Understanding the spatial clustering of severe acute respiratory syndrome (SARS) in Hong Kong

Towards circular consumer behavior: Analysis of discount schemes on coffee cup use

Through a natural experiment setting in Hong Kong, this study examines the effects of financial incentives and nudges on consumer choices among three types of coffee cups: bring-your-own-cup (BYOC), shop-provided reusable cups, and disposable cups. Our dataset comprises 223 structured observations of coffee shops with 522 data points. The financial incentive-a direct price instrument set as a discount-is offered exclusively to customers who bring their own cups, while shop-provided (reusable) cups are not eligible. The results indicate that a financial incentive is not associated with a positive change in the behavior of the rewarded consumers: In this study, the discount does not significantly encourage consumers to bring their own cups. However, we find negative effects related to the choice of cup by consumers not rewarded by the incentive: A negative spillover effect emerges: consumers who have not brought their cups and thus who do not qualify for the discount are more likely to choose disposable cups. These findings highlight the limited effectiveness of financial incentives and nudges in reducing disposable cup usage and suggest the need for broader strategies to encourage sustainable consumption.

Economic globalization and the COVID-19 pandemic: global spread and inequalities

In just a few weeks, COVID-19 has become a global crisis and there is no longer any question of it being a major pandemic. The spread of the disease and the speed of transmission need to be squared with the forms and characteristics of economic globalization, disparities in development between the world's different regions and the highly divergent degree of their interconnectedness. Combining a geographic approach based on mapping the global spread of the virus with the collection of data and socio-economic variables, we drew up an OLS model to identify the impact of certain socio-economic factors on the number of cases observed worldwide. Globalization and the geography of economic relations were the main drivers of the spatial structuring and speed of the international spread of the COVID-19.

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.

Spatial and Temporal Analysis of COVID-19 in the Elderly Living in Residential Care Homes in Portugal

Background: The goal of this study is to identify geographic areas for priority actions in order to control COVID-19 among the elderly living in Residential Care Homes (RCH). We also describe the evolution of COVID-19 in RHC throughout the 278 municipalities of continental Portugal between March and December 2020. Methods: A spatial population analysis of positive COVID-19 cases reported by the Portuguese National Health Service (NHS) among the elderly living in RCH. The data are for COVID-19 testing, symptomatic status, comorbidities, and income level by municipalities. COVID-19 measures at the municipality level are the proportion of positive cases of elderly living in RCH, positive cases per elderly living in RCH, symptomatic to asymptomatic ratio, and the share of comorbidities cases. Spatial analysis used the Kernel density estimation (KDE), space-time statistic Scan, and geographic weighted regression (GWR) to detect and analyze clusters of infected elderly. Results: Between 3 March and 31 December 2020, the high-risk primary cluster was located in the regions of Braganca, Guarda, Vila Real, and Viseu, in the Northwest of Portugal (relative risk = 3.67), between 30 September and 13 December 2020. The priority geographic areas for attention and intervention for elderly living in care homes are the regions in the Northeast of Portugal, and around the large cities, Lisbon and Porto, which had high risk clusters. The relative risk of infection was spatially not stationary and generally positively affected by both comorbidities and low-income. Conclusion: The regions with a population with high comorbidities and low income are a priority for action in order to control COVID-19 in the elderly living in RCH. The results suggest improving both income and health levels in the southwest of Portugal, in the environs of large cities, such as Lisbon and Porto, and in the northwest of Portugal to mitigate the spread of COVID-19.

The impact of geographic mobility on the spread of COVID-19 in Hong Kong

The modern highly globalised economy is jeopardising human health as the increased mobility and interconnectedness has the potential to rapidly transmit pathogens across the globe. This was recently confirmed by the coronavirus disease 2019 outbreak, which quickly led to localised outbreaks in virtually every country. As the existing health systems were unprepared, the world has witnessed a critical shortage of life-supporting and health-sustaining resources. In the absence of effective non-pharmaceutical interventions to suppress the virus transmission, many governments imposed total or partial lockdowns, with devastating economic consequences. The case of Hong Kong in quickly suppressing the virus from spreading can thus be a lesson for all. In this study, open data sources of infected individuals are employed to compile maps of disease incidents at various geographic scales with the aim of better understanding the transmission dynamics and discern spatial variability. Our findings show that tracking human mobility patterns can improve awareness of spatiotemporal factors driving the risks of human exposure to viruses. Moreover, we have demonstrated that spatial tools can be successfully employed to explore connections between individuals and wider communities with the aim of informing adaptation of policies at different spatial scales and for different time periods. As was shown in the case of Hong Kong, disease control encompasses the interrelated tasks of reducing social interactions and encouraging adoption of protective behaviours.

Spatiotemporal Analysis of Covid-19 in Turkey

The Covid-19 pandemic continues to threaten public health around the world. Understanding the spatial dimension of this impact is very important in terms of controlling and reducing the spread of the pandemic. This study measures the spatial association of the Covid-19 outbreak in Turkey between February 8 and May 28, 2021 and reveals its spatiotemporal pattern. In this context, global and local spatial autocorrelation was used to determine whether there is a spatial association of Covid-19 infections, while the spatial regression model was employed to reveal the geographical relationship of the potential factors affecting the number of Covid-19 cases. As a result of the analyzes made in this context, it has been observed that there are spatial associations and distinct spatial clusters in Covid-19 cases at the provincial level in Turkey. The results of the spatial regression model showed that population density and elderly dependency ratio are very important in explaining the model of Covid-19 case numbers. Additionally, it has been revealed that Covid-19 is affected by the Covid-19 numbers of neighboring provinces, apart from the said explanatory variables. The findings of the study revealed that spatial analysis is helpful in understanding the spread of the pandemic in Turkey. It has been determined that geographical location is an important factor to be considered in the investigation of the factors affecting Covid-19.

Two-stage algorithms for visually exploring spatio-temporal clustering of avian influenza virus outbreaks in poultry farms

The development of visual tools for the timely identification of spatio-temporal clusters will assist in implementing control measures to prevent further damage. From January 2015 to June 2020, a total number of 1463 avian influenza outbreak farms were detected in Taiwan and further confirmed to be affected by highly pathogenic avian influenza subtype H5Nx. In this study, we adopted two common concepts of spatio-temporal clustering methods, the Knox test and scan statistics, with visual tools to explore the dynamic changes of clustering patterns. Since most (68.6%) of the outbreak farms were detected in 2015, only the data from 2015 was used in this study. The first two-stage algorithm performs the Knox test, which established a threshold of 7 days and identified 11 major clusters in the six counties of southwestern Taiwan, followed by the standard deviational ellipse (SDE) method implemented on each cluster to reveal the transmission direction. The second algorithm applies scan likelihood ratio statistics followed by AGC index to visualize the dynamic changes of the local aggregation pattern of disease clusters at the regional level. Compared to the one-stage aggregation approach, Knox-based and AGC mapping were more sensitive in small-scale spatio-temporal clustering.

Modelling the impact of travel restrictions on COVID-19 cases in Hong Kong in early 2020

BACKGROUND

Coronavirus Disease 2019 (COVID-19) led to pandemic that affected almost all countries in the world. Many countries have implemented border restriction as a public health measure to limit local outbreak. However, there is inadequate scientific data to support such a practice, especially in the presence of an established local transmission of the disease.

OBJECTIVE

To apply a metapopulation Susceptible-Exposed-Infectious-Recovered (SEIR) model with inspected migration to investigate the effect of border restriction as a public health measure to limit outbreak of coronavirus disease 2019.

METHODS

We apply a modified metapopulation SEIR model with inspected migration with simulating population migration, and incorporating parameters such as efficiency of custom inspection in blocking infected travelers in the model. The population sizes were retrieved from government reports, while the number of COVID-19 patients were retrieved from Hong Kong Department of Health and China Centre for Disease Control (CDC) data. The R₀ was obtained from previous clinical studies.

RESULTS

Complete border closure can help to reduce the cumulative COVID-19 case number and mortality in Hong Kong by 13.99% and 13.98% respectively. To prevent full occupancy of isolation facilities in Hong Kong; effective public health measures to reduce local R₀ to below 1.6 was necessary, apart from having complete border closure.

CONCLUSIONS

Early complete travel restriction is effective in reducing cumulative cases and mortality. However, additional anti-COVID-19 measures to reduce local R₀ to below 1.6 are necessary to prevent COVID-19 cases from overwhelming hospital isolation facilities.

Modeling Hospital Resource Management during the COVID-19 Pandemic: An Experimental Validation

One of the main challenges posed by the healthcare crisis generated by COVID-19 is to avoid hospital collapse. The occupation of hospital beds by patients diagnosed by COVID-19 implies the diversion or suspension of their use for other specialities. Therefore, it is useful to have information that allows efficient management of future hospital occupancy. This article presents a robust and simple model to show certain characteristics of the evolution of the dynamic process of bed occupancy by patients with COVID-19 in a hospital by means of an adaptation of Kaplan-Meier survival curves. To check this model, the evolution of the COVID-19 hospitalization process of two hospitals between 11 March and 15 June 2020 is analyzed. The information provided by the Kaplan-Meier curves allows forecasts of hospital occupancy in subsequent periods. The results shows an average deviation of 2.45 patients between predictions and actual occupancy in the period analyzed.

Preventing Re-Emergence of COVID-19: A National Survey of Public Risk Perceptions and Behavioural Intentions Concerning Travel Plan Among Taiwanese

Background: The objectives of this study were to investigate risk perceptions and travel intention among the general public in Taiwan during the COVID-19 outbreak. Methods: This study used a cross-sectional online survey to collect data. The questionnaire was disseminated via the social media platform (LINE and Facebook) to the general public. Results: A total of 3,237 complete responses were received, of whom 5.8% (95% CI 5.1-6.7) of the participants reported intent to travel to overseas countries with an apparent community spread and 5.5% (95% CI 4.7-6.3) reported intent to travel to other overseas countries in the next 1 month. A relatively higher proportion (46.5%; 95% CI 44.7-48.2) reported intention for domestic travelling. Participants who viewed travelling to only be risky for older adults or those with medical conditions (OR = 2.19; 95% CI 1.38-3.47) and who perceived that one will not get infected if one takes recommended precautionary measures (OR = 3.12; 95% CI 1.85-5.27) reported higher travelling intention to overseas countries with an apparent community spread. Conclusions: Overall, the findings suggest that risk perceptions were depicted as a strong influence of travel intentions.

Infectious Disease Relational Data Analysis Using String Grammar Non-Euclidean Relational Fuzzy C-Means

Statistical analysis in infectious diseases is becoming more important, especially in prevention policy development. To achieve that, the epidemiology, a study of the relationship between the occurrence and who/when/where, is needed. In this paper, we develop the string grammar non-Euclidean relational fuzzy C-means (sgNERF-CM) algorithm to determine a relationship inside the data from the age, career, and month viewpoint for all provinces in Thailand for the dengue fever, influenza, and Hepatitis B virus (HBV) infection. The Dunn's index is used to select the best models because of its ability to identify the compact and well-separated clusters. We compare the results of the sgNERF-CM algorithm with the string grammar relational hard C-means (sgRHCM) algorithm. In addition, their numerical counterparts, i.e., relational hard C-means (RHCM) and non-Euclidean relational fuzzy C-means (NERF-CM) algorithms are also applied in the comparison. We found that the sgNERF-CM algorithm is far better than the numerical counterparts and better than the sgRHCM algorithm in most cases. From the results, we found that the month-based dataset does not help in relationship-finding since the diseases tend to happen all year round. People from different age ranges in different regions in Thailand have different numbers of dengue fever infections. The occupations that have a higher chance to have dengue fever are student and teacher groups from the central, north-east, north, and south regions. Additionally, students in all regions, except the central region, have a high risk of dengue infection. For the influenza dataset, we found that a group of people with the age of more than 1 year to 64 years old has higher number of influenza infections in every province. Most occupations in all regions have a higher risk of infecting the influenza. For the HBV dataset, people in all regions with an age between 10 to 65 years old have a high risk in infecting the disease. In addition, only farmer and general contractor groups in all regions have high chance of infecting HBV as well.

Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China

Few study has revealed spatial transmission characteristics of COVID-19 in Wuhan, China. We aimed to analyze the spatiotemporal spread of COVID-19 in Wuhan and its influence factors. Information of 32,682 COVID-19 cases reported through March 18 were extracted from the national infectious disease surveillance system. Geographic information system methods were applied to analysis transmission of COVID-19 and its influence factors in different periods. We found decrease in effective reproduction number (Rt) and COVID-19 related indicators through taking a series of effective public health measures including restricting traffic, centralized quarantine and strict stay-at home policy. The distribution of COVID-19 cases number in Wuhan showed obvious global aggregation and local aggregation. In addition, the analysis at streets-level suggested population density and the number of hospitals were associated with COVID-19 cases number. The epidemic situation showed obvious global and local spatial aggregations. High population density with larger number of hospitals may account for the aggregations. The epidemic in Wuhan was under control in a short time after strong quarantine measures and restrictions on movement of residents were implanted.

Real-time spatial health surveillance: Mapping the UK COVID-19 epidemic

Introduction The COVID-19 pandemic has highlighted the need for robust data linkage systems and methods for identifying outbreaks of disease in near real-time. Objectives The primary objective of this study was to develop a real-time geospatial surveillance system to monitor the spread of COVID-19 across the UK. Methods Using self-reported app data and the Secure Anonymised Information Linkage (SAIL) Databank, we demonstrate the use of sophisticated spatial modelling for near-real-time prediction of COVID-19 prevalence at small-area resolution to inform strategic government policy areas. Results We demonstrate that using a combination of crowd-sourced app data and sophisticated geo-statistical techniques it is possible to predict hot spots of COVID-19 at fine geographic scales, nationally. We are also able to produce estimates of their precision, which is an important pre-requisite to an effective control strategy to guard against over-reaction to potentially spurious features of 'best guess' predictions. Conclusion In the UK, important emerging risk-factors such as social deprivation or ethnicity vary over small distances, hence risk needs to be modelled at fine spatial resolution to avoid aggregation bias. We demonstrate that existing geospatial statistical methods originally developed for global health applications are well-suited to this task and can be used in an anonymised databank environment, thus preserving the privacy of the individuals who contribute their data.

Examining the diffusion of coronavirus disease 2019 cases in a metropolis: a space syntax approach

Background

The urban built environment (BE) has been globally acknowledged as one of the main factors that affects the spread of infectious disease. However, the effect of the street network on coronavirus disease 2019 (COVID-19) incidence has been insufficiently studied. Severe acute respiratory syndrome coronavirus 2, which causes COVID-19, is far more transmissible than previous respiratory viruses, such as severe acute respiratory syndrome coronavirus, which highlights the role of the spatial configuration of street network in COVID-19 spread, as it is where humans have contact with each other, especially in high-density areas. To fill this research gap, this study utilized space syntax theory and investigated the effect of the urban BE on the spatial diffusion of COVID-19 cases in Hong Kong.

Method

This study collected a comprehensive dataset including a total of 3815 confirmed cases and corresponding locations from January 18 to October 5, 2020. Based on the space syntax theory, six space syntax measures were selected as quantitative indicators for the urban BE. A linear regression model and Geographically Weighted Regression model were then applied to explore the underlying relationships between COVID-19 cases and the urban BE. In addition, we have further improved the performance of GWR model considering the spatial heterogeneity and scale effects by adopting an adaptive bandwidth.

Result

Our results indicated a strong correlation between the geographical distribution of COVID-19 cases and the urban BE. Areas with higher integration (a measure of the cognitive complexity required for a pedestrians to reach a street) and betweenness centrality values (a measure of spatial network accessibility) tend to have more confirmed cases. Further, the Geographically Weighted Regression model with adaptive bandwidth achieved the best performance in predicting the spread of COVID-19 cases.

Conclusion

In this study, we revealed a strong positive relationship between the spatial configuration of street network and the spread of COVID-19 cases. The topology, network accessibility, and centrality of an urban area were proven to be effective for use in predicting the spread of COVID-19. The findings of this study also shed light on the underlying mechanism of the spread of COVID-19, which shows significant spatial variation and scale effects. This study contributed to current literature investigating the spread of COVID-19 cases in a local scale from the space syntax perspective, which may be beneficial for epidemic and pandemic prevention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12942-021-00270-4.

A first insight about spatial dimension of COVID-19: analysis at municipality level

Background

This paper is about spatial patterns of by corona virus disease-2019 (COVID-19).

Methods

Using data for the first 21 weeks from municipalities in Catalonia, we analyse whether reported positive cases appear randomly or following some kind of spatial dependence. Global and local measures of spatial autocorrelation are used.

Results

There are some clusters alongside Catalan municipalities that change over time.

Conclusions

Use of spatial analysis techniques is suggested to identify spatial disease patterns and to provide spatially disaggregated public health policy recommendations.

Spatial analysis of the impact of urban geometry and socio-demographic characteristics on COVID-19, a study in Hong Kong

The World Health Organization considered the wide spread of COVID-19 over the world as a pandemic. There is still a lack of understanding of its origin, transmission, and treatment methods. Understanding the influencing factors of COVID-19 can help mitigate its spread, but little research on the spatial factors has been conducted. Therefore, this study explores the effects of urban geometry and socio-demographic factors on the COVID-19 cases in Hong Kong. For each patient, the places they visited during the incubation period before going to hospital were identified, and matched with corresponding attributes of urban geometry (i.e., building geometry, road network and greenspace) and socio-demographic factors (i.e., demographic, educational, economic, household and housing characteristics) based on the coordinates. The local cases were then compared with the imported cases using stepwise logistic regression, logistic regression with case-control of time, and least absolute shrinkage and selection operator regression to identify factors influencing local disease transmission. Results show that the building geometry, road network and certain socio-economic characteristics are significantly associated with COVID-19 cases. In addition, the results indicate that urban geometry is playing a more important role than socio-demographic characteristics in affecting COVID-19 incidence. These findings provide a useful reference to the government and the general public as to the spatial vulnerability of COVID-19 transmission and to take appropriate preventive measures in high-risk areas.

Identification of superspreading environment under COVID-19 through human mobility data

COVID-19 reaffirms the vital role of superspreaders in a pandemic. We propose to broaden the research on superspreaders through integrating human mobility data and geographical factors to identify superspreading environment. Six types of popular public facilities were selected: bars, shopping centres, karaoke/cinemas, mega shopping malls, public libraries, and sports centres. A historical dataset on mobility was used to calculate the generalized activity space and space-time prism of individuals during a pre-pandemic period. Analysis of geographic interconnections of public facilities yielded locations by different classes of potential spatial risk. These risk surfaces were weighed and integrated into a "risk map of superspreading environment" (SE-risk map) at the city level. Overall, the proposed method can estimate empirical hot spots of superspreading environment with statistical accuracy. The SE-risk map of Hong Kong can pre-identify areas that overlap with the actual disease clusters of bar-related transmission. Our study presents first-of-its-kind research that combines data on facility location and human mobility to identify superspreading environment. The resultant SE-risk map steers the investigation away from pure human focus to include geographic environment, thereby enabling more differentiated non-pharmaceutical interventions and exit strategies to target some places more than others when complete city lockdown is not practicable.

Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020)

Understanding the spatial distribution of coronavirus disease 2019 (COVID-19) cases can provide valuable information to anticipate the world outbreaks and in turn improve public health policies. In this study, the cumulative incidence rate (CIR) and cumulative mortality rate (CMR) of all countries affected by the new corona outbreak were calculated at the end of March and April, 2020. Prior to the implementation of hot spot analysis, the spatial autocorrelation results of CIR were obtained. Hot spot analysis and Anselin Local Moran's I indices were then applied to accurately locate high and low-risk clusters of COVID-19 globally. San Marino and Italy revealed the highest CMR by the end of March, though Belgium took the place of Italy as of 30th April. At the end of the research period (by 30th April), the CIR showed obvious spatial clustering. Accordingly, southern, northern and western Europe were detected in the high-high clusters demonstrating an increased risk of COVID-19 in these regions and also the surrounding areas. Countries of northern Africa exhibited a clustering of hot spots, with a confidence level above 95%, even though these areas assigned low CIR values. The hot spots accounted for nearly 70% of CIR. Furthermore, analysis of clusters and outliers demonstrated that these countries are situated in the low-high outlier pattern. Most of the surveyed countries that exhibited clustering of high values (hot spot) with a confidence level of 99% (by 31st March) and 95% (by 30th April) were dedicated higher CIR values. In conclusion, hot spot analysis coupled with Anselin local Moran's I provides a scrupulous and objective approach to determine the locations of statistically significant clusters of COVID-19 cases shedding light on the high-risk districts.

Spatial super-spreaders and super-susceptibles in human movement networks

As lockdowns and stay-at-home orders start to be lifted across the globe, governments are struggling to establish effective and practical guidelines to reopen their economies. In dense urban environments with people returning to work and public transportation resuming full capacity, enforcing strict social distancing measures will be extremely challenging, if not practically impossible. Governments are thus paying close attention to particular locations that may become the next cluster of disease spreading. Indeed, certain places, like some people, can be “super-spreaders”. Is a bustling train station in a central business district more or less susceptible and vulnerable as compared to teeming bus interchanges in the suburbs? Here, we propose a quantitative and systematic framework to identify spatial super-spreaders and the novel concept of super-susceptibles, i.e. respectively, places most likely to contribute to disease spread or to people contracting it. Our proposed data-analytic framework is based on the daily-aggregated ridership data of public transport in Singapore. By constructing the directed and weighted human movement networks and integrating human flow intensity with two neighborhood diversity metrics, we are able to pinpoint super-spreader and super-susceptible locations. Our results reveal that most super-spreaders are also super-susceptibles and that counterintuitively, busy peripheral bus interchanges are riskier places than crowded central train stations. Our analysis is based on data from Singapore, but can be readily adapted and extended for any other major urban center. It therefore serves as a useful framework for devising targeted and cost-effective preventive measures for urban planning and epidemiological preparedness.

Confluent impact of housing and geology on indoor radon concentrations in Atlanta, Georgia, United States

Radon is a naturally released radioactive carcinogenic gas. To estimate radon exposure, studies have examined various risk factors, but limited information exists pertaining to the confluent impact of housing characteristics and geology. This study evaluated the efficacy of housing and geological characteristics to predict radon risk in DeKalb County, Georgia, USA. Four major types of data were used: (1) three databases of indoor radon concentrations (n = 6757); (2) geologic maps of rock types and fault zones; (3) a database of 402 in situ measurements of gamma emissions, and (4) two databases of housing characteristics. The Getis-Ord method was used to delineate hot spots of radon concentrations. Empirical Bayesian Kriging was used to predict gamma radiation at each radon test site. Chi-square tests, bivariate correlation coefficients, and logistic regression were used to examine the impact of geological and housing factors on radon. The results showed that indoor radon levels were more likely to exceed the action level-4 pCi/L (148 Bq/m³) designated by the U.S. Environmental Protection Agency-in fault zones, were significantly positively correlated to gamma readings, but significantly negatively related to the presence of a crawlspace foundation and its combination with a slab. The findings suggest that fault mapping and in situ gamma ray measurements, coupled with analysis of foundation types and delineation of hot spots, may be used to prioritize areas for radon screening.

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

Background

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

Methods

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

Results

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

Conclusion

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

Socio-environmental exposures and health outcomes among persons with sickle cell disease

There is much variability in the expression of sickle cell disease (SCD) and recent works suggest that environmental and social factors may also influence this variability. This paper aims to use geographic information systems technology to examine the association between socio-environmental exposures and health outcomes in all persons who have attended or currently attend the Sickle Cell Unit in Jamaica. Rural patients presented for clinical care at older ages and had less annual visits to clinic. Persons travelled relatively long distances to seek SCD care and those travelling longer had less health maintenance visits. Urban patients had a higher prevalence of significant pain crises (69.4% vs. 55.8%, p value<0.001) and respiratory events (21.2% vs. 14%, p value<0.001). Prevalence of leg ulcers did not vary between rural and urban patients but was higher in males than in females. Females also had lower odds of having respiratory events but there was no sex difference in history of painful crises. Persons with more severe genotypes lived in higher poverty and travelled longer for healthcare services. Persons in areas with higher annual rainfall, higher mean temperatures and living farther from factories had less painful crises and respiratory events. The paper highlights a need for better access to healthcare services for Jamaicans with SCD especially in rural areas of the island. It also reports interesting associations between environmental climatic exposures and health outcomes.

How domain growth is implemented determines the long-term behavior of a cell population through its effect on spatial correlations

Domain growth plays an important role in many biological systems, and so the inclusion of domain growth in models of these biological systems is important to understanding how these systems function. In this work we present methods to include the effects of domain growth on the evolution of spatial correlations in a continuum approximation of a lattice-based model of cell motility and proliferation. We show that, depending on the way in which domain growth is implemented, different steady-state densities are predicted for an agent population. Furthermore, we demonstrate that the way in which domain growth is implemented can result in the evolution of the agent density depending on the size of the domain. Continuum approximations that ignore spatial correlations cannot capture these behaviors, while those that account for spatial correlations do. These results will be of interest to researchers in developmental biology, as they suggest that the nature of domain growth can determine the characteristics of cell populations.

Spatial methods for infectious disease outbreak investigations: systematic literature review

Investigations of infectious disease outbreaks are conventionally framed in terms of person, time and place. Although geographic information systems have increased the range of tools available, spatial analyses are used relatively infrequently. We conducted a systematic review of published reports of outbreak investigations worldwide to estimate the prevalence of spatial methods, describe the techniques applied and explore their utility. We identified 80 reports using spatial methods published between 1979 and 2013, ca 0.4% of the total number of published outbreaks. Environmental or waterborne infections were the most commonly investigated, and most reports were from the United Kingdom. A range of techniques were used, including simple dot maps, cluster analyses and modelling approaches. Spatial tools were usefully applied throughout investigations, from initial confirmation of the outbreak to describing and analysing cases and communicating findings. They provided valuable insights that led to public health actions, but there is scope for much wider implementation and development of new methods.

A Model for Coupled Outbreaks Contained by Behavior Change

Large epidemics such as the recent Ebola crisis in West Africa occur when local efforts to contain outbreaks fail to overcome the probabilistic onward transmission to new locations. As a result, there may be large differences in total epidemic size from similar initial conditions. This work seeks to determine the extent to which the effects of behavior changes and metapopulation coupling on epidemic size can be characterized. While mathematical models have been developed to study local containment by social distancing, intervention and other behavior changes, their connection to larger-scale transmission is relatively underdeveloped. We make use of the assumption that behavior changes limit local transmission before susceptible depletion to develop a time-varying birth-death process capturing the dynamic decrease of the transmission rate associated with behavior changes. We derive an expression for the mean outbreak size of this model and show that the distribution of outbreak sizes is approximately geometric. This allows a probabilistic extension whereby infected individuals may initiate new outbreaks. From this model we characterize the overall epidemic size as a function of the behavior change rate and the probability that an infected individual starts a new outbreak. We find good agreement between the analytical results and stochastic simulations leading to novel findings including critical learning rates that demarcate large and small epidemic sizes.

System tradeoffs in siting a solar photovoltaic material recovery infrastructure

The consumption and disposal of rare and hazardous metals contained in electronics and emerging technologies such as photovoltaics increases the material complexity of the municipal waste stream. Developing effective waste policies and material recovery systems is required to inhibit landfilling of valuable and finite resources. This work developed a siting and waste infrastructure configuration model to inform the management and recovery of end-of-life photovoltaics. This model solves the siting and waste location-allocation problem for a New York State case study by combining multi-criteria decision methods with spatial tools, however this methodology is generalizable to any geographic area. For the case study, the results indicate that PV installations are spatially statistically significant (i.e., clustered). At least 9 sites, which are co-located with landfills and current MRFs, were 'highly' suitable for siting according to our criteria. After combining criteria in an average weighted sum, 86% of the study area was deemed unsuitable for siting while less than 5% is characterized as highly suitable. This method implicitly prioritized social and environmental concerns and therefore, these concerns accounted for the majority of siting decisions. As we increased the priority of economic criteria, the likelihood of siting near ecologically sensitive areas such as coastline or socially vulnerable areas such as urban centers increased. The sensitivity of infrastructure configurations to land use and waste policy are analyzed. The location allocation model results suggest current tip fees are insufficient to avoid landfilling of photovoltaics. Scenarios where tip fees were increased showed model results where facilities decide to adopt limited recycling technologies that bypass compositionally complex materials; a result with strong implications for global PV installations as well as other waste streams. We suggest a multi-pronged approach that lowers technology cost, imposes a minimum collection rate, and implements higher tip fees would encourage exhaustive material recovery for solar photovoltaic modules at end-of-life, beyond New York State. These results have important implications for policy makers and waste managers especially in locations where there is rapid adoption of renewable energy technologies.

Cluster of human infections with avian influenza A (H7N9) cases: a temporal and spatial analysis

Objectives: This study aims to describe the spatial and temporal characteristics of human infections with H7N9 virus in China using data from February 2013 to March 2014 from the websites of every province’s Population and Family Planning Commission. Methods: A human infection with H7N9 virus dataset was summarized by county to analyze its spatial clustering, and by date of illness onset to analyze its space-time clustering using the ESRI^® Geographic Information System (GIS) software ArcMap™ 10.1 and SatScan. Results: Based on active surveillance data, the distribution map of H7N9 cases shows that compared to the rest of China, the areas from near the Yangtze River delta (YRD) to farther south around the Pearl River delta (PRD) had the highest densities of H7N9 cases. The case data shows a strong space-time clustering in the areas on and near the YRD from 26 March to 18 April 2013 and a weak space-time clustering only in the areas on and near the PRD between 3 and 4 February 2014. However, for the rest of the study period, H7N9 cases were spatial-temporally randomly distributed. Conclusions: Our results suggested that the spatial-temporal clustering of H7N9 in China between 2013 and 2014 is fundamentally different.

Global distribution patterns of highly pathogenic H5N1 avian influenza: environmental vs. socioeconomic factors

In this report, we quantitatively analyzed the essential ecological factors that were strongly correlated with the global outbreak of highly pathogenic H5N1 avian influenza. The ecological niche modeling (ENM) was used to reveal the potential outbreak hotspots of H5N1. A two-step modeling procedure has been proposed: we first used BioClim model to obtain the coarse suitable areas of H5N1, and then those suitable areas with very high probabilities were retained as the inputs of multiple-variable autologistic regression analysis (MAR) for model refinement. MAR was implemented taking spatial autocorrelation into account. The final performance of ENM was evaluated using the areas under the curve (AUC) of receiver-operating characteristic. In addition, principal component analysis (PCA) was employed to reveal the most important variables and relevant ecological gradients of H5N1 outbreak. Niche visualization was used to identify potential spreading trend of H5N1 along important ecological gradients. For the first time, we combined socioeconomic and environmental variables as joint predictors in developing ecological niche modeling. Environmental variables represented the natural element related to H5N1 outbreak, whereas socioeconomic ones represented the anthropogenic element. Our results indicated that: (1) the high-risk hotspots are mainly located in temperate zones (indicated by ENM)-correspondingly, we argued that the "ecoregions hypothesis" was reasonable to some extent; (2) evaporation, humidity, human population density, livestock population density were the first four important factors (in descending order) that were associated with the H5N1 global outbreak (indicated by PCA); (3) influenza had a tendency to expand into areas with low evaporation (indicated by niche visualization). In conclusion, our study substantiates that both the environmental and socioeconomic variables jointly determined the global spreading trend of H5N1, but environmental variables played a more important role. Consequently, our study is consistent with the assumption that the natural element is more important than the anthropogenic element as the underlying ecological mechanisms explaining global H5N1 transmission.

Generalizing the spatial relative risk function

The spatial relative risk function is defined as the ratio of densities describing respectively the spatial distribution of cases and controls. It has proven to be an effective tool for visualizing spatial variation in risk in many epidemiological applications over the past 20 years. We discuss the generalization of this function to spatio-temporal case-control data, and also to situations where there are covariates available that may affect the spatial patterns of disease. We examine estimation of the generalized relative risk functions using kernel smoothing, including asymptotic theory and data-driven bandwidth selection. We also consider construction of tolerance contours. Our methods are illustrated on spatio-temporal data describing the 2001 outbreak of foot-and-mouth disease in the United Kingdom, with farm size as a covariate.

Simplified method for including spatial correlations in mean-field approximations

Biological systems involving proliferation, migration, and death are observed across all scales. For example, they govern cellular processes such as wound healing, as well as the population dynamics of groups of organisms. In this paper, we provide a simplified method for correcting mean-field approximations of volume-excluding birth-death-movement processes on a regular lattice. An initially uniform distribution of agents on the lattice may give rise to spatial heterogeneity, depending on the relative rates of proliferation, migration, and death. Many frameworks chosen to model these systems neglect spatial correlations, which can lead to inaccurate predictions of their behavior. For example, the logistic model is frequently chosen, which is the mean-field approximation in this case. This mean-field description can be corrected by including a system of ordinary differential equations for pairwise correlations between lattice site occupancies at various lattice distances. In this work we discuss difficulties with this method and provide a simplification in the form of a partial differential equation description for the evolution of pairwise spatial correlations over time. We test our simplified model against the more complex corrected mean-field model, finding excellent agreement. We show how our model successfully predicts system behavior in regions where the mean-field approximation shows large discrepancies. Additionally, we investigate regions of parameter space where migration is reduced relative to proliferation, which has not been examined in detail before and find our method is successful at correcting the deviations observed in the mean-field model in these parameter regimes.

A geographic analysis of population density thresholds in the influenza pandemic of 1918-19

BACKGROUND

Geographic variables play an important role in the study of epidemics. The role of one such variable, population density, in the spread of influenza is controversial. Prior studies have tested for such a role using arbitrary thresholds for population density above or below which places are hypothesized to have higher or lower mortality. The results of such studies are mixed. The objective of this study is to estimate, rather than assume, a threshold level of population density that separates low-density regions from high-density regions on the basis of population loss during an influenza pandemic. We study the case of the influenza pandemic of 1918-19 in India, where over 15 million people died in the short span of less than one year.

METHODS

Using data from six censuses for 199 districts of India (n=1194), the country with the largest number of deaths from the influenza of 1918-19, we use a sample-splitting method embedded within a population growth model that explicitly quantifies population loss from the pandemic to estimate a threshold level of population density that separates low-density districts from high-density districts.

RESULTS

The results demonstrate a threshold level of population density of 175 people per square mile. A concurrent finding is that districts on the low side of the threshold experienced rates of population loss (3.72%) that were lower than districts on the high side of the threshold (4.69%).

CONCLUSIONS

This paper introduces a useful analytic tool to the health geographic literature. It illustrates an application of the tool to demonstrate that it can be useful for pandemic awareness and preparedness efforts. Specifically, it estimates a level of population density above which policies to socially distance, redistribute or quarantine populations are likely to be more effective than they are for areas with population densities that lie below the threshold.

Social deprivation and prevalence of chronic kidney disease in the UK: workload implications for primary care

BACKGROUND

The 'inverse care law' suggests that populations with the poorest health outcomes also tend to have poorer access to high-quality care. The new general practitioner (GP) contract in the UK aimed to reduce variations in care between areas by collecting information on processes and outcomes of chronic disease management. This study investigated whether, despite reductions in inequalities, primary care in deprived areas is still at a disadvantage due to the higher prevalence of chronic diseases, using chronic kidney disease (CKD) as an example.

METHODS

Initially, data from a hospital-based cohort of CKD patients were analysed to investigate the clustering of CKD patients across area-level deprivation using a geographical information system that employed kernel density estimation. Data from the Quality and Outcomes Framework were then analysed to explore the burden of CKD and associated non-communicable chronic diseases (NCD) and assess the potential impact on GPs' workload by area-level deprivation.

RESULTS

There was a significant clustering of CKD patients referred to the hospital in the most deprived areas. Both the prevalence of CKD and associated conditions and caseload per GP were significantly higher in deprived areas.

CONCLUSION

In the most deprived areas, there is an increased burden of major chronic disease and a higher caseload for clinicians. These reflect significant differences in workload for practices in deprived areas, which needs to be addressed.

Spatio-temporal epidemiology: principles and opportunities

Space-time analysis of disease data has historically involved the search for patterns in aggregated data to identify how regions of high and low risk change through time. Space-time analysis of aggregated data has great value, but represents only a subset of space-time epidemiologic applications. Technological advances for tracking and mapping individuals (e.g., global positioning systems) have introduced mobile populations as an important element in space-time epidemiology. We review five domains critical to the developing field of spatio-temporal epidemiology: (1) spatio-temporal epidemiologic theory, (2) selection of appropriate spatial scale of analysis, (3) choice of spatial/spatio-temporal method for pattern identification, (4) individual-level exposure assessment in epidemiologic studies, and (5) assessment and consideration of locational and attribute uncertainty. This review provides an introduction to principles of space-time epidemiology and highlights future research opportunities.

Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?

Background

Although syndromic surveillance is increasingly used to detect unusual illness, there is a debate whether it is useful for detecting local outbreaks. We evaluated whether syndromic surveillance detects local outbreaks of lower-respiratory infections (LRIs) without swamping true signals by false alarms.

Methods and Findings

Using retrospective hospitalization data, we simulated prospective surveillance for LRI-elevations. Between 1999–2006, a total of 290762 LRIs were included by date of hospitalization and patients place of residence (>80% coverage, 16 million population). Two large outbreaks of Legionnaires disease in the Netherlands were used as positive controls to test whether these outbreaks could have been detected as local LRI elevations. We used a space-time permutation scan statistic to detect LRI clusters. We evaluated how many LRI-clusters were detected in 1999–2006 and assessed likely causes for the cluster-signals by looking for significantly higher proportions of specific hospital discharge diagnoses (e.g. Legionnaires disease) and overlap with regional influenza elevations. We also evaluated whether the number of space-time signals can be reduced by restricting the scan statistic in space or time. In 1999–2006 the scan-statistic detected 35 local LRI clusters, representing on average 5 clusters per year. The known Legionnaires' disease outbreaks in 1999 and 2006 were detected as LRI-clusters, since cluster-signals were generated with an increased proportion of Legionnaires disease patients (p:<0.0001). 21 other clusters coincided with local influenza and/or respiratory syncytial virus activity, and 1 cluster appeared to be a data artifact. For 11 clusters no likely cause was defined, some possibly representing as yet undetected LRI-outbreaks. With restrictions on time and spatial windows the scan statistic still detected the Legionnaires' disease outbreaks, without loss of timeliness and with less signals generated in time (up to 42% decline).

Conclusions

To our knowledge this is the first study that systematically evaluates the performance of space-time syndromic surveillance with nationwide high coverage data over a longer period. The results show that syndromic surveillance can detect local LRI-outbreaks in a timely manner, independent of laboratory-based outbreak detection. Furthermore, since comparatively few new clusters per year were observed that would prompt investigation, syndromic hospital-surveillance could be a valuable tool for detection of local LRI-outbreaks.

A small-area study of environmental risk assessment of outdoor falls

Falls in public places are an issue of great health concern especially for the elderly. Falls among the elderly is also a major health burden in many countries. This study describes a spatial approach to assess environmental causes of outdoor falls using a small urban community in Hong Kong as an example. The method involves collecting data on fall occurrences and mapping their geographic positions to examine circumstances and environmental evidence that contribute to falls. High risk locations or hot spots of falls are identified on the bases of spatial proximity and concentration of falls within a threshold distance by means of kernel smoothing and standard deviational ellipses. This method of geographic aggregation of individual fall incidents for a small-area study yields hot spots of manageable sizes. The spatial clustering approach is effective in two ways. Firstly, it allows visualisation and isolation of fall hot spots to draw focus. Secondly and especially under conditions of resource decline, policy makers are able to target specific locations to examine the underlying causal mechanisms and strategise effective response and preventive measures based on the types of environmental risk factors identified.

The space-time clustering of highly pathogenic avian influenza (HPAI) H5N1 outbreaks in Bangladesh

Bangladesh faced two epidemic waves of highly pathogenic avian influenza (HPAI) H5N1 in two consecutive years. The peaks of the waves were observed in February-July 2007 and January-April 2008, respectively. We examined the spatial and temporal patterns of the 293 outbreaks in 143 subdistricts in 2007 and 2008. Global clustering assessed by K-function was seen at distances 150-300 km between subdistricts. Significant local clusters were detected by space-time scan statistics. In both waves, significant primary clusters of HPAI outbreaks were identified in the central part of the country dominated by commercial production systems and in the northwestern part primarily in backyard production systems. Secondary clusters varied from the northwestern part in 2007 and the southern part in 2008. The findings are highly relevant for the successful planning and implementation of control, prevention and surveillance strategies by highlighting areas where detailed investigations should be initiated.

An ecological study of physical environmental risk factors for elderly falls in an urban setting of Hong Kong

Elderly fall has become an issue of great public health concern and typically important to the aging population in Hong Kong because it carries a great burden to the individuals and the society. More accurate information about environmental risk factors to falls among the elderly could alleviate if not overcome the situation. Conventional approaches to elderly falls were mainly conducted using statistical methods and clinical tests on falls. This study employs ecological and associative analysis using the geographic information systems (GIS) technology to visualize spatial association of falls and environmental factors. The study identified eleven hot spots of elderly falls with unique environmental characteristics. Amongst various environmental attributes, busy streets and junctions, outdoor markets, and refuse collection points, exhibit a strong spatial relationship with the hot spots. The results have demonstrated that GIS can offer an excellent synergic platform to explore the role of space and pattern in fall occurrences.

Spatial distribution of diarrhoea and microbial quality of domestic water during an outbreak of diarrhoea in the Tshikuwi community in Venda, South Africa

Total microbial quality assessment and geographical information system were used for evaluating the quality of water and the spatial distribution of diarrhoea cases in Tshikuwi, a rural community in South Africa, during an outbreak of diarrhoea. The water-abstraction points included two groundwater storage tanks, namely Tank 1 and Tank 2 and the Khandanama river. Indicator microbial counts for total coliforms, faecal coliforms, enterococci, and heterotrophic bacteria exceeded the limit for no risk as stipulated by the South African water-quality guidelines for domestic use for Tank 1 and the Khandanama river. Vibrio, Salmonella, and Shigella species were prevalent in the Khandanama river. The spatial distribution of diarrhoea cases showed a hot-spot of diarrhoea cases close to Tank 1 and the Khandanama river. Results of chi-square analysis showed that the proportion of infection from each water source was different or that infection depends on the type of water source (alpha = 0.05). The demonstrated spatial clustering of diarrhoea cases might have been influenced by the poor microbial quality of water used from Tank 1 and the Khandanama river. The results further highlight the urgent need of water-treatment facilities and monitoring of water quality in rural communities of South Africa.

Spatial-temporal patterns of dengue in areas at risk of dengue hemorrhagic fever in Kaohsiung, Taiwan, 2002

OBJECTIVE

This study aimed to examine whether spatial-temporal patterns of dengue can be used to identify areas at risk of dengue hemorrhagic fever (DHF).

METHODS

Three indices - probability of case-occurrence, mean duration per wave, and transmission intensity - were used to differentiate eight local spatial-temporal patterns of dengue during the 2002 epidemic in Kaohsiung, Taiwan. DHF densities (DHF cases/km(2) per 100 dengue cases) in each spatial-temporal typed area were compared.

RESULTS

Areas with three high indices correlated with the highest DHF density: (1) high transmission intensity only; (2) long duration of wave only, and (3) high transmission intensity plus long duration of wave. However, cumulative incidences of dengue cases were not correlated with DHF densities.

CONCLUSION

Three spatial-temporal indices of dengue could provide useful information to identify areas at high risk of DHF.

Occurrence and potential risks of harmful algal blooms in the East China Sea

Harmful algal blooms (HABs) have drawn great attention in coastal areas worldwide in the past decades because of their multiple effects on marine ecosystems as well as public health. This study utilized geographic information system (GIS) techniques to analyze the primary data on HABs, as well as shellfish toxins data, in the East China Sea from 2000 to 2006. The frequency of HABs was mapped by kernel density estimation, and the relative risk posed by HABs was assessed based on their physical-chemical characteristics. In addition, the spatial patterns and the trend of HAB events were examined by nearest neighbor analysis and time series analysis, respectively. The results revealed that HAB events not only had an increasing trend and significant seasonality, but also were clustered in space and time. HAB events displayed a higher frequency and a higher risk in Zhejiang coastal waters, particularly in the Zhoushan Archipelago, the largest marine fishery in China. Shellfish toxins were detected in areas with high HAB risk, but were not correlated with the risk. This paper provides a novel method to assess the relative risk caused by HABs and some useful information for HAB monitoring and management and aquaculture development.

Geographical spread of SARS in mainland China

Objectives  To describe the spatiotemporal diffusion of the severe acute respiratory syndrome (SARS) epidemic in mainland China, and to analyse the spatial pattern of SARS transmission from the Beijing epicentre to its neighbouring areas.

Methods  Probable SARS cases occurring between November 2002 and May 2003 in mainland China were compiled from different sources and geo‐coded into a geographical information database based on onset location. Spatial analyses including kernel density estimation, and spatial statistical and tracking analyses were performed to characterise the spatiotemporal distribution of SARS cases based on onset location/date. SARS cases that got infected in Beijing but were reported in three provinces surrounding Beijing were mapped, and logistic regression using a ‘case–control’ design at the county level was performed to analyse the impact of travel‐related risk factors in the diffusion pattern.

Results  The SARS epidemic in mainland China spanned a large geographical extent but clustered in two areas: first in Guangdong Province, and about 3 months later in Beijing with its surrounding areas in Shanxi Province, Inner Mongolia Autonomic Region, Hebei Province and Tianjin. Counties in the neighbourhood of Beijing that were crossed by a national highway or inter‐provincial freeway showed the highest risk of acquiring SARS infections, even after correction for population density and medical staff density. Being intersected by a railway did not significantly associate with risk of SARS.

Conclusions  This study provides the first complete documentation of the spatial and temporal characteristics of the SARS epidemic in mainland China. Our analyses confirmed that SARS had benefited from national highways and inter‐provincial freeways for its spread from epicentres to neighbouring areas, whereas trains showed no significant association. This knowledge may be important for the control of re‐emerging SARS, or other future emerging human‐to‐human transmittable infections.

Spatial analysis of falls in an urban community of Hong Kong

Background

Falls are an issue of great public health concern. This study focuses on outdoor falls within an urban community in Hong Kong. Urban environmental hazards are often place-specific and dependent upon the built features, landscape characteristics, and habitual activities. Therefore, falls must be examined with respect to local situations.

Results

This paper uses spatial analysis methods to map fall occurrences and examine possible environmental attributes of falls in an urban community of Hong Kong. The Nearest neighbour hierarchical (Nnh) and Standard Deviational Ellipse (SDE) techniques can offer additional insights about the circumstances and environmental factors that contribute to falls. The results affirm the multi-factorial nature of falls at specific locations and for selected groups of the population.

Conclusion

The techniques to detect hot spots of falls yield meaningful results that enable the identification of high risk locations. The combined use of descriptive and spatial analyses can be beneficial to policy makers because different preventive measures can be devised based on the types of environmental risk factors identified. The analyses are also important preludes to establishing research hypotheses for more focused studies.

Localized spatial clustering of HIV infections in a widely disseminated rural South African epidemic

BACKGROUND

South Africa contains more than one in seven of the world's HIV-positive population. Knowledge of local variation in levels of HIV infection is important for prioritization of areas for intervention. We apply two spatial analytical techniques to investigate the micro-geographical patterns and clustering of HIV infections in a high prevalence, rural population in KwaZulu-Natal, South Africa.

METHODS

All 12,221 participants who consented to an HIV test in a population under continuous demographical surveillance were linked to their homesteads and geo-located in a geographical information system (accuracy of <2 m). We then used a two-dimensional Gaussian kernel of radius 3 km to produce robust estimates of HIV prevalence that vary across continuous geographical space. We also applied a Kulldorff spatial scan statistic (Bernoulli model) to formally identify clusters of infections (P < 0.05).

RESULTS

The results reveal considerable geographical variation in local HIV prevalence (range = 6-36%) within this relatively homogenous population and provide clear empirical evidence for the localized clustering of HIV infections. Three high-risk, overlapping spatial clusters [Relative Risk (RR) = 1.34-1.62] were identified by the Kulldorff statistic along the National Road (P < or = 0.01), whereas three low risk clusters (RR = 0.2-0.38) were identified elsewhere in the study area (P < or = 0.017).

CONCLUSIONS

The findings show the existence of several localized HIV epidemics of varying intensity that are partly contained within geographically defined communities. Despite the overall high prevalence of HIV in many rural South African settings, the results support the need for interventions that target socio-geographic spaces (communities) at greatest risk to supplement measures aimed at the general population.

Using models to identify routes of nosocomial infection: a large hospital outbreak of SARS in Hong Kong

Two factors dominated the epidemiology of severe acute respiratory syndrome (SARS) during the 2002-2003 global outbreak, namely super-spreading events (SSE) and hospital infections. Although both factors were important during the first and the largest hospital outbreak in Hong Kong, the relative importance of different routes of infection has not yet been quantified. We estimated the parameters of a novel mathematical model of hospital infection using SARS episode data. These estimates described levels of transmission between the index super-spreader, staff and patients, and were used to compare three plausible hypotheses. The broadest of the supported hypotheses ascribes the initial surge in cases to a single super-spreading individual and suggests that the per capita risk of infection to patients increased approximately one month after the start of the outbreak. Our estimate for the number of cases caused by the SSE is substantially lower than the previously reported values, which were mostly based on self-reported exposure information. This discrepancy suggests that the early identification of the index case as a super-spreader might have led to biased contact tracing, resulting in too few cases being attributed to staff-to-staff transmission. We propose that in future outbreaks of SARS or other directly transmissible respiratory pathogens, simple mathematical models could be used to validate preliminary conclusions concerning the relative importance of different routes of transmission with important implications for infection control.

Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV)

Severe acute respiratory syndrome (SARS) is caused by a coronavirus (CoV), SARSCoV. SARS-CoV belongs to the family Coronaviridae, which are enveloped RNA viruses in the order Nidovirales. Global research efforts are continuing to increase the understanding of the virus, the pathogenesis of the disease it causes (SARS), and the “heterogeneity of individual infectiousness” as well as shedding light on how to prepare for other emerging viral diseases. Promising drugs and vaccines have been identified. The milestones achieved have resulted from a truly international effort. Molecular studies dissected the adaptation of this virus as it jumped from an intermediary animal, the civet, to humans, thus providing valuable insights into processes of molecular emergence.