Spatiotemporal Surveillance Research Articles

A Novel Bayesian Spatio-Temporal Surveillance Metric to Predict Emerging Infectious Disease Areas of High Disease Risk.

Identification of areas of high disease risk has been one of the top goals for infectious disease public health surveillance. Accurate prediction of these regions leads to effective resource allocation and faster intervention. This paper proposes a novel prediction surveillance metric based on a Bayesian spatio-temporal model for infectious disease outbreaks. Exceedance probability, which has been commonly used for cluster detection in statistical epidemiology, was extended to predict areas of high risk. The proposed metric consists of three components: the area's risk profile, temporal risk trend, and spatial neighborhood influence. We also introduce a weighting scheme to balance these three components, which accommodates the characteristics of the infectious disease outbreak, spatial properties, and disease trends. Thorough simulation studies were conducted to identify the optimal weighting scheme and evaluate the performance of the proposed prediction surveillance metric. Results indicate that the area's own risk and the neighborhood influence play an important role in making a highly sensitive metric, and the risk trend term is important for the specificity and accuracy of prediction. The proposed prediction metric was applied to the COVID-19 case data of South Carolina from March 12, 2020, and the subsequent 30 weeks of data.

Spatiotemporal patterns and surveillance artifacts in maternal mortality in the United States: a population-based study

Reports of high and rising maternal mortality ratios (MMR) in the United States have caused serious concern. We examined spatiotemporal patterns in cause-specific MMRs, in order to obtain insights into the cause for the increase. The study included all maternal deaths recorded by the Centers for Disease Control and Prevention from 1999 to 2021. Changes in overall and cause-specific MMRs were quantified nationally; in low-vs high-MMR states (i.e., MMRs <20 vs≥26 per 100,000 live births in 2018-2021); and in California vs Texas (populous states with low vs high MMRs). Cause-specific MMRs included those due to unambiguous causes (e.g., selected obstetric causes such as pre-eclampsia/eclampsia) and less-specific/potentially incidental causes (e.g., "other specified pregnancy-related conditions", chronic hypertension, and malignant neoplasms). MMRs increased from 9.60 (n=1543) in 1999-2002 to 23.5 (n=3478) per 100,000 live births in 2018-2021. The temporal increase in MMRs was smaller in low-MMR states (from 7.82 to 14.1 per 100,000 live births) compared with high-MMR states (from 11.1 to 31.4 per 100,000 live births). MMRs due to selected obstetric causes decreased to a similar extent in low-vs high-MMR states, whereas the increase in MMRs from less-specific/potentially incidental causes was smaller in low- vs high-MMR states (MMR ratio (RR) 5.57, 95% CI 4.28, 7.25 vs 7.07, 95% CI 5.91, 8.46), and in California vs Texas (RR 1.67, 95% CI 1.03, 2.69 vs 10.8, 95% CI 6.55, 17.7). The change in malignant neoplasm-associated MMRs was smaller in California vs Texas (RR 1.21, 95% CI 0.08, 19.3 vs 91.2, 95% CI 89.2, 94.8). MMRs from less-specific/potentially incidental causes increased in all race/ethnicity groups. Spatiotemporal patterns of cause-specific MMRs, including similar reductions in unambiguous obstetric causes of death and variable increases in less-specific/potentially incidental causes, suggest misclassified maternal deaths and overestimated maternal mortality in some US states. This work received no funding.

Archival mitogenomes identify invasion by the Batrachochytrium dendrobatidis CAPE lineage caused an African amphibian extinction in the wild.

Outbreaks of emerging infectious diseases are influenced by local biotic and abiotic factors, with host declines occurring when conditions favour the pathogen. Deterioration in the population of the micro-endemic Tanzanian Kihansi spray toad (Nectophrynoides asperginis) occurred after the construction of a hydropower dam, implicating habitat modification in this species decline. Population recovery followed habitat augmentation; however, a subsequent outbreak of chytridiomycosis caused by Batrachochytrium dendrobatidis (Bd) led to the spray toad's extinction in the wild. We show using spatiotemporal surveillance and mitogenome assembly of Bd from archived toad mortalities that the outbreak was caused by invasion of the BdCAPE lineage and not the panzootic lineage BdGPL. Molecular dating reveals an emergence of BdCAPE across southern Africa overlapping with the timing of the spray toad's extinction. That our post-outbreak surveillance of co-occurring amphibian species in the Udzungwa Mountains shows widespread infection by BdCAPE yet no signs of ill-health or decline suggests these other species can tolerate Bd when environments are stable. We conclude that, despite transient success in mitigating the impact caused by dams' construction, invasion by BdCAPE caused the ultimate die-off that led to the extinction of the Kihansi spray toad.

Adapting vector surveillance using Bayesian experimental design: An application to an ongoing tick monitoring program in the southeastern United States

Maps of the distribution of medically-important ticks throughout the US remain lacking in spatial and temporal resolution in many areas, leading to holes in our understanding of where and when people are at risk of tick encounters, an important baseline for informing public health response. In this work, we demonstrate the use of Bayesian Experimental Design (BED) in planning spatiotemporal surveillance of disease vectors. We frame survey planning as an optimization problem with the objective of identifying a calendar of sampling locations that maximizes the expected information regarding some goal. Here we consider the goals of understanding associations between environmental factors and tick presence and minimizing uncertainty in high risk areas. We illustrate our proposed BED workflow using an ongoing tick surveillance study in South Carolina parks. Following a model comparison study based on two years of initial data, several techniques for finding optimal surveys were compared to random sampling. Two optimization algorithms found surveys better than all replications of random sampling, while a space-filling heuristic performed favorably as well. Further, optimal surveys of just 20 visits were more effective than repeating the schedule of 111 visits used in 2021. We conclude that BED shows promise as a flexible and rigorous means of survey design for vector control, and could help alleviate pressure on local agencies by limiting the resources necessary for accurate information on arthropod distributions. We have made the code for our BED workflow publicly available on Zenodo to help promote the application of these methods to future surveillance efforts.

Artificial Intelligence Approach for Severe Dengue Early Warning System.

Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complications. Dengue fever is one of the global burden diseases which annually have 50-100 million cases with 500,000 cases of severe dengue fever, of which 22,000 deaths occur mostly in children. Despite the discovery of vaccines, vector control is still the main approach for prevention efforts. Early detection and accessibility to medical care can reduce severe Dengue mortality rate from 50% to 2%. In the previous study, both statistical and machine learning methods have the potential for predicting a Dengue outbreak, but the study is still fragmented and limited on implementing the generated model into an early warning system application. In this study, we developed an artificial intelligence model with spatiotemporal to predict Dengue outbreak and Dengue incidence case which is ready to be implemented into an early warning system application. Indonesia, especially Semarang City, has experienced an endemic Dengue. We used Semarang City spatiotemporal, meteorological, climatological, and Dengue surveillance epidemiology data from January 2014 to December 2021 in 16 districts of Semarang City. We reviewed 7208 samples from 16 districts and 1 city per week during 8 years. The entire dataset was divided into training (80%) and testing (20%) to develop a prediction model. We used machine learning and Long Short Term Memory (LSTM) to predict Dengue outbreak 1 week before the event for each district. and machine learning to predict Dengue incident cases 1 week before the event for each district. Accuracy, area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score were considered to evaluate the Dengue outbreak prediction model. The Dengue incidence cases prediction model will evaluate using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R2). Extra Trees Classifier model shown outperform in Dengue outbreak prediction, with accuracy 0.8925, AUROC 0. 9529, Recall 0.6117, precision 0.8880, and F1 score 0.7238. CatBoost Regressor model is shown to outperform in Dengue incidence cases prediction, with R2 0.5621, MAE 0.6304, MSE 1.1997, and RMSE 1.0891. The study proves that Artificial Intelligence (AI) with a spatiotemporal approach can give higher performance in Dengue outbreak and incidence cases prediction. Utilization of AI approaches that are sensitive with spatiotemporal feasibility to implement in Dengue early warning system application may contribute to increase the policy makers and community attention to do accurate community-based vector control.

What, How, When, and Where: Spatiotemporal Water Quality Hazards of Cyanotoxins in Subtropical Eutrophic Reservoirs.

Though toxins produced during harmful blooms of cyanobacteria present diverse risks to public health and the environment, surface water quality surveillance of cyanobacterial toxins is inconsistent, spatiotemporally limited, and routinely relies on ELISA kits to estimate total microcystins (MCs) in surface waters. Here, we employed liquid chromatography tandem mass spectrometry to examine common cyanotoxins, including five microcystins, three anatoxins, nodularin, cylindrospermopsin, and saxitoxin in 20 subtropical reservoirs spatially distributed across a pronounced annual rainfall gradient. Probabilistic environmental hazard analyses identified whether water quality values for cyanotoxins were exceeded and if these exceedances varied spatiotemporally. MC-LR was the most common congener detected, but it was not consistently observed with other toxins, including MC-YR, which was detected at the highest concentrations during spring with many observations above the California human recreation guideline (800 ng/L). Cylindrospermopsin was also quantitated in 40% of eutrophic reservoirs; these detections did not exceed a US Environmental Protection Agency swimming/advisory level (15,000 ng/L). Our observations have implications for routine water quality monitoring practices, which traditionally use ELISA kits to estimate MC levels and often limit collection of surface samples during summer months near reservoir impoundments, and further indicate that spatiotemporal surveillance efforts are necessary to understand cyanotoxins risks when harmful cyanobacteria blooms occur throughout the year.

2077. Utility of a Real-Time Spatiotemporal Mapping Surveillance System in Detection of Healthcare-Associated Acute Respiratory Viral Infection Clusters in a Tertiary Healthcare Institution

Abstract Background Transition to endemic COVID-19 has been associated with a rise in community respiratory viral infections (ARIs) with a corresponding increase in healthcare-associated ARIs (HA-ARIs) (Figure 1). 4D-Disease Outbreak Surveillance System (4D-DOSS) is a real-time spatiotemporal mapping surveillance system being developed to detect healthcare-associated infection clusters. We aimed to assess 4D-DOSS’s utility in detection of HA-ARI clusters. Methods 4D-DOSS is a system that integrates and maps clinical, laboratory and patient movement data onto a digital twin of the hospital’s physical space. In addition to a virtual mapping replica of Singapore General Hospital, a 2000-bedded tertiary hospital in Singapore, it constitutes detailed healthcare cloud architecture and surveillance algorithms. Respiratory specimens from inpatients with ARI symptoms are tested for 16 human respiratory viral pathogens via a respiratory virus multiplex PCR (RV16) panel. HA-ARI constitutes first positive sample beyond the maximum incubation period of the corresponding virus (from admission date). Earlier positive test is categorized as community-associated ARI (CA-ARI). Two or more patients with spatial temporal overlap of three-days or less, during the infectious period (7-days) of an index patient are deemed a HA-ARI cluster. Results Incidence of HA-ARI, as per 10,000 patient-days was 7.4 pre-COVID-19 pandemic (Jan 2018 to Dec 2019), 1.5 during pandemic (Jan 2020 to Dec 2022) and 6.4 during transition to endemicity (Jan 2023 – April 2023). Between September 2018 and December 2018, one influenza cluster of ten inpatients (Nov 2018) was identified in the proof-of-concept version of 4D-DOSS. There were four HA-ARI clusters during the Jan 2020-Dec 2022 COVID-19 pandemic phase. 19 HA-ARI clusters were identified between Jan 2023-April 2023. Conclusion 4D-DOSS can detect HA-ARI clusters and has potential to trigger an alert-response process for more effective infection prevention. It enables study of infectious disease transmission kinetics in real-time. Disclosures Sean Whiteley, BSc (IT), Axomem: Board Member|Axomem: Fees received for service and software licenses Maybelle Auw, MBBS, Axomem: Work for company that received fees for service and licensing

Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis.

The SARS-CoV-2 pandemic has been characterized by the repeated emergence of genetically distinct virus variants of increased transmissibility and immune evasion compared to pre-existing lineages. In many countries, their containment required the intervention of public health authorities and the imposition of control measures. While the primary role of testing is to identify infection, target treatment, and limit spread (through isolation and contact tracing), a secondary benefit is in terms of surveillance and the early detection of new variants. Here we study the spatial invasion and early spread of the Alpha, Delta and Omicron (BA.1 and BA.2) variants in England from September 2020 to February 2022 using the random neighbourhood covering (RaNCover) method. This is a statistical technique for the detection of aberrations in spatial point processes, which we tailored here to community PCR (polymerase-chain-reaction) test data where the TaqPath kit provides a proxy measure of the switch between variants. Retrospectively, RaNCover detected the earliest signals associated with the four novel variants that led to large infection waves in England. With suitable data our method therefore has the potential to rapidly detect outbreaks of future SARS-CoV-2 variants, thus helping to inform targeted public health interventions.

Spatiotemporal health surveillance accounting for risk factors and spatial correlation

AbstractMost of the current public health surveillance methods used in epidemiological studies to identify hotspots of diseases assume that the regional disease case counts are independently distributed and they lack the ability of adjusting for confounding covariates. This article proposes a new approach that uses a simultaneous autoregressive (SAR) model, a popular spatial regression approach, within the classical space‐time cumulative sum (CUSUM) framework for detecting changes in the spatial distribution of count data while accounting for risk factors and spatial correlation. We develop expressions for the likelihood ratio test monitoring statistics based on a SAR model with covariates, leading to the proposed space‐time CUSUM test statistic. The effectiveness of the proposed monitoring approach in detecting and identifying step shifts is studied by simulation of various shift scenarios in regional counts. A case study for monitoring regional COVID‐19 infection counts while adjusting for social vulnerability, often correlated with a community's susceptibility towards disease infection, is presented to illustrate the application of the proposed methodology in public health surveillance.

Genome characterization, phylogenomic assessment and spatio-temporal dynamics study of highly mutated BA variants from India

PurposeThe emergence of highly mutated and transmissible BA variants has caused an unprecedented surge in COVID-19 infections worldwide. Thorough analysis of its genome structure and phylogenomic evolutionary details will serve as scientific reference for future research. MethodHere, we have analyzed the BA variants from India using whole-genome sequencing, spike protein mutation study, spatio-temporal surveillance, phylogenomic assessment and epitope mapping. ResultsThe predominance of BA.2/BA.2-like was observed in India during COVID-19 third wave. Genome analysis and mutation study highlighted the existence of 2128 amino acid changes within BA as compared to NC_045512.2. Presence of 23 unknown mutation sites (spanning region 61–831) were observed among the Indian BA variants as compared to the global BA strains. Unassigned probable Omicron showed the highest number of mutations (370) followed by BA.1 (104), BA.2.3 (56), and BA.2 (27). Presence of mutations ‘Q493R ​+ ​Q498R ​+ ​N501Y’, and ‘K417 ​N ​+ ​E484A ​+ ​N501Y’ remained exclusive to BA.2 as well as unassigned probable Omicron. The time-tree and phylogenomic network assessed the evolutionary relationship of the BA variants. Existence of 424 segregating sites and 113 parsimony informative sites within BA genomes were observed through haplotype network analysis. Epitope mapping depicted the presence of unique antigenic sites within the receptor binding domain of the BA variants that could be exploited for robust vaccine development. ConclusionThese findings provide important scientific insights about the nature, diversity, and evolution of Indian BA variants. The study further divulges in the avenues of therapeutic upgradation for better management and eventual eradication of COVID-19.

Constrained Bayesian optimization and spatio-temporal surveillance for sensor network design in the presence of measurement errors

ABSTRACT The optimal placement of sensors is studied to construct a surveillance sensor network for a complicated stochastic system with random measurement errors. The problem is formulated as a joint problem of constrained black-box optimization for the fast detection of an anomaly event and spatio-temporal change-point detection for a low false alarm rate. An algorithm is proposed called Confidence-Set based Constrained Bayesian Optimization (CSCBO) that models performance measures as Gaussian Processes (GPs) and provides a flexible and easy-to-implement framework for handling noisy black-box constraints. As the decision variables of this problem are high-dimensional binary variables, the Wasserstein similarity metric is introduced as a distance measure among different solutions to capture the similarity among solutions properly. Finally, a newly proposed detection statistic for spatio-temporal surveillance is combined with CSCBO to identify the optimal sensor placement while controlling the false alarm rate. The combined procedure is applied to the Altamaha River.

Spatiotemporal Surveillance of SARS-CoV-2 in the Sewage of Three Major Urban Areas in Peru: Generating Valuable Data Where Clinical Testing Is Extremely Limited.

Peru has been severely affected by the COVID-19 pandemic. By January 2022, Peru had surpassed 200 000 COVID-19 deaths, constituting the highest death rate per capita worldwide. Peru has had several limitations during the pandemic: insufficient testing access, limited contact tracing, a strained medical infrastructure, and many economic hurdles. These limitations hindered the gathering of accurate information about infected individuals with spatial resolution in real time, a critical aspect of effectively controlling the pandemic. Wastewater monitoring for SARS-CoV-2 RNA offered a promising alternative for providing needed population-wide information to complement health care indicators. In this study, we demonstrate the feasibility and value of implementing a decentralized SARS-CoV-2 RNA wastewater monitoring system to assess the spatiotemporal distribution of COVID-19 in three major cities in Peru: Lima, Callao, and Arequipa. Our data on viral loads showed the same trends as health indicators such as incidence and mortality. Furthermore, we were able to identify hot spots of contagion within the surveyed urban areas to guide the efforts of health authorities. Viral decay in the sewage network of the cities studied was found to be negligible (<2%). Overall, our results support wastewater monitoring for SARS-CoV-2 as a valuable and cost-effective tool for monitoring the COVID-19 pandemic in the Peruvian context.

The where of when: Geographic variation in the timing of recent increases in US county-level heart disease death rates

PurposeWithin the context of local increases in US heart disease death rates, we estimated when increasing heart disease death rates began by county among adults aged 35–64 years and characterized geographic variation. MethodsWe applied Bayesian spatiotemporal models to vital statistics data to estimate the timing (i.e., the year) of increasing county-level heart disease death rates during 1999–2019 among adults aged 35–64 years. To examine geographic variation, we stratified results by US Census region and urban-rural classification. ResultsThe onset of increasing heart disease death rates among adults aged 35–64 years spanned the two-decade study period from 1999 to 2019. Overall, 43.5% (95% CI: 41.3, 45.6) of counties began increasing before 2011, with early increases more prevalent outside of the most urban counties and outside of the Northeast. Roughly one-in-five (18.4% [95% CI: 15.6, 20.7]) counties continued to decline throughout the study period. ConclusionsThis variation suggests that factors associated with these geographic classifications may be critical in establishing the timing of changing trends in heart disease death rates. These results reinforce the importance of spatiotemporal surveillance in the early identification of adverse trends and in informing opportunities for tailored policies and programs.

COVID-19, air quality and space monitoring.

Due to the worldwide spread of the coronavirus disease 2019 (COVID-19), human mobility and economic activity have slowed down considerably since early 2020. A relatively high number of those infected develop serious pneumonia leading to progressive respiratory failure, system disease and often death. Apart from close human-to-human contact, the acceleration and global diffusion of this pandemic has been shown to be associated with changes in atmospheric chemistry and air pollution by microscopic particulate matter (PM). Breathing air with high concentrations of nitrogen dioxide and PM can result in over-expression of the angiotensin converting enzyme-2 (ACE-2) leading to stress of organs, such as heart and kidneys. Satellite monitoring can play a crucial role in spatio-temporal surveillance of the disease by producing data on pollution as proxy for industrial activity, transport and traffic circulation. Real-time monitoring of COVID-19 in air and chemical pollution of the atmospheric boundary layer available from Earth-observing satellites commuting with Health Information Systems (HIS) would be useful for decision makers involved with public health.

On the crack characterization of reinforced concrete structures: Experimental and data-driven numerical study

In today’s rapid infrastructure development, reinforced concrete (RC) construction accounts for majority of the total residential, commercial and industrial projects. These RC structures, once constructed, deteriorates significantly during their service life. The deterioration of RC structural elements increases mainly with the ageing and sustained loading on the structure and depends on various parameters. One of the primary parameter to access the serviceability of an RC structure is cracking. In this work, an experimental and a numerical study is carried out on the flexural crack behaviour of reinforced concrete beam members. The experimental investigation is focused on the effect of flexural crack by varying the percentage of tensile steel on beam sections. Later, a computer vision-based data-driven numerical tool for crack representation and quantification is developed and validated based on the real-time spatio-temporal video surveillance data of the flexural testing on beams. The developed numerical tool based on the experimental study can be used by the structural designers and practitioners for a robust serviceability-based design of reinforced concrete members and systems.

Advances in spatiotemporal models for non-communicable disease surveillance.

Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public.

A real-time spatio-temporal syndromic surveillance system with application to small companion animals

Lack of disease surveillance in small companion animals worldwide has contributed to a deficit in our ability to detect and respond to outbreaks. In this paper we describe the first real-time syndromic surveillance system that conducts integrated spatio-temporal analysis of data from a national network of veterinary premises for the early detection of disease outbreaks in small animals. We illustrate the system’s performance using data relating to gastrointestinal disease in dogs and cats. The data consist of approximately one million electronic health records for dogs and cats, collected from 458 UK veterinary premises between March 2014 and 2016. For this illustration, the system predicts the relative reporting rate of gastrointestinal disease amongst all presentations, and updates its predictions as new data accrue. The system was able to detect simulated outbreaks of varying spatial geometry, extent and severity. The system is flexible: it generates outcomes that are easily interpretable; the user can set their own outbreak detection thresholds. The system provides the foundation for prompt detection and control of health threats in companion animals.

To reduce or not to reduce: a study on spatio-temporal surveillance

The majority of control charts based on scan statistics for spatio-temporal surveillance use full observation vectors. In high-dimensional applications, dimension-reduction techniques are usually applied. Typically, the dimension reduction is conducted as a post-processing step rather than in the data acquisition stage and thus, a full sample covariance matrix is required. When the dimensionality of data is high, (i) the sample covariance matrix tends to be ill-conditioned due to a limited number of samples; (ii) the inversion of such a sample covariance matrix causes numerical issues; and (iii) aggregating information from all variables may lead to high communication costs in sensor networks. In this paper, we propose a set of reduced-dimension (RD) control charts that perform dimension reduction during the data acquisition process by spatial scanning. The proposed methods avoid computational difficulties and possibly high communication costs. We derive a theoretical measure that characterizes the performance difference between the RD approach and the full observation approach. The numerical results show that the RD approach has little performance loss under several commonly used spatial models while enjoying all the benefits of implementation. A case study on water quality monitoring demonstrates the effectiveness of the proposed methods in real applications.

Dynamic and integrative approaches to understanding pathogen spillover.

Dynamic and integrative approaches to understanding pathogen spillover.

A model for spatiotemporal injury surveillance: implications for the evolution of a trauma system.

Geographic variations in case volume have important implications for trauma system configuration and have been recognized for some time. However, temporal trends in these distributions have received relatively little attention. The aim of this study was to propose a model to facilitate the spatiotemporal surveillance of injuries, using Scotland as a case study. Retrospective analysis of 5 years (2009-2013) of trauma incident location data. We analyzed the study population as a whole, as well as predefined subgroups, such as those with abnormal physiologic signs. To leverage sufficient statistical power to detect temporal trends in rare events over short time periods and small spatial units, we used a geographically weighted regression model. There were 509,725 incidents. There were increases in case volume in Glasgow, the central southern part of the country, the northern parts of the Highlands, the Northeast, and the Orkney and Shetland Islands. Statistically significant changes were mostly restricted to major cities. Decreases in the number of incidents were seen in the Hebrides, Western Scotland, Fife and Lothian, and the Borders. Statistically significant changes were seen mostly in Fife and Lothian, the West, some areas of the Borders, and in the Peterhead area. Subgroup analyses showed markedly different spatiotemporal patterns. This project has demonstrated the feasibility of population-based spatiotemporal injury surveillance. Even over a relatively short period, the geographic distribution of where injuries occur may change, and different injuries present different spatiotemporal patterns. These findings have implications for health policy and service delivery. Epidemiologic study, level V.