Abstract—Super-spreading events for infectiousdiseases oc- cur when some infected individuals infect more than the average number of secondary cases. Several super-spreading individuals have been identified for the 2003 outbreak of severe acute respiratory syndrome (SARS). We develop a model for super- spreading events of infectiousdiseases, which is based on the outbreak of SARS. Using this model we describe two methods for estimating the parameters of the model, which we demonstrate with the small-scale SARS outbreak at the Amoy Gardens, Hong Kong, and the large-scale outbreak in the entire Hong Kong Special Administrative Region. One method is based on parameters calculated for the classical susceptible - infected - removed (SIR) disease model. The second is based on parameter estimates found in the literature. Using the parameters calculated for the SIR model, our model predicts an outcome similar to that for the SIR model. On the other hand, using parameter estimates from SARS literature our model predicts a much more serious epidemic.
An individual host may acquire coinfection by being infected sequentially or simulta- neously with different strains (or diseases). A major concern with disease coinfection is that coinfecting pathogens or strains usually interact with one another (Balmer and Tanner, 2011). Interactions within coinfection may lead to an increased susceptibility of the host to other infections due to waning immunity or decreased susceptibility of the host to similar strains due to cross-immunity. For example, infection with a strain of dengue fever has been found to enhance the transmission of another strain (see, for example, Ferguson et al. (1999)) and infection with HIV suppresses the immune system of the host making it more vulnerable for Tuberculosis transmission (see, for example, Newman and Ferrario (2013)). On the other hand, studies have observed the existence of cross-immunity between different subtypes of Influenza (see, for example, Epstein (2006)), with strong cross-protection existing among variants of antigenic drifts evolved from the same influenza subtype, see, for example, Barry et al. (2008). Understanding the transmission dynamics of disease coinfection is key to finding effective prophylactic and/or treatment measures to combat the diseases in an event of coepidemics, see, for example, Hoti et al. (2009) and Lipsitch (1997), for the use of vaccination in the pre- vention of Streptococcus pneumoniae and coinfection of Streptococcus pneumoniae and Haemophilus influnzae, respectively. Lipsitch (1997) observes that vaccination could of- fer full, partial or cross immunity to certain serotypes (strains) of the diseases. However, a serotype-targeted vaccine could give rise to an increased carriage of other out-competed non-target serotypes. This raises the question of how well diseases coinfection dynamics is understood.
distributed [1,2,7,23,24]. It was for this reason in 2010 the InfectiousDiseases Society of America (IDSA) and the European Society for Microbiology and InfectiousDiseases endorsed the use of fosfomycin tromethamine as a first treatment for uncomplicated urinary tract infections and cystitis [3,7]. However, because US physicians do not commonly use the drug there is little experience and confidence in it. Data has been sparse in the United States concerning the treatment of ESBL infections especially with uncomplicated UTIs. However, recent studies by Linsenmyer et al. and Sastry el al have shown promising results with susceptibilities to fosfomycin at 96% and 100% respectively [1,25]. Fosfomycin has significant activity against vancomycin-resistant enterococci (VRE) with Sultan and colleagues reporting 98% 100% susceptibility against fosfomycin [26,27]. In this study, we noted susceptibility rates of 95% for almost all the pathogens. Klebsiella Proteus had a varied susceptibility rates 90-95%.
Although indigenous populations in the Pacific have developed complex stratagems and adapted highly effectively to their sometimes extreme environment, Australian Aboriginal & Torres Straits Islanders, Micronesian, Melanesian, and Polynesian populations are overrepresented among severe cases and deaths related to certain diseases, both communicable and non-communicable (2–5). A partial explanation of this over-representation is the challenges faced in many Pacific island countries in the delivery and uptake of: health services, improved water and sanitation, and education; problems faced in many low-income countries globally. However, factors specific to the region and its people, such as rapidly changing diets and the historic isolation of these ethnic groups, need also be considered. Studies conducted on the relationships between host genetics, microbiome, and diseases are mainly carried out in Europe, Asia, or USA, and include very few or no indigenous participants from the Pacific despite the high burden of some diseases they face. Conclusions drawn from studies conducted in other geographical areas determine global trends, but have little specific application to Pacific populations (6–8), leading to a dearth of knowledge on how the microbiome may impact upon and/or interact with susceptibility to disease and immunomodulation.
problem in areas that had low to medium VL endemicities (≤5 cases per 10 000 people per year) prior to the start of interven- tions. For higher precontrol endemicities, an extended attack phase (intensive IRS and ACD) may be required. Maintaining the target level of incidence might require the same level of interventions that was required to achieve it. Whether IRS or reducing onset-to-treatment time is the more effective inter- vention depends on the relative infectiousness of asymptomatic and symptomatic individuals and the efficacy of IRS, both still important gaps in knowledge. If most asymptomatic individuals are infectious to sandflies (even if only 1/80th as infectious as symptomatic individuals ) and their duration of infection is as long and their rate of developing VL as low as estimated, then they will act as the main source of transmission. In this case, increasing IRS coverage will cause a greater reduction in transmission than reducing delays to treatment, provided IRS is effective in killing sandflies . However, if asymptomatic individuals are not infectious to sandflies, or only a very small proportion of them are, so that clinical cases drive transmission, then reducing delays to treatment will lead to a greater decrease in incidence . These varying assumptions, some of which are covered by the different submodels, influence the time taken to reach elimination.
dence intervals were obtained by the 'logistic' function in Stata. Significant factors to the level of P < 0.2 from the univariate analysis and a priori important factors such as sex, age and underlying diseases were included in the mul- tivariate analysis. Multivariate analysis was performed by automated and manual backwards step-wise logistic regression where factors with P > 0.2 were removed from the model. We present four logistic regression models using different subsets of the study population analyzing cases of laboratory-confirmed bloodstream infection (n = 216) as well as cases of clinically suspected systemic infec- tion (n = 1527), and for each category we re-analyzed the data for those who had known HIV status (n = 128 and n = 790, respectively). Comparisons of medians of time var- iables were done by Wilcoxon rank-sum (Mann-Whitney) test.
cases diagnosed in the health services of the Lazio region (central Italy) in the period from September 2010 – September 2011. Before the study started, preliminary meetings with all hospital infectiousdiseases departments and the infectiousdiseases departments of the local health units (LHU) wereheld. In these meetings, clinicians and LHU operators were invited to collect the supplementary data required together with the case report forms usually filled in for surveillance reporting. Data were collected by patient interviews and consulting clinical records. Information was collected on: 1) presence and dates of onset of symptoms that could be associated with the suspicion of tuberculosis (i.e. cough, haemoptysis, fever, night sweats, chest pain, unintentional weight loss, loss of appetite, fatigue), date of first contact with health service structures (i.e. general practitioner, emergency departments, outpatient clinics, hospitals), presence of some comorbidities (i.e. diabetes, cancer, HIV), diagnostic exams performed (i.e. x-ray, CT scan, bronchoscopy, standard haematologic tests, echography, sputum microscopic examinations) and drugs prescribed (e.g. antibiotics) in the period between the onset of symptoms and diagnosis. Information was also retrieved from clinical notes and case notification forms. Only extra-PTB cases and relapses were excluded from the survey.
Furthermore, because the InfectiousDiseases Society of America and Pediatric InfectiousDiseases Society pneumonia guidelines do not recommend radiography in the outpatient setting, the reliance of examination findings with limited reliability to diagnose CAP has the potential to increase antibiotic overuse, resulting in the spread of antimicrobial resistance, antibiotic- associated adverse effects, and increased cost. It is reassuring that the findings of respiratory rate and retractions, often abnormal in pneumonia, had acceptable reliability. However, the limited reliability of many other findings that are hallmarks of the clinical diagnosis of CAP suggests that either interventions to improve examination skills are necessary, a more standardized approach to the diagnosis is required, or more objective tools are needed to aid in CAP diagnosis in children.
The epidemiology of infectiousdiseases is a complex and multi-factoral subject [16, 151, 74]. As such, no single review can hope to cover all aspects. Here we fo- cus largely on the population dynamics of infectiousdiseases – how the number of individuals infected changes dynamically over time. This article therefore relies heavily on tools from mathematics and theoretical physics (the theory of differential equations and dynamical systems, statistical mechanics, and stochastic processes). However, there are multiple disciplines that feed into epidemiology that we do not cover. Statistics is one with which many readers will be familiar; statistics plays a vital role in both inter- preting the observed infection data (accounting for the many biases in reporting) and in parameter inference when we attempt to fit models to data. Obviously, a range of biological sciences from microbiology to immunology to ecology are needed to inform our understanding of the pathogen, the host and their interaction; while we do not attempt a comprehensive review of such knowledge, the next section does provide suf- ficient biological background to motivate and justify the choice of model formulation. Finally, in recent years it has become apparent that other disciplines have a role to play: economics is vital to underpin cost-effectiveness studies that are key to assessing control programmes; sociology and psychology help to explain and predict human response to outbreaks or new treatments; while medical insights are needed to understand the link between the individual as a host for the pathogen and the individual as a patient that requires treatment. Therefore, when developing models for the spread of an infectious disease we not only need a range of mathematical skills but must account for the in- sights provided by many other disciplines. Throughout, we had attempted to draw on citations from the mathematical and biological literature whenever possible, so as to introduce the reader to this (possible novel) fields of scientific publication.
We performed a subanalysis restricting the cohort to children who received an antibiotic on the first or second hospital day to best represent the population of children with suspected bacterial pneumonia. Additionally, we attempted to identify patients who were hospitalized with severe or complicated pneumonia. Patients were considered to have severe or complicated pneumonia if they were either admitted to an ICU or underwent a pleural drainage procedure on the first or second day of their hospitalization, respectively. Pleural drainage was defined by International Classification of Diseases, Ninth Revision, Clinical Modification procedure codes for thoracentesis (34.91), chest tube placement (34.04), video-assisted thoracoscopic surgery (34.21), and thoracotomy (34.02 and 34.09). 18