Global Spread Of Infectious Diseases Research Articles

Smart Nanozymes for Diagnosis of Bacterial Infection: The Next Frontier from Laboratory to Bedside Testing.

The global spread of infectious diseases caused by pathogenic bacteria significantly poses public health concerns, and methods for sensitive, selective, and facile diagnosis of bacteria can efficiently prevent deterioration and further spreading of the infections. The advent of nanozymes has broadened the spectrum of alternatives for diagnosing bacterial infections. Compared to natural enzymes, nanozymes exhibit the same enzymatic characteristics but offer greater economic efficiency, enhanced durability, and adjustable dimensions. The importance of early diagnosis of bacterial infection and conventional diagnostic approaches is introduced. Subsequently, the review elucidates the definition, properties, and catalytic mechanism of nanozymes. Eventually, the detailed application of nanozymes in detecting bacteria is explored, highlighting their utilization as biosensors that allow for accelerated and highly sensitive identification of bacterial infections and reflecting on the potential of nanozyme-based bacterial detection as a point-of-care testing (POCT) tool. A brief summary of obstacles and future perspectives in this field is presented at the conclusion of this review.

Two-Dimensional Piezoelectric Nanofibrous Webs by Self-Polarized Assembly for High-Performance PM0.3 Filtration.

Particulate matter (PM) pollution has posed a serious threat to public health, especially the global spread of infectious diseases. Most existing air filtration materials are still subjected to a compromise between removal efficiency and air permeability on account of their stacking bulk structures. Here, we proposed a self-polarized assembly technique to create two-dimensional piezoelectric nanofibrous webs (PNWs) directly from polymer solutions. The strategy involves droplets deforming into ultrathin liquid films by inertial flow, liquid films evolving into web-like architectures by instantaneous phase inversion, and enhanced dipole alignment by cluster electrostatics. The assembled continuous webs exhibit integrated structural superiorities of nanoscale diameters (∼20 nm) of the internal fibers and through pores (∼100 nm). Combined with the wind-driven electrostatic property derived from the enhanced piezoelectricity, the PNW filter shows high efficiency (99.48%) and low air resistance (34 Pa) against PM0.3 as well as high transparency (84%), superlight weight (0.7 g m-2), and long-term stable service life. This creation of such versatile nanomaterials may offer insight into the design and upgrading of high-performance filters.

AI-Based Medical Chatbot for Disease Prediction By IJISRT

This research paper presents the development and implementation of an AI-based medical chatbot for disease prediction. Leveraging machine learning and artificial intelligence technologies, the chatbot utilizes natural language processing (NLP) to understand user queries and provide accurate information, guidance, and assistance for various infectious diseases. Motivated by the global spread of infectious diseases and the need for accessible healthcare support, the paper outlines the objectives, algorithm design, dataset description, and application of the chatbot. The algorithm involves receiving user input, extracting symptoms, classifying diseases, and suggesting prevention measures. The dataset, structured in JSON format, facilitates training and pattern recognition. The chatbot interface, accessible across platforms, offers information on symptoms, prevention measures, hospital bed availability, and medication options. In conclusion, the research highlights the potential of AI-based chatbots in revolutionizing healthcare accessibility and personalized diagnosis, thereby bridging the gap between users and healthcare systems.

Trans-Amplifying RNA Vaccines Against Infectious Diseases: A Comparison with Non-Replicating and Self-Amplifying RNA.

The recent COVID-19 pandemic as well as other past and recent outbreaks of newly or re-emerging viruses show the urgent need to develop potent new vaccine approaches, that enable a quick response to prevent global spread of infectious diseases. The breakthrough of first messenger RNA (mRNA)-based vaccines 2019 approved only months after identification of the causative virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), opens a big new field for vaccine engineering. Currently, two major types of mRNA are being pursued as vaccines for the prevention of infectious diseases. One is non-replicating mRNA, including nucleoside-modified mRNA, used in the current COVID-19 vaccines of Moderna and BioNTech (Sahin et al., Nat Rev Drug Discov 13(10):759-780, 2014; Baden et al., N Engl J Med 384(5):403-416, 2021; Polack et al., N Engl J Med 383(27):2603-2615, 2020), the other is self-amplifying RNA (saRNA) derived from RNA viruses. Recently, trans-amplifying RNA, a split vector system, has been described as a third class of mRNA (Spuul et al., J Virol 85(10):4739-4751, 2011; Blakney et al., Front Mol Biosci 5:71, 2018; Beissert et al., Mol Ther 28(1):119-128, 2020). In this chapter we review the different types of mRNA currently used for vaccine development with focus on trans-amplifying RNA.

How enlightened self-interest guided global vaccine sharing benefits all: A modeling study.

Despite consensus that vaccines play an important role in combatting the global spread of infectious diseases, vaccine inequity is still a prevalent issue due to a deep-seated mentality of self-priority. We aimed to evaluate the existence and possible outcomes of a more equitable global vaccine distribution and explore a concrete incentive mechanism that promotes vaccine equity. We designed a metapopulation epidemiological model that simultaneously considers global vaccine distribution and human mobility, which we then calibrated by the number of infections and real-world vaccination records during the coronavirus disease 2019 (COVID-19) pandemic from March 2020 to July 2021. We explored the possibility of the enlightened self-interest incentive mechanism, which comprises improving one's own epidemic outcomes by sharing vaccines with other countries, by evaluating the number of infections and deaths under various vaccine sharing strategies using the proposed model. To understand how these strategies affect the national interests, we distinguished imported from local cases for further cost-benefit analyses that rationalise the enlightened self-interest incentive mechanism behind vaccine sharing. The proposed model accurately reproduces the real-world cumulative infections for both global and regional epidemics (R2>0.990), which can support the following evaluations of different vaccine sharing strategies: High-income countries can reduce 16.7 (95% confidence interval (CI) = 8.4-24.9, P < 0.001) million infection cases and 82.0 (95% CI = 76.6-87.4, P < 0.001) thousand deaths on average by more actively sharing vaccines in an enlightened self-interest manner, where the reduced internationally imported cases outweigh the threat from increased local infections. Such vaccine sharing strategies can also reduce 4.3 (95% CI = 1.2-7.5, P < 0.01) million infections and 7.0 (95% CI = 5.7-8.3, P < 0.001) thousand deaths in middle- and low-income countries, effectively benefiting the whole global population. Lastly, the more equitable vaccine distribution could help largely reduce the global mobility reduction needed for pandemic control. The incentive mechanism of enlightened self-interest we explored here could motivate vaccine equity by realigning the national interest to more equitable vaccine distributions. The positive results could promote multilateral collaborations in global vaccine redistribution and reconcile conflicted national interests, which could in turn benefit the global population.

Designing and Operating an Online Class Review Meeting: A Case Study of Exchange Involving Social Studies Classes between Korea and Japan

2020年初めからの世界的な感染症の拡大により，海外の大学との交流会のほとんどは中止かオンラインへと切り替わった。筆者自身がこれまで行っていた交流会 においてもオンライン化を図 ることとなった。本稿では，オンライン上での韓国と日本の授業検討会を取り上げ，その実際とそれによる効果を検証した。オンライン化により，韓国に出向いての交流では問題視されにくかった言葉や文化の障壁が明示化された。それを乗り越えるための工夫として，①ウェブベースソフトウェアの活用や資料の翻訳，②通訳 の活用，及び③提示すべき資料の検 討の必要性が明らかになった。また，対面では，短期間の交流に集約されることが多かったが，オンライン化による長期的な交流の機会を設けることが授業検討会において効果的であることが分かった。このような手立ては，今後，対面での交流となった場合も継続する必要があるといえよう。非常時のオンラインではなく，「オンライン」だからできること，対面だからできることを見極め，よりよい授業研究会の在り方を検討する必要がある。On account of the global spread of infectious diseases from the beginning of 2020, most exchange events with overseas universities have been canceled or switched to online. In this paper, we have taken up online class review meeting in South Korea and Japan, then examined their actuality and their effects. The online program's presence made any inherent language and cultural barriers more explicit; to overcome these barriers, we introduced (1) web-based software and translation, (2) the use of interpreters, and (3) advance consideration of the materials to be presented in the class. The study findings showed that the advance preparations made for overcoming language and cultural barriers helped to enhance individual participants’ understanding beyond the experience of a single classroom visit. This report showed the program’s design and operation in concrete terms and examined the online version’s effectiveness. It is necessary to examine how class study groups can be organized in a better manner by identifying what can be done “online” and what can be done “face-to-face” rather than what can be done online in emergencies only.

EXTENSION OF A FOREST-FIRE MODEL TO SIMULATE HOW COVID-19 VIRUS CAUSES OSCILLATORY POPULATION DYNAMICS

The global spread of infectious diseases and their associated social impacts have been proved to cause havoc to humans with the recent pandemic invasion of COVID-19. Understanding the spread pattern of COVID-19 and predicting the disease dynamics have been inevitable to support government in the public and private health fields in setting up strategies for alleviating the dire seriousness of the pandemic around the World. The spread of infectious disease and the resulting population dynamics could be modeled and simulated by extending the classic "forest-fire" model with an introduction of 3 model parameters of p, q, r that control the probability of infection, immunity, and spread. Then, by applying the mathematical model of damped harmonic oscillation from physics, the effect of these parameters on the amplitude and frequency of oscillations of an epidemic could be characterized. We concluded that this model could predict a wide range of behaviors including Covid-10 that could aid epidemiologists and policymakers. Further study might be required for practical application.

Epidemiological characteristics of imported acute infectious diseases in Guangzhou, China, 2005-2019.

The global spread of infectious diseases is currently a prominent threat to public health, with the accelerating pace of globalization and frequent international personnel intercourse. The present study examined the epidemiological characteristics of overseas imported cases of acute infectious diseases in Guangzhou, China. We retrospectively investigated the distribution of diseases, demographic characteristics, and temporal and spatial variations of imported cases of acute infectious diseases in Guangzhou based on the surveillance data of notifiable infectious diseases from 2005 to 2019, provided by Guangzhou center for Disease Control and Prevention. The Cochran-Armitage trend test was applied to examine the trend in the number of imported cases over time. A total of 1,025 overseas imported cases of acute infectious diseases were identified during the study period. The top three diseases were dengue (67.12%), malaria (12.39%), and influenza A (H1N1) pdm09 (4.10%). Imported cases were predominantly males, with a sex ratio of 2.6: 1 and 75.22% of the cases were those aged 20-49 years. Businessmen, workers, students and unemployed persons accounted for a large proportion of the cases (68.49%) and many of the cases came from Southeast Asia (59.02%). The number of imported cases of acute infectious diseases increased during the study period and hit 318 in 2019. A clear seasonal pattern was observed in the number of imported cases with a peak period between June and November. Imported cases were reported in all of the 11 districts in Guangzhou and the central districts were more seriously affected compared with other districts. The burden of dengue imported from overseas was substantial and increasing in Guangzhou, China, with the peak period from June to November. Dengue was the most common imported disease. Most imported cases were males aged 20-49 years and businessmen. Further efforts, such as strengthening surveillance of imported cases, paying close attention to the epidemics in hotspots, and improving the ability to detect the imported cases from overseas, are warranted to control infectious diseases especially in the center of the city with a higher population density highly affected by imported cases.

Transmission of Antimicrobial Resistant Bacteria at the Hajj: A Scoping Review.

The Hajj is an annual religious mass gathering event held in Makkah, Saudi Arabia. With millions of participants from across the globe attending the Hajj, the risk of importation, transmission, and global spread of infectious diseases is high. The emergence of antimicrobial resistant (AMR) bacteria is of worldwide concern and the Hajj poses a serious risk to its dissemination. This review aims to synthesize published literature on AMR bacteria acquisition and transmission associated with the Hajj. We searched electronic databases to identify literature published between January 1990 and December 2021. The search strategy included medical subject headings and keyword terms related to AMR bacteria and the Hajj. After screening 2214 search results, 51 studies were included in the analysis. The review found 6455 AMR bacteria transmissions related to the Hajj. Thirty predominantly enteric or respiratory disease-causing AMR bacterial species were reported with isolates identified in cases on five continents. Most were male, aged above 50 years and were diagnosed in Makkah. Most cases were identified through hospital-based research; few cases were detected in community or primary health care settings. This review provides a contemporary account of knowledge related to AMR transmission at the Hajj. It emphasizes the need for the enhancement of surveillance for AMR bacteria globally.

Significantly correlation between tourism and COVID-19: evidence from 178 countries and territories.

The high development of tourism is considered a factor that facilitates the global spread of infectious diseases. The association between tourism and the epidemic of coronavirus diseases 2019 (COVID-19) remains unclear. We retrieved the data of COVID-19 in 178 countries/territories from the Center for Systems Science and Engineering at Johns Hopkins University. Data on tourism indicators were collected from the World Tourism Organization. We used Spearman's correlation analysis to explore the association between tourism and the epidemic of COVID-19. We find that international tourism expenditure, international tourism receipts, international tourist arrivals, and international tourism exports were significantly correlated with the total number of cases (rs=0.86, rs=0.79, rs=0.80, rs=0.81, respectively), the daily growth of cases of COVID-19 (rs=0.84, rs=0.76, rs=0.78, rs=0.78, respectively), and the number of cases (per million persons) (rs=0.52, rs=0.53, rs=0.36, rs=0.53, respectively) (p < 0.0001 for all), especially in places with high-income. Tourism as percentage of exports was slightly associated with the total number of cases and the daily growth of cases (rs=-0.33, rs=-0.33) (p < 0.0001 for both). The clinical and public health care providers must realize the potential for the transmission of infections across regions and put more effort to prevent and respond to future infections.

The Impact of Urban Form on the Spread of Infectious Diseases: Focusing on COVID-19 Outbreak in the Seoul Metropolitan Area

COVID-19 is raising awareness about the risk of the global spread of infectious diseases. As a result, various measures have been proposed to reduce the spread of infectious diseases. This study attempts to analyze the impact of the urban form on the spread of infectious diseases and suggests an urban planning approach to prevent their spread. Recently, studies have shown that confirmed cases increase in high-density, compact cities, but this seems to be the result of simply looking at urban forms in terms of density. Therefore, this study attempted to examine urban forms by considering not only density but also accessibility, self-sufficiency, and land-use mixes. In this study, a structural equation model is used to illustrate the spread of infectious diseases in relation to urban forms, with variables representing the number of confirmed cases. This research focuses on Seoul and Gyeonggi-do, two densely populated, urbanized areas in South Korea, to study the relationship between urban spatial structures and the number of confirmed COVID-19 cases. The study found that urban forms prone to sprawl are vulnerable to the spread of infectious diseases. Moreover, the higher a region’s self-sufficiency and the denser and more compact the cities, the more resistant they are to infectious diseases. This means that it is necessary for future urban planning to consider aiming for dense, compact designs in order to create cities resistant to infectious diseases, such as COVID-19. Moreover, it has been determined that cities will be able to prevent the spread of infectious diseases if urban plans, such as community plans, are promoted to enhance self-sufficiency.

Extreme first-passage times for random walks on networks.

Many biological, social, and communication systems can be modeled by "searchers" moving through a complex network. For example, intracellular cargo is transported on tubular networks, news and rumors spread through online social networks, and the rapid global spread of infectious diseases occurs through passengers traveling on the airport network. To understand the timescale of search (or "transport" or "spread"), one commonly studies the first-passage time (FPT) of a single searcher (or "transporter" or "spreader") to a target. However, in many scenarios the relevant timescale is not the FPT of a single searcher to a target, but rather the FPT of the fastest searcher to a target out of many searchers. For example, many processes in cell biology are triggered by the first molecule to find a target out of many, and the time it takes an infectious disease to reach a particular city depends on the first infected traveler to arrive out of potentially many infected travelers. Such fastest FPTs are called extreme FPTs. In this paper, we study extreme FPTs for a general class of continuous-time random walks on networks (which includes continuous-time Markov chains). In the limit of many searchers, we find explicit formulas for the probability distribution and all the moments of the kth fastest FPT for any fixed k≥1. These rigorous formulas depend only on network parameters along a certain geodesic path(s) from the starting location to the target since the fastest searchers take a direct route to the target. Hence, the extreme FPTs are independent of the details of the network outside this geodesic(s) and can be drastically faster and less variable than conventional FPTs of single searchers. Furthermore, our results allow one to estimate if a particular system is in a regime characterized by fast extreme FPTs. We also prove similar results for mortal searchers on a network that are conditioned to find the target before a fast inactivation time. We illustrate our results with numerical simulations and uncover potential pitfalls of modeling diffusive or subdiffusive processes involving extreme statistics. In particular, we find that the many searcher limit does not commute with the diffusion limit for random walks, and thus care must be taken when choosing spatially continuous versus spatially discrete diffusion models.

A SIR-Poisson Model for COVID-19: Evolution and Transmission Inference in the Maghreb Central Regions.

2019-nCoV is a virulent virus belonging to the coronavirus family that caused the new pneumonia (COVID-19) which has spread internationally very rapidly and has become pandemic. In this research paper, we set forward a statistical model called SIR-Poisson that predicts the evolution and the global spread of infectious diseases. The proposed SIR-Poisson model is able to predict the range of the infected cases in a future period. More precisely, it is used to infer the transmission of the COVID-19 in the three Maghreb Central countries (Tunisia, Algeria, and Morocco). Using the SIR-Poisson model and based on daily reported disease data, since its emergence until end April 2020, we attempted to predict the future disease period over 60 days. The estimated average number of contacts by an infected individual with others was around 2 for Tunisia and 3 for Algeria and Morocco. Relying on inferred scenarios, although the pandemic situation would tend to decline, it has not ended. From this perspective, the risk of COVID-19 spreading still exists after the deconfinement act. It is necessary, therefore, to carry on the containment until the estimated infected number achieves 0.

Infectious Diseases Spreading on an Adaptive Metapopulation Network

When an emerging acute infectious disease occurs, travel restrictions, one-way or two-way, are often taken to prevent its global spread. In order to investigate the impact of two-way travel restrictions in the global spread of infectious diseases, this paper defines a risk indicator according to the relative infection density. Based on this risk indicator and an intervention time on two-way travel restrictions, we define an adaptive metapopulation network. Then a susceptible-infectious-removed (SIR) metapopulation model on this network is proposed. The mathematical analysis shows that the basic reproduction number is independent of human mobility. Furthermore, this essay compares the effects of one-way travel restrictions and two-way travel restrictions on the global spread of infectious diseases. It is shown that the adaptive metapopulation network under two-way travel restrictions can effectively suppress the global spread of infectious diseases. We also obtain a threshold of risk indicator to prevent the global spread of infectious diseases by simulations. The earlier the intervention time on two-way travel restriction is, the better to curb the global spread of the disease. Even if two-way travel restrictions are not implemented, controlling the mobility of infectious persons would help prevent the global spread of the disease. This work will throw lights on the prevention and control of the globally spreading of an emerging infectious disease.

Risk communication and infectious diseases in an age of digital media

In a digital world where the public’s voice is growing increasingly strong, how can health experts best exert influence to contain the global spread of infectious diseases? Digital media sites provide an important source of health information, however are also powerful platforms for the public to air personal experiences and concerns. This has led to a growing phenomenon of civil skepticism towards health issues including Emerging Infectious Diseases and epidemics. Following the shift in the role of the public from recipients to a vocal entity, this book explores the different organizational strategies for communicating public health information and identifies common misconceptions that can inhibit effective communication with the public. Drawing on original research and a range of global case studies, this timely volume offers an important assessment of the complex dynamics at play in managing risk and informing public health decisions. Providing thought-provoking analysis of the implications for future health communication policy and practice, this book is primarily suitable for academics and graduate students interested in understanding how public health communication has changed. It may also be useful to health care professionals.

Analyzing large-scale human mobility data: a survey of machine learning methods and applications

Human mobility patterns reflect many aspects of life, from the global spread of infectious diseases to urban planning and daily commute patterns. In recent years, the prevalence of positioning methods and technologies, such as the global positioning system, cellular radio tower geo-positioning, and WiFi positioning systems, has driven efforts to collect human mobility data and to mine patterns of interest within these data in order to promote the development of location-based services and applications. The efforts to mine significant patterns within large-scale, high-dimensional mobility data have solicited use of advanced analysis techniques, usually based on machine learning methods, and therefore, in this paper, we survey and assess different approaches and models that analyze and learn human mobility patterns using mainly machine learning methods. We categorize these approaches and models in a taxonomy based on their positioning characteristics, the scale of analysis, the properties of the modeling approach, and the class of applications they can serve. We find that these applications can be categorized into three classes: user modeling, place modeling, and trajectory modeling, each class with its characteristics. Finally, we analyze the short-term trends and future challenges of human mobility analysis.

Biomineralized vaccine nanohybrid for needle-free intranasal immunization.

Frequent outbreaks and the rapid global spread of infectious diseases have increased the urgent need for massive vaccination especially in countries with limited resources. Intranasal vaccination facilitates the mass vaccination via needle-free delivery of vaccine through nasal mucosal surfaces. Inspired by the strong capability of calcium phosphate (CaP) materials to adhere to cells and tissues, we propose to improve nasal vaccination by using a biomineralization-based strategy. The vaccine nanohybrid was obtained by covering the viral surface with CaP nanoshell, which changed the physiochemical properties of original vaccine, resulting in the increase of mucosal adhesion to the nasal tissues. The core-shell structure was beneficial for the receptor-independent uptake and the induction of elevated local IgA response within the nasal cavity. Moreover, the vaccine complex elicited enhanced systemic antibody response that neutralized wild type of dengue virus and promoted the systemic cellular immune responses. This achievement presents the potential of CaP based vaccine biomineralization for the fabrication of needle-free vaccine formulation.

Transdermal delivery of vaccines - Recent progress and critical issues.

In 2010, the number of deaths from infectious diseases globally was approximately 15 million. It has been reported that two-thirds of deaths from infections are caused by around 20 species, mainly bacteria and viruses. Transnational migration caused by war and the development of transportation facilities have led to the global spread of infectious diseases. Subcutaneous vaccination, though widespread, has a number of problems: the need for trained healthcare personnel, pain, needle-related injuries as well as storage difficulties. Two layers of the human skin- epidermis and dermis- are populated by dendritic cells (DCs), which are potent antigen-presenting cells (APCs). Transcutaneous immunization has therefore become an attractive and alternative route for vaccination. In this review, the various techniques for enhancing vaccine delivery are discussed. These techniques include iontophoresis, elastic liposomes as well as microneedles. Progress made so far with these techniques and the critical issues facing scientists will be highlighted.

Spatial Modeling and Mapping of Tuberculosis Using Bayesian Hierarchical Approaches

Global spread of infectious disease threatens the well-being of human, domestic, and wildlife health. A proper understanding of global distribution of these diseases is an important part of disease management and policy making. However, data are subject to complexities by heterogeneity across host classes. The use of frequentist methods in biostatistics and epidemiology is common and is therefore extensively utilized in answering varied research questions. In this paper, we applied the hierarchical Bayesian approach to study the spatial distribution of tuberculosis in Kenya. The focus was to identify best fitting model for modeling TB relative risk in Kenya. The Markov Chain Monte Carlo (MCMC) method via WinBUGS and R packages was used for simulations. The Deviance Information Criterion (DIC) proposed by [1] was used for models comparison and selection. Among the models considered, unstructured heterogeneity model perfumes better in terms of modeling and mapping TB RR in Kenya. Variation in TB risk is observed among Kenya counties and clustering among counties with high TB Relative Risk (RR). HIV prevalence is identified as the dominant determinant of TB. We find clustering and heterogeneity of risk among high rate counties. Although the approaches are less than ideal, we hope that our formulations provide a useful stepping stone in the development of spatial methodology for the statistical analysis of risk from TB in Kenya.

Epidemiology and geographical distribution of enteric protozoan infections in sydney, australia.

BackgroundEnteric protozoa are associated with diarrhoeal illnesses in humans; however there are no recent studies on their epidemiology and geographical distribution in Australia. This study describes the epidemiology of enteric protozoa in the state of New South Wales and incorporates spatial analysis to describe their distribution.Design and methodsLaboratory and clinical records from four public hospitals in Sydney for 910 patients, who tested positive for enteric protozoa over the period January 2007 - December 2010, were identified, examined and analysed. We selected 580 cases which had residence post code data available, enabling us to examine the geographic distribution of patients, and reviewed the clinical data of 252 patients to examine possible links between protozoa, demographic and clinical features.ResultsFrequently detected protozoa were Blastocystis spp. (57%), Giardia intestinalis (27%) and Dientamoeba fragilis (12%). The age distribution showed that the prevalence of protozoa decreased with age up to 24 years but increasing with age from 25 years onwards. The geographic provenance of the patients indicates that the majority of cases of Blastocystis (53.1%) are clustered in and around the Sydney City Business District, while pockets of giardiasis were identified in regional/rural areas. The distribution of cases suggests higher risk of protozoan infection may exist for some communities.ConclusionsThese findings provide useful information for policy makers to design and tailor interventions to target high risk communities. Follow-up investigation into the risk factors for giardiasis in regional/rural areas is needed.Significance for public healthThis research is significant since it provides the most recent epidemiological update on the common enteric protozoa affecting Australians. It reveals that enteric protozoa cause considerable disease burden in high risk city dwellers, and provides the evidence base for development of targeted interventions for their prevention and control in high risk populations. The prevalence of enteric protozoa in this metropolitan setting underscores that microorganisms do not respect borders and that a collaborative approach is needed to contain the global spread of infectious diseases. Incorporating spatial analysis is valuable in providing a compelling picture of the geographical distribution of these often neglected diseases. Local and State Public Health departments can use this information to support further inves-