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Emerging infectious diseases can present serious threats to wildlife, even to the point of causing extinction. Whitenose fungus (Pseudogymnoascus destructans) is causing an epizootic in bats that is expanding rapidly, both geographically and taxonomically. Little is known of the ecology and distributional potential of this intercontinental pathogen. We address this gap via ecological niche models that characterise coarse resolution niche differences between fungus populations on different continents, identifying areas potentially vulnerable to infection in South America. Here we explore a novel approach to identifying areas of potential distribution across novel geographic regions that avoids perilious extrapolation into novel environments. European and North American fungus populations show differential use of environmental space, but rather than niche differentiation, we find that changes are best attributed to climatic differences between the two continents. Suitable areas for spread of the pathogen were identified across southern South America; however caution should be taken to avoid underestimating the potential for spread of this pathogen in South America.

Hepatitis B is an important public health challenge facing China. Understanding the long-term epidemiological trends and evolving spatial distribution patterns is critical for optimizing prevention strategies and achieving the World Health Organization's 2030 hepatitis elimination targets. This study aimed to explore the epidemic trends and spatial distribution characteristics of hepatitis B in China from 2004 to 2020. This study used data on hepatitis B incidence from 2004 to 2020 from the China Public Health Science Data Center to analyze the time trend of hepatitis B incidence by joinpoint regression. The age-period-cohort model was used to analyze the age, period, and cohort effects of hepatitis B onset. Spatial autocorrelation analysis was used to explore the spatial distribution characteristics of hepatitis B in China. From 2004 to 2020, China reported a total of 17,449,842 cases of hepatitis B, with an average annual incidence rate of 76.30/100,000. The incidence of hepatitis B in China showed an increasing trend from 2004 to 2007, with an average annual percentage change (AAPC) of 9.49 (95% CI 2.12-17.39), and a decreasing trend from 2007 to 2014, with an AAPC of -3.77 (95% CI -5.93 to -1.55). The incidence of hepatitis B in China tended to be stable from 2014 to 2020, with an AAPC of -0.46 (95% CI -2.86 to 2.01). Age, period, and cohort effect significantly affect the incidence of hepatitis B. The age effect showed that the incidence rate of hepatitis B reached its peak at the age of 22 years, with an average incidence rate of 176.173/100,000. The period effect showed that the highest level during the study period occurred during 2004-2006. The cohort effect showed that the risk of hepatitis B increased first and then decreased, with the turning point of 1924-1974. The incidence of hepatitis B varies significantly among regions. The incidence in the northeast and northwest regions has decreased, that in the south and southwest regions has increased, and that in other regions has remained stable. China has achieved remarkable results in the prevention and control of hepatitis B, but there are still differences in the incidence rate among different age groups and regions. These results suggest the need to further strengthen the identification and screening of high-risk populations and promote supplementary adult hepatitis B vaccination. Future intervention strategies should fully consider regional differences, implement precise intervention measures based on the epidemic trends and spatial distribution characteristics of each region, optimize resource allocation, and enhance the overall effectiveness of hepatitis B prevention and control.

Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

Background Despite the biological plausibility of hotspots fueling malaria transmission, the evidence to support this concept has been mixed. If transmission spreads from high burden to low burden households in a consistent manner, then this could have important implications for control and elimination program development. Methods Data from a longitudinal cohort in The Gambia was analyzed. All consenting individuals residing in 12 villages across the country were sampled monthly from June (dry season) to December 2013 (wet season), in April 2014 (mid dry season), and monthly from June to December 2014. A study nurse stationed within each village recorded passively detected malaria episodes between visits. Plasmodium falciparum infections were determined by polymerase chain reaction and analyzed using a geostatistical model. Results Household-level observed monthly incidence ranged from 0 to 0.50 infection per person (interquartile range = 0.02–0.10) across the sampling months, and high burden households exist across all study villages. There was limited evidence of a spatio-temporal pattern at the monthly timescale irrespective of transmission intensity. Within-household transmission was the most plausible hypothesis examined to explain the observed heterogeneity in infections. Conclusions Within-village malaria transmission patterns are concentrated in a small proportion of high burden households, but patterns are stochastic regardless of endemicity. Our findings support the notion of transmission occurring at the household and village scales but not the use of a targeted approach to interrupt spreading of infections from high to low burden areas within villages in this setting.

The outbreak of SARS-CoV-2 in Wuhan, China in late December 2019 became the harbinger of the COVID-19 pandemic. During the pandemic, geospatial techniques, such as modeling and mapping, have helped in disease pattern detection. Here we provide a synthesis of the techniques and associated findings in relation to COVID-19 and its geographic, environmental, and socio-demographic characteristics, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology for scoping reviews. We searched PubMed for relevant articles and discussed the results separately for three categories: disease mapping, exposure mapping, and spatial epidemiological modeling. The majority of studies were ecological in nature and primarily carried out in China, Brazil, and the USA. The most common spatial methods used were clustering, hotspot analysis, space-time scan statistic, and regression modeling. Researchers used a wide range of spatial and statistical software to apply spatial analysis for the purpose of disease mapping, exposure mapping, and epidemiological modeling. Factors limiting the use of these spatial techniques were the unavailability and bias of COVID-19 data—along with scarcity of fine-scaled demographic, environmental, and socio-economic data—which restrained most of the researchers from exploring causal relationships of potential influencing factors of COVID-19. Our review identified geospatial analysis in COVID-19 research and highlighted current trends and research gaps. Since most of the studies found centered on Asia and the Americas, there is a need for more comparable spatial studies using geographically fine-scaled data in other areas of the world.

The purpose of this study was to evaluate the feasibility of using global positioning system (GPS) methods to understand the spatial context of obesity and hypertension risk among a sample of low-income housing residents in New York City (n = 120). GPS feasibility among participants was measured with a pre- and post-survey as well as adherence to a protocol which included returning the GPS device as well as objective data analysed from the GPS devices. We also conducted qualitative interviews with 21 of the participants. Most of the sample was overweight (26.7%) or obese (40.0%). Almost one-third (30.8%) was pre-hypertensive and 39.2% was hypertensive. Participants reported high ratings of GPS acceptability, ease of use and low levels of wear-related concerns in addition to few concerns related to safety, loss or appearance, which were maintained after the baseline GPS feasibility data collection. Results show that GPS feasibility increased over time. The overall GPS return rate was 95.6%. Out of the total of 114 participants with GPS, 112 (98.2%) delivered at least one hour of GPS data for one day and 84 (73.7%) delivered at least one hour on 7 or more days. The qualitative interviews indicated that overall, participants enjoyed wearing the GPS devices, that they were easy to use and charge and that they generally forgot about the GPS device when wearing it daily. Findings demonstrate that GPS devices may be used in spatial epidemiology research in low-income and potentially other key vulnerable populations to understand geospatial determinants of obesity, hypertension and other diseases that these populations disproportionately experience.

The municipal, spatial pattern of male stomach cancer mortality in Spain, spanning the period 1989-2008, was studied, comparing the results of depicting mortality using different expected-case computation methods in a spatial and spatio- temporal modelling context. Expected cases for each municipality were first calculated by two methods: (i) using reference rates for each 5-year period; and (ii) using average reference rates for the overall period. This was visualised by two types of models: (i) independent maps for each period based on the model proposed by Besag, York and Mollié; and (ii) a series of maps over time based on a model with spatio-temporal interaction terms. An additional model, based on mortality rate ratios as an alternative to the traditional use of standardised mortality ratios, was also fitted. Integrated nested Laplace approximations were used as the Bayesian inference tool. The results show that, in general, the geographical pattern was maintained across the study period, and that the maps differed appreciably according to the method used to obtain the expected number of cases. While the use of average reference rates appears to be the most suitable choice where the aim is to study time trends by area, it may nevertheless mask the spatial pattern in situations where the time trend is very marked and the study period is long. When it comes to studying changes in the spatial pattern of stomach cancer mortality, we feel that it is most useful to plot independent maps by period and use the \"local\" rates for each period as reference in the computation of expected cases.

Background Obesity rates are recognized to be at epidemic levels throughout much of the world, posing significant threats to both the health and financial security of many nations. The causes of obesity can vary but are often complex and multifactorial, and while many contributing factors can be targeted for intervention, an understanding of where these interventions are needed is necessary in order to implement effective policy. This has prompted an interest in incorporating spatial context into the analysis and modeling of obesity determinants, especially through the use of geographically weighted regression (GWR). Method This paper provides a critical review of previous GWR models of obesogenic processes and then presents a novel application of multiscale (M)GWR using the Phoenix metropolitan area as a case study. Results Though the MGWR model consumes more degrees of freedom than OLS, it consumes far fewer degrees of freedom than GWR, ultimately resulting in a more nuanced analysis that can incorporate spatial context but does not force every relationship to become local a priori . In addition, MGWR yields a lower AIC and AICc value than GWR and is also less prone to issues of multicollinearity. Consequently, MGWR is able to improve our understanding of the factors that influence obesity rates by providing determinant-specific spatial contexts. Conclusion The results show that a mix of global and local processes are able to best model obesity rates and that MGWR provides a richer yet more parsimonious quantitative representation of obesity rate determinants compared to both GWR and ordinary least squares.

Scrub typhus is transmitted by the larval stage of trombiculid mites. Environmental factors, including land cover and land use, are known to influence breeding and survival of trombiculid mites and, thus, also the spatial heterogeneity of scrub typhus risk. Here, a spatially autoregressive modelling framework was applied to scrub typhus incidence data from Taiwan, covering the period 2003 to 2011, to provide increased understanding of the spatial pattern of scrub typhus risk and the environmental and socioeconomic factors contributing to this pattern. A clear spatial pattern in scrub typhus incidence was observed within Taiwan, and incidence was found to be significantly correlated with several land cover classes, temperature, elevation, normalized difference vegetation index, rainfall, population density, average income and the proportion of the population that work in agriculture. The final multivariate regression model included statistically significant correlations between scrub typhus incidence and average income (negatively correlated), the proportion of land that contained mosaics of cropland and vegetation (positively correlated) and elevation (positively correlated). These results highlight the importance of land cover on scrub typhus incidence: mosaics of cropland and vegetation represent a transitional land cover type which can provide favourable habitats for rodents and, therefore, trombiculid mites. In Taiwan, these transitional land cover areas tend to occur in less populated and mountainous areas, following the frontier establishment and subsequent partial abandonment of agricultural cultivation, due to demographic and socioeconomic changes. Future land use policy decision-making should ensure that potential public health outcomes, such as modified risk of scrub typhus, are considered.

Background In 2015, the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreak’s expected geographic scale and prevalence of cases, and were therefore unable to plan and allocate surveillance resources in a timely and effective manner. Methods In this work, we present a dynamic neural network model to predict the geographic spread of outbreaks in real time. The modeling framework is flexible in three main dimensions (i) selection of the chosen risk indicator, i.e., case counts or incidence rate; (ii) risk classification scheme, which defines the high-risk group based on a relative or absolute threshold; and (iii) prediction forecast window (1 up to 12 weeks). The proposed model can be applied dynamically throughout the course of an outbreak to identify the regions expected to be at greatest risk in the future. Results The model is applied to the recent Zika epidemic in the Americas at a weekly temporal resolution and country spatial resolution, using epidemiological data, passenger air travel volumes, and vector habitat suitability, socioeconomic, and population data for all affected countries and territories in the Americas. The model performance is quantitatively evaluated based on the predictive accuracy of the model. We show that the model can accurately predict the geographic expansion of Zika in the Americas with the overall average accuracy remaining above 85% even for prediction windows of up to 12 weeks. Conclusions Sensitivity analysis illustrated the model performance to be robust across a range of features. Critically, the model performed consistently well at various stages throughout the course of the outbreak, indicating its potential value at any time during an epidemic. The predictive capability was superior for shorter forecast windows and geographically isolated locations that are predominantly connected via air travel. The highly flexible nature of the proposed modeling framework enables policy makers to develop and plan vector control programs and case surveillance strategies which can be tailored to a range of objectives and resource constraints.