Space-time Analysis Research Articles

Space–Time Analysis of the COVID-19 Pandemic and Its Relationship with Socioeconomic and Demographic Variables in the Metropolitan Region of São Paulo, Brazil

Space–Time Analysis of the COVID-19 Pandemic and Its Relationship with Socioeconomic and Demographic Variables in the Metropolitan Region of São Paulo, Brazil

Whooping cough – a recurrent epidemic

Abstract Background Whooping cough (WC), or Pertussis, is a vaccine-preventable and highly contagious infectious disease. Although it has a relatively low fatality rate, approximately 13% of reported cases develop secondary bacterial pneumonia. This was because of several recurrent epidemics across Europe in the last decades, with the latest beginning in mid-2023 and still ongoing. The Central Region of Portugal is currently being affected, and this study aims to describe the current epidemic at the regional level. Methods Data from epidemiological inquiries of confirmed and probable cases of WC pertaining to the Central Region of Portugal from January 2020 till April 2024 was gathered from the mandatory report national epidemiological surveillance system (SINAVE). Both a temporal comparison of number of cases and a statistical space-time scan with permutation were conducted (software SaTScan v10.) Results A comparison by month of reported cases shows an increase in cases from September 2023 until December 2023 followed by an exponential increase from January 2024 until April 2024 denoting an ongoing epidemic. In the statistical space-time analysis of 107 reported cases between September 2023 and April 2024, 8 nonsignificant clusters were identified with different locations involved in non-overlapping periods. Conclusions The finding of several unrelated clusters, even though nonsignificant, during a short time period indicates that Pertussis transmission is ongoing at the local level probably through asymptomatic or barely symptomatic cases that act as reservoirs. Clinicians must hold a high clinical suspicion for a timely diagnosis, treatment and report and health authorities must reinforce control measures to limit the spread of this disease. Key messages • Pertussis remains a disease of significant concern recurrent outbreaks across Europe show that public health local authorities have an important role in timely management of cases and outbreaks. • Local level transmission and intervention may play a pivotal role in the epidemiologic dynamic of Pertussis.

Space-time analysis, severity of the wilt disease in escabeche pepper (Capsicum baccatum var. Pendulum) and identification of the causal agent (Phytophthora capsici L.) under subtropical climate conditions in Peru

Phytophthora capsici is an aggressive pathogen in escabeche pepper on the Peruvian coast. Root rot has a strong correlation with humidity and environment. Disease behavior was evaluated epidemiologically using spatiotemporal variables. Severity was evaluated according to the advance of the secondary symptom according to grades 1 to 5. Then, coordinates of each plant were established by photogrammetric survey of a field with 1705 escabeche pepper plants. For temporal analysis, severity was adjusted to an exponential model (R2 = 0.909) and incidence to a Gompertz model (R2 = 0.921) that detected an initial delay of the disease due to temperature. For the spatial analysis, the Global Moran Index (Ii) showed a high spatial dependence of the disease reaching a peak of 0.4 and 0.7 for severity and incidence, respectively. Also, heat maps related to the Local Ii were generated from which an initial source of infestation was determined where the furrow irrigation started in random infestations. Then, the infestation spots were settled in areas of surface water accumulation. Also, rhizosphere samples were collected per plant by degree of severity on V8 or CMA whit PARB and PDA-A selective medium. As a result, significant differences were obtained between grade 1, grade 2, 3, 4 and grade 5. In addition, the effect on yield was significant for plants with grade 4 and 5 with respect to fruit weight (22.3 and 18.5g/fruit) and weight per plant (509.5 and 371.8g/plant), respectively.

Temporal trends, spatial and spatiotemporal clusters of cervical cancer mortality in Brazil from 2000 to 2021

Cervical cancer, despite being preventable through primary and secondary prevention strategies, remains one of the leading causes of morbidity and mortality among women in Brazil. This study aimed to analyze the temporal, spatial, and space-time patterns of cervical cancer mortality in Brazil. An ecological study was conducted using temporal, spatial, and space-time analysis techniques, using death certificates with cervical cancer as the underlying cause or associated condition among females in Brazil from 2000 to 2021. Death certificate and population data were provided by the Department of Health Informatics of the Unified Health System (DATASUS) and the Brazilian Institute of Geography and Statistics (IBGE), respectively. A total of 123,306 deaths associated with cervical cancer among women were registered during the study period. A rising trend in mortality was detected since 2014 onwards, after 14 years of decline. Particularly, an increase in mortality was observed among the younger age groups, and in the North and Northeast regions regardless of age. Heterogeneity in the spatial distribution of cervical cancer mortality was observed, with high mortality clusters around the country, but mostly concentrated in the North and Northeast regions. These findings suggest a need and an opportunity to develop efficient and effective health policies targeting those regions and groups of women at higher risk which in turn will allow for fast and significant reductions in cervical cancer mortality in Brazil.

Detection and Attribution of Changes in Precipitation Extremes in China and Its Different Climate Zones

Abstract Based on the observations and the phase 6 of Coupled Model Intercomparison Project (CMIP6) multimodel simulations, we conducted a detection and attribution analysis for the observed changes in intensity and frequency indices of extreme precipitation during 1961–2014 over the whole of China and within distinct climate regions across the country. A space–time analysis is simultaneously applied in detection so that spatial structure on the signals is considered. Results show that the CMIP6 models can simulate the observed general increases of extreme precipitation indices during the historical period except for the drying trends from southwestern to northeastern China. The anthropogenic (ANT) signal is detectable and attributable to the observed increase of extreme precipitation over China, with human-induced greenhouse gas (GHG) increases being the dominant contributor. Additionally, we also detected the ANT and GHG signals in China’s temperate continental, subtropical–tropical monsoon, and plateau mountain climate zones, demonstrating the role of human activity in historical extreme precipitation changes on much smaller spatial scales. Significance Statement The observed intensification of extreme precipitation globally has been attributed to human influences. Here, we demonstrate that anthropogenic forcing has discernably intensified extreme precipitation over the period 1961–2014, over China and in three of its four climate zones, with human-induced greenhouse gas increases being the dominant contributor. Our results strengthen the body of evidence that greenhouse gas increases are intensifying extreme precipitation by quantifying their role in observed changes at smaller regional scales than previously reported.

Space–Time Analysis of Refueling Patterns of Alternative Fuel Vehicles Using GPS Trajectory Data and Machine Learning

ABSTRACTThe urgency to combat climate change has led countries worldwide to embrace clean energy solutions across various sectors, including transportation, to reduce greenhouse gas emissions. This shift is evident in the growing popularity and adoption of alternative fuel vehicles (AFVs), such as natural gas vehicles and electric vehicles. AFVs have significantly lower carbon footprints compared to conventional petrol‐powered vehicles with internal combustion engines. Consequently, there arises a need to gain a deeper understanding of AFV refueling demand to optimize the distribution of refueling stations. To address this, our research proposes an innovative space–time method that integrates GPS trajectory data with the support vector machine technique to accurately identify and analyze patterns in AFV refueling behavior. The results highlight distinct space–time patterns, notably the clustering of refueling activities in areas like Shuiguohu, Shouyilu, Houhu, Hanshuiqiao, Zhoutou, and Yongfeng around noon, influenced by taxi drivers' breaks. This underscores the importance of increasing staff levels at refueling stations in these areas during peak refueling periods, forming alliances with local eateries, and coordinating taxi shift hours to evenly distribute refueling demand throughout the day, ultimately reducing congestion during peak refueling periods in these areas. The proposed method by this research is applicable to urban contexts worldwide and equips policymakers and planners with a powerful tool for effective planning of future AFV refueling stations.

High-risk spatiotemporal patterns of leprosy in the southeastern region of Yunnan province from 2010 to 2022: an analysis at the township level

BackgroundDespite being preventable and curable, leprosy remains endemic in some undeveloped regions, including China. Wenshan Zhuang and Miao Autonomous Prefecture (Wenshan prefecture) currently bears the highest leprosy burden in China. In this ecological study, we aimed to analyze the epidemiological characteristics as well as identify and visualize the high-risk townships of Wenshan prefecture using the most updated leprosy data from 2010 to 2022.MethodsGeographical information system combined with spatial scan statistics was used for newly detected leprosy cases abstracted from the Leprosy Management Information System in China. Global Moran’s I index was used to uncover the spatial pattern of leprosy at the township level. Spatial scan statistics, encompassing purely temporal, purely spatial, spatial variation in temporal trends, and space-time analysis, were implemented for detecting the risk clusters.ResultsBetween 2010 and 2022, Wenshan prefecture detected 532 new leprosy cases, comprising 352 (66.17%) males and 180 (33.83%) females. The aggregated time primarily occurred between October 2010 and March 2014. The distribution pattern of newly detected leprosy cases was spatially clustered. We identified four high-risk spatial clusters encompassing 54.51% of the new cases. Furthermore, spatial variation in temporal trends highlighted one cluster as a potential high-risk area. Finally, two space-time clusters were detected, and the most likely cluster was predominantly located in the central and northwest regions of Wenshan prefecture, spanning from January 2010 to September 2013.ConclusionsIn this ecology study, we characterized the epidemiological features and temporal and spatial patterns of leprosy in Wenshan prefecture using the most recent leprosy data between 2010 and 2022. Our findings offer scientific insights into the epidemiological profiles and spatiotemporal dynamics of leprosy in Wenshan prefecture. Clinicians and policymakers should pay particular attention to the identified clusters for the prevention and control of leprosy.

Space-time dynamics of the dengue epidemic in Brazil, 2024: an insight for decision making

BackgroundDengue is a vector-borne viral infection caused by the dengue virus transmitted to humans primarily by Aedes aegypti. The year 2024 has been a historic year for dengue in Brazil, with the highest number of probable cases ever registered. Herein, we analyze the temporal trend and spatio-temporal dynamics of dengue cases in Brazil during the first nine epidemiological weeks (EW) of 2024.MethodsThis is an ecological study, including all probable cases of dengue in Brazil during the period, carried out in two steps: time series analysis to assess the temporal trend and spatial analysis to identify high-risk clusters.Results1,345,801 probable cases of dengue were reported. The regions with the highest increasing trend were the Northeast with an average epidemiologic week percent change (AEPC) of 52.4 (95% CI: 45.5–59.7; p < 0.001) and the South with 35.9 (95% CI: 27.7–44.5; p < 0.001). There was a statistically significant increasing trend in all states, except Acre (AEPC = -4.1; 95% CI: -16.3–10; p = 0.55), Amapá (AEPC = 1.3; 95% CI: -16.2–22.3; p = 0.9) and Espírito Santo (AEPC = 8.9; 95% CI: -15.7–40.6; p = 0.5). The retrospective space-time analysis showed a cluster within the Northeast, Central-West and Southeast regions, with a radius of 515.3 km, in which 1,267 municipalities and 525,324 of the cases were concentrated (RR = 6.3; p < 0.001). Regarding the spatial variation of the temporal trend, 21 risk areas were found, all of them located in Southeast or Central-West states. The area with the highest relative risk was Minas Gerais state, where 5,748 cases were concentrated (RR = 8.1; p < 0.001). Finally, a purely spatial analysis revealed 25 clusters, the one with the highest relative risk being composed of two municipalities in Acre (RR = 6.9; p < 0.001).ConclusionsWe described a detailed temporal-spatial analysis of dengue cases in the first EWs of 2024 in Brazil, which were mainly concentrated in the Southeast and Central-West regions. Overall, it is recommended that governments adopt public policies to control the the vector population in high-risk areas, as well as to prevent the spread of dengue fever to other areas of Brazil.

Gaussian Process Regression as a precrash velocity determination method–subcompact vehicle class

The following paper presents an innovative approach to determining vehicle precrash velocity when hitting an immovable obstacle facing forward. Precrash velocity is necessary in order to perform a crash reconstruction. It is needed for the time-space analysis of the events, as well as to assesscrash mitigation and to evaluate drivers’ technique and tactics. For this task, the authors are using Gaussian Process Regression (GPR). Such an approach offers a number of advantages over the currently used methods that prove to be outdated when considering modern vehicles. The mathematical model was trained on a database shared by the National Highway Traffic Safety Administration.. This database covers a large number of crash tests of different kind, however authors focus on frontal collisions of the subcompact car class. Due to low accuracy of linear methods used up till now, Authors developed an innovative approach to determine the EES parameter utilizing Gaussian process regression. The newly developed method is an effective and accurate way to determine the vehicle’s velocity and shows promising results, as is demonstrated in this paper.

Long-term changes of agricultural land over the last century in Romania. The showcase of Romanian plain

The current paper is a space-time analysis of agricultural land transitions in the Romanian Plain, which has been significantly impacted during the past century by the fundamental structural and functional transformations. Five maps, derived from historical cartographic materials and the recent CORINE Land Cover databases, were spatially analysed using a transition matrix procedure. The aim was to identify and measure the magnitude of the main land use/cover change flows related to agricultural lands. Furthermore, several underlying factors of the spatial and functional transformations have been discussed so as to understand the evolution of the historical agricultural lands over time. The results show that the most significant changes occurred between 1912 and 1970, when 38 % of the Romanian Plain’s surface underwent transformations, with notable regional differences. The analysis of the change flows in relation to the underlying factors was instrumental in explaining the land processes, an approach that can be used to understand future landscape transformations.

Coupled space-time scale analysis for obtaining enhanced accuracy in species dynamics

Scale effects significantly affect the rationality and accuracy of ecological models, so temporal and spatial scales are included in the construction and application of ecological studies. However, few ecological pattern analyses focus on both of these scales simultaneously, which is especially important in terms of synchrony. In this study, based on fishery catch data and simulated data, we propose a framework for coupled space-time scale analysis and reveal possible deficiencies in conventional scale studies. We verified the feasibility and reliability of the scheme using simulated data with different combinations of temporal and spatial scales. The results showed that our scheme can simultaneously identify and localize scale features in the variation in species spatial-temporal patterns, specifically regarding the synchrony and amplitude of temporal dynamics (or spatial patterns) of multiple sites (or times). Our scheme revealed that spatial scales had an important effect on the intrinsic 13-year scale features, which significantly improved our understanding of global Rajiformes fishery dynamics. This showed that the spatial-temporal patterns and scale features obtained based on the coupled space-time scale analysis were complete and more accurate. At the same time, our scheme correctly identified complex pattern structures, such as data stratification and multiple combinations of time and space scales, which can reduce possible errors in practical applications. This scheme can be applied to the identification and prediction of the spatial-temporal patterns in biodiversity in the future and will help in formulating effective policies promoting sustainable fishery resources management and comprehensive conservation of endangered marine species.

Prevalence and distribution of carbapenem-resistant Enterobacterales in companion animals: A nationwide study in the United States using commercial laboratory data.

Carbapenem-resistant Enterobacterales (CRE) are a concern in both human and animal medicine globally. Despite extensive research in humans, limited data exist on CRE in companion animals, with a lack of nationwide prevalence estimates. To assess the occurrence and trends of CRE in cats and dogs across the United States by analyzing 4 years of commercial antimicrobial susceptibility testing (AST) data. Between 2019 and 2022, 477 426 ASTs were conducted on Enterobacterales isolates against imipenem. Isolates were derived from 379 598 dogs and 97 828 cats. Animal origin was not disclosed. In this retrospective study, antimicrobial susceptibility test data from IDEXX Laboratories were analyzed. Analysis included resistance estimations to imipenem stratified by sampling site, an assessment of resistance patterns over time and location, and the application of space-time cluster analysis to identify potential outbreaks. Antibiograms were produced for carbapenem-resistant isolates. Susceptibility to imipenem was high, at 98.86%. Temporal analysis indicated stability in susceptibility, with an unexplained reduction in susceptible isolates in June 2019. Spatial analysis identified 2 high-risk clusters along the Western Coast (relative risk [RR]: 23.26; P < .001) and in Texas (RR: 10.72; P < .001) in that month. Three other clusters were found, in Missouri (RR: 39.55; P = .038), Florida (RR: 4.53; P < .001), and New York (RR: 9.20; P < .001). CRE are present at a low prevalence in dogs and cats across the United States. Variations in prevalence across patient-level and environmental factors highlight the need for tailored stewardship programs.

Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis

Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

Spatiotemporal analysis and seasonality of tuberculosis in Pudong New Area of Shanghai, China, 2014–2023

BackgroundSpatiotemporal analysis is a vital method that plays an indispensable role in monitoring epidemiological changes in diseases and identifying high-risk clusters. However, there is still a blank space in the spatial and temporal distribution of tuberculosis (TB) incidence rate in Pudong New Area, Shanghai. Consequently, it is crucial to comprehend the spatiotemporal distribution of TB in this district, this will guide the prevention and control of TB in the district.MethodsOur research used Geographic Information System (GIS) visualization, spatial autocorrelation analysis, and space-time scan analysis to analyze the TB incidence reported in the Pudong New Area of Shanghai from 2014 to 2023, and described the spatiotemporal clustering and seasonal hot spot distribution of TB incidence.ResultsFrom 2014 to 2023, the incidence of TB in the Pudong New Area decreased, and the mortality was at a low level. The incidence of TB in different towns/streets has declined. The spatial autocorrelation analysis revealed that the incidence of TB was spatially clustered in 2014, 2016–2018, and 2022, with the highest clusters in 2014 and 2022. The high clustering area was mainly concentrated in the northeast. The space-time scan analysis indicated that the most likely cluster was located in 12 towns/streets, with a period of 2014–2018 and a radiation radius of 15.74 km. The heat map showed that there was a correlation between TB incidence and seasonal variations.ConclusionsFrom 2014 to 2023, the incidence of TB in the Pudong New Area of Shanghai declined, but there were spatiotemporal clusters and seasonal correlations in the incidence area. Local departments should formulate corresponding intervention measures, especially in high-clustering areas, to achieve accurate prevention and control of TB within the most effective time and scope.

Economic paradigms, public ethics, socio-economic institutions: dynamics and relationships

The article studies the relationship between ethics and economics in the context of current system’s functioning of the most important social institutions. The purpose of the study is to theoretically analyze the relation between the economic and ethical structure of society and to develop recommendations for the practical application of the results ща designing a strategy for the country's socio-economic development. The research is based on the principles of the systems approach, space-time analysis and the methodology of systemic socio-economic theory. The structure of economic paradigms is compared to the structure of ethical paradigms. The conclusion is made about their mutual conditionality and influence on the type of social structure. The dual nature of the relationship between economics and ethics is substantiated. This duality is manifested in the separation of the subject of economics and ethics in systemic terms: if the economic subjects are the processes aimed at mastering the forces of nature, then the subjects of ethics are the processes aimed at improving the system of intra-societal relations. Economics in society is comprehensive, while ethics has the property of widespread penetration into the consciousness of economic subjects. The economic and ethical spheres all together determine the structure and functions of socio-economic space-time. Strengthening the institution of reputation contributes to the dissemination and consolidation of traditional values and raising the ethical level of society. It is necessary to represent it in the strategy of the country's socio-economic development along with target guidelines for economic dynamics, as well as the desired state of the spiritual sphere of the population and generally accepted principles of ethical behavior. The possibilities of applying ethical principles in solving the problems of location and development of productive forces in the country are explored.

Supply of bioelectricity from sugarcane bagasse in Brazil: a space–time analysis

Bioelectricity generation from sugarcane is significant across Brazil and is related to regional market structure characteristics where the mills are located. To understand the distribution and conjuncture of this sector, this study analyzes the pattern of location, concentration and clustering of the bioelectricity supply from sugarcane bagasse in Brazil, for 2017 and 2022. The data were obtained from the Brazilian National Electric Energy Agency, and the methodology was based on concentration indices and scan statistics. The results showed that the Southeast region presented the most thermoelectric power plants and installed capacity. The Southeast and Midwest regions were highly concentrated in terms of quantity and sugarcane bioelectricity installed capacity. Five clusters were identified for the number of power plants in 2017; for 2022, there were eight clusters. Regarding installed potential, there were 14 clusters in 2017 and 23 clusters in 2022, all statistically significant. The existence of clusters provides information on the competitive advantages in the national market, which can drive new investments in more densified areas or in the neighborhood. Identification of the location and concentration pattern showed that facilities in the state of São Paulo and the Northeast coast were responsible for the most important share of supply. These results indicate to investors the impact of electricity generation on the sector and the most relevant location for installing new thermoelectric plants.

Space–time isogeometric analysis of tire aerodynamics with complex tread pattern, road contact, and tire deformation

AbstractThe space–time (ST) computational method “ST-SI-TC-IGA” and recently-introduced complex-geometry isogeometric analysis (IGA) mesh generation methods have enabled high-fidelity computational analysis of tire aerodynamics with near-actual tire geometry, road contact, tire deformation, and aerodynamic influence of the car body. The tire geometries used in the computations so far included the longitudinal and transverse grooves. Here, we bring the tire geometry much closer to an actual tire geometry by using a complex, asymmetric tread pattern. The complexity of the tread pattern required an updated version of the NURBS Surface-to-Volume Guided Mesh Generation (NSVGMG) method, which was introduced recently and is robust even in mesh generation for complex shapes with distorted boundaries. The core component of the ST-SI-TC-IGA is the ST Variational Multiscale (ST-VMS) method, and the other key components are the ST Slip Interface (ST-SI) and ST Topology Change (ST-TC) methods and the ST Isogeometric Analysis (ST-IGA). They all play a key role. The ST-TC, uniquely offered by the ST framework, enables moving-mesh computation even with the topology change created by the contact between the tire and the road. It deals with the contact while maintaining high-resolution flow representation near the tire.The computational analysis we present is the first of its kind and shows the effectiveness of the ST-SI-TC-IGA and NSVGMG in tire aerodynamic analysis with complex tread pattern, road contact, and tire deformation.

A methodological approach for filling the gap in extreme daily temperature data: an application in the Calabria region (Southern Italy)

Regional studies are crucial for monitoring and managing the impacts of extreme climatic events. This phenomenon is particularly important in some areas, such as the Mediterranean region, which has been identified as one of the most responsive regions to climate change. In this regard, the analysis of large space-time sets of climatic data can provide potentially valuable information, although the datasets are commonly affected by the issue of missing data. This approach can significantly reduce the reliability of inferences derived from space-time data analysis. Consequently, the selection of an effective missing data recovery method is crucial since a poor dataset reconstruction could lead to misleading the decision makers’ judgments. In the present paper, a methodology that can enhance the confidence of the statistical analysis performed on the reconstructed data is presented. The basic assumption of the proposed methodology is that missing data within certain percentages cannot significantly change the shape or parameters of the complete data distribution. Therefore, by applying several missing data recovery methods whose reconstructed dataset better overlaps the original dataset, larger confidence is needed. After the gap filling procedure, the temporal tendencies of the annual daily minimum temperature (T < 0 °C) were analysed in the Calabria region (southern Italy) by applying a test for trend detection to 8 temperature series over a 30-year period (1990–2019). The results showed that there was a constant reduction in the duration of frosty days, indicating the reliability of the effect of climate change.

A Perspective of the Epidemiology of Rabies in South Africa, 1998-2019.

Despite the implementation of various control strategies aimed at eliminating canine-mediated rabies, the disease is still endemic in up to 150 countries across the world. Rabies remains endemic to South Africa, with various reservoir species (both wildlife species and domestic dogs) capable of maintaining rabies infection, and the epidemiology of the disease is yet to be adequately defined. As such, this study used surveillance data collected between 1998 and 2019 from the two diagnostic laboratories in the country for a statistical space-time analysis to determine regions where significant disease clusters could occur. In addition, the robustness of surveillance activities across the country was evaluated through the mathematical evaluation and visualization of testing rates based on the average number of samples tested per species group. In our study, various significant disease clusters were detected for domestic animals, wildlife and livestock. The significant disease clusters for domestic animals and livestock were primarily restricted to eastern South Africa, while the significant disease clusters in wildlife species were detected across northern and western South Africa. Furthermore, the testing rates identified districts from various provinces where surveillance activities could be considered inadequate, consequently influencing the geographical range of the observed clusters. These results could be used to direct intervention campaigns towards high-risk areas, while also allocating the required resources to improve surveillance in the surrounding areas where surveillance was deemed inadequate.

Temporal trends and space-time distribution of leprosy relapse in Brazil from 2001 to 2021.

To analyse the temporal trends and spatiotemporal distribution of leprosy relapse in Brazil from 2001 to 2021. An ecological study with a temporal trend approach and space-time analysis of leprosy relapse in Brazil was carried out with data from the Notifiable Diseases Information System. A total of 31334 patients who experienced leprosy relapse were identified. The number of recurrent cases tended to increase throughout the study period, and this increase was significant among females and in almost all age groups, except for those <15, 50-59 and ≥70y. Several clusters of high- and low-risk patients were identified across all regions with a heterogeneous distribution. The burden of relapse showed an increasing trend in some groups and was distributed in all regions.