Spatial co-location patterns between early COVID-19 risk and urban facilities: a case study of Wuhan, China

Introduction

COVID-19, being a new type of infectious disease, holds significant implications for scientific prevention and control to understand its spatiotemporal transmission process. This study examines the diverse spatial patterns of COVID-19 within Wuhan by analyzing early case data alongside urban infrastructure information.

Methods

Through co-location analysis, we assess both local and global spatial risks linked to the epidemic. In addition, we use the Geodetector, identifying facilities displaying unique spatial risk characteristics, revealing factors contributing to heightened risk.

Results

Our findings unveil a noticeable spatial distribution of COVID-19 in the city, notably influenced by road networks and functional zones. Higher risk levels are observed in the central city compared to its outskirts. Specific facilities such as parking, residence, ATM, bank, entertainment, and hospital consistently exhibit connections with COVID-19 case sites. Conversely, facilities like subway station, dessert restaurant, and movie theater display a stronger association with case sites as distance increases, hinting at their potential as outbreak focal points.

Discussion

Despite our success in containing the recent COVID-19 outbreak, uncertainties persist regarding its origin and initial spread. Some experts caution that with increased human activity, similar outbreaks might become more frequent. This research provides a comprehensive analytical framework centered on urban facilities, contributing quantitatively to understanding their impact on the spatial risks linked with COVID-19 outbreaks. It enriches our understanding of the interconnectedness between urban facility distribution and transportation flow, affirming and refining the distance decay law governing infectious disease risks. Furthermore, the study offers practical guidance for post-epidemic urban planning, promoting the development of safer urban environments resilient to epidemics. It equips government bodies with a reliable quantitative analysis method for more accurately predicting and assessing infectious disease risks. In conclusion, this study furnishes both theoretical and empirical support for tailoring distinct strategies to prevent and control COVID-19 epidemics.

Mapping the risk distribution of Borrelia burgdorferi sensu lato in China from 1986 to 2020: a geospatial modelling analysis

Lyme borreliosis, recognized as one of the most important tick-borne diseases worldwide, has been increasing in incidence and spatial extent. Currently, there are few geographic studies about the distribution of Lyme borreliosis risk across China. Here we established a nationwide database that involved Borrelia burgdorferi sensu lato (B. burgdorferi) detected in humans, vectors, and animals in China. The eco-environmental factors that shaped the spatial pattern of B. burgdorferi were identified by using a two-stage boosted regression tree model and the model-predicted risks were mapped. During 1986−2020, a total of 2,584 human confirmed cases were reported in 25 provinces. Borrelia burgdorferi was detected from 35 tick species with the highest positive rates in Ixodes granulatus, Hyalomma asiaticum, Ixodes persulcatus, and Haemaphysalis concinna ranging 20.1%−24.0%. Thirteen factors including woodland, NDVI, rainfed cropland, and livestock density were determined as important drivers for the probability of B. burgdorferi occurrence based on the stage 1 model. The stage 2 model identified ten factors including temperature seasonality, NDVI, and grasslands that were the main determinants used to distinguish areas at high or low-medium risk of B. burgdorferi, interpreted as potential occurrence areas within the area projected by the stage 1 model. The projected high-risk areas were not only concentrated in high latitude areas, but also were distributed in middle and low latitude areas. These high-resolution evidence-based risk maps of B. burgdorferi was first created in China and can help as a guide to future surveillance and control and help inform disease burden and infection risk estimates.

Assessment of the spatiotemporal risk of avian influenza between waterfowl and poultry farms during the annual cycle: A spatial prediction study focused on seasonal distribution changes in resident waterfowl in South Korea

Previous studies and efforts to prevent and manage avian influenza (AI) outbreaks have mainly focused on the wintering season. However, outbreaks of AI have been reported in the summer, including the breeding season of waterfowl. Additionally, the spatial distribution of waterfowl can easily change during the annual cycle due to their life-cycle traits and the presence of both migrants and residents in the population. Thus, we assessed the spatiotemporal variation in AI exposure risk in poultry due to spatial distribution changes in three duck species included in both major residents and wintering migrants in South Korea, the mandarin, mallard and spot-billed duck, during wintering (October-March), breeding (April-June) and whole annual seasons. To estimate seasonal ecological niche variations among the three duck species, we applied pairwise ecological niche analysis using the Pianka index. Subsequently, seasonal distribution models were projected by overlaying the monthly ranges estimated by the maximum entropy model. Finally, we overlaid each seasonal distribution range onto a poultry distribution map of South Korea. We found that the mandarin had less niche overlap with the mallard and spot-billed duck during the wintering season than during the breeding season, whereas the mallard had less niche overlap with the mandarin and spot-billed duck during the breeding season than during the wintering season. Breeding and annual distribution ranges of the mandarin and spot-billed duck, but not the mallard, were similar or even wider than their wintering ranges. Similarly, the mandarin and spot-billed duck showed more extensive overlap proportions between poultry and their distributional ranges during both the breeding and annual seasons than during the wintering season. These results suggest that potential AI exposure in poultry can occur more widely in the summer than in winter, depending on sympatry with the host duck species. Future studies considering the population density and variable pathogenicity of AI are required.

Modeling Antimicrobial Prescriptions in Scotland: A Spatiotemporal Clustering Approach

In 2016, the British government acknowledged the importance of reducing antimicrobial prescriptions to avoid the long-term harmful effects of overprescription. Prescription needs are highly dependent on the factors that have a spatiotemporal component, such as bacterial outbreaks and urban densities. In this context, density-based clustering algorithms are flexible tools to analyze data by searching for group structures and therefore identifying peer groups of GPs with similar behavior. The case of Scotland presents an additional challenge due to the diversity of population densities under the area of study. We propose here a spatiotemporal clustering approach for modeling the behavior of antimicrobial prescriptions in Scotland. Particularly, we consider the density-based spatial clustering of applications with noise algorithm (DBSCAN) due to its ability to include both spatial and temporal data. We extend this approach into two directions. For the temporal analysis, we use dynamic time warping to measure the dissimilarity between time series while taking into account effects such as seasonality. For the spatial component, we propose a new way of weighting spatial distances with continuous weights derived from a Kernel density estimation-based process. This makes our approach suitable for cases with different local densities, which presents a well-known challenge for the original DBSCAN. We apply our approach to antibiotic prescription data in Scotland, demonstrating how the findings can be used to compare antimicrobial prescription behavior within a group of similar peers and detect regions of extreme behaviors.

Spatial risk distribution and determinants of E. coli contamination in household drinking water: a case study of Bangladesh

The Escherichia coli (E. coli) contamination in the household (HH) drinking water is often a public health concern. Very few studies explore the associated factors and spatial risk modeling together for E. coli contamination in Bangladesh, this research gap motivates to explore this fact further by utilizing Bangladesh Multiple Indicator Cluster Survey (MICS) 2012-13 data. A Bayesian spatial ordered logit model was used to examine the associated factors and spatial risks of the E. coli contamination. The results show that 62% of HH water samples were contaminated with E. coli. After controlling for different factors, a high level of E. coli contamination was observed among HHs who had access to non-improved water sources. Moreover, no significant rural-urban difference was observed. The spatial prediction of the high-risk contamination was prominent in districts like Dhaka and Bandarban. The study findings can provide insights into the planning of policy activities in Bangladesh.

Risk factors and spatial relative risk assessment for influenza A virus in poultry and swine in backyard production systems of central Chile

Backyard production systems (BPS) are a common form of poultry and swine production worldwide. The limited implementation of biosecurity standards in these operations makes BPS a potential source for the emergence of pathogens that have an impact on both animal and public health. Information regarding circulation of influenza A virus (IAV) in poultry and swine raised in BPS is scarce; particularly in South American countries. The objective of this study was to estimate prevalence and seroprevalence of IAV in BPS in central Chile, identify subtype diversity, evaluate risk factors and spatial relative risk for IAV. Samples were collected from 329 BPS from central Chile. Seroprevalence at BPS level was 34.7% (95% CI: 23.1%–46.2%), 19.7% (95% CI: 9.9%–30.6%) and 11.7% (95% CI: 7.2%–16.4%), whereas prevalence at BPS level was 4.2% (95% CI: 0.0%–8.8%), 8.2% (95% CI: 0.8%–14.0%) and 9.2% (95% CI: 4.8%–13.1%), for the Metropolitan, Valparaiso and LGB O’Higgins regions, respectively. Spatial analysis revealed that central‐western area of Metropolitan region and the southern province of Valparaiso region could be considered as high‐risk areas for IAV (spatial relative risk = 2.2, p < .05). Logistic regression models identified the practice of breeding both poultry and pigs at the BPS as a risk factor (95% CI 1.06–3.75). From 75 IAV ELISA‐positive sera, 20 chicken sera had haemagglutination inhibition titres ranging from 20 to 160, and of these, 11 had microneutralization titres ranging from 40 to 960 for one or more IAV subtypes. Identified subtypes were H1, H3, H4, H9, H10 and H12. Results from this study highlight the need for further IAV surveillance programmes in BPS in Chile. Early detection of IAV strains circulating in backyard animals, especially in regions with large human populations, could have an enormous impact on animal and public health.

Targeting at risk households in Bangladesh exposed to arsenic contamination: a spatial analysis

This study seeks to identify the spatial risk pattern of households (HHs) exposed to arsenic contamination in Bangladesh by adjusting potential socio-economic, demographic factors. Data from Bangladesh Multiple Indicator Cluster Survey 2012-13 are used where hierarchical Bayesian spatial ordered logit model is implemented. The analysis shows that 25% of HH water samples were arsenic contaminated, although the majority (95%) of HHs used improved water sources. Arsenic contamination risk in the HH water was significantly associated with water source type and location, place of residence and districts. The model-based spatial prediction reveals that the north-east and south-west parts of Bangladesh have a high risk of contamination. To ensure the quality of HH water, our findings suggest that chemical test should be promoted considering the spatial risk of arsenic contaminations variations among HHs of Bangladesh. Furthermore, the study findings can effectively contribute in the planning of future interventions and programs.

Spatial Risk Analysis of Power Systems Resilience During Extreme Events

The increased frequency of extreme events in recent years highlights the emerging need for the development of methods that could contribute to the mitigation of the impact of such events on critical infrastructures, as well as boost their resilience against them. This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events. A Severity Risk Index (SRI) with the support of real-time monitoring assesses the impact of the extreme events on the power system resilience, with application to the effect of windstorms on transmission networks. The index considers the spatial and temporal evolution of the extreme event, system operating conditions, and the degraded system performance during the event. SRI is based on probabilistic risk by condensing the probability and impact of possible failure scenarios while the event is spatially moving across a power system. Due to the large number of possible failures during an extreme event, a scenario generation and reduction algorithm is applied in order to reduce the computation time. SRI provides the operator with a probabilistic assessment that could lead to effective resilience-based decisions for risk mitigation. The IEEE 24-bus Reliability Test System has been used to demonstrate the effectiveness of the proposed online risk analysis, which was embedded in a sequential Monte Carlo simulation for capturing the spatiotemporal effects of extreme events and evaluating the effectiveness of the proposed method.

An Integrated Framework for Environmental Multi-Impact Spatial Risk Analysis

Quantitative risk analysis is being extensively employed to support policymakers and provides a strong conceptual framework for evaluating decision alternatives under uncertainty. Many problems involving environmental risks are, however, of a spatial nature, i.e., containing spatial impacts, spatial vulnerabilities, and spatial risk-mitigation alternatives. Recent developments in multicriteria spatial analysis have enabled the assessment and aggregation of multiple impacts, supporting policymakers in spatial evaluation problems. However, recent attempts to conduct spatial multicriteria risk analysis have generally been weakly conceptualized, without adequate roots in quantitative risk analysis. Moreover, assessments of spatial risk often neglect the multidimensional nature of spatial impacts (e.g., social, economic, human) that are typically occurring in such decision problems. The aim of this article is therefore to suggest a conceptual quantitative framework for environmental multicriteria spatial risk analysis based on expected multi-attribute utility theory. The framework proposes: (i) the formal assessment of multiple spatial impacts; (ii) the aggregation of these multiple spatial impacts; (iii) the assessment of spatial vulnerabilities and probabilities of occurrence of adverse events; (iv) the computation of spatial risks; (v) the assessment of spatial risk mitigation alternatives; and (vi) the design and comparison of spatial risk mitigation alternatives (e.g., reductions of vulnerabilities and/or impacts). We illustrate the use of the framework in practice with a case study based on a flood-prone area in northern Italy.

Spatial variation in hyperthermia emergency department visits among those with employer-based insurance in the United States - a case-crossover analysis

BACKGROUND

Predictions of intense heat waves across the United States will lead to localized health impacts, most of which are preventable. There is a need to better understand the spatial variation in the morbidity impacts associated with extreme heat across the country to prevent such adverse health outcomes.

METHODS

Hyperthermia-related emergency department (ED) visits were obtained from the Truven Health MarketScan(®) Research dataset for 2000-2010. Three measures of daily ambient heat were constructed using meteorological observations from the National Climatic Data Center (maximum temperature, heat index) and the Spatial Synoptic Classification. Using a time-stratified case crossover approach, odds ratio of hyperthermia-related ED visit were estimated for the three different heat measures. Random effects meta-analysis was used to combine the odds ratios for 94 Metropolitan Statistical Areas (MSA) to examine the spatial variation by eight latitude categories and nine U.S. climate regions.

RESULTS

Examination of lags for all three temperature measures showed that the odds ratio of ED visit was statistically significant and highest on the day of the ED visit. For heat waves lasting two or more days, additional statistically significant association was observed when heat index and synoptic classification was used as the temperature measure. These results were insensitive to the inclusion of air pollution measures. On average, the maximum temperature on the day of an ED visit was 93.4°F in 'South' and 81.9°F in the 'Northwest' climatic regions of United States. The meta-analysis showed higher odds ratios of hyperthermia ED visit in the central and the northern parts of the country compared to the south and southwest.

CONCLUSION

The results showed spatial variation in average temperature on days of ED visit and odds ratio for hyperthermia ED visits associated with extreme heat across United States. This suggests that heat response plans need to be customized for different regions and the potential role of hyperthermia ED visits in syndromic surveillance for extreme heat.

Identifying Areas Suitable for the Occurrence of Rift Valley Fever in North Africa: Implications for Surveillance

Rift Valley fever (RVF) is a vector-borne zoonotic disease that has caused widespread outbreaks throughout Africa and the Arabian Peninsula, with serious consequences for livestock-based economies and public health. Although there have never been any reports of RVF in Morocco, Algeria, Tunisia and Libya, it is a priority disease in the Maghreb, due to the threat of introduction of the virus through transboundary livestock movements or infected mosquito vectors. However, the implementation of surveillance activities and early warning contingency plans requires better knowledge of the epidemiological situation. We conducted a multicriteria decision analysis, integrating host distribution with a combination of important ecological factors that drive mosquito abundance, to identify hotspots and suitable time periods for RVF enzootic circulation (i.e. stable transmission at a low to moderate level for an extended period of time) and an RVF epizootic event (i.e. a sudden occurrence of a large number of infected animals over a large geographic area) in the Maghreb. We also modelled vector species distribution using available information on vector presence and habitat preference. We found that the northern regions of the Maghreb were moderately suitable for RVF enzootics, but highly suitable for RVF epizootics. The vector species distribution model identified these regions as the most favourable mosquito habitats. Due to the low density of animal hosts and arid conditions, the desert region showed low RVF suitability, except in oases. However, the presence of competent vectors in putative unsuitable areas underlines the need for further assessments of mosquito habitat preference. This study produced monthly RVF suitability maps useful for animal health managers and veterinary services involved in designing risk-based surveillance programmes. The suitability maps can be further enhanced using existing country-specific sources of information and by incorporating knowledge - as it becomes available - on the epidemiology of the disease and distribution of vectors in the Maghreb.

Dying in their prime: determinants and space-time risk of adult mortality in rural South Africa

A longitudinal dataset was used to investigate adult mortality in rural South Africa in order to determine location, trends, high impact determinants and policy implications. Adult (15-59 years) mortality data for the period 1993-2010 were extracted from the health and demographic surveillance system in the rural sub-district of Agincourt. A Bayesian geostatistical frailty survival model was used to quantify significant associations between adult mortality and various multilevel (individual, household and community) variables. It was found that adult mortality significantly increased over time with a reduction observed late in the study period. Non-communicable disease mortality appeared to increase and decrease in parallel with communicable mortality, whilst deaths due to external causes remained constant. Male gender, unemployment, circular (labour) migrant status, age and gender of household heads, partner and/or other household death, low education and low household socio-economic status were identified as significant and highly attributable determinants of adult mortality. Health facility remoteness was a risk for adult mortality and households falling outside a critical buffering zone were identified. Spatial foci of higher adult mortality risk were observed, indicating a strong non-random pattern. Communicable diseases differed from non-communicable diseases with respect to spatial distribution of mortality. Areas with significant excess mortality risk (hot spots) were found to be part of a complex interaction of highly attributable factors that continues to drive differential space-time risk patterns of communicable (HIV/AIDS and tuberculosis) mortality in Agincourt. The impact of HIV mortality and its subsequent lowering due to the introduction of antiretroviral therapy was found to be clearly evident in this rural population.