Spatial Pattern Of Risk Research Articles

Deciphering the Hantavirus Host Range Combining Virology and Species Distribution Models with an Emphasis on the Yellow Pygmy Rice Rat (Oligoryzomys flavescens)

Hantaviruses are the causative agents of hantavirus pulmonary syndrome (HPS) in the Americas. In Central and South America, 28 hantavirus lineages were associated with different Sigmodontinae rodents. Of these, Lechiguanas hantavirus was initially described as a lineage associated with HPS cases in the central region of Argentina. Initial studies on the rodent hosts and viral lineages performed between 1999 and 2005 showed that HPS cases in Uruguay were distributed mostly in the southern region of the country, and that the Lechiguanas hantavirus (LECV) and the closely related Andes Central Plata hantaviruses were the viral lineages most frequently associated with HPS cases, both carried by the yellow pygmy rice rat (Oligoryzomys flavescens). Although these rodents are present all across the Uruguayan territory, determining the extent of the risk areas for hantavirus transmission based on the distribution of the infected rodents may be a useful tool for disease control and prevention. Distribution models are positioned as an effective instrument in the prediction of diseases affecting human health. Assessment of the potential distribution of rodent reservoir hosts and analysis of the influence of environmental factors on hantavirus transmission can help to understand the spatial patterns of disease transmission risk. In the present study, virological studies and species distribution models were integrated to understand the hantavirus infection risk pattern in Uruguay. Virological analyses confirmed that in Uruguay, the primary hantavirus reservoir host for both viral lineages is the yellow pygmy rice rat. Additionally, we report an Azara’s grass mouse (Akodon azarae) infected with the Andes Central Plata viral lineage. Based on the seropositive and nonseropositive yellow pygmy rice rats tested, the distribution models emphasized that favorable environmental conditions for the infected rodents are mainly related to the availability of human-disturbed rural environments with high humidity. We conclude that the innovative application of the methodologies reported herein allowed for the assessment of the current risk territory for HPS in Uruguay.

Examining heat inequity in a Brazilian metropolitan region

Urban heat islands (UHIs) are one of the major global issues that need to be addressed because of the negative effects that higher temperatures can cause to people and the environment, such as health issues and higher energy consumption. Within the literature on climate justice, specifically heat inequity, there are very few studies about Global South urban areas. Our study examines the spatial relationships between heat risk, urban form composition, and vulnerable social groups in Belo Horizonte Metropolitan Region (BHMR), in Brazil. We evaluated the spatial pattern of heat risk and concluded that the study area was experiencing UHIs in 2015. We estimated spatial regressions and found that the non-White population, low-income residents, and the elderly population were statistically significantly associated with heat risk. This case study indicates that even though Global South urban areas have the opposite spatial distribution of social groups (i.e., high-income residents living in the center and low-income living in the periphery) when compared to the Global North, areas where vulnerable social groups reside are experiencing similar inequities concerning the UHI effects in both South and North. Our case study exemplifies that climate justice is not taking place in BHMR, and specifically, heat inequity is being experienced by vulnerable social groups.

Dynamic evaluation of cropland degradation risk by combining multi-temporal remote sensing and geographical data in the Black Soil Region of Jilin Province, China

Cropland degradation has attracted increasing attention because the important production functions of cropland and its sensitivity to climate change. Based on multi-temporal remote sensing and geographical data, this study developed a comprehensive index of cropland degradation risk (CDRI) using the improved k-means method and applied it to the Black Soil Region of Jilin Province, China, to analyze the changes in cropland degradation risk from 2005 to 2020. The average cropland degradation risk in 2020 was generally higher than that in 2005. Croplands with changes in degradation risk types accounted for 14.27% of the total cropland area, of which croplands showing an increase and decrease in degradation risk accounted for 8.47% and 5.80% of total, respectively. From 2005 to 2020, areas of croplands with a low degradation risk decreased, but those of croplands with a medium degradation risk increased significantly. The synthetic image of bare soil, temperature, precipitation, and soil nutrients contributed greatly to the CDRI. Degradation risk of croplands distributed in black soil, chernozem, and meadow soil fluctuated significantly. The proposed CDRI method comprehensively considers the degree and spatial pattern of cropland degradation risk and has good prospects for the rapid detection of cropland degradation on a large scale.

Assessment of ecological resilience in Nanchang based on "risk-connectivity-potential".

With the rapid development of urban construction, land for construction continues to expand and the ecological land is shrinking. There are various risks and degradation phenomena. The evaluation of urban ecological resilience thus becomes particularly important. From the perspective of urbanization-induced landscape changes, we evaluated the ecological resilience level of Nanchang from 2005 to 2020 by means of "risk-connectivity-potential" model, spatial autocorrelation model and kernel density assessment, as well as its spatial-temporal variations. The results showed that from 2005 to 2020, the spatial pattern of risk, connectivity, and potential subsystems in the study area was stable, with significant differences. The ecological risk was high in the middle and low in the east and west, while the spatial distribution of connectivity and ecological potential was opposite to the ecological risk, showing a pattern of low in the middle and high in the east and west. The overall level of ecological resilience was not high, with medium and low resilience levels as the main body. The low level continued to grow, and high value areas gradually decreased. The "high-high" cluster type of ecological resilience was decreasing, while the "low-low" cluster type was gradually increasing. Results of Kernel density assessment showed that the overall resilience level of Nanchang had little change, with obvious spatial difference. The number and distribution of different landscape types should be adjusted according to the characteristics of the ecological resilience level of different regions, in combination with the urban planning layout. The connectivity and agglomeration of spatial distribution of different landscape types should be changed to achieve the optimization and improvement of the overall regional ecological resilience level. The results could provide a theoretical basis for urban spatial planning and layout.

Changes in wildfire occurrence and risk to homes from 1990 through 2019 in the Southern Rocky Mountains,USA

AbstractWildfires and housing development have increased since the 1990s, presenting unique challenges for wildfire management. However, it is unclear how the relative influences of housing growth and changing wildfire occurrence have altered risk to homes, or the potential for wildfire to threaten homes. We used a random forests model to predict burn probability in relation to weather variables at 1‐km resolution and monthly intervals from 1990 through 2019 in the Southern Rocky Mountains ecoregion. We quantified risk by combining the predicted burn probabilities with decadal housing density. We then compared the predicted burn probabilities and risk across the study area with observed values and quantified trends. Finally, we evaluated how housing growth and changes in burn probability influenced risk individually and combined. Fires burned 9055 km2and exposed more than 8500 homes from 1990 to 2019. Observed burned area increased 632% from the 1990s to the 2000s, which combined with housing growth, resulted in a 1342% increase in homes exposed. Increases continued in the 2010s but at lower rates; burned area by 65% and exposure by 32%. The random forests model had excellent fit and high correlation with observations (AUC = 0.88 andr = 0.9). Observed values were within the 95% uncertainty interval for all years except 2016 (burned area) and 2000 (exposure). However, our model overpredicted in years with low observed burned area and underpredicted in years with high observed burned area. Overpredictions in risk resulted in lower rates of change in predicted risk compared with change in observed exposure. Increases in risk between the 1990s and 2000s were primarily due to warmer and drier weather conditions and secondarily because of housing growth. However, increases between the 2000s and 2010s were primarily due to housing growth. Our modeling approach identifies spatial and temporal patterns of wildfire potential and risk, which is critical information to guide decision‐making. Because the drivers behind risk shift over time, strategies to mitigate risk may need to account for multiple drivers simultaneously.

Landscape Ecological Risk Assessment during 2000–2020 Based on Land Use and Land Cover Changes in Lanzhou City

A landscape ecological risk model was constructed to evaluate the landscape ecological risk pertaining to Lanzhou City based on the land use and land cover (LULC) changes obtained using remote sensing data for the years 2000, 2010, and 2020 covering the duration 2010–2020. Results show that during 2010–2020, the LULC types in Lanzhou City mainly comprised of cropland, grassland, and construction land. In terms of LULC changes in Lanzhou City the trend shows a decrease in cropland and increase in construction land with an increase in the fragmentation and separation of cropland, grassland, and shrubland, which is pronounced for cropland and shrubland. The spatial distribution pattern of ecological risk shows very high and very low levels of risk in the main urban area in the south and in the northwest of the city, respectively. From 2010 to 2020, the ecological risk level of the landscape shows a gradual increase with evidence for significant changes. The results of the study provide a scientific basis for the optimization of the landscape pattern, rational utilization and development of land resources in Lanzhou City, and lay a solid foundation for the formulation of ecological risk control measures.

도시쇠퇴 유형별 재난⋅재해 위험의 공간분포 패턴 분석

This study aims to compare the difference in the spatial pattern of the Risk Diagnosis Index(RDI) and the Comprehensive Diagnosis Index(CDI) by type of urban decline. First, CDI decreases the risk of disasters as it comes close to the urbanized area in all case areas. Various strategies are needed to increase the reduction capacity, focusing on areas with a high risk of disasters. Second, there is a difference in the spatial pattern of declined and non-declined areas in the city, and this trend is different for the urban type. Seosan has relatively little influence in the reduction capacity sector, while Daegu has a high influence in the reduction capacity sector, which affects the spatial pattern of CDI. It implies CDI by urban size and regional attributes should be differentiated. Third, both RDI and CDI tend to be spatially clustered. The spatial clustering pattern of disaster risk in the declined areas is higher than that of other areas. Although the pattern of disaster risk is high in declined areas, this varies depending on urban size and declined type.

Spatial and temporal patterns of supply and demand risk for ecosystem services in the Weihe River Main Stream, NW China.

The rapid development of society and economy in the post-industrial era has exacerbated the spatial matching contradiction between the demand of humans and the supply of the natural environment, while ecosystem service (ES) as a bridge linking nature and society, identifying and assessing its supply-demand risk, was beneficial to ecosystem management and promoted regional high-quality development. Based on the data of multi-source remote sensing and statistics, the supply and demand levels of four ESs, which contain food supply, carbon storage, soil conservation, and water yield in the main stem of the Weihe River in 2000, 2010, and 2020, were quantitatively measured. The spatial and temporal analysis of the supply, demand, and supply-demand ratio of each service was carried out using spatial mapping. The spatio-temporal pattern of the supply-demand risk was recognized by the method of spatial overlay, which means overlaying the supply and demand for material quality, ratio, trend, and the degree of trade-off coordination together between each service. The results showed that (1) the demand for water yield decreased slightly while the demand for food and the supply of carbon storage remained stable. In addition, the supply and demand of other services showed an upward trend. (2) The spatial distribution of the supply-demand ratio of each service shows "high in the south and low in the north" and "high in the east and low in the west," among which the supply-demand ratio of carbon storage is decreasing. (3) The overall supply-demand risk of soil conservation in the study area is low with characteristics of small range and high degree, the risk distribution characteristics of the other services are high in the east and low in the west, and the risk is high in the city center and low around. Otherwise, the supply-demand risk of food supply showed a downward trend, the risk of carbon storage showed an upward trend, the risk of soil conservation remained stable, and the risk of water yield showed a significant downward trend. Based on static supply-demand risk identification, this study assesses supply-demand risk over two periods and analyzes the trend of supply-demand risk changes over time. It clarifies the extent and direction of supply-demand risk shifts, as well as provides improved theoretical support for ecosystem service management.

Malaria transmission in Nepal under climate change: anticipated shifts in extent and season, and comparison with risk definitions for intervention

BackgroundClimate and climate change affect the spatial pattern and seasonality of malaria risk. Season lengths and spatial extents of mapped current and future malaria transmission suitability predictions for Nepal were assessed for a combination of malaria vector and parasites: Anopheles stephensi and Plasmodium falciparum (ASPF) and An. stephensi and Plasmodium vivax (ASPV) and compared with observed estimates of malaria risk in Nepal.MethodsThermal bounds of malaria transmission suitability for baseline (1960–1990) and future climate projections (RCP 4.5 and RCP 8.5 in 2030 and 2050) were extracted from global climate models and mapped for Nepal. Season length and spatial extent of suitability between baseline and future climate scenarios for ASPF and ASPV were compared using the Warren’s I metric. Official 2010 DoHS risk districts (DRDs) and 2021 DoHS risk wards (DRWs), and spatiotemporal incidence trend clusters (ITCs) were overlaid on suitability season length and extent maps to assess agreement, and potential mismatches.ResultsShifts in season length and extent of malaria transmission suitability in Nepal are anticipated under both RCP 4.5 and RCP 8.5 scenarios in 2030 and 2050, compared to baseline climate. The changes are broadly consistent across both future climate scenarios for ASPF and ASPV. There will be emergence of suitability and increasing length of season for both ASPF and ASPV and decreasing length of season for ASPV by 2050. The emergence of suitability will occur in low and no-risk DRDs and outside of high and moderate-risk DRWs, season length increase will occur across all DRD categories, and outside of high and moderate-risk DRWs. The high and moderate risk DRWs of 2021 fall into ITCs with decreasing trend.ConclusionsThe study identified areas of Nepal where malaria transmission suitability will emerge, disappear, increase, and decrease in the future. However, most of these areas are anticipated outside of the government’s current and previously designated high and moderate-risk areas, and thus outside the focus of vector control interventions. Public health officials could use these anticipated changing areas of malaria risk to inform vector control interventions for eliminating malaria from the country, and to prevent malaria resurgence.

RETRACTED: Exploring and forecasting spatial and temporal patterns of fire hazard risk in Nepal's tiger conservation zones

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/locate/withdrawalpolicy).This article has been retracted at the request of the Principal Editor. After a thorough investigation, the Editor has concluded that the review process for this article was compromised. The acceptance was based on information from at least two reviewer reports that were submitted from email accounts provided to the journal as suggested reviewers during the submission of the article. Although purportedly real reviewer accounts, the Editor has concluded that these were not of appropriate, independent reviewers.This manipulation of the peer-review process represents a clear violation of the fundamentals of peer review, our publishing policies, and publishing ethics standards. Apologies are offered to the reviewers whose identities were assumed and to the readers of the journal that this deception was not detected during the submission process.

Environmental and anthropogenic features mediate risk from human hunters and wolves for moose

AbstractLandscape characteristics, seasonal changes in the environment, and daylight conditions influence space use and detection of prey and predators, resulting in spatiotemporal patterns of predation risk for the prey. When predators have different hunting modes, the combined effects of multiple predators are mediated by the physical landscape and can result in overlapping or contrasting patterns of predation risk. Humans have become super‐predators in many anthropogenic landscapes by harvesting game species and competing with large carnivores for prey. Here, we used the locations of wolf (Canis lupus)‐killed and hunter‐killed moose (Alces alces) in south‐central Scandinavia to investigate whether environmental and anthropogenic features influenced where wolves and hunters killed moose. We predicted that the combined effects of wolves and hunters would result in contrasting spatial risk patterns due to differences in hunting modes. We expected these contrasting spatial risk patterns also to differ temporally. During the hunting season, the probability of a wolf kill increased with distance to bogs, whereas it decreased with increasing building density and distance to clearcuts and young forests. After the hunting season, the probability of a wolf kill increased with increasing terrain ruggedness and decreased with increasing building density, distance to main roads, and distance to clearcuts and young forests. The probability of a hunter kill was highest closer to bogs, main and secondary roads, in less rugged terrain and in areas with lower building density. Hunters killed all moose during the day, whereas wolves killed most moose at night during and after the hunting season. Our findings suggest that environmental and anthropogenic features mediate hunting and wolf predation risk. Additionally, we found that hunter‐ and wolf‐killed moose exhibited contrasting spatial associations to landscape features, most likely due to the different hunting modes displayed by hunters and wolves. However, wolf predation and hunting risks also contrasted over time since wolves killed mostly at night and hunters were restricted to hunting during daytime and during the hunting season. This temporal segregation in risk might therefore suggest that moose could minimize risk exposure by taking advantage of spatiotemporally vacant hunting domains.

A systems approach framework for evaluating tree restoration interventions for social and ecological outcomes in rural tropical landscapes.

The science guiding design and evaluation of restoration interventions in tropical landscapes is dominated by ecological processes and outcomes and lacks indicators and methods that integrate human wellbeing into the restoration process. We apply a new systems approach framework for tree restoration in forest-agricultural landscapes to show how this shortcoming can be addressed. Demonstrating 'proof of concept', we tested statistical models underlying the framework pathways with data collected from a case study in Tanzania. Local community perceptions of nature's values were not affected by levels of self-reported wildlife-induced crop damage. But mapped predictions from the systems approach under a tree restoration scenario suggested differential outcomes for biodiversity indicators and altered spatial patterns of crop damage risk, expected to jeopardize human wellbeing. The predictions map anticipated trade-offs in costs and benefits of restoration scenarios, which we have started to explore with stakeholders to identify restoration opportunities that consider local knowledge, value systems and human wellbeing. We suggest that the framework be applied to other landscapes to identify commonalities and differences in forest landscape restoration outcomes under varying governance and land use systems. This should form a foundation for evidence-based implementation of the global drive for forest landscape restoration, at local scales. This article is part of the theme issue 'Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration'.

Spatial disparities of HIV prevalence in South Africa. Do sociodemographic, behavioral, and biological factors explain this spatial variability?

In 2021, an estimated 38 million people were living with human immunodeficiency virus (HIV) globally, with over two-thirds living in African regions. In South Africa, ~20% of South African adults are living with HIV. Accurate estimation of the risk factors and spatial patterns of HIV risk using individual-level data from a nationally representative sample is invaluable for designing geographically targeted intervention and control programs. Data were obtained from the 2016 South Africa Demographic and Health Survey (SDHS16). The study involved all men and women aged 15 years and older, who responded to questions and tested for HIV in the SDHS. Generalized additive models (GAMs) were fitted to our data with a nonparametric bivariate smooth term of spatial location parameters (X and Y coordinates). The GAMs were used to assess the spatial disparities and the potential contribution of sociodemographic, biological, and behavioral factors to the spatial patterns of HIV prevalence in South Africa. A significantly highest risk of HIV was observed in east coast, central and north-eastern regions. South African men and women who are widowed and divorced had higher odds of HIV as compared to their counterparts. Additionally, men and women who are unemployed had higher odds of HIV as compared to the employed. Surprisingly, the odds of HIV infection among men residing in rural areas were 1.60 times higher (AOR 1.60, 95% CI 1.12, 2.29) as compared to those in urban areas. But men who were circumcised had lower odds of HIV (AOR 0.73, 95% CI 0.52, 0.98), while those who had STI in the last 12 months prior to the survey had higher odds of HIV (AOR 1.76, 95% CI 1.44, 3.68). Spatial heterogeneity in HIV risk persisted even after covariate adjustment but differed by sex, suggesting that there are plausible unobserved influencing factors contributing to HIV uneven variation. This study's findings could guide geographically targeted public health policy and effective HIV intervention in South Africa.

Tigers on the edge: mortality and landscape change dominate individual-based spatially-explicit simulations of a small tiger population

ContextReductions in the tiger’s (Panthera tigris) range in Southeast Asia have been concurrent with large infrastructure expansion and landscape change. Thailand’s Dong Phayayen-Khao Yai Forest Complex (DPKY), a landscape of tiger conservation priority, may be particularly vulnerable to such changes, necessitating investigations into effects on population dynamics.ObjectivesEvaluate relative effects of landscape change scenarios on the probability of tiger persistence in DPKY and sensitivity of predictions to spatially-explicit mortality risk, landscape resistance, and tiger population density.MethodsWe utilize individual-based, spatially-explicit population modelling to evaluate the trajectory of tiger population dynamics across 11 landscape change scenarios. Concurrently, we evaluate sensitivity of predictions to landscape resistance transformation, maximum population density, and spatially-explicit mortality across 20 generations.ResultsSpatially-explicit mortality risk dominated predictions of population persistence, frequently resulting in population declines/extinction. Adjustment of moderate mortality risk to slightly convex and concave forms shifted extinction rates from 46 to 12% and 85%, respectively. Holding mortality constant at moderate levels, strong negative effects were predicted in landscape change scenarios incorporating road expansion (46%-74% extinction) and construction of dams (52%). Strong negative effects of combined development persisted even when habitat restoration measures were applied (96% extinction). Adjusting resistance and maximum population density had marginal effects.ConclusionsThe high sensitivity and variability of predictions to spatial patterns of mortality risk suggest a population on a proverbial knife’s edge. Our results underscore the importance of incorporating spatial patterns of mortality risk in population modelling, highlighting their potentially dominating influence on population dynamics and extinction risk.

Urban–Rural Disparities in Deaths of Despair: A County-Level Analysis 2004–2016 in the U.S.

The purpose of this study is to examine nationwide disparities in drug, alcohol, and suicide mortality; evaluate the association between county-level characteristics and these mortality rates; and illustrate spatial patterns of mortality risk to identify areas with elevated risk. The authors applied a Bayesian spatial regression technique to investigate the association between U.S. county-level characteristics and drug, alcohol, and suicide mortality rates for 2004-2016, accounting for spatial correlation that occurs among counties. Mortality risks from drug, alcohol, and suicide were positively associated with the degree of rurality, the proportion of vacant housing units, the population with a disability, the unemployed population, the population with low access to grocery stores, and the population with no health insurance. Conversely, risks were negatively associated with Hispanic population, non-Hispanic Black population, and population with a bachelor's degree or higher. Spatial disparities in drug, alcohol, and suicide mortality exist at the county level across the U.S. social determinants of health; educational attainment, degree of rurality, ethnicity, disability, unemployment, and health insurance status are important factors associated with these mortality rates. A comprehensive strategy that includes downstream interventions providing equitable access to healthcare services and upstream efforts in addressing socioeconomic conditions is warranted to effectively reduce these mortality burdens.

SIMULATION OF ECOLOGICAL RISK IN BEIJING USING MOP-PLUS MODEL

Abstract. The accelerated urbanisation, threatening the integrity of ecological environment. The lack of future simulation in ecological risk assessment in current studies. Especially in metropolis, to address this problem, this study uses the Multiple objective programming (MOP) and the improved patch-generating land use simulation (PLUS) models to simulate land use in Beijing in 2035 under the Natural Development (ND) scenario and the Liveable City (LC) scenario; the changes in land types and land transfers under different scenarios are analysed. In addition, a landscape ecological risk assessment method was applied to analyse the ecological risks caused by land use in each scenario. The study found that, in general, the dominant trend of land use change in Beijing is the shift from construction land to ecological land. The spatial pattern of ecological risk is polarized from east to west, and the conversion of a large amount of built-up land to arable land or grassland, and the high vulnerability of arable land and grassland to human destruction, are the reasons for the continuous increase in ecological risk in Beijing. The area of construction land in the LC scenario is closer to the planned area than in the ND scenario, and the ecological risks faced by the LC scenario are slightly lower than those of the ND scenario. Therefore, it is reasonable to use the LC scenario to simulate the land use situation in Beijing in 2035.

Water use efficiency-based assessment of risk to terrestrial ecosystems in China under global warming targets of 1.5 °C and 2.0 °C

Assessing ecosystem risk is crucial to ecosystem management and to achieving the Paris Agreement’s 1.5 °C or 2.0 °C global warming targets, but the spatial pattern and drivers of ecosystem risk are highly controversial. By simulating China’s ecosystem water use efficiency using the Lund–Potsdam–Jena global vegetation model, the ecosystem risk at the eco-geographical and vegetation-type scales was assessed in this study to identify the regions at risk under global warming, and the impacts of the species richness (ASR) and micro-meteorological factors on the ecosystem risk were investigated in the identified regions using the structural equation model (SEM). Our results indicate that about 30.5% and 32.2% of the ecosystems in China will face risk under global warming targets of 1.5 °C and 2.0 °C, respectively. The ecosystem risk is spatially heterogeneous and biome-specific. The ecosystem risk will mainly occur in the mid-temperate humid/sub-humid region, warm temperate humid/sub-humid region, northern subtropical humid region, mid-subtropical humid region, and the Tibetan Plateau region in China, of which cropland, grassland, and deciduous broadleaf forests will account for 39%, 17%, and 14%, respectively. The results of the SEM analysis indicate that the micro-meteorological factors and ASR explain 48% of the variation in the ecosystem risk, and the micro-meteorological factors and ASR have similar but opposite influences on the ecosystem risk. For the ecosystems identified as at risk under global warming, the negative total effect of the micro-meteorological factors on the ecosystem risk is mainly due to the dominant role of the shortwave radiation in regulating the ecosystem risk; and the positive total effect of the ASR on the ecosystem risk indicates that the ecosystems with higher species richness may face more severe damage when the risk occurs. Therefore, we should carefully balance the effect of the ASR on the ecosystems’ abilities to adapt to changes and ecosystems’ abilities to recover from damage. Our results contribute to precise ecosystem management in China and provide evidence to support the idea that properly optimizing biodiversity can prevent and reduce the damage to ecosystems caused by global warming.

Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling

Spatiotemporal patterns and trends of COVID-19 at a local spatial scale using Bayesian approaches are hardly observed in literature. Also, studies rarely use satellite-derived long time-series data on the environment to predict COVID-19 risk at a spatial scale. In this study, we modelled the COVID-19 pandemic risk using a Bayesian hierarchical spatiotemporal model that incorporates satellite-derived remote sensing data on land surface temperature (LST) from January 2020 to October 2021 (89 weeks) and several socioeconomic covariates of the 140 neighbourhoods in Toronto. The spatial patterns of risk were heterogeneous in space with multiple high-risk neighbourhoods in Western and Southern Toronto. Higher risk was observed during Spring 2021. The spatiotemporal risk patterns identified 60% of neighbourhoods had a stable, 37% had an increasing, and 2% had a decreasing trend over the study period. LST was positively, and higher education was negatively associated with the COVID-19 incidence. We believe the use of Bayesian spatial modelling and the remote sensing technologies in this study provided a strong versatility and strengthened our analysis in identifying the spatial risk of COVID-19. The findings would help in prevention planning, and the framework of this study may be replicated in other highly transmissible infectious diseases.

Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique.

Most analyses of spatial patterns of disease risk using health survey data fail to adequately account for the complex survey designs. Particularly, the survey sampling weights are often ignored in the analyses. Thus, the estimated spatial distribution of disease risk could be biased and may lead to erroneous policy decisions. This paper aimed to present recent statistical advances in disease-mapping methods that incorporate survey sampling in the estimation of the spatial distribution of disease risk. The methods were then applied to the estimation of the geographical distribution of child malnutrition in Malawi, and child fever and diarrhoea in Mozambique. The estimation of the spatial distributions of the child disease risk was done by Bayesian methods. Accounting for sampling weights resulted in smaller standard errors for the estimated spatial disease risk, which increased the confidence in the conclusions from the findings. The estimated geographical distributions of the child disease risk were similar between the methods. However, the fits of the models to the data, as measured by the deviance information criteria (DIC), were different.

Realizable accessibility: evaluating the reliability of public transit accessibility using high-resolution real-time data.

The widespread availability of high spatial and temporal resolution public transit data is improving the measurement and analysis of public transit-based accessibility to crucial community resources such as jobs and health care. A common approach is leveraging transit route and schedule data published by transit agencies. However, this often results in accessibility overestimations due to endemic delays due to traffic and incidents in bus systems. Retrospective real-time accessibility measures calculated using real-time bus location data attempt to reduce overestimation by capturing the actual performance of the transit system. These measures also overestimate accessibility since they assume that riders had perfect information on systems operations as they occurred. In this paper, we introduce realizable real-time accessibility based on space–time prisms as a more conservative and realistic measure. We, moreover, define accessibility unreliability to measure overestimation of schedule-based and retrospective accessibility measures. Using high-resolution General Transit Feed Specification real-time data, we conduct a case study in the Central Ohio Transit Authority bus system in Columbus, Ohio, USA. Our results prove that realizable accessibility is the most conservative of the three accessibility measures. We also explore the spatial and temporal patterns in the unreliability of both traditional measures. These patterns are consistent with prior findings of the spatial and temporal patterns of bus delays and risk of missing transfers. Realizable accessibility is a more practical, conservative, and robust measure to guide transit planning.