Spatial Patterns Of Disease Research Articles

Spatial Analysis of Chronic Kidney Disease of Unknown Aetiology (CKDu) Prevalence in Badulupura Village, Sri Lanka

This study investigated spatial patterns of chronic kidney disease of unknown aetiology (CKDu) patients in Badulupura village. The analysis utilized published locational data for all households, including households affected by CKDu, and all wells used by CKDu-affected patients (CPA wells). The study employed Average Nearest Neighbour (ANN) and Hot Spot Analysis (Getis-Ord Gi*) tools to assess the spatial clustering of CKDu households and CPA wells. The study also explored the impact of topographical factors such as elevation, slope, and distance to watershed boundaries on the spatial distribution of CKDu patients. The ANN analysis revealed a dispersed pattern for all wells, and a random distribution for all households. However, both CKDu households and CPI wells exhibited a clustered pattern, suggesting a location-specific cause for the spatial distribution of the disease. Additionally, overlaying CKDu-affected households on the digital elevation model revealed a concentration of CKDu hot spots in high-elevation areas, while cold spots were observed in low-elevation regions. Furthermore, the t-test was employed to investigate the significance of elevation, slope, and distance to the watershed boundaries in relation to CKDu distribution. The results indicated a significant (p<0.05) prevalence of CKDu in high-elevation areas, while no significant association was found with slope or distance to watershed boundaries. The study revealed the presence of localized CKDu hot spots and cold spots within a small geographical area, highlighting the importance of considering topographical factors and conducting comprehensive hydrogeological and geological studies to gain valuable insights into the spatial distribution of CKDu.

Grapevine Pinot gris virus spreads in infected vineyards: latent infections have no direct impact on grape production

BackgroundGrapevine Pinot gris virus (GPGV) infects grapevines worldwide and causes symptoms such as chlorotic mottling and deformations on leaves, stunted shoots and short panicles, or none of these symptoms if it appears as latent infection. So far, the consequences of GPGV infections for winegrowers are difficult to assess since important information such as plant performance at different GPGV infection levels and symptom expression are not fully clarified.MethodsIn order to investigate the course of GPGV spread, annual visual evaluations and ELISA tests were conducted over 3–4 consecutive years in four GPGV-infected vineyards in southern Germany: GEM, HEC, NIM, and REI. The program PATCHY was used to analyze spatial disease patterns. Sanger sequencing was used to determine virus isolates in vines at different GPGV infection levels, to test their respective influence on symptom expression. Yield and GrapeScan (FTIR) analyses were conducted to test the impact of different GPGV infection levels and isolates on fruit quantity and quality.ResultsGPGV infections significantly increased in all four vineyards (GEM 22–32%, HEC 50–99%, NIM 83–90%, REI 56–76%) with significant spreading patterns across and along rows. Specific symptom progression patterns were not observed. According to our results, the virus isolate has an influence on whether symptoms develop during a GPGV infection. While yield analyses revealed that yield losses only occur in symptomatic vines and range from 13 to 96% depending on the severity of symptoms, latent infections have no impact on grape production. No relevant effects of GPGV infections on must quality were observed.ConclusionsSecondary spread of GPGV was observed in all vineyards monitored, indicating vector-borne transmission that is likely to be accelerated by human viticultural management. GPGV should be further monitored to prevent the accumulation of detrimental symptomatic isolates. The results of this study can be used to assess the risk of GPGV to viticulture and should be considered when developing management strategies against the virus.

Gene interactions analysis of brain spatial transcriptome for Alzheimer's disease

Recent studies have explored the spatial transcriptomics patterns of Alzheimer's disease (AD) brain by spatial sequencing in mouse models, enabling the identification of unique genome-wide transcriptomic features associated with different spatial regions and pathological status. However, the dynamics of gene interactions that occur during amyloid-β accumulation remain largely unknown. In this study, we performed analyses on ligand-receptor communication, transcription factor regulatory network, and spot-specific network to reveal the dependence and the dynamics of gene associations/interactions on spatial regions and pathological status with mouse and human brains. We first used a spatial transcriptomics dataset of the AppNL-G-F knock-in AD and wild-type mouse model. We revealed 17 ligand-receptor pairs with opposite tendencies throughout the amyloid-β accumulation process and showed the specific ligand-receptor interactions across the hippocampus layers at different extents of pathological changes. We then identified nerve function related transcription factors in the hippocampus and entorhinal cortex, as well as genes with different transcriptomic association degrees in AD versus wild-type mice. Finally, another independent spatial transcriptomics dataset from different AD mouse models and human single-nuclei RNA-seq data/AlzData database were used for validation. This is the first study to identify various gene associations throughout amyloid-β accumulation based on spatial transcriptomics, establishing the foundations to reveal advanced and in-depth AD etiology from a novel perspective based on the comprehensive analyses of gene interactions that are spatio-temporal dependent.

Identification of trends, direction of distribution and spatial pattern of tuberculosis disease (2015-2017) in Penang

Identification of trends, direction of distribution and spatial pattern of tuberculosis disease (2015-2017) in Penang

Temporal, spatial and space-time distribution of infections caused by five major enteric pathogens, Ontario, Canada, 2010-2017.

In Canada, enteric diseases pose substantial health and economic burdens. The distribution of these diseases is uneven across both geography and time and understanding these patterns is therefore important for the prevention of future outbreaks. We evaluated temporal, spatial and space-time clustering of laboratory-confirmed cases of Campylobacter spp. (n = 28,728), non-typhoidal Salmonella spp. (n = 22,640), Shiga toxin-producing Escherichia coli (STEC; n = 1340), Yersinia spp. (n = 1674) and Listeria monocytogenes (n = 471) infections, reported between 2010 and 2017 inclusive in Ontario, the most populous province in Canada (population ~ 13,500,000 in 2016). For each enteric pathogen, we calculated the mean incidence rates (IRs) for Ontario's 35 public health unit (PHU) areas and visualized them using choropleth maps. We identified temporal, spatial and space-time high infection rate clusters using retrospective Poisson scan statistics. Campylobacter and Salmonella infections had the highest IRs, while Listeria infections had the lowest. Campylobacter, Salmonella, STEC and Listeria mostly clustered temporally in the spring/summer and sometimes extended into fall, while Yersinia showed a less clear seasonal pattern. The IR visualizations and spatial and space-time scan statistics showed geographic heterogeneity of infection rates with high infection rate clusters detected mainly in PHUs across the southwestern and central-western regions of Ontario for Campylobacter, Salmonella and STEC infections, and mainly in PHUs located in the central-eastern regions for Yersinia and Listeria. A high proportion of cases in some of the significant Salmonella, STEC and Listeria infection clusters were linked to disease outbreaks. Results from this study will inform heightened public health surveillance, and prevention and control programmes, in populations and regions of high infection rates. Further research is needed to determine the pathogen-specific socioeconomic, environmental and agricultural risk factors that may be related to the temporal and spatial disease patterns we observed in our study.

Exploring spatial patterns of cardiovascular disease among women in India

Abstract Cardiovascular disease (CVD) has become a leading cause of mortality and morbidity in India, surpassing the global rate. While CVD is influenced by gender due to sex-specific risk factors in women, it remains underdiagnosed, and undertreated in women. WHO provides a list of ‘best buys’ which does not consider sex-specific risk factors. Hence, we assessed prevalence, unawareness and spatial variations of CVDs in women (15-49 years). We used NFHS (2019-20) WHO causal links between underlying drivers, behavioral risk factors, metabolic/physiological risk factors, and CVDs. We used ordinary kriging interpolation techniques to measure the spatial heterogeneity of CVD prevalence and spatial autocorrelation techniques using global Moran I statistic. Furthermore, ordinary least square were used to examine spatial predictors and geographically weighted regression and multiscale geographically weighted regression model were used to map the local spatial variations. CVDs prevalence in women is 11.7% [95%, CI: 0.117-0.118], its unawareness is 12% [95%, CI: 0.116-0.117] among women. Spatial regression analysis suggests observed geographical variation of CVDs significantly correlated with risk factors- metabolic (BMI), behavioral (tobacco consumption), and underlying (sex-specific - women's education as a proxy for autonomy and socioeconomic depriviation- household wealth). India has a high burden of CVDs in women, with state-level disparities and a lack of awareness about the disease. Women are not a target population for CVDs in SDG 3.4 and India's National Health policy, 2017; however, its rising epidemic burden on women demands a need for policy choices targeted at women. The study provides evidence to inform the policy choices in reducing CVDs burden in women. It suggests a need to develop gender-specific guidelines for CVD prevention and management in India. Key messages • The study provides evidence to inform the policy choices in reducing CVDs burden in women and suggests a need to develop gender-specific guidelines for CVD prevention and management in India. • India has high burden & unawareness of CVDs in women with risk factors- BMI, tobacco consumption, sex-specific - women's education as a proxy for autonomy; socioeconomic deprivation- household wealth.

A practical illustration of spatial smoothing methods for disconnected regions with INLA: spatial survey on overweight and obesity in Malaysia

BackgroundNational prevalence could mask subnational heterogeneity in disease occurrence, and disease mapping is an important tool to illustrate the spatial pattern of disease. However, there is limited information on techniques for the specification of conditional autoregressive models in disease mapping involving disconnected regions. This study explores available techniques for producing district-level prevalence estimates for disconnected regions, using as an example childhood overweight in Malaysia, which consists of the Peninsular and Borneo regions separated by the South China Sea. We used data from Malaysia National Health and Morbidity Survey conducted in 2015. We adopted Bayesian hierarchical modelling using the integrated nested Laplace approximation (INLA) program in R-software to model the spatial distribution of overweight among 6301 children aged 5–17 years across 144 districts located in two disconnected regions. We illustrate different types of spatial models for prevalence mapping across disconnected regions, taking into account the survey design and adjusting for district-level demographic and socioeconomic covariates.ResultsThe spatial model with split random effects and a common intercept has the lowest Deviance and Watanabe Information Criteria. There was evidence of a spatial pattern in the prevalence of childhood overweight across districts. An increasing trend in smoothed prevalence of overweight was observed when moving from the east to the west of the Peninsular and Borneo regions. The proportion of Bumiputera ethnicity in the district had a significant negative association with childhood overweight: the higher the proportion of Bumiputera ethnicity in the district, the lower the prevalence of childhood overweight.ConclusionThis study illustrates different available techniques for mapping prevalence across districts in disconnected regions using survey data. These techniques can be utilized to produce reliable subnational estimates for any areas that comprise of disconnected regions. Through the example, we learned that the best-fit model was the one that considered the separate variations of the individual regions. We discovered that the occurrence of childhood overweight in Malaysia followed a spatial pattern with an east–west gradient trend, and we identified districts with high prevalence of overweight. This information could help policy makers in making informed decisions for targeted public health interventions in high-risk areas.

Wide mismatches in the sequences of primers and probes for monkeypox virus diagnostic assays.

Rapid and accurate diagnosis of infections is fundamental to containment of disease. Several monkeypox virus (MPV) real-time diagnostic assays have been recommended by the CDC; however, the specificity of the primers and probes in these assays for the ongoing MPV outbreak has not been investigated. We analyzed the primer and probe sequences present in the CDC recommended MPVgeneric real-time PCR assay by aligning those sequences against 1730 MPV complete genomes reported in 2022 worldwide. Sequence mismatches were found in 99.08% and 97.46% of genomes for the MPV generic forward and reverse primers, respectively. Mismatch-corrected primers were synthetized and compared to the generic assay for MPV detection. Results showed that the two primer-template mismatches resulted in a ~11-fold underestimation of initial template DNA in the reaction and 4-fold increase in the 95% LOD. We further evaluated the specificity of seven other real-time PCR assays used for MPV and orthopoxvirus (OPV) detection and identified two assays with the highest matching score (>99.6%) to the global MPV genome database in 2022. Genetic variations in the primer-probe regions across MPV genomes could indicate the temporal and spatial emergence pattern of monkeypox disease. Our results show that the current MPV real-time generic assay may not be optimal to accurately detect MPV, and the mismatch-corrected assay with full complementarity between primers and current MPV genomes could provide a more sensitive and accurate detection of MPV.

Spatial pattern of mottled stripe disease (Herbaspirillum rubrisubalbicans) in sugar cane

Sugar cane (Saccharum spp. hybrids) is the most important agroindustrial crop of the tropics. Recently, sugar cane plants with chlorotic leaf streaking associated with the bacterium Herbaspirillum rubrisubalbicans have been observed. This disease impacts photosynthetic capacity and yield. Characterizing the spatio-temporal behavior of chlorotic mottling using prediction maps is an important element of integrated disease management. Here, we determine the spatial distribution of mottled stripe disease in sugar cane in southeastern Mexico. To do this, we randomly chose and georeferenced 80 points in commercial plots in 2016 and 2017 to determine the disease incidence. We generated an experimental semivariogram based on a predetermined theoretical model and estimated Kriging. The incidence was 2.93% in 2016 and 5.36% in 2017 in varieties ICP-MEX-92-1420, CP-72-2086, ITV-92-373, MEX-79434 and MEX-69-290. The spatial behavior of the bacteria fit the pentaspherical model in 2016 and the spherical model in 2017. Spatial interpolation was validated by Mean Error (ME), Root Mean Square Error (RMSE) and Mean Standardized Prediction Error (MSPE) values near zero, visualized using the generated map. The results will be used to guide management of mottled stripe disease in sugar cane in the affected area. Highlights H. rubrisubalbicans can persist from sugar cane growing season to the next and spread from one plot to another by different mechanisms. The chlorotic mottling of sugar cane showed an aggregate type spatial behavior at the regional level. The differences in spatial patterns between seasons show the dispersion potential of the disease in this crop system, as well as a latent infestation in the area. The semivariograms and maps show aggregate behavior of this disease, expressed as different spatial patterns given by the spatial dependence among points and the severity of the effects in each plot.

Spatial and temporal patterns of white spot disease in Rayong Province, Thailand, from october 2015 to september 2018

This study aimed to analyze the spatial and temporal patterns of white spot disease (WSD) in shrimp farms in Rayong Province, Thailand, between October 2015 and September 2018. The longitudinal data of all active shrimp farms were collected and categorized into two groups: cases and non-cases. A space–time permutation scan, epidemic curve, and time-series analysis were used to evaluate the spatiotemporal patterns. We assessed a total of 1126 ponds across 176 farms in two districts (Klaeng and Mueang Rayong) and identified three significant (P < 0.05) clusters of WSD cases. The biggest cluster encompassed 21 geographical coordinates. This cluster had a radius of 1.14 km and occurred between January 31, 2017, and February 28, 2017. The epidemic curve showed that the biggest outbreak peaked from December 2017 to February 2018. In the time-series analysis, the highest probability of actual WSD cases was at the beginning of each calendar year, consistent with the prominent high probability recorded in WSD forecasts. Our analysis presents the interaction between hotspot areas and time period. These results should help the relevant authorities implement appropriate surveillance programs and control measures to limit the occurrence and transmission of WSD.

Social determinants and spatio-temporal variation of Ischemic Heart Disease in Manitoba

IntroductionThe aim was to study any spatial and/or temporal patterns of ischemic heart disease (IHD) prevalence and measure the effects of selected social determinants on these spatial and space-time patterns.MethodsData were obtained from the Population Research Data Repository housed at the Manitoba Centre for Health Policy to identify persons who were diagnosed with IHD between 1995 and 2018. These persons were geocoded to 96 geographic regions of Manitoba. An area-level socioeconomic factor index (SEFI-2) and the proportion of the population who was Indigenous were calculated for each geographic region using the 2016 Canadian Census data. Associations between these factors and IHD prevalence were measured using Bayesian spatial Poisson regression models. Temporal trends and spatio-temporal trends were measured using Bayesian spatio-temporal Poisson regression models.ResultsUnivariable models showed a significant association with increased regional Indigenous population proportion associated with a higher prevalence of IHD (RR: 0.07, 95% CredInt: (0.05, 0.10)) and for SEFI-2 (RR: 0.17, 95% CredInt: (0.11, 0.23)). Using a multivariable model, after accounting for the proportion of the population that was Indigenous, there was no evidence of an association between IHD prevalence and area-level socioeconomic factor. Spatio-temporal models showed no significant overall temporal trend in IHD prevalence, but there were significant spatially varying temporal trends within the 96 regions.ConclusionsAssociation between Indigenous population proportion and IHD is consistent with previous research. No significant overall temporal trend was measured. However, regions with significantly increasing trends and significantly decreasing trends in IHD prevalence were identified.

Fine‐scale spatial patterns of wildlife disease are common and understudied

Abstract All parasites are heterogeneous in space, yet little is known about the prevalence and scale of this spatial variation, particularly in wild animal systems. To address this question, we sought to identify and examine spatial dependence of wildlife disease across a wide range of systems. Conducting a broad literature search, we collated 31 datasets featuring 89 replicates and 71 unique host–parasite combinations, only 51% of which had previously been used to test spatial hypotheses. We analysed these datasets for spatial dependence within a standardised modelling framework using Bayesian linear models, and we then meta‐analysed the results to identify generalised determinants of the scale and magnitude of spatial autocorrelation. We detected spatial autocorrelation in 48/89 model replicates (54%) across 21/31 datasets (68%), spread across parasites of all groups. Even some very small study areas (under 0.01 km2) exhibited substantial spatial variation. Despite the common manifestation of spatial variation, our meta‐analysis was unable to identify host‐, parasite‐, or sampling‐level determinants of this heterogeneity across systems. Parasites of all transmission modes had easily detectable spatial patterns, implying that structured contact networks and susceptibility effects are potentially as important in spatially structuring disease as are environmental drivers of transmission efficiency. Our findings demonstrate that fine‐scale spatial patterns of infection manifest frequently and across a range of wild animal systems, and many studies are able to investigate them—whether or not the original aim of the study was to examine spatially varying processes. Given the widespread nature of these findings, studies should more frequently record and analyse spatial data, facilitating development and testing of spatial hypotheses in disease ecology. Ultimately, this may pave the way for an a priori predictive framework for spatial variation in novel host–parasite systems. A free Plain Language Summary can be found within the Supporting Information of this article.

Spatial and Temporal Patterns of Ross River Virus in Queensland, 2001-2020.

Ross River virus (RRV), the most common human arbovirus infection in Australia, causes significant morbidity and substantial medical costs. About half of Australian cases occur in Queensland. We describe the spatial and temporal patterns of RRV disease in Queensland over the past two decades. RRV notifications, human population data, and weather data from 2001 to 2020 were analysed by the Statistical Area Level 2 (SA2) area. Spatial interpolation or linear extrapolation were used for missing weather values and the estimated population in 2020, respectively. Notifications and incidence rates were analysed through space and time. During the study period, there were 43,699 notifications in Queensland. The highest annual number of notifications was recorded in 2015 (6182), followed by 2020 (3160). The average annual incidence rate was 5 per 10,000 people and the peak period for RRV notifications was March to May. Generally, SA2 areas in northern Queensland had higher numbers of notifications and higher incidence rates than SA2 areas in southern Queensland. The SA2 areas with high incidence rates were in east coastal areas and western Queensland. The timely prediction may aid disease prevention and routine vector control programs, and RRV management plans are important for these areas.

Unraveling spatial patterns of COVID-19 in Italy: Global forces and local economic drivers.

This article investigates the spatial patterns of coronavirus disease 2019 (COVID‐19) infection in Italy and its determinants from March 9 to June 15, 2020, a time interval covering the so‐called first wave of COVID pandemics in Europe. The results, based on negative binomial regressions and linear spatial models, confirm the importance of multiple factors that positively correlate with the number of recorded cases. Economic forces, including urban agglomeration, industrial districts, concentration of large companies (both before and after the beginning of the ‘lockdown’) and a north–south gradient, are the most significant predictors of the strength of COVID‐19 infection. These effects are statistically more robust in the spatial models than in the aspatial ones. We interpretate our results in the light of pitfalls related to data reliability, and we discuss policy implications and possible avenues for future research.

Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States.

Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study’s findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19.

Socio-demographic, not environmental, risk factors explain fine-scale spatial patterns of diarrhoeal disease in Ifanadiana, rural Madagascar.

Precision health mapping is a technique that uses spatial relationships between socio-ecological variables and disease to map the spatial distribution of disease, particularly for diseases with strong environmental signatures, such as diarrhoeal disease (DD). While some studies use GPS-tagged location data, other precision health mapping efforts rely heavily on data collected at coarse-spatial scales and may not produce operationally relevant predictions at fine enough spatio-temporal scales to inform local health programmes. We use two fine-scale health datasets collected in a rural district of Madagascar to identify socio-ecological covariates associated with childhood DD. We constructed generalized linear mixed models including socio-demographic, climatic and landcover variables and estimated variable importance via multi-model inference. We find that socio-demographic variables, and not environmental variables, are strong predictors of the spatial distribution of disease risk at both individual and commune-level (cluster of villages) spatial scales. Climatic variables predicted strong seasonality in DD, with the highest incidence in colder, drier months, but did not explain spatial patterns. Interestingly, the occurrence of a national holiday was highly predictive of increased DD incidence, highlighting the need for including cultural factors in modelling efforts. Our findings suggest that precision health mapping efforts that do not include socio-demographic covariates may have reduced explanatory power at the local scale. More research is needed to better define the set of conditions under which the application of precision health mapping can be operationally useful to local public health professionals.

Identification of Ross River virus hot spots along urban fringes of South East Queensland, Australia

Background/Aim: Ross River virus (RRV) is responsible for the most common vector-borne disease of humans reported in Australia. The virus circulates in enzootic cycles between multiple species of mosquitoes, wildlife reservoir hosts and humans. Despite regular outbreaks, ongoing morbidity and substantial economic costs, the underlying determinants of epidemics remain unclear. Public health concern about RRV has recently increased due to rising incidence rates in Australian urban centres, along with increased circulation in Pacific Island countries. We aimed to provide a detailed analysis of disease trends and explore potential links between disease patterns and transmission pathways of RRV. Methods: We assessed the spatial and temporal distribution of notified RRV cases, and associated epidemiological features in south east Queensland, Australia, from 2001-2016. This included fine-scale analysis of disease patterns across the suburbs of the endemic capital city of Brisbane, and those of 8 adjacent Local Government Areas. Results: The mean annual incidence rate for the region was 41/100,000 with a consistent seasonal peak between February and May. The highest RRV incidence was in adults aged from 30-64 years (mean incidence rate: 59/100,000), and females had higher incidence rates than males (mean incidence rates: 44/100,000 and 34/100,000, respectively). Spatial patterns of disease were heterogeneous between years, and there was a wide distribution of disease across both urban and rural areas of SEQ. Overall, the highest incidence rates were reported from predominantly rural suburbs to the north of Brisbane City, with significant hot spots located in peri-urban suburbs where residential, agricultural and conserved natural land use types intersect. Conclusions: Although RRV is endemic across all of SEQ, transmission is most concentrated in areas where urban and peri-urban environments intersect. The drivers of RRV transmission across rural-urban landscapes should be prioritised for further investigation, including identification of specific vectors and hosts that mediate human spillover.

Spatial and temporal patterns of Ross River virus in south east Queensland, Australia: identification of hot spots at the rural-urban interface

BackgroundRoss River virus (RRV) is responsible for the most common vector-borne disease of humans reported in Australia. The virus circulates in enzootic cycles between multiple species of mosquitoes, wildlife reservoir hosts and humans. Public health concern about RRV is increasing due to rising incidence rates in Australian urban centres, along with increased circulation in Pacific Island countries. Australia experienced its largest recorded outbreak of 9544 cases in 2015, with the majority reported from south east Queensland (SEQ). This study examined potential links between disease patterns and transmission pathways of RRV.MethodsThe spatial and temporal distribution of notified RRV cases, and associated epidemiological features in SEQ, were analysed for the period 2001–2016. This included fine-scale analysis of disease patterns across the suburbs of the capital city of Brisbane, and those of 8 adjacent Local Government Areas, and host spot analyses to identify locations with significantly high incidence.ResultsThe mean annual incidence rate for the region was 41/100,000 with a consistent seasonal peak in cases between February and May. The highest RRV incidence was in adults aged from 30 to 64 years (mean incidence rate: 59/100,000), and females had higher incidence rates than males (mean incidence rates: 44/100,000 and 34/100,000, respectively). Spatial patterns of disease were heterogeneous between years, and there was a wide distribution of disease across both urban and rural areas of SEQ. Overall, the highest incidence rates were reported from predominantly rural suburbs to the north of Brisbane City, with significant hot spots located in peri-urban suburbs where residential, agricultural and conserved natural land use types intersect.ConclusionsAlthough RRV is endemic across all of SEQ, transmission is most concentrated in areas where urban and peri-urban environments intersect. The drivers of RRV transmission across rural-urban landscapes should be prioritised for further investigation, including identification of specific vectors and hosts that mediate human spillover.

Reactivation of latent infections with migration shapes population-level disease dynamics.

Annual migration is common across animal taxa and can dramatically shape the spatial and temporal patterns of infectious disease. Although migration can decrease infection prevalence in some contexts, these energetically costly long-distance movements can also have immunosuppressive effects that may interact with transmission processes in complex ways. Here, we develop a mechanistic model for the reactivation of latent infections driven by physiological changes or energetic costs associated with migration (i.e. 'migratory relapse') and its effects on disease dynamics. We determine conditions under which migratory relapse can amplify or reduce infection prevalence across pathogen and host traits (e.g. infectious periods, virulence, overwinter survival, timing of relapse) and transmission phenologies. We show that relapse at either the start or end of migration can dramatically increase prevalence across the annual cycle and may be crucial for maintaining pathogens with low transmissibility and short infectious periods in migratory populations. Conversely, relapse at the start of migration can reduce the prevalence of highly virulent pathogens by amplifying culling of infected hosts during costly migration, especially for highly transmissible pathogens and those transmitted during migration or the breeding season. Our study provides a mechanistic foundation for understanding the spatio-temporal patterns of relapsing infections in migratory hosts, with implications for zoonotic surveillance and understanding how infection patterns will respond to shifts in migratory propensity associated with environmental change. Further, our work suggests incorporating within-host processes into population-level models of pathogen transmission may be crucial for reconciling the range of migration-infection relationships observed across migratory species.

Remote Sensing of Diseases.

Detection, identification, and quantification of plant diseases by sensor techniques are expected to enable a more precise disease control, as sensors are sensitive, objective, and highly available for disease assessment. Recent progress in sensor technology and data processing is very promising; nevertheless, technical constraints and issues inherent to variability in host-pathogen interactions currently limit the use of sensors in various fields of application. The information from spectral [e.g., RGB (red, green, blue)], multispectral, and hyperspectral sensors that measure reflectance, fluorescence, and emission of radiation or from electronic noses that detect volatile organic compounds released from plants or pathogens, as well as the potential of sensors to characterize the health status of crops, is evaluated based on the recent literature. Phytopathological aspects of remote sensing of plant diseases across different scales and for various purposes are discussed, including spatial disease patterns, epidemic spread of pathogens, crop characteristics, and links to disease control. Future challenges in sensor use are identified.