and health survey were included. ArcGIS version 10.7, SaT Scan version 9.6, and GWR version 4.0 Software was used to handle mapping, hotspot, ordinary least square, Bernoulli model analysis, and to model spatial relationships. Finally, a statistical decision was made at a p-value< 0.05 and at 95% confidence interval.

MAIN FINDINGS

The proportion of mothers who had zero utilization of a standard maternal continuum of care was 48.8% (95% CI: 47.3-50.4). Hot spot (high risk) regions for zero utilization of maternal continuum of care was detected in Afder, Warder, Korahe and Gode Zones of Somali region and West Arsi Zone of Oromia region. Respondents who had poor wealth index, uneducated mothers, and mothers who declared distance as a big problem could increase zero utilization of maternal continuum of care by 0.24, 0.27, and 0.1 times.

CONCLUSION

Five women out of ten could not utilize any components of a standard maternal continuum of care. Hot spot (high risk) areas was detected in Afder, Warder, Korahe and Gode Zones of Somali region and West Arsi Zone of Oromia region. Poor wealth index, uneducated mothers, and mothers who declare distance as a big problem were factors significantly associated with zero utilization of maternal continuum of care. Thus, geographical based intervention could be held to curve the high prevalence of zero utilization of maternal continuum of care.

Spatiotemporal patterns of the COVID-19 epidemic in Mexico at the municipality level

In recent history, Coronavirus Disease 2019 (COVID-19) is one of the worst infectious disease outbreaks affecting humanity. The World Health Organization has defined the outbreak of COVID-19 as a pandemic, and the massive growth of the number of infected cases in a short time has caused enormous pressure on medical systems. Mexico surpassed 3.7 million confirmed infections and 285,000 deaths on October 23, 2021. We analysed the spatio-temporal patterns of the COVID-19 epidemic in Mexico using the georeferenced confirmed cases aggregated at the municipality level. We computed weekly Moran’s I index to assess spatial autocorrelation over time and identify clusters of the disease using the “flexibly shaped spatial scan” approach. Finally, we compared Euclidean, cost, resistance distances and gravitational model to select the best-suited approach to predict inter-municipality contagion. We found that COVID-19 pandemic in Mexico is characterised by clusters evolving in space and time as parallel epidemics. The gravitational distance was the best model to predict newly infected municipalities though the predictive power was relatively low and varied over time. This study helps us understand the spread of the epidemic over the Mexican territory and gives insights to model and predict the epidemic behaviour.

Red deer reveal spatial risks of Crimean-Congo haemorrhagic fever virus infection

Crimean-Congo haemorrhagic fever virus (CCHFV) continues to cause new human cases in Iberia while its spatial distribution and ecological determinants remain unknown. The virus remains active in a silent tick-animal cycle to which animals contribute maintaining the tick populations and the virus itself. Wild ungulates, in particular red deer, are essential hosts for Hyalomma ticks in Iberia, which are the principal competent vector of CCHFV. Red deer could be an excellent model to understand the ecological determinants of CCHFV as well as to predict infection risks for humans because it is large, gregarious, abundant and the principal host for Hyalomma lusitanicum. We designed a cross-sectional study, analysed the presence of CCHFV antibodies in 1444 deer from 82 populations, and statistically modelled exposure risk with host and environmental predictors. The best-fitted statistical model was projected for peninsular Spain to map infection risks. Fifty out of 82 deer populations were seropositive, with individual population prevalence as high as 88%. The highest prevalence of exposure to CCHFV occurred in the southwest of the Iberian Peninsula. Climate and ungulate abundance were the most influential predictors of the risk of exposure to the virus. The highest risk regions were those where H. lusitanicum is most abundant. Eight of the nine primary human cases occurred in or bordering these regions, demonstrating that the model predicts human infection risk accurately. A recent human case of CCHF occurred in northwestern Spain, a region that the model predicted as low risk, pointing out that it needs improvement to capture all determinants of the CCHFV infection risk. In this study, we have been able to identify the main ecological determinants of CCHFV, and we have also managed to create an accurate model to assess the risk of CCHFV infection.

Assessment of Retrospective COVID-19 Spatial Clusters with Respect to Demographic Factors: Case Study of Kansas City, Missouri, United States

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The United States (U.S.) has the highest number of reported COVID-19 infections and related deaths in the world, accounting for 17.8% of total global confirmed cases as of August 2021. As COVID-19 spread throughout communities across the U.S., it became clear that inequities would arise among differing demographics. Several researchers have suggested that certain racial and ethnic minority groups may have been disproportionately impacted by the spread of COVID-19. In the present study, we used the daily data of COVID-19 cases in Kansas City, Missouri, to observe differences in COVID-19 clusters with respect to gender, race, and ethnicity. Specifically, we utilized a retrospective Poisson spatial scan statistic with respect to demographic factors to detect daily clusters of COVID-19 in Kansas City at the zip code level from March to November 2020. Our statistical results indicated that clusters of the male population were more widely scattered than clusters of the female population. Clusters of the Hispanic population had the highest prevalence and were also more widely scattered. This demographic cluster analysis can provide guidance for reducing the social inequalities associated with the COVID-19 pandemic. Moreover, applying stronger preventive and control measures to emerging clusters can reduce the likelihood of another epidemic wave of infection.

Issues in the Current Practices of Spatial Cluster Detection and Exploring Alternative Methods

Local Moran and local G-statistic are commonly used to identify high-value (hot spot) and low-value (cold spot) spatial clusters for various purposes. However, these popular tools are based on the concept of spatial autocorrelation or association (SA), but do not explicitly consider if values are high or low enough to deserve attention. Resultant clusters may not include areas with extreme values that practitioners often want to identify when using these tools. Additionally, these tools are based on statistics that assume observed values or estimates are highly accurate with error levels that can be ignored or are spatially uniform. In this article, problems associated with these popular SA-based cluster detection tools were illustrated. Alternative hot spot-cold spot detection methods considering estimate error were explored. The class separability classification method was demonstrated to produce useful results. A heuristic hot spot-cold spot identification method was also proposed. Based on user-determined threshold values, areas with estimates exceeding the thresholds were treated as seeds. These seeds and neighboring areas with estimates that were not statistically different from those in the seeds at a given confidence level constituted the hot spots and cold spots. Results from the heuristic method were intuitively meaningful and practically valuable.

Space-Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination

Malaria in Bhutan has fallen significantly over the last decade. As Bhutan attempts to eliminate malaria in 2022, this study aimed to characterize the space-time clustering of malaria from 2010 to 2019. Malaria data were obtained from the Bhutan Vector-Borne Disease Control Program data repository. Spatial and space-time cluster analyses of Plasmodium falciparum and Plasmodium vivax cases were conducted at the sub-district level from 2010 to 2019 using Kulldorff's space-time scan statistic. A total of 768 confirmed malaria cases, including 454 (59%) P. vivax cases, were reported in Bhutan during the study period. Significant temporal clusters of cases caused by both species were identified between April and September. The most likely spatial clusters were detected in the central part of Bhutan throughout the study period. The most likely space-time cluster was in Sarpang District and neighboring districts between January 2010 to June 2012 for cases of infection with both species. The most likely cluster for P. falciparum infection had a radius of 50.4 km and included 26 sub-districts with a relative risk (RR) of 32.7. The most likely cluster for P. vivax infection had a radius of 33.6 km with 11 sub-districts and RR of 27.7. Three secondary space-time clusters were detected in other parts of Bhutan. Spatial and space-time cluster analysis identified high-risk areas and periods for both P. vivax and P. falciparum malaria. Both malaria types showed significant spatial and spatiotemporal variations. Operational research to understand the drivers of residual transmission in hotspot sub-districts will help to overcome the final challenges of malaria elimination in Bhutan.

Real time surveillance of COVID-19 space and time clusters during the summer 2020 in Spain

BACKGROUND

On June 21st de-escalation measures and state-of-alarm ended in Spain after the COVID-19 first wave. New surveillance and control strategy was set up to detect emerging outbreaks.

AIM

To detect and describe the evolution of COVID-19 clusters and cases during the 2020 summer in Spain.

METHODS

A near-real time surveillance system to detect active clusters of COVID-19 was developed based on Kulldorf's prospective space-time scan statistic (STSS) to detect daily emerging active clusters.

RESULTS

Analyses were performed daily during the summer 2020 (June 21st - August 31st) in Spain, showing an increase of active clusters and municipalities affected. Spread happened in the study period from a few, low-cases, regional-located clusters in June to a nationwide distribution of bigger clusters encompassing a higher average number of municipalities and total cases by end-August.

CONCLUSION

STSS-based surveillance of COVID-19 can be of utility in a low-incidence scenario to help tackle emerging outbreaks that could potentially drive a widespread transmission. If that happens, spatial trends and disease distribution can be followed with this method. Finally, cluster aggregation in space and time, as observed in our results, could suggest the occurrence of community transmission.

Using geographically weighted regression analysis to cluster under-nutrition and its predictors among under-five children in Ethiopia: Evidence from demographic and health survey

BACKGROUND

Malnutrition among under-five children is a common public health problem and it is one of the main cause for the mortality of under-five children in developing countries, including Ethiopia. Therefore, lack of evidence about geographic heterogeneity and predictors of under-nutrition hinders for evidence-based decision-making process for the prevention and control programs of under-nutrition in Ethiopia. Thus, this study aimed to address this gap.

METHODS

The data were obtained from the Ethiopian Demographic and Health Survey (EDHS) 2016. A total of 9,384 under-five children nested in 645 clusters were included with a stratified two-stage cluster sampling. ArcGIS version 10.5 software was used for global, local and ordinary least square analysis and mapping. The spatial autocorrelation (Global Moran's I) statistic was held in order to assess the pattern of wasting, stunting, and underweight whether it was dispersed, clustered, or randomly distributed. In addition, a Bernoulli model was used to analyze the purely spatial cluster detection of under-nutrition indicators through SaTScan version 9.6 software. Geographically weighted regression (GWR) version 4.0 software was used to model spatial relationships in the GWR analysis. Finally, a statistical decision was made at p-value<0.05 with 95%CI for ordinary least square analysis and geographically weighted regression.

MAIN FINDINGS

Childhood under-nutrition showed geographical variations at zonal levels in Ethiopia. Accordingly, Somali region (Afder, Gode, Korahe, Warder Zones), Afar region (Zone 2), Tigray region (Southern Zone), and Amhara region (Waghmira Zones) for wasting, Amhara region (West Gojam, Awi, South Gondar, and Waghmira Zones) for stunting and Amhara region (South Wollo, North Wollo, Awi, South Gondar, and Waghmira zones), Afar region (Zone 2), Tigray region (Eastern Zone, North Western Zone, Central Zone, Southern Zone, and Mekele Special Zones), and Benshangul region (Metekel and Assosa Zones) for underweight were detected as hot spot (high risk) regions. In GWR analysis, had unimproved toilet facility for stunting, wasting and underweight, father had primary education for stunting and wasting, father had secondary education for stunting and underweight, mothers age 35-49 years for wasting and underweight, having female children for stunting, having children eight and above for wasting, and mother had primary education for underweight were significant predictors at (p<0.001).

CONCLUSIONS

Our study showed that the spatial distribution of under-nutrition was clustered and high-risk areas were identified in all forms of under-nutrition indicators. Predictors of under-nutrition were identified in all forms of under-nutrition indicators. Thus, geographic-based nutritional interventions mainly mobilizing additional resources could be held to reduce the burden of childhood under-nutrition in hot spot areas. In addition, improving sanitation and hygiene practice, improving the life style of the community, and promotion of parent education in the identified hot spot zones for under-nutrition should be more emphasized.

Spatial distribution of under immunization among children 12-23 months old in Butajira HDSS, southern Ethiopia

Background

Immunization is essential to prevent between 2 and 3 million deaths globally each year and it is widely accepted that it is one of the most cost-effective health interventions. Despite all its advantages, immunization in Ethiopia is still far from the target set by the United Nations Sustainable Development Goals to achieve universal immunization by all countries in 2030. The 2016 Ethiopian Demographic and Health Survey (EDHS) reported an overall full immunization rate of only 38.3%. The objective of this study was to evaluate the spatial distribution of under immunization in 12 to 23 months old children and further identify the determinants of under immunization clustering in the Butajira Health and Demographic Surveillance Site (HDSS).

Methods

We conducted a community based sectional survey from March to April, 2016 in Butajira HDSS. We collected data on immunization status from a total of 482 children between the age of 12 to 23 months. We randomly selected household and interviewed mothers and /or observed vaccination cards when available to collect data on child’s immunization status. We also collected the geographic location of all villages within the ten Kebeles using a Handheld Global Positioning System (GPS) (Garmin GPSMAP®). We analyzed the spatial distribution of under immunization and clustering using the SatScan® software which employs a purely spatial Bernoulli’s model. We also ran a logistic regression model to help evaluate the causes of clustering.

Results

We found that only 22.4% [95% CI: 18.9, 26.4%] of children were fully immunized. This study identified one significant cluster of under immunization among children 12–23 months of age within the Butajira HDSS (relative risk (RR) = 1.24,P < 0·01). We found that children residing in this cluster had more than 1.24 times risk of under immunization compared with children residing outside of the identified cluster. We found significant differences with regard to Maternal Tetanus Toxoid immunization status and place of delivery between cases found within a spatial cluster and cases found outside the cluster. For example, the odds of home delivery is more than two times [AOR 2.21: 95%CI; 1.06, 4.63] among children within an identified spatial cluster than the odds among children found outside the identified cluster.

Conclusions

Under immunization of 12–23 months old children and under immunization with specific vaccines such as Polio, BCG, DPT (1–3) and Measles clustered geographically. Spatial studies could be effective in identifying geographic areas of under immunization for targeted intervention like in this study to gear health education to the specific locality.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-021-02690-4.

Spatial patterns of pulmonary tuberculosis (TB) cases in Zimbabwe from 2015 to 2018

INTRODUCTION

Accurate mapping of spatial heterogeneity in tuberculosis (TB) cases is critical for achieving high impact control as well as guide resource allocation in most developing countries. The main aim of this study was to explore the spatial patterns of TB occurrence at district level in Zimbabwe from 2015 to 2018 using GIS and spatial statistics as a preamble to identifying areas with elevated risk for prioritisation of control and intervention measures.

METHODS

In this study Getis-Ord Gi* statistics together with SaTscan were used to characterise TB hotspots and clusters in Zimbabwe at district level from 2015 to 2018. GIS software was used to map and visualise the results of cluster analysis.

RESULTS

Results show that TB occurrence exhibits spatial heterogeneity across the country. The TB hotspots were detected in the central, western and southern part of the country. These areas are characterised by artisanal mining activities as well as high poverty levels.

CONCLUSIONS AND RECOMMENDATIONS

Results of this study are useful to guide TB control programs and design effective strategies which are important in achieving the United Nations Sustainable Development goals (UNSDGs).

Stage 1 registered report: spatiotemporal patterns of the COVID-19 epidemic in Mexico at the municipality level

In this stage 1 registered report, we propose an analysis of the spatio-temporal patterns of the COVID-19 epidemic in Mexico using the georeferenced confirmed cases aggregated at the municipality level. We will compute weekly Moran index to assess spatial autocorrelation over time and identify clusters of the disease using the “flexibly shaped spatial scan” approach. Finally, different distance models will be compared to select the best suited to predict inter-municipality contagion. This study will help us understand the spread of the epidemic over the Mexican territory and give insights to model and predict the epidemic behavior.

Estimating the local spatio-temporal distribution of malaria from routine health information systems in areas of low health care access and reporting

BACKGROUND

Reliable surveillance systems are essential for identifying disease outbreaks and allocating resources to ensure universal access to diagnostics and treatment for endemic diseases. Yet, most countries with high disease burdens rely entirely on facility-based passive surveillance systems, which miss the vast majority of cases in rural settings with low access to health care. This is especially true for malaria, for which the World Health Organization estimates that routine surveillance detects only 14% of global cases. The goal of this study was to develop a novel method to obtain accurate estimates of disease spatio-temporal incidence at very local scales from routine passive surveillance, less biased by populations' financial and geographic access to care.

METHODS

We use a geographically explicit dataset with residences of the 73,022 malaria cases confirmed at health centers in the Ifanadiana District in Madagascar from 2014 to 2017. Malaria incidence was adjusted to account for underreporting due to stock-outs of rapid diagnostic tests and variable access to healthcare. A benchmark multiplier was combined with a health care utilization index obtained from statistical models of non-malaria patients. Variations to the multiplier and several strategies for pooling neighboring communities together were explored to allow for fine-tuning of the final estimates. Separate analyses were carried out for individuals of all ages and for children under five. Cross-validation criteria were developed based on overall incidence, trends in financial and geographical access to health care, and consistency with geographic distribution in a district-representative cohort. The most plausible sets of estimates were then identified based on these criteria.

RESULTS

Passive surveillance was estimated to have missed about 4 in every 5 malaria cases among all individuals and 2 out of every 3 cases among children under five. Adjusted malaria estimates were less biased by differences in populations' financial and geographic access to care. Average adjusted monthly malaria incidence was nearly four times higher during the high transmission season than during the low transmission season. By gathering patient-level data and removing systematic biases in the dataset, the spatial resolution of passive malaria surveillance was improved over ten-fold. Geographic distribution in the adjusted dataset revealed high transmission clusters in low elevation areas in the northeast and southeast of the district that were stable across seasons and transmission years.

CONCLUSIONS

Understanding local disease dynamics from routine passive surveillance data can be a key step towards achieving universal access to diagnostics and treatment. Methods presented here could be scaled-up thanks to the increasing availability of e-health disease surveillance platforms for malaria and other diseases across the developing world.

Leptospirosis and its spatial and temporal relations with natural disasters in six municipalities of Santa Catarina, Brazil, from 2000 to 2016

Leptospirosis is a serious bacterial infection that occurs worldwide, with fatality rate of up to 40% in the most severe cases. The number of cases peaks during the rainy season and may reach epidemic proportions in the event of flooding. It is possible that people living in areas affected by natural disasters are at greater risk of contracting the disease. The aim of this study was to identify clusters of relatively higher risk for leptospirosis occurrence, both in space and time, in six municipalities of Santa Catarina, Brazil, which had the highest incidence of the disease between 2000 and 2016, and to evaluate if these clusters coincide with the occurrence of natural disasters. The cases were geocoded with the geographic coordinates of patients' home addresses, and the analysis was performed using SaTScan software. The areas mapped as being at risk for hydrological and mass movements were compared with the locations of detected leptospirosis clusters. The disease was more common in men and in the age group from 15 to 69 years. In the scan statistics performed, only space-time showed significant results. Clusters were detected in all municipalities in 2008, when natural disasters preceded by heavy rainfall occurred. One of the municipalities also had clusters in 2011. In these clusters, most of the cases lived in urban areas and areas at risk for experiencing natural disasters. The interaction between time (time of disaster occurrence) and space (areas at risk of experiencing natural disasters) were the determining factors affecting cluster formation.

Prioritizing COVID-19 tests based on participatory surveillance and spatial scanning

OBJECTIVES

This study aimed to identify, describe and analyze priority areas for COVID-19 testing combining participatory surveillance and traditional surveillance.

DESIGN

It was carried out a descriptive transversal study in the city of Caruaru, Pernambuco state, Brazil, within the period of 20/02/2020 to 05/05/2020. Data included all official reports for influenza-like illness notified by the municipality health department and the self-reports collected through the participatory surveillance platform Brasil Sem Corona.

METHODS

We used linear regression and loess regression to verify a correlation between Participatory Surveillance (PS) and Traditional Surveillance (TS). Also a spatial scanning approach was deployed in order to identify risk clusters for COVID-19.

RESULTS

In Caruaru, the PS had 861 active users, presenting an average of 1.2 reports per user per week. The platform Brasil Sem Corona started on March 20th and since then, has been officially used by the Caruaru health authority to improve the quality of information from the traditional surveillance system. Regarding the respiratory syndrome cases from TS, 1588 individuals were positive for this clinical outcome. The spatial scanning analysis detected 18 clusters and 6 of them presented statistical significance (p-value < 0.1). Clusters 3 and 4 presented an overlapping area that was chosen by the local authority to deploy the COVID-19 serology, where 50 individuals were tested. From there, 32 % (n = 16) presented reagent results for antibodies related to COVID-19.

CONCLUSION

Participatory surveillance is an effective epidemiological method to complement the traditional surveillance system in response to the COVID-19 pandemic by adding real-time spatial data to detect priority areas for COVID-19 testing.

Lack of Consistent Malaria Incidence Hotspots in a Highland Kenyan Area During a 10-Year Period of Very Low and Unstable Transmission

The use of spatial data in malaria elimination strategies is important to understand whether targeted interventions against malaria can be used, particularly in areas with limited resources. We previously documented consistent areas of increased malaria incidence in the epidemic-prone area of Kipsamoite in highland Kenya from 2001 to 2004. In this area and a neighboring subcounty (Kapsisiywa), malaria incidence decreased substantially in 2005, going from peak incidence of 31.7 per 1,000 persons in June 2004 to peak incidence of 7.4 per 1,000 persons in May 2005. Subsequently, the use of indoor residual spraying and artemisinin combination therapy malaria treatment led to a possible interruption of malaria transmission for a 13-month period from 2007 to 2008, after which the incidence returned to very low levels until an epidemic in April-July 2013. In the present study, we used novel kernel density estimation methods to determine whether areas of increased malaria incidence were consistent in six periods of peak incidence from 2003 to 2013, and to assess patterns of incidence in the period before versus. after the period of possible interruption. Areas of highest incidence differed during peak malaria transmission periods over the years 2003-2013, and differed before and after the potential malaria interruption. In this epidemic-prone region with very low malaria transmission, consistent malaria "hotspots" identified in a time of higher transmission are no longer present. Ongoing assessment of spatial malaria epidemiology to identify and target current areas of elevated malaria risk may be important in campaigns to control or eliminate malaria in epidemic-prone areas.

Spatial and spatio-temporal analysis of malaria cases in Zimbabwe

Background

Although effective treatment for malaria is now available, approximately half of the global population remain at risk of the disease particularly in developing countries. To design effective malaria control strategies there is need to understand the pattern of malaria heterogeneity in an area. Therefore, the main objective of this study was to explore the spatial and spatio-temporal pattern of malaria cases in Zimbabwe based on malaria data aggregated at district level from 2011 to 2016.

Methods

Geographical information system (GIS) and spatial scan statistic were applied on passive malaria data collected from health facilities and aggregated at district level to detect existence of spatial clusters. The global Moran’s I test was used to infer the presence of spatial autocorrelation while the purely spatial retrospective analyses were performed to detect the spatial clusters of malaria cases with high rates based on the discrete Poisson model. Furthermore, space-time clusters with high rates were detected through the retrospective space-time analysis based on the discrete Poisson model.

Results

Results showed that there is significant positive spatial autocorrelation in malaria cases in the study area. In addition, malaria exhibits spatial heterogeneity as evidenced by the existence of statistically significant (P < 0.05) spatial and space-time clusters of malaria in specific geographic regions. The detected primary clusters persisted in the eastern region of the study area over the six year study period while the temporal pattern of malaria reflected the seasonality of the disease where clusters were detected within particular months of the year.

Conclusions

Geographic regions characterised by clusters of high rates were identified as malaria high risk areas. The results of this study could be useful in prioritizing resource allocation in high-risk areas for malaria control and elimination particularly in resource limited settings such as Zimbabwe. The results of this study are also useful to guide further investigation into the possible determinants of persistence of high clusters of malaria cases in particular geographic regions which is useful in reducing malaria burden in such areas.

Spatial-temporal trends in forced migrant mortality, 2014-2018

INTRODUCTION

The identification of spatial-temporal clusters of forced migrant mortality is urgently needed to inform preventative policies and humanitarian response. As a first step towards understanding the geography of forced migrant mortality, this study investigates spatial-temporal patterns in death at a global scale.

METHODS

We used information on the location and dates of forced migrant deaths reported in the International Organization for Migration's Missing Migrant Project from 2014 to 2018. Kulldorff's spatial-temporal and seasonal scans were used to detect spatial-temporal and temporal heterogeneity in mortality.

RESULTS

A total of 16 314 deaths were reported during the study period. A preponderance of deaths occurred at sea each year (range 26%-54% across 5 years). Twelve spatial-temporal clusters of forced migrant mortality were detected by maximum likelihood testing. Annually, the period of August-October was associated with a 40-percentage-point increase in the risk of mortality, relative to other time periods.

CONCLUSIONS

Death during forced migration occurs close to national borders and during periods of intense conflict. This evidence may inform the design of policies and targeting of interventions to prevent forced migration-related deaths.

Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering

BACKGROUND

Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time.

OBJECTIVE

The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source.

METHODS

We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran's I statistics to investigate the extent of the outbreak in both space and time within the affected area.

RESULTS

Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak.

CONCLUSIONS

Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.

Application of GIS Spatial Analysis and Scanning Statistics in the Gynecological Cancer Clustering Pattern and Risk Screening: A Case Study in Northern Jiangxi Province, China

OBJECTIVE

The incidence of gynecological cancer is high in China, and the effects of related treatments and preventive measures need to be improved.

METHODS

This study uses GIS spatial analysis methods and a scanning statistical analysis to study the major gynecological cancers in northern Jiangxi Province from 2016 to 2018.

RESULTS

The incidence and spatial pattern of cervical cancer, ovarian cancer, and uterine cancer had agglomeration characteristics and changes during the study period. The gynecological cancer had a spatial autocorrelation and agglomeration in its spatial pattern. The Moran's Index of the overall gynecological cancer incidence rate was 0.289 (p = 0.005). Ripley's L(d) function showed that the agglomeration radius was between 51.40 and 52.82 km. The results of the kernel density estimation showed that the cases of gynecological cancer were concentrated in the central and northeastern areas of the study area. The overall county-level incidence of gynecological cancer varied from 0.26 to 11.14 per 100,000. The results of the gravity center analysis showed that the spatial distribution of the gravity center point of gynecological cancer had moved toward the east during the past three years. The results of a hotspot analysis showed that there were five hotspot areas that had gynecological cancers. The most likely clusters of gynecological cancer at the county level in northern Jiangxi Province were distributed in the adjacent areas of Jiujiang, Yichun, and Nanchang, with a relative risk of 1.85.

CONCLUSION

The research shows that GIS can display the distribution of cancer cases and can use spatial analysis methods and scanning statistical techniques to obtain key areas of cancer incidence. These results can provide data and key areas for the formulation of regional public health policies and provide recommendations for cancer screening and the rational allocation of health resources.

Spatial and temporal patterns of Lyme Neuroborreliosis on Funen, Denmark from 1995-2014

In Europe, Lyme neuroborreliosis (LNB) is the most severe manifestation of Lyme borreliosis and has recently been added to the communicable disease surveillance list for EU/EEA by the European Commission. In Northern Europe, LNB is primarily caused by the spirochete Borrelia garinii and transmitted by the tick Ixodes ricinus. This Danish observational epidemiologic case-control study includes every identified LNB patient (n = 401) on Funen, Denmark, from 1995-2014. We display spatial and temporal LNB incidence variation, seasonal distribution of cases and local spatial case clustering. Seasonal patterns show LNB symptom-onset peaking in July and a significant seasonal difference in number of cases (p < 0.01). We found no significant change in seasonality patterns over time when dividing the study period into 5-year intervals. We identified a significant local geographical hot-spot of cases with a relative risk of 2.44 (p = 0.013). Analysis revealed a significantly shorter distance to nearest forest for cases compared with controls (p < 0.001). We present a novel map of the focal geographical distribution of LNB cases in a high endemic borreliosis area. Continued studies of case clustering in the epidemiology of LNB are of key importance in guiding intervention strategies.

Precision Surveillance for Viral Respiratory Pathogens: Virome Capture Sequencing for the Detection and Genomic Characterization of Severe Acute Respiratory Infection in Uganda

Background

Precision public health is a novel set of methods to target disease prevention and mitigation interventions to high-risk subpopulations. We applied a precision public health strategy to syndromic surveillance for severe acute respiratory infection (SARI) in Uganda by combining spatiotemporal analytics with genomic sequencing to detect and characterize viral respiratory pathogens with epidemic potential.

Methods

Using a national surveillance network we identified patients with unexplained, influenza-negative SARI from 2010 to 2015. Spatiotemporal analyses were performed retrospectively to identify clusters of unexplained SARI. Within clusters, respiratory viruses were detected and characterized in naso- and oropharyngeal swab samples using a novel oligonucleotide probe capture (VirCapSeq-VERT) and high-throughput sequencing platform. Linkage to conventional epidemiologic strategies further characterized transmission dynamics of identified pathogens.

Results

Among 2901 unexplained SARI cases, 9 clusters were detected, accounting for 301 (10.4%) cases. Clusters were more likely to occur in urban areas and during biannual rainy seasons. Within detected clusters, we identified an unrecognized outbreak of measles-associated SARI; sequence analysis implicated cocirculation of endemic genotype B3 and genotype D4 likely imported from England. We also detected a likely nosocomial SARI cluster associated with a novel picobirnavirus most closely related to swine and dromedary viruses.

Conclusions

Using a precision approach to public health surveillance, we detected and characterized the genomics of vaccine-preventable and zoonotic respiratory viruses associated with clusters of severe respiratory infections in Uganda. Future studies are needed to assess the feasibility, scalability, and impact of applying similar approaches during real-time public health surveillance in low-income settings.

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 space-time analysis of recurrent malnutrition-related hospitalisations in Kilifi, Kenya for children under-5 years

Background

Globally, malnutrition underlies 45% of under-5 s mortality, mainly from potentiating common infections such as diarrhoea and pneumonia. Malnutrition as a public health problem is not evenly disbursed because of disparities in food insecurity and health, and children commonly suffer recurrent episodes of opportunistic infections. We aimed to understand better the spatial and temporal structure of multiple paediatric hospital admissions associated with malnutrition-related illnesses. This paper aimed to investigate the spatial-temporal variations in malnutrition-related recurrent morbidity of children under-5 years from the Kilifi County in Kenya between 2002 and 2015.

Methods

The study included data from children under-5 years old who had more than one admission to a rural district hospital in Kenya within the Kilifi Health and Demographic Surveillance System (KHDSS). The primary outcome was a malnutrition-related admission, based on wasting (WHZ < -2) or nutritional oedema. Individual, household and environmental level covariates were examined as exposures. We first fitted a SARIMA model for the temporality, and the Moran’s Index affirmed spatial clustering in malnutrition admissions. Kulldorf Statistics using SaTScan were applied to detect hotspots. Then, bivariate analysis was done using repeated values tabulation and analysis of covariance (ANCOVA). Inferential analysis was done using a mixed effect multivariable negative-binomial regression model, adjusting for spatiotemporal random effects.

Results

A total of 2821 children were admitted more than once, giving a total of 6375 admissions. Of these 6375 admissions, 1866 were malnutrition-related, and 3.9% (109/2821) of the children with repeat admissions died. There was a seasonal pattern of re-admissions, peaking from May to July over the years. Hotspots were found in both the Northern and Southern areas of the KHDSS, while the areas near Kilifi Town were least affected. We found that disease severity was most likely associated with a malnutrition re-admission to the hospital.

Conclusion

Disease severity was strongly associated with admission with malnutrition but its effect reduced after adjusting for the spatial and temporal random effects. Adjusting for clustering in space and in time (spatial-temporal) in models helps to improve the understanding of recurrent hospitalisations involving malnutrition.

Electronic supplementary material

The online version of this article (10.1186/s40795-019-0296-5) contains supplementary material, which is available to authorized users.

Space-time dynamics of a triple epidemic: dengue, chikungunya and Zika clusters in the city of Rio de Janeiro

Dengue, an arboviral disease transmitted by Aedes mosquitoes, has been endemic in Brazil for decades. However, vector-control strategies have not led to a significant reduction in the disease burden and have not been sufficient to prevent chikungunya and Zika entry and establishment in the country. In Rio de Janeiro city, the first Zika and chikungunya epidemics were detected between 2015 and 2016, coinciding with a dengue epidemic. Understanding the behaviour of these diseases in a triple epidemic scenario is a necessary step for devising better interventions for prevention and outbreak response. We applied scan statistics analysis to detect spatio-temporal clustering for each disease separately and for all three simultaneously. In general, clusters were not detected in the same locations and time periods, possibly owing to competition between viruses for host resources, depletion of susceptible population, different introduction times and change in behaviour of the human population (e.g. intensified vector-control activities in response to increasing cases of a particular arbovirus). Simultaneous clusters of the three diseases usually included neighbourhoods with high population density and low socioeconomic status, particularly in the North region of the city. The use of space–time cluster detection can guide intensive interventions to high-risk locations in a timely manner, to improve clinical diagnosis and management, and pinpoint vector-control measures.

Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data

Long term surveillance of vectors and arboviruses is an integral aspect of disease prevention and control systems in countries affected by increasing risk. Yet, little effort has been made to adjust space-time risk estimation by integrating disease case counts with vector surveillance data, which may result in inaccurate risk projection when several vector species are present, and when little is known about their likely role in local transmission. Here, we integrate 13 years of dengue case surveillance and associated Aedes occurrence data across 462 localities in 63 districts to estimate the risk of infection in the Republic of Panama. Our exploratory space-time modelling approach detected the presence of five clusters, which varied by duration, relative risk, and spatial extent after incorporating vector species as covariates. The Ae. aegypti model contained the highest number of districts with more dengue cases than would be expected given baseline population levels, followed by the model accounting for both Ae. aegypti and Ae. albopictus. This implies that arbovirus case surveillance coupled with entomological surveillance can affect cluster detection and risk estimation, potentially improving efforts to understand outbreak dynamics at national scales.

Dengue cases have increased in tropical regions worldwide owing to urbanization, globalization, and climate change facilitating the spread of Aedes mosquito vectors. National surveillance programs monitor trends in dengue fever and inform the public about epidemiological scenarios where outbreak preventive actions are most needed. Yet, most estimations of dengue risk so far derive only from disease case data, ignoring Aedes occurrence as a key aspect of dengue transmission dynamic. Here we illustrate how incorporating vector presence and absence as a model covariate can considerably alter the characteristics of space-time cluster estimations of dengue cases.

Spatial distribution of tuberculosis and its socioeconomic influencing factors in mainland China 2013-2016

BACKGROUND

To assess the spatial epidemic characteristics of TB and identify the key areas for disease prevention and control.

OBJECTIVE

To explore the spatial distribution and socioeconomic influencing factors of TB in mainland China from 2013 to 2016.

METHODS

Spatial autocorrelation was used to explore the spatial distribution characteristics of TB at the quantitative level. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were conducted to explore the association between factors and TB incidence from both global and local perspectives.

RESULTS

There was a significant positive spatial autocorrelation of TB at the provincial level (P < 0.05): hot spots were mainly located in the west of Xinjiang and Tibet, and cold spots in the eastern coastal areas. Four latent factors on the socioeconomic dimension, involving the proportion of illiterate people aged 15 and over, per capita disposable income in rural areas, the number of health technicians per 1000 population and the urban population density, were associated with TB incidence. The GWR model showed that the effect of the same factor on TB incidence varied with geographical location.

CONCLUSIONS

Spatial clustering of TB incidence in mainland China still exists. The differences of socioeconomic factors in different locations can be confirmed by GWR model. Targeted preventive and control measures or policies will be conducive in effectively reducing the incidence of TB, especially in hot spots.

Spatiotemporal analysis of the dengue outbreak in Guangdong Province, China

Background

Dengue is becoming a major public health concern in Guangdong (GD) Province of China. The problem was highlighted in 2014 by an unprecedented explosive outbreak, where the number of cases was larger than the total cases in previous 30 years. The present study aimed to clarify the spatial and temporal patterns of this dengue outbreak.

Methods

Based on the district/county-level epidemiological, demographic and geographic data, we first used Moran’s I statistics and Spatial scan method to uncover spatial autocorrelation and clustering of dengue incidence, and then estimated the spatial distributions of mosquito ovitrap index (MOI) by using inverse distance weighting. We finally employed a multivariate time series model to quantitatively decompose dengue cases into endemic, autoregressive and spatiotemporal components.

Results

The results indicated that dengue incidence was highly spatial-autocorrelated with the inclination of clustering and nonuniformity. About 12 dengue clusters were discovered around Guangzhou and Foshan with significant differences by district/county, where the most likely cluster with the largest relative risk located in central Guangzhou in October. Three significant high-MOI areas were observed around Shaoguan, Qingyuan, Shanwei and Guangzhou. It was further found the districts in Guagnzhou and Foshan were prone to local autoregressive transmission, and most region in southern and central GD exhibited higher endemic components. Moreover, nearly all of districts/counties (especially the urban area) have cases that were infected in adjacent regions.

Conclusions

The study can help to clarify the heterogeneity and the associations of dengue transmission in space and time, and thus provide useful information for public health authorities to plan dengue control strategies.

Spatiotemporal patterns of malaria at cross-boundaries area in Menoreh Hills, Java, Indonesia

Background

Comprehensive reports of malaria in Menoreh Hills, Central Java, Indonesia, a unique district cross-boundaries area under three districts and two provinces have been published previously. However, no study was performed to identify the hotspots of malaria in this cross-boundaries area, Kaligesing and Bagelen Subdistricts in Purworejo, Jawa Tengah Province and Kokap Subdistrict in Kulon Progo, Yogyakarta Province, using a longitudinal spatial data.

Methods

Monthly reports of malaria cases at primary health centres during 2005–2015 were collected and processed with ArcGIS and SaTScan to identify the malaria distribution at the village level. Malaria distribution was analysed using global spatial autocorrelation (Moran index) in ArcGIS. Cluster analysis was conducted using SaTScan purely spatial clustering and purely temporal clustering. Cluster characteristics resulted from three different approach were compared and analysed.

Results

During the last 11 years, 3812 malaria cases were reported and the number of high case incidence (HCI) villages were increased continuously. Malaria spatial distribution in Menoreh Hills was clustered spatially. Using three different approaches of time period ranges, consistent conclusion was found i.e. most likely clusters always occurred in the Purworejo district while the secondary clusters appeared later in the cross-boundaries districts.

Conclusion

Spatiotemporal analysis of an 11 years surveillance data showed that hotspots of malaria cases in Menoreh Hills were continuously located in Purworejo district. The success of malaria elimination in the cross boundaries area of Menoreh Hills might be depended on the success in malaria case management and surveillance in this hotspot area.

Spatial overlaps in the distribution of HIV/AIDS and malaria in Zimbabwe

BACKGROUND

In most developing economies particularly in Africa, more people are likely to die of HIV/AIDS and malaria compared to other diseases. HIV/AIDS tends to be superimposed on the long standing malaria burden particularly in sub-Saharan Africa. The detection and understanding of spatial overlaps in disease occurrence is important for integrated and targeted disease control. Integrated disease control can enhance efficiency and cost-effectiveness through the development of drugs targeting multiple infections in the same geographic space.

METHODS

Using Zimbabwe as a case study, this study tests the hypothesis that malaria clusters coincide with HIV/AIDS clusters in space. Case data for the two diseases were obtained from the Ministry of Health and Child Care in Zimbabwe at district level via the District Health Information System (DHIS). Kulldorff's spatial scan statistic was used to test for spatial overlaps in clusters of high cases of HIV/AIDS and malaria at district level. The spatial scan test was used to identify areas with higher cases of HIV/AIDS and malaria than would be expected under spatial randomness.

RESULTS

Results of this study indicate that primary clusters of HIV/AIDS and malaria were not spatially coincident in Zimbabwe. While no spatial overlaps were detected between primary clusters of the two diseases, spatial overlaps were detected among statistically significant secondary clusters of HIV/AIDS and malaria. Spatial overlaps between HIV/AIDS and malaria occurred in five districts in the northern and eastern regions of Zimbabwe. In addition, findings of this study indicate that HIV/AIDS is more widespread in Zimbabwe compared to malaria.

CONCLUSIONS

The results of this study may therefore be used as a basis for spatially-targeted control of HIV/AIDS and malaria particularly in high disease burden areas. This is important as previous interventions have targeted the two diseases separately. Thus, targeted control could assist in resource allocation through prioritising areas in greatest need hence maximising the impact of disease control.

Malaria epidemiology in Suriname from 2000 to 2016: trends, opportunities and challenges for elimination

Background

Suriname has experienced a significant change in malaria transmission risk and incidence over the past years. The country is now moving toward malaria elimination. The first objective of this study is to describe malaria epidemiological trends in Suriname between 2000 and 2016. The second objective is to identify spatiotemporal malaria trends in notification points between 2007 and 2016.

Methods

National malaria surveillance data resulting from active and passive screening between 2000 and 2016 were used for the temporal trend analysis. A space–time cluster analysis using SaTScan™ was conducted on Malaria Programme-data from 2007 to 2016 comparing cases (people tested positive) with controls (people tested negative).

Results

Suriname experienced a period of high malaria incidence during 2000–2005, followed by a steep decline in number of malaria cases from 2005 onwards. Imported malaria cases, mostly of Brazilian nationality and travelling from French Guiana, were major contributors to the reported number of cases, exceeding the national malaria burden (94.2% of the total). Most clusters in notification points are found in the border area between Suriname and French Guiana. Clustering was also found in the migrant clinic in Paramaribo.

Conclusions

Suriname has successfully reduced malaria to near-elimination level in the last 17 years. However, the high malaria import rate resulting from cross-border moving migrants is a major challenge for reaching elimination. This requires continued investment in the national health system, with a focus on border screening and migrant health. A regional approach to malaria elimination within the Guianas and Brazil is urgently needed.

Malaria intensity in Colombia by regions and populations

Determining the distribution of disease prevalence among heterogeneous populations at the national scale is fundamental for epidemiology and public health. Here, we use a combination of methods (spatial scan statistic, topological data analysis and epidemic profile) to study measurable differences in malaria intensity by regions and populations of Colombia. This study explores three main questions: What are the regions of Colombia where malaria is epidemic? What are the regions and populations in Colombia where malaria is endemic? What associations exist between epidemic outbreaks between regions in Colombia? Plasmodium falciparum is most prevalent in the Pacific Coast, some regions of the Amazon Basin, and some regions of the Magdalena Basin. Plasmodium vivax is the most prevalent parasite in Colombia, particularly in the Northern Amazon Basin, the Caribbean, and municipalities of Sucre, Antioquia and Cordoba. We find an acute peak of malarial infection at 25 years of age. Indigenous and Afrocolombian populations experience endemic malaria (with household transmission). We find that Plasmodium vivax decreased in the most important hotspots, often with moderate urbanization rate, and was re-introduced to locations with moderate but sustained deforestation. Infection by Plasmodium falciparum, on the other hand, steadily increased in incidence in locations where it was introduced in the 2009-2010 generalized epidemic. Our findings suggest that Colombia is entering an unstable transmission state, where rapid decreases in one location of the country are interconnected with rapid increases in other parts of the country.

Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016

Vector-borne diseases (VBDs) infect over one billion people and are responsible for over one million deaths each year, globally. Chikungunya (CHIK) and Dengue Fever (DENF) are emerging VBDs due to overpopulation, increases in urbanization, climate change, and other factors. Colombia has recently experienced severe outbreaks of CHIK AND DENF. Both viruses are transmitted by the Aedes mosquitoes and are preventable with a variety of surveillance and vector control measures (e.g. insecticides, reduction of open containers, etc.). Spatiotemporal statistics can facilitate the surveillance of VBD outbreaks by informing public health officials where to allocate resources to mitigate future outbreaks. We utilize the univariate Kulldorff space-time scan statistic (STSS) to identify and compare statistically significant space-time clusters of CHIK and DENF in Colombia during the outbreaks of 2015 and 2016. We also utilize the multivariate STSS to examine co-occurrences (simultaneous excess incidences) of DENF and CHIK, which is critical to identify regions that may have experienced the greatest burden of VBDs. The relative risk of CHIK and DENF for each Colombian municipality belonging to a univariate and multivariate cluster is reported to facilitate targeted interventions. Finally, we visualize the results in a three-dimensional environment to examine the size and duration of the clusters. Our approach is the first of its kind to examine multiple VBDs in Colombia simultaneously, while the 3D visualizations are a novel way of illustrating the dynamics of space-time clusters of disease.

Geographical Clustering and Environmental Determinants of Schistosomiasis from 2007 to 2012 in Jianghan Plain, China

This study compared changes in the spatial clustering of schistosomiasis in Jianghan Plain, China by applying Kulldorff's spatial scan statistic. The Geodetector software was employed to detect the environmental determinants of schistosomiasis annually from 2007 to 2012. The most likely spatial cluster in 2007 covered the north-central part of Jianghan Plain, whereas those observed from 2008 to 2012 were toward the south, with extended coverage in generally the same areas across various periods, and some variation nevertheless in precise locations. Furthermore, the 2007 period was more likely to be clustered than any other period. We found that temperature, land use, and soil type were the most critical factors associated with infection rates in humans. In addition, land use and soil type had the greatest impact on the prevalence of schistosomiasis in 2009, whereas this effect was minimal in 2007. The effect of temperature on schistosomiasis prevalence reached its maximum in 2010, whereas in 2008, this effect was minimal. Differences observed in the effects of those two factors on the spatial distribution of human schistosomiasis were inconsistent, showing statistical significance in some years and a lack thereof in others. Moreover, when two factors operated simultaneously, a trend of enhanced interaction was consistently observed. High-risk areas with strong interactions of affected factors should be targeted for disease control interventions.