Patterns Of Disease Spread Research Articles

Systematic shifts in the variation among host individuals must be considered in climate-disease theory.

To make more informed predictions of host-pathogen interactions under climate change, studies have incorporated the thermal performance of host, vector and pathogen traits into disease models to quantify effects on average transmission rates. However, this body of work has omitted the fact that variation in susceptibility among individual hosts affects disease spread and long-term patterns of host population dynamics. Furthermore, and especially for ectothermic host species, variation in susceptibility is likely to be plastic, influenced by variables such as environmental temperature. For example, as host individuals respond idiosyncratically to temperature, this could affect the population-level variation in susceptibility, such that there may be predictable functional relationships between variation in susceptibility and temperature. Quantifying the relationship between temperature and among-host trait variation will therefore be critical for predicting how climate change and disease will interact to influence host-pathogen population dynamics. Here, we use a model to demonstrate how short-term effects of temperature on the distribution of host susceptibility can drive epidemic characteristics, fluctuations in host population sizes and probabilities of host extinction. Our results emphasize that more research is needed in disease ecology and climate biology to understand the mechanisms that shape individual trait variation, not just trait averages.

Modeling precaution, immunity loss and dispersal on disease dynamics: a two-patch SIRS model

An SIRS model is developed to account for precautionary measures and immunity loss. The existence and stability of equilibria are studied. It is shown that precautionary measures can induce Hopf bifurcations leading to the occurrence of cyclical behavior. The model is then extended to a two-patch scenario to explore how disease spread patterns are influenced by dispersal (travel). Theoretical analyses establish the stability of the disease-free equilibrium, the basic reproduction number, and thresholds related to travel rates. The persistence of the system, as well as the existence of boundary and endemic equilibria, are also discussed. Using Hopf bifurcation theory, we further examine the interaction between nonlinear incidence functions, travel rates, and precaution delay effects in shaping the stability of the endemic equilibrium. The findings reveal a strong connection between reduced infection rates due to precaution and the emergence of Hopf bifurcations, emphasizing the importance of timely and accurate disease information in curbing the spread of diseases. Additionally, the study highlights the significant impact of different infection force functions on equilibrium stability, underscoring the critical role of precautionary measures in disease transmission mechanisms. The results also show the diverse effects of travel rates on disease spread, suggesting that restricting travel may not always lead to favorable outcomes. This underscores the necessity for governments to consider multiple factors comprehensively in their efforts to prevent and control diseases.

Stochastic SEIR Model for Simulating Epidemic Dynamics in Limited Populations

The stochastic SEIR model offers an innovative approach to understanding the spread of infectious diseases, particularly tuberculosis, in limited populations. This study adopts a stochastic model to capture random variability in individual interactions, often overlooked in deterministic models. The population is divided into four main categories: Susceptible (S), Exposed (E), Infected (I), and Recovered (R), with transitions between categories determined by probabilities based on epidemiological parameters. Through simulations, the model demonstrates its capability to depict more realistic patterns of disease spread, including fluctuations in case numbers and epidemic duration. The findings indicate that stochastic variability plays a crucial role in understanding the dynamics of tuberculosis transmission, especially in small populations or when the number of individual contacts is limited. The stochastic SEIR model can serve as an effective tool for policymakers to evaluate various intervention strategies, such as vaccination, transmission control, and treatment, as well as to design public health policies that are data-driven and adaptive to epidemiological uncertainties.

Application of K-Medoids Clustering Method on Disease Clustering Based on Patient Medical Records

Dr. Fauziah Bireuen Regional General Hospital (RSUD) faces daily challenges in managing the ever-increasing medical record data. Currently, the medical record data only consists of reports containing information on the number of patients and their diseases, which are then archived without further processing to generate valuable information. This research aims to cluster diseases based on patient medical records using the K-Medoids Clustering method, thereby providing information on the patterns of disease spread in various regions of the Bireuen Regency. The data used are patient medical records from RSUD Dr. Fauziah Bireuen from 2021–2023, focusing on five common diseases: stroke, hypertension, schizophrenia, dyspepsia, and pneumonia. We conducted Clustering in 17 sub-districts in Bireuen Regency using the K-Medoids method and determined the optimal number of clusters using the Elbow method. The research results show that the K-Medoids method successfully grouped each disease into 3 clusters: high, medium, and low. The results showed that the K-Medoids method successfully grouped each disease into 3 clusters: high, medium, and low. The cluster distribution for stroke disease consists of 7 sub-districts in the high cluster, 7 in the medium, and 3 in the low. Hypertension disease consists of 6 sub-districts in the high cluster, 3 in the medium, and 8 in the low. Schizophrenia disease comprises seven sub-districts in the high cluster, 8 in the medium, and 2 in the low. Dyspepsia disease includes six sub-districts in the high cluster, 2 in the medium, and 9 in the low. Meanwhile, pneumonia disease consists of 8 sub-districts in the high cluster, 5 in the medium, and 4 in the low.

Spatial distribution of poultry farms using point pattern modelling: A method to address livestock environmental impacts and disease transmission risks.

The distribution of farm locations and sizes is paramount to characterize patterns of disease spread. With some regions undergoing rapid intensification of livestock production, resulting in increased clustering of farms in peri-urban areas, measuring changes in the spatial distribution of farms is crucial to design effective interventions. However, those data are not available in many countries, their generation being resource-intensive. Here, we develop a farm distribution model (FDM), which allows the prediction of locations and sizes of poultry farms in countries with scarce data. The model combines (i) a Log-Gaussian Cox process model to simulate the farm distribution as a spatial Poisson point process, and (ii) a random forest model to simulate farm sizes (i.e. the number of animals per farm). Spatial predictors were used to calibrate the FDM on intensive broiler and layer farm distributions in Bangladesh, Gujarat (Indian state) and Thailand. The FDM yielded realistic farm distributions in terms of spatial clustering, farm locations and sizes, while providing insights on the factors influencing these distributions. Finally, we illustrate the relevance of modelling realistic farm distributions in the context of epidemic spread by simulating pathogen transmission on an array of spatial distributions of farms. We found that farm distributions generated from the FDM yielded spreading patterns consistent with simulations using observed data, while random point patterns underestimated the probability of large outbreaks. Indeed, spatial clustering increases vulnerability to epidemics, highlighting the need to account for it in epidemiological modelling studies. As the FDM maintains a realistic distribution of farm location and sizes, its use to inform mathematical models of disease transmission is particularly relevant for regions where these data are not available.

MATHEMATICAL ANALYSIS OF SIR MODEL WITH INCUBATION PERIOD

The SIPmodel is a fundamental tool for simulating epidemic dynamics, but it has limitations in accurately representing real-world scenarios. This paper presents a comprehensive review and mathematical analysis of the SIR model when incorporating the incubation period of infectious diseases. We discuss the significance of the incubation period in disease transmission dynamics and explore the modifications made to the SIR model to integrate this parameter. By analyzing the mathematical equations governing the modified SIR model, we demonstrate its enhanced accuracy in predicting disease spread patterns and its implications for public health interventions. Our findings evaluated the importance of incorporating the incubation period into epidemic models.

Modeling different strategies towards control of lung cancer: leveraging early detection and anti-cancer cell measures

The global population has encountered significant challenges throughout history due to infectious diseases. To comprehensively study these dynamics, a novel deterministic mathematical model, TCD I L 2 Z, is developed for the early detection and treatment of lung cancer. This model incorporates I L 2 cytokine and anti-PD-L1 inhibitors, enhancing the immune system’s anticancer response within five epidemiological compartments. The TCD I L 2 Z model is analyzed qualitatively and quantitatively, emphasizing local stability given the limited data-a critical component of epidemic modeling. The model is systematically validated by examining essential elements such as equilibrium points, the reproduction number ( R 0 ), stability, and sensitivity analysis. Next-generation techniques based on R 0 that track disease transmission rates across the sub-compartments are fed into the system. At the same time, sensitivity analysis helps model how a particular parameter affects the dynamics of the system. The stability on the global level of such therapy agents retrogrades individuals with immunosuppression or treated with I L 2 and anti-PD-L1 inhibitors admiring the Lyapunov functions’ applications. NSFD scheme based on the implicit method is used to find the exact value and is compared with Euler’s method and RK4, which guarantees accuracy. Thus, the simulations were conducted in the MATLAB environment. These simulations present the general symptomatic and asymptomatic consequences of lung cancer globally when detected in the middle and early stages, and measures of anticancer cells are implemented including boosting the immune system for low immune individuals. In addition, such a result provides knowledge about real-world control dynamics with I L 2 and anti-PD-L1 inhibitors. The studies will contribute to the understanding of disease spread patterns and will provide the basis for evidence-based intervention development that will be geared toward actual outcomes.

Determining epidemiological patterns in disease identification using mathematical models on machine learning based multilayer structures

The object of the study is epidemiological grouping using the SEIR mathematical model on a machine learning-based multilayer network. The problems in this research are related to managing epidemiological data on a large scale to determine disease patterns and identification such as the number of recovered cases, number of infected cases and number of deaths and demographic factors. In the process, traditional methods make it difficult to carry out processes such as determining patterns and identifying diseases. So, it is necessary to use machine learning and the SEIR (Susceptible-Exposed-Infectious-Recovered) mathematical model which can be integrated with multilayer networks to increase accuracy and effectiveness in identifying diseases and determining patterns. The results obtained from this research are a model that can identify and determine patterns of disease spread in large-scale epidemiological data. In its application, the SEIR mathematical model combined into a social layer and an environmental layer in a multilayer network. This research is research with a level of novelty in the application of the SEIR mathematical model to multilayer networks and machine learning so that the model formed can be used to view the distribution of epidemiological data for disease-related information. Machine learning aims to process large-scale data in minimal time resulting in clustering and identification of diseases such as flu, Covid-19 and pneumonia. This research has an accuracy of 94 % using the MAPE evaluation technique. It is hoped that the resulting model can be used in the world of health for disease mapping in certain areas as a reference for mitigating the spread of disease

The Peritoneum: Anatomy, Pathologic Findings, and Patterns of Disease Spread.

Disease spread in the abdomen and pelvis generally occurs in a predictable pattern in relation to anatomic landmarks and fascial planes. Anatomically, the abdominopelvic cavity is subdivided into several smaller spaces or compartments by key ligaments and fascial planes. The abdominal cavity has been traditionally divided into peritoneal, retroperitoneal, and pelvic extraperitoneal spaces. Recently, more clinically relevant classifications have evolved. Many pathologic conditions affect the abdominal cavity, including traumatic, inflammatory, infectious, and neoplastic processes. These abnormalities can extend beyond their sites of origin through various pathways. Identifying the origin of a disease process is the first step in formulating a differential diagnosis and ultimately reaching a final diagnosis. Pathologic conditions differ in terms of pathways of disease spread. For example, simple fluid tracks along fascial planes, respecting anatomic boundaries, while fluid from acute necrotizing pancreatitis can destroy fascial planes, resulting in transfascial spread without regard for anatomic landmarks. Furthermore, neoplastic processes can spread through multiple pathways, with a propensity for spread to noncontiguous sites. When the origin of a disease process is not readily apparent, recognizing the spread pattern can allow the radiologist to work backward and ultimately arrive at the site or source of pathogenesis. As such, a cohesive understanding of the peritoneal anatomy, the typical organ or site of origin for a disease process, and the corresponding pattern of disease spread is critical not only for initial diagnosis but also for establishing a road map for staging, anticipating further disease spread, guiding search patterns and report checklists, determining prognosis, and tailoring appropriate follow-up imaging studies. ©RSNA, 2024 Supplemental material is available for this article.

Retroperitoneum revisited: a review of radiological literature and updated concept of retroperitoneal fascial anatomy with imaging features and correlating anatomy

PurposeSpread of disease in the retroperitoneum is dictated by the complex anatomy of retroperitoneal fasciae and is still incompletely understood. Conflicting reports have led to insufficient and incorrect anatomical concepts in radiological literature.MethodsThis review will discuss previous concepts prevalent in radiological literature and their shortcomings will be highlighted. New insights from recent anatomical and embryological research, together with imaging examples, will be used to clarify patterns of disease spread in the retroperitoneum that remain unexplained by these concepts.ResultsThe fusion fascia and the renal fascia in particular give rise to planes and spaces that act as vectors for spread of disease in the retroperitoneum. Some of these planes and structures, such as the caudal extension of the renal fascia, have previously not been described in radiological literature.ConclusionNew insights, including the various fasciae, potential spaces and planes, are incorporated into an updated combined retroperitoneal fascial concept.

Exploring the urban arbovirus landscape in Rio de Janeiro, Brazil: transmission dynamics and patterns of disease spread

This study focuses on urban arboviruses, specifically dengue (DENV), chikungunya (CHIKV), and Zika (ZIKV), which pose a significant public health challenge in Rio de Janeiro state, Southeast Brazil. In our research, we highlight critical findings on the transmission dynamics of these arboviruses in Rio de Janeiro, identifying distinct patterns of disease spread. By combining genomic data with case reports from the Brazilian Ministry of Health, we have analysed the phylogenetics, prevalence and spatial distribution of these endemic viruses within the state. Our results revealed sustained DENV transmission primarily in the northern part of the state, a significant ZIKV epidemic in 2016 affecting all mesoregions, and two major CHIKV outbreaks in 2018 and 2019, predominantly impacting the northern and southern areas. Our analysis suggests an inverse relationship between arboviral case incidence and urban density, with less populous regions experiencing higher transmission rates, potentially attributed to a complex interplay of factors such as the efficacy of vector control measures, environmental conditions, local immunity levels, and human mobility. Furthermore, our investigation unveiled distinct age and gender trends among affected individuals. Notably, dengue cases were predominantly observed in young adults aged 32, while chikungunya cases were more prevalent among individuals over 41. In contrast, cases of ZIKV were concentrated around the 33-year age group. Intriguingly, females accounted for nearly 60% of the cases, suggesting a potential gender-based difference in infection rates. Our findings underscore the complexity of arbovirus transmission and the need for interventions tailored to different geographical mesoregions. Enhanced surveillance and genomic sequencing will be essential for a deeper, more nuanced understanding of regional arbovirus dynamics. Identifying potential blind spots within the state will be pivotal for developing and implementing more effective public health strategies, specifically designed to address the unique challenges posed by these viruses throughout the state. This study was supported by the National Institutes of Health USA grant U01 AI151698 for the United World Arbovirus Research Network (UWARN) and the CRP-ICGEB RESEARCH GRANT 2020 Project CRP/BRA20-03.

Role of Ga68 Prostate-Specific Membrane Antigen Positron Emission Tomography-Computed Tomography in Prostate Cancer Imaging

AbstractThe introduction of prostate-specific membrane antigen (PSMA) in clinical practice has revolutionized the evaluation of biochemical recurrence (BCR) of prostate cancer after curative-intent treatment. The high expression of this glycoprotein in prostate cancer cells makes PSMA imaging superior to the current conventional staging methods, namely bone scanning and computed tomography. The high capability of PSMA imaging for identifying very small previously undetected lesions has been widely demonstrated in the literature, leading to a rethinking of patient management by treating physicians. The usual and predictable patterns of spread in prostate cancer are still more prevalent, such as spread to pelvic lymph nodes and bone metastasis, but different patterns of disease spread are becoming more commonly recognized with higher reliability because PSMA imaging allows the detection of more usual and unusual lesions than conventional imaging. The expanding use of PSMA positron emission tomography (PET) has also revealed PSMA ligand uptake in diverse nonprostatic diseases, which raised questions about the specificity of this imaging modality. It is important for the reading physician to recognize and understand the usual disease spread, the most prevalent unusual sites of relapse, and the nonprostatic conditions which are PSMA avid not only to heighten the relevancy of reports but also to improve imaging consultancy in multispecialty oncologic practice. This article aims to brief the role of PSMA PET in the initial staging of multitude of clinical scenarios, BCR, castration-resistant prostate cancer, usual and unusual patterns of recurrence and metastatic spread diagnosed with PSMA PET, normal variants, pitfalls, and nonprostatic disorders showing PSMA expression.

Apex Nasi: What’s Happening near the Tip of the Nose?

The apex nasi (colloquially, the tip of the nose) is an anatomically complex region that can be divided into 2 parts: the soft tissue/cartilaginous framework of the most anterior-inferior aspect of the nose and the anterior nasal cavity. Detailed knowledge of local anatomy is helpful to characterize pathology and understand patterns of disease spread. Although many lesions of the apex nasi are readily assessed on physical examination, CT and MR imaging can provide important, often complementary, information about this region. CT well demonstrates osseous anatomy, while MR imaging offers increased specificity for certain conditions, given its superior soft-tissue contrast. Regarding specific pathologies of the region, because cutaneous cancers are common and often involve the apex nasi, they will be discussed in detail, along with potential imaging pitfalls such as perineural tumor spread or coexistence with rhinophyma. Certain rare non-neoplastic or developmental lesions, notably nasolabial cysts and pyriform aperture stenosis, are unique to this region. Vascular lesions such as angiosarcoma and venous malformation may involve the apex nasi, as may nasal septal hematoma and abscess, the latter necessitating immediate treatment. Given the high prevalence of rhinoplasty, the imaging appearance of cosmetic nasal implants and selected rare procedural complications is shown.Learning Objective: To describe the anatomic landmarks of the external nose and anterior nasal cavity and to illustrate common and uncommon pathologies in these locations.

Prostate-specific membrane antigen-positron emission tomography (PSMA-PET) of prostate cancer: current and emerging applications.

Prostate-specific membrane antigen-positron emission tomography (PSMA-PET) is transforming the management of patients with prostate cancer. In appropriately selected patients, PSMA-PET offers superior sensitivity and specificity compared to conventional imaging (e.g., computed tomography and bone scintigraphy) as well as choline and fluciclovine PET, with the added benefit of consolidating bone and soft tissue evaluation into a single study. Despite being a newly available imaging tool, PSMA-PET has established indications, interpretation guidelines, and reporting criteria, which will be reviewed. The prostate cancer care team, from imaging specialists to those delivering treatment, should have knowledge of physiologic PSMA radiotracer uptake, patterns of disease spread, and the strengths and limitations of PSMA-PET. In this review, current and emerging applications of PSMA-PET, including appropriateness use criteria as well as image interpretation and pitfalls, will be provided with an emphasis on clinical implications.

Visualization of Covid-19 Data in Indonesia in 2022 through the Google Data Studio Dashboard

The COVID-19 pandemic has presented significant challenges to governments, researchers and the general public in understanding and monitoring the spread of this disease. In an effort to analyse the spread of COVID-19 disease in Indonesia effectively, this study uses Google Data Studio as a tool for data visualization and better understanding. This review is based on collecting data on the spread of COVID-19 disease in Indonesia which has been collected from various reliable sources. , including the World Health Organization (WHO) and national health agencies. This data is then processed and processed using Google Data Studio to produce informative visualizations. The results of the study show that Google Data Studio can be used effectively to analyse the spread of the COVID-19 disease in Indonesia. Through the use of available features, such as interactive graphs, maps, and tables, researchers can easily describe patterns of disease spread, infection rates, and recovery rates from an area or country. The quality of data collected from different sources may vary, and this can affect the accuracy and reliability of the resulting visualizations. Elements of the Scorecard that displays some important information related to the Covid-19 pandemic from 1 January 2019 to 31 January 2022. Information regarding the Covid-19 displayed on the Scorecard is as follows. The total survivors of the Covid-19 disease are 3,234,336,858 people. This indicates the number of people who have successfully recovered and recovered from infection with the Covid-19 virus during the period in question. The total number of deaths due to Covid-19 is 89,398,496 people. This reflects the number of people who died due to complications caused by the Covid-19 virus in that period.

Longitudinal flortaucipir, metabolism and volume differ between phonetic and prosodic speech apraxia.

Progressive apraxia of speech (PAOS) is a neurodegenerative motor-speech disorder that most commonly arises from a four-repeat tauopathy. Recent studies have established that progressive apraxia of speech is not a homogenous disease but rather there are distinct subtypes: the phonetic subtype is characterized by distorted sound substitutions, the prosodic subtype by slow and segmented speech and the mixed subtype by a combination of both but lack of predominance of either. There is some evidence that cross-sectional patterns of neurodegeneration differ across subtypes, although it is unknown whether longitudinal patterns of neurodegeneration differ. We examined longitudinal patterns of atrophy on MRI, hypometabolism on 18F-fluorodeoxyglucose-PET and tau uptake on flortaucipir-PET in a large cohort of subjects with PAOS that had been followed for many years. Ninety-one subjects with PAOS (51 phonetic, 40 prosodic) were recruited by the Neurodegenerative Research Group. Of these, 54 (27 phonetic, 27 prosodic) returned for annual follow-up, with up to seven longitudinal visits (total visits analysed = 217). Volumes, metabolism and flortaucipir uptake were measured for subcortical and cortical regions, for all scans. Bayesian hierarchical models were used to model longitudinal change across imaging modalities with PAOS subtypes being compared at baseline, 4 years from baseline, and in terms of rates of change. The phonetic group showed smaller volumes and worse metabolism in Broca's area and the striatum at baseline and after 4 years, and faster rates of change in these regions, compared with the prosodic group. There was also evidence of faster spread of hypometabolism and flortaucipir uptake into the temporal and parietal lobes in the phonetic group. In contrast, the prosodic group showed smaller cerebellar dentate, midbrain, substantia nigra and thalamus volumes at baseline and after 4 years, as well as faster rates of atrophy, than the phonetic group. Greater hypometabolism and flortaucipir uptake were also observed in the cerebellar dentate and substantia nigra in the prosodic group. Mixed findings were observed in the supplementary motor area and precentral cortex, with no clear differences observed across phonetic and prosodic groups. These findings support different patterns of disease spread in PAOS subtypes, with corticostriatal patterns in the phonetic subtype and brainstem and thalamic patterns in the prosodic subtype, providing insight into the pathophysiology and heterogeneity of PAOS.

Spatial-temporal evolution of overweight and obesity among Chinese adolescents from 2016 to 2020

This study examines the spatial-temporal evolution of overweight and obesity among Chinese adolescents aged 14-17. Data from five national surveys conducted between 2016 and 2020 were analyzed to determine distribution patterns and trends. Results showed that overweight and obesity exhibit spatial clustering, with greater severity in the north and less severity in the south. The issue has spread from the northeast to the southwest of Mainland China. Using a local autocorrelation model, the regions were divided into a northern disease cold spot area (Inner Mongolia) and a southern disease hot spot area (Guangxi). Over the past five years, overweight rates among Chinese adolescents have not been effectively curbed, but obesity has shown some success in control and reversal until 2019. Future efforts should focus on the spatial-temporal pattern of disease spread, targeting hotspot areas and abnormal values for regional synergy and precise prevention and control.

Theoretical exploration of behavioral change in disease control: Sex work as a case study for HSV-2 transmission dynamics

Currently, there are non effective vaccines that prevents herpes simplex virus-2 (HSV-2) and the recommended treatments are sub-optimal, that implies other methods can be employed in an attempt to reduce the prevalence of the disease. To address this, we developed a new deterministic mathematical model that seeks to investigate HSV-2 transmission dynamics, emphasizing the role of risky sexual behaviors in the form of sex work. The reproduction number and the equilibria states of the model were determined and their stabilities analyzed. Our analysis identified key transmission parameters, allowing us to forecast possible disease spread patterns and to evaluate the impact of behavioral change to complement sub-optimal treatment and vaccine absence. Our model managed to highlight the importance of behavior change, especially among latent individuals, suggesting the need for combined preventive strategies of behavior change and treatment. The results also indicated that the virus can still spread even in the absence of high-risk sexual behaviors like sex workers, implying that other hidden factors influencing HSV-2 transmission. Our results emphasize on the multi-layered challenge of HSV-2 control and the need for comprehensive strategies addressing both the disease spread and associated high-risk behaviors.

Low Rectal Cancers at Initial Staging MRI.

Low rectal cancers, which are associated with increased risk of local recurrent disease and poorer prognosis, have unique anatomic considerations and issues for staging and treatment that do not apply to mid and high rectal cancers. Although tumor histology help drive the staging and treatment of all rectal cancers, it is of particular importance in low rectal cancers, which may involve the anal canal, to help establish whether a low rectal mass should be staged and treated as a rectal cancer (ie, adenocarcinoma) or an anal cancer (ie, squamous cell carcinoma). Optimal staging and treatment of rectal cancer are contingent on tumor location and local extent, which help guide management decisions including neoadjuvant therapy and curative surgical treatment strategies. Tumor location in the low rectum and local involvement of the anal canal, sphincter, and pelvic floor help determine whether a patient can undergo sphincter-preserving resection such as a low anterior resection versus abdominoperineal resection to achieve negative surgical margins. Issues exist related to the anatomy and patterns of disease spread that are unique to the low rectum and include how to determine and stage anal sphincter involvement, mesorectal fascia status at the pelvic floor, and nodal status of extramesorectal nodes such as the external iliac and inguinal lymph nodes. For these reasons, it is imperative that radiologists who interpret rectal cancer staging MRI examinations feel comfortable with the unique anatomy of the low rectum and anal canal, nuances of low rectal cancer local disease spread, and treatment paradigms for low rectal cancer. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Gollub in this issue.

Climate Change and its Impact on Human Health: A Medical Geography Perspective

This research discusses the impact of climate change on human health using a medical geography approach. Climate change affects health through extreme heatwaves, changing patterns of infectious disease spread and intensification of natural disasters. A medical geography perspective provides insight into how geographical factors such as climate, topography and population distribution affect vulnerability to these impacts. The importance of understanding medical geography in meeting these challenges and the need for global cooperation to address the impacts of climate change on human health are emphasized. The article underscores the need for appropriate mitigation measures to protect future human health in the face of increasingly apparent climate change.