Disease Transmission Patterns Research Articles

Population Movement and Poliovirus Spread across Pakistan and Afghanistan in 2023.

Population movement dynamics are a critical part of understanding communicable disease transmission patterns and determining where, when, and with whom to deliver appropriate prevention interventions. This study aimed to identify the origin of the Afghan population and their patterns of movement within Karachi, to assess the polio vaccination status of children under the age of five, and to investigate the travel history and guest arrival patterns of individuals from Afghanistan and other regions known to be affected by wild poliovirus type 1 (WPV1) within the past six months. A cross-sectional survey was conducted in selected 12 union councils of Karachi, Pakistan. The data were collected through interviews with Afghan household members and from the frontline workers (FLWs) responsible for the polio vaccination of the children of the same households. Cohen's kappa was used to check the agreement between information provided by the household participant and FLWs. A total of 409 Afghan household members were interviewed. Travel of any household member outside the city within the last six months was reported by 105 (25.7%) participants, 140 (34.2%) hosted guests within the last six months, and 92 (22.5%) participants reported that guest children were vaccinated in their households. A total of 230 (56.2%) participants observed polio teams at relatives' households within Karachi, and 127 (31.1%) observed polio teams at relatives' households outside Karachi in different districts of Pakistan and Afghanistan. Fair to moderate agreement was observed between information provided by the household members and FLWs on the variable's duration of living at current residence (Kappa = 0.370), travel history (Kappa = 0.429), guest arrival (Kappa = 0.395), and household children vaccinated for OPV (Kappa = 0.419). Substantial population mobility was observed between Afghanistan and Pakistan as well as significant movement of the Afghan population within Karachi in the last six months. These findings warrant attention and targeted implementation of interventions to enhance and sustain both routine and supplementary immunization activities within this demographic group.

Methodology for assessing the impact of emergencies on the spread of infectious diseases

The spread of infectious diseases is significantly influenced by emergencies, particularly military conflicts, which disrupt healthcare systems and increase the risks of epidemics. The full-scale Russian invasion of Ukraine has exacerbated these challenges, causing environmental damage, mass displacement, and the breakdown of healthcare services, all of which contribute to the spread of infectious diseases. This study aims to develop a comprehensive methodology for assessing the impact of emergencies on the spread of infectious diseases, focusing on the full-scale invasion of Ukraine. The object of this study is to address epidemic threats posed by emergencies, particularly the increased spread of infectious diseases due to war-related disruptions. The subject of this study is methods and models of infectious disease transmission under conditions of emergencies, emphasizing the Russian full-scale invasion of Ukraine. The tasks of this study are to provide an analysis of the current state of research and develop a methodology for assessing the impact of emergencies on the spread of infectious diseases. The proposed methodology includes several key components. Comprehensive data from public health organizations includes infectious disease statistics, demographic shifts, healthcare disruptions, and environmental factors exacerbated by emergencies. Data preprocessing removes inconsistencies, standardization of formats, and normalization for population size differences. Machine learning models, including convolutional neural networks and recurrent neural networks, have been developed to simulate the spread of diseases based on demographic, environmental, and healthcare-related variables. Deep learning models analyze spatial and temporal patterns, whereas compartmental models such as SIR estimate changes in reproductive numbers (R₀ and Re). Additionally, models of excess mortality incorporate mixed effects to account for regional and time-based variations. The methodology incorporates real-time monitoring of epidemic threats using real-time data from multiple sources, enabling dynamic assessments of disease spread and facilitating predictive modeling. The models were trained on historical data and validated using cross-validation techniques to ensure robustness and reliability, with a specific focus on the pre- and post-invasion phases in Ukraine.Results: The study provides a comprehensive framework for collecting and processing data on infectious diseases and epidemic threats in emergencies. The proposed model introduces advanced machine learning and epidemiological models trained on pre- and post-invasion data to analyze disease transmission patterns and forecast future epidemic dynamics. Conclusion: The proposed methodology addresses current gaps in infectious disease during emergencies by integrating real-time data and machine learning techniques. This research improves decision-making in public health management and biosafety during crises, particularly in war-affected regions like Ukraine.

Visual analysis of hot spots and trends in research of meteorology and hemorrhagic fever with renal syndrome: a bibliometric analysis based on CiteSpace and VOSviewer

Objective: We here displayed the global research trends of meteorology and hemorrhagic fever with renal syndrome (HFRS) as a visual knowledge map by using bibliometrics and revealed the research directions, hotspots, trends, and frontiers in this field.Methods: Using Web of Science core collection as the data source and with CiteSpace and VOSviewer software, we collected and analyzed the annual number of papers, cooperative relationships (countries, institutions, authors, etc.), citations (literature citation, literature co-citation, literature publication, etc.), keywords (emergence, clustering, etc.) of meteorology, and HFRS-related research data for the past 30 years, and drew a visual map.Results: In total, this study included 313 papers investigating the relationship between meteorology and HFRS. The first paper was published in 1992. Globally, United States had the largest number of publications in this field, and the Chinese Center for Disease Control and Prevention was the most influential institution conducting related research (20 articles published, and the mediation centrality was 0.24). Several small author cooperation clusters were formed; however, the number of papers published by the same scholar and the co-citation frequency were low. Cazelles Bernard (7 articles) published the highest number of articles in this field, and Gubler DJ was the author with the most co-citations (55 times). The most frequently cited journal was Emerging Infectious Diseases. In this field, the top three high-frequency keywords were “hemorrhagic fever,” “transmission,” and “temperature.” According to keyword cluster analysis, the top three themes were dengue, dechlorane plus, and bank voles. The timeline spectrum exhibited that dengue clustering had a good temporal continuity. The trend analysis of emergent words revealed that the research on “temperature,” “meteorological factors” and “Puumala hantavirus” has gradually appeared in recent years.Conclusion: This study represents the first comprehensive exploration of global trends, hotspots, frontiers, and developments in the relationship between meteorology and HFRS, utilizing CiteSpace and VOSviewer software. The findings of this study are crucial for elucidating the influence of climate change on disease transmission patterns and offering novel insights for forthcoming epidemiological research and public health interventions.

Association between air pollution, meteorological factors and hand, foot, and mouth disease incidence in Zhejiang Province: A Bayesian spatio-temporal modelling study

Temperature and humidity are known to influence seasonal patterns of hand, foot and mouth disease (HFMD) transmission. However, the effect of air pollution and climate-air pollution interaction on the intensity of HFMD epidemics is poorly understood. This study aims to quantify the exposure-response effects of air pollution on HFMD risk by level of meteorological indicators in Zhejiang Province. A spatiotemporal Bayesian hierarchical model was used to determine the exposure-response association between air pollution, meteorological factors and the relative risk (RR) of HFMD. The model was fitted to monthly HFMD case data for the 92 counties in Zhejiang Province between January 2013, and December 2021. We assessed both linear interaction effects between air pollutants and meteorological factors on HFMD, and the modification role of extreme weather conditions on the nonlinear air pollutant-HFMD association. A 10 μg/m³ increase in PM10 and NO2 concentration raised the HFMD risk by 6.6% (RR = 1.066, 95% CI: 1.042, 1.092) and 20.8% (RR = 1.208, 95% CI: 1.162, 1.256) respectively. HFMD was negatively associated with O3 (RR = 0.980, 95% CI: 0.968, 0.993), SO2 (RR = 0.820, 95% CI: 0.776, 0.868) and CO (RR = 0.770, 95% CI: 0.621, 0.956). Temperature, humidity, and precipitation positively interacted with most air pollutants on HFMD, while wind speed had a negative interaction effect. In higher temperature conditions, the HFMD risk associated with PM10 increased. Elevated humidity levels amplified the risk associated with PM10, as did CO concentrations within a certain range. Heavy rainfall intensified the risk of HFMD associated with PM10, SO2, and CO. Results of this study showed that air pollution was associated with HFMD incidence, with different climate factors playing various modification roles in the associations and primarily leading to increased risk under extreme conditions. These findings provide a theoretical basis for personal protection and government prevention and control measures.

Cochlosoma anatis in Poultry and its Industry Impact.

Cochlosoma anatis is a flagellated protozoan parasite classified in the Trichomonadidae family and is the causative agent of cochlosomiasis, an enteric disease of turkeys, waterfowl, and other wild birds. Cochlosomiasis symptoms largely consist of watery diarrhea, lethargic birds, depressed weight gain, and widespread flock morbidity causing flock nonuniformity. The known distribution of C. anatis is centered around areas of turkey production farms in the southeast United States, e.g., North Carolina, Missouri and Arkansas, but has been reported in other states and some other countries. Diagnosis is confirmed through examination of enteric mucosal scrapings using light microscopy. Following the withdrawal of approval of effective antiprotozoal medications for use in commercial animal production, cochlosomiasis has become a greater concern for commercial turkey industry professionals. Transmission of C. anatis occurs via the fecal-oral route, but the organism is fragile outside the host, suggesting the implication of a vector in the introduction of disease to susceptible farms. Research regarding C. anatis pathogenicity, transmission, and environmental involvement has been limited, creating a gap in cochlosomiasis knowledge. Future research is needed to further explore ways to prevent and treat cochlosomiasis, with needs centered on disease pathogenesis, transmission patterns, and prophylaxis and treatment methods.

Unveiling ecological/evolutionary insights in HIV viral load dynamics: Allowing random slopes to observe correlational changes to CpG-contents and other molecular and clinical predictors

In the context of infectious diseases, the dynamic interplay between ever-changing host populations and viral biology demands a more flexible modeling approach than common fixed correlations. Embracing random-effects regression models allows for a nuanced understanding of the intricate ecological and evolutionary dynamics underlying complex phenomena, offering valuable insights into disease progression and transmission patterns. In this article, we employed a random-effects regression to model an observed decreasing median plasma viral load (pVL) among individuals with HIV in Mexico City during 2019–2021. We identified how these functional slope changes (i.e. random slopes by year) improved predictions of the observed pVL median changes between 2019 and 2021, leading us to hypothesize underlying ecological and evolutionary factors. Our analysis involved a dataset of pVL values from 7325 ART-naïve individuals living with HIV, accompanied by their associated clinical and viral molecular predictors. A conventional fixed-effects linear model revealed significant correlations between pVL and predictors that evolved over time. However, this fixed-effects model could not fully explain the reduction in median pVL; thus, prompting us to adopt random-effects models. After applying a random effects regression model—with random slopes and intercepts by year—, we observed potential "functional changes" within the local HIV viral population, highlighting the importance of ecological and evolutionary considerations in HIV dynamics: A notably stronger negative correlation emerged between HIV pVL and the CpG content in the pol gene, suggesting a changing immune landscape influenced by CpG-induced innate immune responses that could impact viral load dynamics. Our study underscores the significance of random effects models in capturing dynamic correlations and the crucial role of molecular characteristics like CpG content. By enriching our understanding of changing host-virus interactions and HIV progression, our findings contribute to the broader relevance of such models in infectious disease research. They shed light on the changing interplay between host and pathogen, driving us closer to more effective strategies for managing infectious diseases. Significance of the studyThis study highlights a decreasing trend in median plasma viral loads among ART-naïve individuals living with HIV in Mexico City between 2019 and 2021. It uncovers various predictors significantly correlated with pVL, shedding light on the complex interplay between host-virus interactions and disease progression. By employing a random-slopes model, the researchers move beyond traditional fixed-effects models to better capture dynamic correlations and evolutionary changes in HIV dynamics. The discovery of a stronger negative correlation between pVL and CpG content in HIV-pol sequences suggests potential changes in the immune landscape and innate immune responses, opening avenues for further research into adaptive changes and responses to environmental shifts in the context of HIV infection. The study's emphasis on molecular characteristics as predictors of pVL adds valuable insights to epidemiological and evolutionary studies of viruses, providing new avenues for understanding and managing HIV infection at the population level.

An Agent-Based Model for Disease Epidemics in Greece

In this research, we present the first steps toward developing a data-driven agent-based model (ABM) specifically designed for simulating infectious disease dynamics in Greece. Amidst the ongoing COVID-19 pandemic caused by SARS-CoV-2, this research holds significant importance as it can offer valuable insights into disease transmission patterns and assist in devising effective intervention strategies. To the best of our knowledge, no similar study has been conducted in Greece. We constructed a prototype ABM that utilizes publicly accessible data to accurately represent the complex interactions and dynamics of disease spread in the Greek population. By incorporating demographic information and behavioral patterns, our model captures the specific characteristics of Greece, enabling accurate and context-specific simulations. By using our proposed ABM, we aim to assist policymakers in making informed decisions regarding disease control and prevention. Through the use of simulations, policymakers have the opportunity to explore different scenarios and predict the possible results of various intervention measures. These may include strategies like testing approaches, contact tracing, vaccination campaigns, and social distancing measures. Through these simulations, policymakers can assess the effectiveness and feasibility of these interventions, leading to the development of well-informed strategies aimed at reducing the impact of infectious diseases on the Greek population. This study is an initial exploration toward understanding disease transmission patterns and a first step towards formulating effective intervention strategies for Greece.

Dynamical Analysis of an Improved Bidirectional Immunization SIR Model in Complex Network.

In order to investigate the impact of two immunization strategies-vaccination targeting susceptible individuals to reduce their infection rate and clinical medical interventions targeting infected individuals to enhance their recovery rate-on the spread of infectious diseases in complex networks, this study proposes a bilinear SIR infectious disease model that considers bidirectional immunization. By analyzing the conditions for the existence of endemic equilibrium points, we derive the basic reproduction numbers and outbreak thresholds for both homogeneous and heterogeneous networks. The epidemic model is then reconstructed and extensively analyzed using continuous-time Markov chain (CTMC) methods. This analysis includes the investigation of transition probabilities, transition rate matrices, steady-state distributions, and the transition probability matrix based on the embedded chain. In numerical simulations, a notable concordance exists between the outcomes of CTMC and mean-field (MF) simulations, thereby substantiating the efficacy of the CTMC model. Moreover, the CTMC-based model adeptly captures the inherent stochastic fluctuation in the disease transmission, which is consistent with the mathematical properties of Markov chains. We further analyze the relationship between the system's steady-state infection density and the immunization rate through MCS. The results suggest that the infection density decreases with an increase in the immunization rate among susceptible individuals. The current research results will enhance our understanding of infectious disease transmission patterns in real-world scenarios, providing valuable theoretical insights for the development of epidemic prevention and control strategies.

Numerical study on the onset of global-scale flow from individual buoyant plumes: Implications for indoor disease transmission

Transport of exhaled droplets and aerosol suspension is a main route for the transmission of highly infectious respiratory diseases. A poorly ventilated room, where human body heat drives the flow and the pathogen motion, is one such paradigmatic situation with an elevated risk of viral transmission. Here, we report a numerical study on human body heat-driven buoyancy convection in a slender rectangular geometry with the geometric size of 12 × 1 × 3 m3. Using large-scale three-dimensional simulations, we reveal how different spacings between human body heat sources can potentially spread pathogenic species between occupants in a room. Morphological transition in airflow takes place as the distance between human heat sources is varied, which shapes distinct patterns of disease transmission: For sufficiently large distance, individual buoyant plume creates a natural barrier, forming buoyant jets that block suspension spread between occupants. Thermal plumes exhibit significant individual effects. However, for small distances, a collective effect emerges and thermal plumes condense into superstructure, facilitating long-distance suspension transport via crossing between convection rolls. In addition, we quantify the impact of morphological transition on the transport of viral particles by introducing tracer particles. The quantitative analysis shows that under certain critical distances, the infection risk becomes significantly elevated due to this transition and collective behavior. Our findings highlight the importance of reasonable social distancing to reduce indoor cross-transmission of viral particles between people and provide new insights into the hidden transitional behavior of pathogen transmission in indoor environments.

Individual and temporal variation in movement patterns of wild alpine reindeer and implications for disease management

Animal behaviour is important for prevalence and outbreaks of infectious diseases, for instance by affecting individual interactions. Increasing the knowledge of individual movement patterns can provide better insight into disease prevalence and spread, helping to target efforts to minimise disease outbreaks. Chronic wasting disease (CWD) is a fatal prion disease affecting cervids. CWD is transmitted by animal‐to‐animal contact and through the environment, thus individual variation in space use and social associations may influence disease transmission patterns and infection risk. CWD was detected in Norwegian alpine reindeer Rangifer tarandus tarandus in 2016, and eradication of the infected population was implemented. A 3:1 infection rate between males and females suggests sex‐specific behavioural drivers. We utilised an extensive individual‐based dataset of 149 GPS‐marked wild reindeer to investigate individual variation in movement patterns in terms of inter‐ and intra‐annual home range size and site fidelity, and variation in home range overlap and distances between individuals. We aimed to identify patterns which could indicate higher potential disease risk. Females had larger annual and seasonal home ranges than males, except during calving and rut. Greater home range overlaps and shorter between‐individual distances were found between same‐sex individuals than different‐sex individuals, except during the rut. Accordingly, the rut season stands out with greater male home ranges, greater home range overlap and shorter distances between males and between males and females, which could indicate that this season is critical for disease transmission. Measures to prevent disease spread should lower contact rates, e.g. by reducing the abundance of adult males before they mix with other groups during the rut. This can be achieved for instance by allowing earlier hunt on adult males when they are distributed in small male groups, to reduce the transmission risk and keep disturbance of other individuals low.

A systematic overview of bovine brucellosis and its implications for public health

Comprehensive study reveals the complex nature of bovine brucellosis, with special emphasis on its potential for transmission to humans. By adopting a systemic methodology thorough literature search was done, including publications published during 1897 to 2022, form academic databases, research repositories, and scholarly credible literature sources. The criteria for being included in this study ensured an in-depth investigation of the previous and current facets of the disease. The methodological extraction and processing of data enabled for the categorising of pertinent findings into particular groups, which enhanced the comprehension of characteristics, transmission trends, clinical aspects, diagnostic tools, treatment options, prevention strategies, vaccination, and public health concerns. This study highlights the importance of this zoonotic threat indicating the compelling need of effective solutions of this problem by keeping in view the zoonotic variables and epidemiological views. Understanding disease origins and transmission patterns help design effective control strategies that are tailored to each mode of transmission. A detailed evaluation of symptoms shows that humans and animals need appropriate diagnosis. Comprehensive treatment and preventative measures are supported by the review. The study of vaccination as a preventive measure underlines its importance in eliminating bovine brucellosis. Public health issues require knowledge, surveillance, and economic concerns. In conclusion, this comprehensive study not only explores uncharted research grounds but also serves as a beacon for researchers and policy makers. Through an in-depth analysis of the interaction between bovine brucellosis and public health, findings in this study provide crucial information that is useful to a wide range of stakeholders. This systemic review provides a clear and comprehensive perspective of the public health implication of bovine brucellosis by emphasising the need of specific interventions and preventive measures.

The Dynamics of Tuberculosis through BSEIR Model with Immigration in Malaysia

Tuberculosis or known as TB is an airborne disease that exists in Malaysia caused by Mycobacterium Tuberculosis. Despite that, TB infection is curable with early diagnosis and treatment. The disease can be prevented through Bacillus Calmette-Guérin (BCG) vaccine, which is given among infants at birth. Numerous mathematical models on infectious diseases have been formulated in earlier studies since it is crucial to comprehend infectious disease transmission patterns and predict future outcomes. However, there is minimal study on the dynamics of TB transmission, particularly in Malaysia concerning immigration. Hence, this study aims to formulate a mathematical BCG-vaccinated, susceptible, exposed, infected, and recovered (BSEIR) model for TB infection in Malaysia by considering the immigration parameter. The model parameters are obtained from the literature and some with reported data in 2013 as initial value for the simulation using MAPLE software. The obtained results revealed that the basic reproduction number, for the model is more than one. The graphical plot shows that the BSEIR model with immigration demonstrates a rise in TB-infected cases. The best strategy to lessen the number of infected individuals in Malaysia is by increasing the transmission rate and recovery rate. Additionally, the local stability analysis on the diseases-free equilibrium point and sensitivity analysis of its parameter is also provided and discussed. To conclude, a proper screening test should be mandated by the authorities before permitting new immigrants and refugees into Malaysia.

In the Lyme light: individual trait determinants of Borrelia burgdorferi infection in Peromyscus mice

Abstract Disease ecologists commonly use abiotic factors (e.g. temperature and moisture) or measures of biodiversity (e.g. species richness) to predict Lyme disease transmission patterns, but variance in infection probability among individuals within a population is poorly understood. Most studies assume intraspecific consistency, but recent evidence suggests that individual traits, such as animal personality, may drive differences in encounter rates with infected vectors and pathogen transmission probabilities through differential space use and microhabitat selection, leading to intraspecific variation in infection probability. In addition, because vectors and hosts are nonrandomly distributed across a landscape, land-use changes that modify key habitat features—such as forest management practices—may substantially alter associations between individual traits and infection probability. To address these gaps in our knowledge, we used a large-scale capture–mark–recapture study targeting Peromyscus mice in Maine, United States, to test whether personality drives probability of Borrelia burgdorferi infection in hosts within managed forest compartments with different silvicultural treatments. Specifically, we tested effects of individual phenotypic traits (physical and behavioral) and environmental traits (microhabitat and forest type) on infection probability within 2 species: P. leucopus and P. maniculatus. We found evidence that boldness negatively influences infection probability in P. maniculatus, and that body mass positively influences infection probability in both species. We found no effect of mouse density, microhabitat, or forest type in our analyses. These results suggest that personalities vary in their functional contributions to the natural cycle of B. burgdorferi, and that broader integration of behavioral diversity in disease ecology studies may aid in identifying key transmission zones for this rapidly expanding vector-borne zoonosis.

Dengue and climate changes: Increase of DENV-1 in São Paulo/Brazil – 2023

Dengue is a vector borne disease caused by virus serotypes DENV-1, DENV-2, DENV-3, and DENV-4, representing a significant public health concern in the Region of the Americas (2,997,097 cases in 2023). This study explores the relationship between dengue incidence and climate changes in the city of São Paulo-Brazil. During the first semester of 2023, Brazil reported the highest number of dengue cases in Americas’ Region. Our data reveals a correlation between the high temperature and rainfall season persistence and the extension of dengue incidence into the winter season. The findings highlight the importance of understanding the relationship between climate change and disease transmission patterns to develop effective strategies for prevention and control.

Harnessing the untapped potential of digital twin technology in digital public health interventions

Digital technologies have garnered more attention in this epoch of public health emergencies like coronavirus disease 2019 (COVID-19) and monkeypox (mpox). Digital twin (DT) is the virtual cybernetic equivalent of a physical object (e.g., a device, a human, a community) used to better understand the complexity of the latter and predict, prevent, monitor, and optimize real-world outcomes. The possible use cases of DT systems in public health ranging from mass vaccination planning to understanding disease transmission patterns have been discussed. Despite potential applications in healthcare, several economic, social, and ethical challenges might hinder the universal implementation of DT. Nevertheless, devising appropriate policies, reinforcing good governance, and launching multinational collaborative efforts ascertain early espousal of DT technology.

Genetic variability of Haemonchus contortus isolates in small ruminants from slaughterhouses in Bangladesh

Haemonchus contortus is a blood-sucking gastrointestinal nematode that infects all ruminants and causes significant economic losses in production. Characterizing the genetic variability of H. contortus populations is crucial for understanding patterns of disease transmission and developing effective control strategies against haemonchosis. This study aimed to identify the genetic variability of H. contortus isolates in small ruminants from slaughterhouses in Bangladesh. During January to December 2015, 400 abomasa samples were collected and 186 were found to be positive for Haemonchus. A 321-bp fragment of the second internal transcribed spacer (ITS-2) of nuclear ribosomal DNA and an 800-bp fragment of the mitochondrial nicotinamide dehydrogenase subunit-4 gene (nad4) were amplified using polymerase chain reaction (PCR) and directly sequenced. The results showed 10 genotypes (ITS-2) and 45 haplotypes (nad4) among the 186 worms. The sequences were 98.5 to 100% identical to reference sequences from the GenBank database. ITS-2 sequence analysis revealed four nucleotide substitutions at positions 30, 41, 42, and 216. There was one transition (C/T) at position 42 and three transversions (C/A at position 30, G/C at position 41, and T/A at position 216). The nad4 gene sequences showed 15 substitutions, all of which were transitions. The pairwise distance of ITS-2 between H. contortus populations ranged from 0.005 to 1.477. The nucleotide diversity (μ) among the populations was 0.009524 using ITS-2 and 0.00394 using nad4. This study indicated low genetic deviation among H. contortus populations in Bangladesh.

Occupation and Industry Data Quality Among Select Notifiable Conditions in Washington State.

Occupation and industry are basic data elements that, when collected during public health investigations, can be key to understanding patterns of disease transmission and developing effective prevention measures. To assess the completeness and quality of occupation and industry data among select notifiable conditions in Washington and discuss potential improvements to current data collection efforts. We evaluated occupation and industry data, collected by local health departments during routine case investigations, for 11 notifiable conditions, selected for inclusion based on an established or plausible link to occupational exposure. Confirmed cases of select notifiable conditions among Washington residents aged 16 to 64 years, for years 2019-2021. We calculated the percentage of cases among working-age adults reported as employed, the percentage with occupation and industry data collected, and the percentage assigned standard occupation and industry codes. We identified the most common responses for occupation and industry and challenges of assigning codes to those responses. Among the 11 conditions evaluated, one-third of cases aged 16 to 64 years were reported as employed. Among the cases reported as employed, 91.5% reported occupation data and 30.5% reported industry data. "Self-employed" was among the top responses for occupation, a response that does not describe a specific job and could not be assigned an occupation code. In the absence of additional information, 4 of the most common responses for industry could not be coded: "health care," "technology," "tech," and "food." Routine collection of informative occupation and industry data among working-age adults is largely absent from case investigations in Washington. Methods of data collection that improve quality while minimizing the burden of collection should be pursued. Suggestions for improving data quality are discussed.

Assessments of epidemic spread in aquaculture: comparing different scenarios of infectious bacteria incursion through spatiotemporal hybrid modeling.

The sustainable development of the aquaculture sector is at risk due to the significant challenges posed by many emerging infectious diseases. While disease prevention and control measures are becoming increasingly critical, there is a dearth of studies on the epidemiological aspects of disease transmission in aquatic ecosystems. This study aims to forecast the spread of a bacterial disease between fish farms in two regions, Romsdalsfjord in Norway and Gujwa in South Korea by applying a DTU-DADS-Aqua spatiotemporal hybrid simulation model. The simulation model assessed the pattern of disease transmission between fish farms under different degrees of transmission power based on the distance between farms (ScalingInf), host susceptibility (RelSusceptibility), the origin site of disease, and the capacity of culling fish. The distance between fish farms was found to have significant associations with disease transmission. In most simulation conditions, the disease transmission between different bay management areas (BMAs) was not evident in Romsdalsfjord. In the Guwja region, where there are relatively narrow distances between fish farms, the spread of infectious disease was greatly affected by ScalingInf. The impact of RelSusceptibility on disease transmission patterns is a critical factor to consider in simulation modeling. When RelSusceptibility ranges from 0.5-1, there is little impact on the likelihood of disease transmission. Conversely, lower ranges (0.2 and 0.05) of RelSusceptibility result in a significant decrease in the area affected by the spread of disease. Eradication measures could control the patterns of infectious disease transmission, but the effectiveness of the depopulation strategy can be dramatically changed depending on the geographical environment. In conclusion, through a comparative analysis of the disease transmission and management scenarios, this study demonstrates the potential use of existing simulation models in predicting the spread of infectious diseases under different epidemiological circumstances and quarantine actions.

A Prospective Study of Injection Drug Use and Jail Readmission in Two Western North Carolina Counties.

Injection drug use among people admitted to rural jails has significant implications for both infectious disease transmission and incarceration patterns. This study examines the relationship between injection drug use, jail readmission, and detention duration to inform interventions designed specifically for this understudied correctional setting. The Comprehensive Addiction and Psychological Evaluation-5 (CAAPE-5) was administered to a random sample of adults admitted to two county detention centers in Western North Carolina. Data regarding readmission and the length of detention were obtained for the 12-month study period. Participants included 420 adults, aged 18-66 years. Nearly three-quarters (70.9%) met criteria for at least one substance use disorder (SUD) and almost half (45.7%) met criteria for multiple SUDs. Approximately half (45.2%) reported injecting drugs, with the majority (71.6%) reporting injecting methamphetamine, followed by 51.1% who injected opioids. People who injected drugs were 1.83 times more likely (95% CI = 1.17-2.87) than those who did not inject to be admitted on multiple occasions and also spent a significantly longer amount of time detained (IRR = 1.34, 95% CI = 1.02-1.77) during the 12-month follow-up period. The study was limited to jails in two counties and injection-related factors may vary from other facilities. The association between drug injection and longer detention periods presents an opportunity to engage people with infectious disease screening and substance use services in jails. Connection to stable primary and behavioral health care after release is a high priority in enhancing community health.

Epidemiology and Evolutionary Dynamics of High Pathogenicity Avian Influenza (HPAI) H5N1 in Bangladesh

Understanding disease clustering and transmission patterns improves the prevention and control of disease. Herein, we described the epizootic characteristics and spatiotemporal dynamics of High Pathogenicity Avian Influenza (HPAI) H5N1 outbreaks as well as clade diversity and phylodynamics of H5N1 over time and across host species in Bangladesh. We used Moran’s I, Geary’s C, Getis-Ord Gi∗, and a space-time permutation model to analyze the spatiotemporal patterns of H5N1 outbreaks. We used Bayesian phylogenetic analysis to generate a time-scaled maximum clade credibility (MCC) tree. Our study revealed nine HPAI H5N1 epizootic waves between 2007 and 2020 that invariably peaked in the wintertime. After vaccination of poultry against H5N1 was introduced in 2012, the incidence of HPAI H5N1 outbreaks and poultry mortality decreased significantly over time. Nonetheless, our research revealed that the virus continued circulating unabatedly in Bangladesh. The various spatiotemporal analyses were identified up to nine space-time clusters across Bangladesh, with the most significant clustering and hotspots of H5N1 outbreaks in and around the district of Dhaka. Since 2007, four H5N1 clades have been detected in Bangladesh, with only clade 2.3.2.1a continuing to circulate since 2011, which was followed up by the reassorted clade 2.3.2.1a in 2012. The HA gene of the H5N1 clade 2.3.2.1a has been reassorted into at least nine subgroups (R1–R9). After 2016, the first eight groups disappeared, with only the R9 group remaining. Spatiotemporal patterns and phylodynamics of H5N1 outbreaks are crucial for developing targeted and appropriate HPAI control and prevention measures. We recommended intensive monitoring of biosecurity measures and disease records in high-priority areas, along with assessing vaccine efficacy to better control HPAI outbreaks in Bangladesh.