A two-component model for counts of infectious diseases

We propose a stochastic model for the analysis of time series of disease counts as collected in typical surveillance systems on notifiable infectious diseases. The model is based on a Poisson or negative binomial observation model with two components: a parameter-driven component relates the disease incidence to latent parameters describing endemic seasonal patterns, which are typical for infectious disease surveillance data. An observation-driven or epidemic component is modeled with an autoregression on the number of cases at the previous time points. The autoregressive parameter is allowed to change over time according to a Bayesian changepoint model with unknown number of changepoints. Parameter estimates are obtained through the Bayesian model averaging using Markov chain Monte Carlo techniques. We illustrate our approach through analysis of simulated data and real notification data obtained from the German infectious disease surveillance system, administered by the Robert Koch Institute in Berlin. Software to fit the proposed model can be obtained from http://www.statistik.lmu.de/ approximately mhofmann/twins.     

Evaluating surveillance strategies for the early detection of low pathogenicity avian influenza infections

In recent years, the early detection of low pathogenicity avian influenza (LPAI) viruses in poultry has become increasingly important, given their potential to mutate into highly pathogenic viruses. However, evaluations of LPAI surveillance have mainly focused on prevalence and not on the ability to act as an early warning system. We used a simulation model based on data from Italian LPAI epidemics in turkeys to evaluate different surveillance strategies in terms of their performance as early warning systems. The strategies differed in terms of sample size, sampling frequency, diagnostic tests, and whether or not active surveillance (i.e., routine laboratory testing of farms) was performed, and were also tested under different epidemiological scenarios. We compared surveillance strategies by simulating within-farm outbreaks. The output measures were the proportion of infected farms that are detected and the farm reproduction number (R(h)). The first one provides an indication of the sensitivity of the surveillance system to detect within-farm infections, whereas R(h) reflects the effectiveness of outbreak detection (i.e., if detection occurs soon enough to bring an epidemic under control). Increasing the sampling frequency was the most effective means of improving the timeliness of detection (i.e., it occurs earlier), whereas increasing the sample size increased the likelihood of detection. Surveillance was only effective in preventing an epidemic if actions were taken within two days of sampling. The strategies were not affected by the quality of the diagnostic test, although performing both serological and virological assays increased the sensitivity of active surveillance. Early detection of LPAI outbreaks in turkeys can be achieved by increasing the sampling frequency for active surveillance, though very frequent sampling may not be sustainable in the long term. We suggest that, when no LPAI virus is circulating yet and there is a low risk of virus introduction, a less frequent sampling approach might be admitted, provided that the surveillance is intensified as soon as the first outbreak is detected.     

基于冠层温湿度模型的日光温室黄瓜霜霉病预警方法

利用温室环境参数构建室内微环境模拟模型,并结合温室病害模型进行预警,便于开展病害生态防治,以减少农药使用,从而保护温室生态环境和保证农产品质量安全.本文利用温室内能量守恒原理和水分平衡原理,构建了日光温室冠层叶片温度和空气相对湿度模拟模型.叶片温度模拟模型考虑了温室内植物与墙体、土壤、覆盖物之间的辐射热交换,以及室内净辐射、叶片蒸腾作用引起的能量变化;相对湿度模拟模型综合了温室内叶片蒸腾、土壤蒸发、覆盖物与叶面的水汽凝结引起的水分变化.将温湿度估计模型输出值作为参数,输入黄瓜霜霉病初侵染和潜育期预警模型中,估计黄瓜霜霉病发病日期,并与田间观测的实际发病日期比较.试验选取2014年9月和10月的温湿度监测数据进行模型验证,冠层叶片温度实际值与模拟值的均方根偏差（RMSD)分别为0.016和0.024 ℃,空气相对湿度实际值与模拟值的RMSD分别为0.15%和0.13%.结合温湿度估计模型结果表明,黄瓜病害预警系统预测黄瓜霜霉病发病日期与田间调查发病日期相吻合.本研究可为黄瓜日光温室病害预警模型及系统构建提供微环境数据支持.     

Exposure versus Susceptibility as Alternative Bases for New Approaches to Surveillance for Schistosoma japonicum in Low Transmission Environments

Currently, schistosomiasis in China provides an excellent example of many of the challenges of moving from low transmission to the elimination of transmission for infectious diseases generally. In response to the surveillance dimension of these challenges, we here explore two strategic approaches to inform priorities for the development of improved methods addressed specifically to schistosomiasis in the low transmission environment. We utilize an individually-based model and the exposure data used earlier to explore surveillance strategies, one focused on exposure assessment and the second on our estimates of variability in individual susceptibility in the practical context of the current situation in China and the theoretical context of the behavior of transmission dynamics near the zero state. Our findings suggest that individual susceptibility is the major single determinant of infection intensity in both the low and medium risk environments. We conclude that there is considerable motivation to search for a biomarker of susceptibility to infection in humans, but that there would also be value in a method for monitoring surface waters for the free-swimming forms of the parasite in endemic or formerly endemic environments as an early warning of infection risk.     

A mixed mover-stayer model for spatiotemporal two-state processes

Studies of recurring infection or chronic disease often collect longitudinal data on the disease status of subjects. Two-state transitional models are useful for analysis in such studies where, at any point in time, an individual may be said to occupy either a diseased or disease-free state and interest centers on the transition process between states. Here, two additional features are present. The data are spatially arranged and it is important to account for spatial correlation in the transitional processes corresponding to different subjects. In addition there are subgroups of individuals with different mechanisms of transitions. These subgroups are not known a priori and hence group membership must be estimated. Covariates modulating transitions are included in a logistic additive framework. Inference for the resulting mixture spatial Markov regression model is not straightforward. We develop here a Monte Carlo expectation maximization algorithm for maximum likelihood estimation and a Markov chain Monte Carlo sampling scheme for summarizing the posterior distribution in a Bayesian analysis. The methodology is applied to a study of recurrent weevil infestation in British Columbia forests.     

Monitoring and surveillance for rare health-related events: a review from the veterinary perspective

Disease monitoring and surveillance systems (MOSSs) have become one of the major components of veterinary activity. Such systems are used to assess the existing levels of prevalence, the effectiveness of control programmes and, after disease eradication, to document the continued absence of disease from a given region or zone. With decreasing disease or infection prevalence, traditional approaches become less reliable and increasingly costly. The objective of this work was to summarize and discuss methodological issues related to veterinary (animal health) MOSSs. There are considerable inconsistencies in the use of the terms 'monitoring' and 'surveillance'. Passive as well as active MOSS have their disadvantages when used for rare health-related events such as emerging and re-emerging diseases. There is a need for evaluation and improvement of these approaches. Integrated systems that call for the use of several parallel surveillance activities seem to be the favoured approach, and analytical methods to combine MOSS data from various sources into a population prevalence, or probability of disease freedom, are under development. The health and safety of the animal and human generations depends on our continuous ability to detect, monitor and control newly emerging or re-emerging livestock diseases and zoonoses rapidly. Uniform surveillance definitions, sound scientifically based approaches that use the resources and data available, and a pool of researchers and veterinary public health officials with sufficient training in epidemiology, are critically important to handle this challenging task.     

Inferring patterns of influenza transmission in swine from multiple streams of surveillance data

Swine populations are known to be an important source of new human strains of influenza A, including those responsible for global pandemics. Yet our knowledge of the epidemiology of influenza in swine is dismayingly poor, as highlighted by the emergence of the 2009 pandemic strain and the paucity of data describing its origins. Here, we analyse a unique dataset arising from surveillance of swine influenza at a Hong Kong abattoir from 1998 to 2010. We introduce a state–space model that estimates disease exposure histories by joint inference from multiple modes of surveillance, integrating both virological and serological data. We find that an observed decrease in virus isolation rates is not due to a reduction in the regional prevalence of influenza. Instead, a more likely explanation is increased infection of swine in production farms, creating greater immunity to disease early in life. Consistent with this, we find that the weekly risk of exposure on farms equals or exceeds the exposure risk during transport to slaughter. We discuss potential causes for these patterns, including competition between influenza strains and shifts in the Chinese pork industry, and suggest opportunities to improve knowledge and reduce prevalence of influenza in the region.     

The use of molecular xenomonitoring for surveillance of mosquito-borne diseases

The scientific community recognizes that molecular xenomonitoring (MX) can allow infected mosquitoes to serve as a proxy for human infection in vector-borne disease surveillance, but developing reliable MX systems for programmatic use has been challenging. The primary aim of this article is to examine the available evidence to recommend how MX can best be used for various purposes. Although much of the literature published within the last 20 years focuses on using MX for lymphatic filariasis elimination, a growing body of evidence supports its use in early warning systems for emerging infectious diseases (EIDs). An MX system design must consider the goal and target (e.g. diseases targeted for elimination versus EIDs), mosquito and pathogen characteristics, and context (e.g. setting and health system). MX is currently used as a ‘supplement’ to human surveillance and will not be considered as a ‘replacement’ until the correlation between pathogen-infection rates in human and mosquito populations is better understood. Establishing such relationships may not be feasible in elimination scenarios, due to increasingly dwindling human infection prevalence after successful control, but may still be possible for EIDs and in integrated disease surveillance systems.This article is part of the theme issue ‘Novel control strategies for mosquito-borne diseases''.     

Using community surveillance data to differentiate between emerging and endemic amphibian diseases

We analyzed submission data from a wildlife care group during amphibian disease surveillance in Queensland, Australia. Between January 1999 and December 2004, 877 white-lipped tree frogs Litoria infrafrenata were classified according to origin, season and presenting category. At least 69% originated from urban Cairns, significantly more than from rural and remote areas. Total submissions increased during the early and late dry seasons compared with the early wet season. Frogs most commonly presented each year with injury, followed by 'other', sparganosis and irreversible emaciation of unknown aetiology. This is the first report of Spirometra erinacei infection in this species. A high prevalence (28%) of visible S. erinacei infection was found in emaciated frogs, but this was not statistically different from that in non-emaciated diseased frogs (25%). However, 14 emaciated specimens that were necropsied all had heavy S. erinacei infections, and the odds of visible sparganosis were statistically greater in emaciated frogs compared with injured, non-diseased frogs. We provide a detailed case definition for a new endemic disease manifesting as irreversible emaciation, for which S. erinacei may be the primary aetiological agent. The lack of significant spatial or temporal patterns in case presentation suggests that this is not a currently emerging disease. We show that community wildlife groups can play a valuable role in monitoring disease trends, particularly in urban areas, but identify a number of limitations associated with passive syndromic surveillance. We conclude that it is critical that professionals be involved in establishing syndromic case definitions, diagnostic pathology, complementary active disease surveillance, and data analysis and interpretation in all wildlife disease investigations.     

Quantitative evaluation of complex surveillance systems for pest and disease detection

Surveillance for disease detection is used primarily for early detection of incursions and to support assertions of freedom from disease.Analytical techniques used to evaluate the efficacy of such surveillance are equally applicable across the domains of invasive species (both plant and animal), diseases and pests of agriculture crops, livestock, and of wildlife. Scenario tree models of surveillance activities may be used to estimate their diagnostic sensitivities, or the probability that the target organism will be detected given that it is present at a defined level. This paper will outline techniques for estimating the sensitivity of both targeted and general surveillance activities, and for the surveillance system as a whole. Probability of freedom from the target organism may be estimated from the surveillance sensitivity, and this Bayesian approach may be extended to estimate current probability of freedom from appropriate use of historical and ongoing surveillance evidence.     

Emerging Infectious Diseases in Free-Ranging Wildlife–Australian Zoo Based Wildlife Hospitals Contribute to National Surveillance

Emerging infectious diseases are increasingly originating from wildlife. Many of these diseases have significant impacts on human health, domestic animal health, and biodiversity. Surveillance is the key to early detection of emerging diseases. A zoo based wildlife disease surveillance program developed in Australia incorporates disease information from free-ranging wildlife into the existing national wildlife health information system. This program uses a collaborative approach and provides a strong model for a disease surveillance program for free-ranging wildlife that enhances the national capacity for early detection of emerging diseases.     

A Focused Ethnographic Study of Sri Lankan Government Field Veterinarians’ Decision Making about Diagnostic Laboratory Submissions and Perceptions of Surveillance

The global public health community is facing the challenge of emerging infectious diseases. Historically, the majority of these diseases have arisen from animal populations at lower latitudes where many nations experience marked resource constraints. In order to minimize the impact of future events, surveillance of animal populations will need to enable prompt event detection and response. Many surveillance systems targeting animals rely on veterinarians to submit cases to a diagnostic laboratory or input clinical case data. Therefore understanding veterinarians’ decision-making process that guides laboratory case submission and their perceptions of infectious disease surveillance is foundational to interpreting disease patterns reported by laboratories and engaging veterinarians in surveillance initiatives. A focused ethnographic study was conducted with twelve field veterinary surgeons that participated in a mobile phone-based surveillance pilot project in Sri Lanka. Each participant agreed to an individual in-depth interview that was recorded and later transcribed to enable thematic analysis of the interview content. Results found that field veterinarians in Sri Lanka infrequently submit cases to laboratories – so infrequently that common case selection principles could not be described. Field veterinarians in Sri Lanka have a diagnostic process that operates independently of laboratories. Participants indicated a willingness to take part in surveillance initiatives, though they highlighted a need for incentives that satisfy a range of motivations that vary among field veterinarians. This study has implications for the future of animal health surveillance, including interpretation of disease patterns reported, system design and implementation, and engagement of data providers.     

Serotype cycles in cholera dynamics

Interest in understanding strain diversity and its impact on disease dynamics has grown over the past decade. Theoretical disease models of several co-circulating strains indicate that incomplete cross-immunity generates conditions for strain-cycling behaviour at the population level. However, there have been no quantitative analyses of disease time-series that are clear examples of theoretically expected strain cycling. Here, we analyse a 40-year (1966-2005) cholera time-series from Bangladesh to determine whether patterns evident in these data are compatible with serotype-cycling behaviour. A mathematical two-serotype model is capable of explaining the oscillations in case patterns when cross-immunity between the two serotypes, Inaba and Ogawa, is high. Further support that cholera's serotype-cycling arises from population-level immunity patterns is provided by calculations of time-varying effective reproductive rates. These results shed light on historically observed serotype dominance shifts and have important implications for cholera early warning systems.     

野生鸟类传染性疾病研究进展

由于具有独特的飞行能力和极强的地理扩散能力,鸟类活动为某些传染性疾病的快速传播和扩散带来了潜在风险.自20世纪以来,以禽霍乱、禽波特淋菌病、西尼罗河热、禽流感等为代表的鸟类疾病频繁暴发,导致为数众多的野生鸟类、家禽甚至人类死亡,给社会造成巨大的经济损失.因此,有关鸟类传染性疾病的研究已引起了国内外学者的广泛关注.从鸟类传染性疾病的生态学特征、疾病对鸟类与人类社会的影响、鸟类对疾病的传播、鸟类疾病的监测、预警和防控等方面对野生鸟类的传染性疾病研究进展进行了综述.不同疾病导致的鸟类死亡量、易感物种数量、暴发频率和地理扩散等特征差异显著.20世纪以来,疾病已成为全球生物多样性的七大威胁因子之一.疾病可能造成鸟类大量死亡,从而对鸟类种群,特别是濒危鸟类种群造成严重影响.其中,人畜共患病还会导致家禽家畜甚至人类的死亡,从而对社会产生严重的影响.野生鸟类作为多种疾病传播的媒介,其移动和迁徙可能会导致疾病的传播与扩散.开展全面的监测活动和建立疾病预警体系,对于疾病的防控具有重要意义.     

Polytomous disease mapping to detect uncommon risk factors for related diseases

A statistical model for jointly analysing the spatial variation of incidences of three (or more) diseases, with common and uncommon risk factors, is introduced. Deaths for different diseases are described by a logit model for multinomial responses (multinomial logit or polytomous logit model). For each area and confounding strata population (i.e. age-class, sex, race) the probabilities of death for each cause (the response probabilities) are estimated. A specic disease, the one having a common risk factor only, acts as the baseline category. The log odds are decomposed additively into shared (common to diseases different by the reference disease) and specic structured spatial variability terms, unstructured unshared spatial terms and confounders terms (such as age, race and sex) to adjust the crude observed data for their effects. Disease specic spatially structured effects are estimated; these are considered as latent variables denoting disease-specic risk factors. The model is presented with reference to a specic application. We considered the mortality data (from 1990 to 1994) relative to oral cavity, larynx and lung cancers in 13 age groups of males, in the 287 municipalities of Region of Tuscany (Italy). All these pathologies share smoking as a common risk factor; furthermore, two of them (oral cavity and larynx cancer) share alcohol consumption as a risk factor. All studies suggest that smoking and alcohol consumption are the major known risk factors for oral cavity and larynx cancers; nevertheless, in this paper, we investigate the possibility of other different risk factors for these diseases, or even the presence of an interaction effect (between smoking and alcohol risk factors) but with different spatial patterns for oral and larynx cancer. For each municipality and age-class the probabilities of death for each cause (the response probabilities) are estimated. Lung cancer acts as the baseline category. The log odds are decomposed additively into shared (common to oral cavity and larynx diseases) and specic structured spatial variability terms, unstructured unshared spatial terms and an age-group term. It turns out that oral cavity and larynx cancer have different spatial patterns for residual risk factors which are not the typical ones such as smoking habits and alcohol consumption. But, possibly, these patterns are due to different spatial interactions between smoking habits and alcohol consumption for the first and the second disease.     

Improving the Modeling of Disease Data from the Government Surveillance System: A Case Study on Malaria in the Brazilian Amazon

The study of the effect of large-scale drivers (e.g., climate) of human diseases typically relies on aggregate disease data collected by the government surveillance network. The usual approach to analyze these data, however, often ignores a) changes in the total number of individuals examined, b) the bias towards symptomatic individuals in routine government surveillance, and; c) the influence that observations can have on disease dynamics. Here, we highlight the consequences of ignoring the problems listed above and develop a novel modeling framework to circumvent them, which is illustrated using simulations and real malaria data. Our simulations reveal that trends in the number of disease cases do not necessarily imply similar trends in infection prevalence or incidence, due to the strong influence of concurrent changes in sampling effort. We also show that ignoring decreases in the pool of infected individuals due to the treatment of part of these individuals can hamper reliable inference on infection incidence. We propose a model that avoids these problems, being a compromise between phenomenological statistical models and mechanistic disease dynamics models; in particular, a cross-validation exercise reveals that it has better out-of-sample predictive performance than both of these alternative models. Our case study in the Brazilian Amazon reveals that infection prevalence was high in 2004–2008 (prevalence of 4% with 95% CI of 3–5%), with outbreaks (prevalence up to 18%) occurring during the dry season of the year. After this period, infection prevalence decreased substantially (0.9% with 95% CI of 0.8–1.1%), which is due to a large reduction in infection incidence (i.e., incidence in 2008–2010 was approximately one fifth of the incidence in 2004–2008).We believe that our approach to modeling government surveillance disease data will be useful to advance current understanding of large-scale drivers of several diseases.     

Evaluating the Performance of Malaria Genetics for Inferring Changes in Transmission Intensity Using Transmission Modeling

Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilize these methodologies for malaria, we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterized using estimated relationships between complexity of infection and age from five regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterize the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance.     

Timing of testing and treatment for asymptomatic diseases

Many papers in the medical literature analyze the cost-effectiveness of screening for diseases by comparing a limited number of a priori testing policies under estimated problem parameters. However, this may be insufficient to determine the best timing of the tests or incorporate changes over time. In this paper, we develop and solve a Markov Decision Process (MDP) model for a simple class of asymptomatic diseases in order to provide the building blocks for analysis of a more general class of diseases. We provide a computationally efficient method for determining a cost-effective dynamic intervention strategy that takes into account (i) the results of the previous test for each individual and (ii) the change in the individual’s behavior based on awareness of the disease. We demonstrate the usefulness of the approach by applying the results to screening decisions for Hepatitis C (HCV) using medical data, and compare our findings to current HCV screening recommendations.     

Bias in Markov models of disease

We examine bias in Markov models of diseases, including both chronic and infectious diseases. We consider two common types of Markov disease models: ones where disease progression changes by severity of disease, and ones where progression of disease changes in time or by age. We find sufficient conditions for bias to exist in models with aggregated transition probabilities when compared to models with state/time dependent transition probabilities. We also find that when aggregating data to compute transition probabilities, bias increases with the degree of data aggregation. We illustrate by examining bias in Markov models of Hepatitis C, Alzheimer’s disease, and lung cancer using medical data and find that the bias is significant depending on the method used to aggregate the data. A key implication is that by not incorporating state/time dependent transition probabilities, studies that use Markov models of diseases may be significantly overestimating or underestimating disease progression. This could potentially result in incorrect recommendations from cost-effectiveness studies and incorrect disease burden forecasts.     

Forecasting infectious disease emergence subject to seasonal forcing

Background

Despite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also anticipate disease emergence in seasonal models.

Methods

We simulated the dynamics of an immunizing infectious pathogen approaching the tipping point to disease endemicity. To explore the effect of seasonality on the reliability of early warning statistics, we varied the amplitude of fluctuations around the average transmission. We proposed and analyzed two new early warning signals based on the wavelet spectrum. We measured the reliability of the early warning signals depending on the strength of their trend preceding the tipping point and then calculated the Area Under the Curve (AUC) statistic.

Results

Early warning signals were reliable when disease transmission was subject to seasonal forcing. Wavelet-based early warning signals were as reliable as other conventional early warning signals. We found that removing seasonal trends, prior to analysis, did not improve early warning statistics uniformly.

Conclusions

Early warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing. Wavelet-based early warning statistics can also be used to forecast infectious disease.