Cross-sectional telephone surveys as a tool to study epidemiological factors and monitor seasonal influenza activity in Malta.

BACKGROUND: Seasonal influenza has major implications for healthcare services as outbreaks often lead to high activity levels in health systems. Being able to predict when such outbreaks occur is vital. Mathematical models have extensively been used to predict epidemics of infectious diseases such as seasonal influenza and to assess effectiveness of control strategies. Availability of comprehensive and reliable datasets used to parametrize these models is limited. In this paper we combine a unique epidemiological dataset collected in Malta through General Practitioners (GPs) with a novel method using cross-sectional surveys to study seasonal influenza dynamics in Malta in 2014-2016, to include social dynamics and self-perception related to seasonal influenza. METHODS: Two cross-sectional public surveys (n = 406 per survey) were performed by telephone across the Maltese population in 2014-15 and 2015-16 influenza seasons. Survey results were compared with incidence data (diagnosed seasonal influenza cases) collected by GPs in the same period and with Google Trends data for Malta. Information was collected on whether participants recalled their health status in past months, occurrences of influenza symptoms, hospitalisation rates due to seasonal influenza, seeking GP advice, and other medical information. RESULTS: We demonstrate that cross-sectional surveys are a reliable alternative data source to medical records. The two surveys gave comparable results, indicating that the level of recollection among the public is high. Based on two seasons of data, the reporting rate in Malta varies between 14 and 22%. The comparison with Google Trends suggests that the online searches peak at about the same time as the maximum extent of the epidemic, but the public interest declines and returns to background level. We also found that the public intensively searched the Internet for influenza-related terms even when number of cases was low. CONCLUSIONS: Our research shows that a telephone survey is a viable way to gain deeper insight into a population's self-perception of influenza and its symptoms and to provide another benchmark for medical statistics provided by GPs and Google Trends. The information collected can be used to improve epidemiological modelling of seasonal influenza and other infectious diseases, thus effectively contributing to public health.

Estimation of force of infection based on different epidemiological proxies: 2009/2010 Influenza epidemic in Malta.

Information about infectious disease outbreaks is often gathered indirectly, from doctor's reports and health board records. It also typically underestimates the actual number of cases, but the relationship between the observed proxies and the numbers that drive the diseases is complicated, nonlinear and potentially time- and state-dependent. We use a combination of data collection from the 2009-2010 H1N1 outbreak in Malta, compartmental modelling and Bayesian inference to explore the effect of using various sources of information (consultations, doctor's diagnose, swabbing and molecular testing) on estimation of the effective basic reproduction ratio, R(t). Different proxies and different sampling rates (daily and weekly) lead to similar behaviour of R(t) as the epidemic unfolds, although individual parameters (force of infection, length of latent and infectious period) vary. We also demonstrate that the relationship between different proxies varies as epidemic progresses, with the first period characterised by high ratio of consultations and influenza diagnoses to actual confirmed cases of H1N1. This has important consequences for modelling that is based on reconstructing influenza cases from doctor's reports.

Parameter estimation and prediction for the course of a single epidemic outbreak of a plant disease.

Many epidemics of plant diseases are characterized by large variability among individual outbreaks. However, individual epidemics often follow a well-defined trajectory which is much more predictable in the short term than the ensemble (collection) of potential epidemics. In this paper, we introduce a modelling framework that allows us to deal with individual replicated outbreaks, based upon a Bayesian hierarchical analysis. Information about 'similar' replicate epidemics can be incorporated into a hierarchical model, allowing both ensemble and individual parameters to be estimated. The model is used to analyse the data from a replicated experiment involving spread of Rhizoctonia solani on radish in the presence or absence of a biocontrol agent, Trichoderma viride. The rate of primary (soil-to-plant) infection is found to be the most variable factor determining the final size of epidemics. Breakdown of biological control in some replicates results in high levels of primary infection and increased variability. The model can be used to predict new outbreaks of disease based upon knowledge from a 'library' of previous epidemics and partial information about the current outbreak. We show that forecasting improves significantly with knowledge about the history of a particular epidemic, whereas the precision of hindcasting to identify the past course of the epidemic is largely independent of detailed knowledge of the epidemic trajectory. The results have important consequences for parameter estimation, inference and prediction for emerging epidemic outbreaks.

An Epidemiological Analysis of the Role of Disease-Induced Root Growth in the Differential Response of Two Cultivars of Winter Wheat to Infection by Gaeumannomyces graminis var. tritici.

ABSTRACT Epidemiological modeling combined with parameter estimation of experimental data was used to examine differences in the contribution of disease-induced root production to the spread of take-all on plants of two representative yet contrasting cultivars of winter wheat, Ghengis and Savannah. A mechanistic model, including terms for primary infection, secondary infection, inoculum decay, and intrinsic and disease-induced root growth, was fitted to data describing changes in the numbers of infected and susceptible roots over time at a low or high density of inoculum. Disease progress curves were characterized by consecutive phases of primary and secondary infection. No differences in root growth were detected between cultivars in the absence of disease and root production continued for the duration of the experiment. However, significant differences in disease-induced root production were detected between Savannah and Genghis. In the presence of disease, root production for both cultivars was characterized by stimulation when few roots were infected and inhibition when many roots were infected. At low inoculum density, the transition from stimulation to inhibition occurred when an average of 5.0 and 9.0 roots were infected for Genghis and Savannah, respectively. At high inoculum density, the transition from stimulation to inhibition occurred when an average of 4.5 and 6.7 roots were infected for Genghis and Savannah, respectively. Differences in the rates of primary and secondary infection between Savannah and Genghis also were detected. At a low inoculum density, Genghis was marginally more resistant to secondary infection whereas, at a high density of inoculum, Savannah was marginally more resistant to primary infection. The combined effects of differences in disease-induced root growth and differences in the rates of primary and secondary infection meant that the period of stimulated root production was extended by 7 and 15 days for Savannah at a low and high inoculum density, respectively. The contribution of this form of epidemiological modeling to the better management of take-all is discussed.

Correlation between mitotic delay and aberration burden, and their role for the analysis of chromosomal damage.

The aim was to investigate further the relationship between radiation-induced mitotic delay and the expression of chromosome damage in V79 cells. Recently published data on the time-course of chromosome aberrations in V79 first-cycle metaphases after exposure to 10.4 MeV u(-1) Ar ions (LET = 1226 keV microm(-1)) were supplemented and reanalysed. A statistical analysis of the distribution of aberrations among cells was performed. Furthermore, cells were grouped into subpopulations carrying 0, 1 -2, 3-4, 5- 6 and 7 or more aberrations. Then, based on the mitotic index, the flux of each subgroup through the first mitosis was determined and the average entrance time to mitosis was estimated. For comparison, the flux of aberrant V79 cells generated by X-irradiation was analysed. Analysis of the Ar ion data revealed that the flux of each subpopulation through the first mitosis is strongly affected by its aberration burden, i.e. a positive correlation between the mitotic delay and the number of aberrations carried by a cell was observed. The distribution of aberrations among cells could be well described by Neyman-type A statistics; the corresponding fit parameters also reflect the damage-dependent mitotic delay. Interestingly, comparison of the flux of Ar ion and X-ray-irradiated V79 cells through mitosis revealed (1) that a direct correlation exists between the number of aberrations carried by a cell and its average entrance time to mitosis, and (2) that this effect is independent of the linear energy transfer. The role of these observations for radiation cytogenetics is discussed.

Bayesian analysis of botanical epidemics using stochastic compartmental models.

A stochastic model for an epidemic, incorporating susceptible, latent, and infectious states, is developed. The model represents primary and secondary infection rates and a time-varying host susceptibility with applications to a wide range of epidemiological systems. A Markov chain Monte Carlo algorithm is presented that allows the model to be fitted to experimental observations within a Bayesian framework. The approach allows the uncertainty in unobserved aspects of the process to be represented in the parameter posterior densities. The methods are applied to experimental observations of damping-off of radish (Raphanus sativus) caused by the fungal pathogen Rhizoctonia solani, in the presence and absence of the antagonistic fungus Trichoderma viride, a biological control agent that has previously been shown to affect the rate of primary infection by using a maximum-likelihood estimate for a simpler model with no allowance for a latent period. Using the Bayesian analysis, we are able to estimate the latent period from population data, even when there is uncertainty in discriminating infectious from latently infected individuals in data collection. We also show that the inference that T. viride can control primary, but not secondary, infection is robust to inclusion of the latent period in the model, although the absolute values of the parameters change. Some refinements and potential difficulties with the Bayesian approach in this context, when prior information on parameters is lacking, are discussed along with broader applications of the methods to a wide range of epidemiological systems.

Epidemiological dynamics and the efficiency of biological control of soil-borne disease during consecutive epidemics in a controlled environment.

• A combination of experimentation and modelling is used to examine the role of epidemiological dynamics on the production and infectivity of inoculum and the efficiency of biocontrol by Trichoderma viride during consecutive epidemics of damping-off disease caused by the pathogen Rhizoctonia solani in crops of radish. • Changes in the net infectivity of inoculum at the beginning of first and second crops caused a switch in epidemiological dynamics. Epidemics of first crops were dominated by secondary infection leading to amplification of inoculum so that epidemics of second crops were overwhelmingly determined by primary infection. • The biocontrol agent reduced primary infection and hence parasitic amplification of inoculum in both first and second crops but the efficiency of control dropped from 91.7% in first crops to 64.8% in second crops, with sudden outbreaks of disease in second crops which had previously been disease-free. • We conclude that parasitic amplification can cause a rapid build-up of disease and inoculum over consecutive crops, leading to loss in the efficiency of biocontrol. This form of inoculum production is supplemented by saprotrophic infestation which can result in sudden outbreaks of disease in protected crops where control of disease had previously been fully successful.

Controlling disease spread on networks with incomplete knowledge.

Models for control of highly infectious diseases on local, small-world, and scale-free networks are considered, with only partial information accessible about the status of individuals and their connections. We consider a case when individuals can be infectious before showing symptoms and thus before detection. For small to moderately severe incidence of infection with a small number of nonlocal links, it is possible to control disease spread by using purely local methods applied in a neighborhood centered around a detected infectious individual. There exists an optimal radius for such a control neighborhood leading to the lowest severity of the epidemic in terms of economic costs associated with disease and treatment. The efficiency of a local control strategy is very sensitive to the choice of the radius. Below the optimal radius, the local strategy is unsuccessful; the disease spreads throughout the system, necessitating treatment of the whole population. At the other extreme, a strategy involving a neighborhood that is too large controls the disease but is wasteful of resources. It is not possible to stop an epidemic on scale-free networks by preventive actions, unless a large proportion of the population is treated.

Mathematical models of radiation-induced mitotic delay: time course analysis and statistics of lesions.

Detailed investigations of high and low LET radiation induced chromosome aberrations in various mammalian cell lines have shown that the registered yield of aberrations depends on cell cycle progression delays. The effect of radiation on the cell kinetics can be analyzed in terms of kinetic growth models. The method yields the number of aberrant cells and the number of aberrations as totals obtained after integration over given time-interval.

Population dynamics of plant-parasite interactions: thresholds for invasion.

Thresholds are derived for the invasion of plant populations by parasites. The theory is developed for a generic model that takes into account two features characteristic of plant-parasite interactions: a dual source of inoculum (infection from primary or externally introduced inoculum and secondary infection from contact between susceptible and infected host tissue) and a host response to infection load. Each of the threshold criteria is shown to be the sum of the individual components for primary and secondary infection. This indicates that if parasite invasion is not possible through primary or secondary infection alone, when the two modes of transmission are combined, the parasite may be able to invade. The invasion criteria demonstrate that there is a threshold population of susceptible hosts below which the parasite is unable to invade. If there are nonlinearities in the population dynamics (arising through either the transmission process or the host response), there are also threshold densities for the infected hosts and parasite populations below which invasion does not occur. The implications of the results for the control of plant disease are discussed.

Predicting variability in biological control of a plant-pathogen system using stochastic models.

A stochastic model for the dynamics of a plant-pathogen interaction is developed and fitted to observations of the fungal pathogen Rhizoctonia solani (Kühn) in radish (Raphanus sativus L.), in both the presence and absence of the antagonistic fungus Trichoderma viride (Pers ex Gray). The model incorporates parameters for primary and secondary infection mechanisms and for characterizing the time-varying susceptibility of the host population. A parameter likelihood is developed and used to fit the model to data from microcosm experiments. It is shown that the stochastic model accounts well for observed variability both within and between treatments. Moreover, it enables us to describe the time evolution of the probability distribution for the variability among replicate epidemics in terms of the underlying epidemiological parameters for primary and secondary infection and decay in susceptibility. Consideration of profile likelihoods for each parameter provides strong evidence that T. viride mainly affects primary infection. By using the stochastic model to study the dependence of the probability distribution of disease levels on the primary infection rate we are therefore able to predict the effectiveness of a widely used biological control agent.

The role of sub-milankovitch climatic forcing in the initiation of the northern hemisphere glaciation

Mechanisms responsible for the initiation of major glaciation in the Northern Hemisphere at about 2.75 million years ago are poorly understood. A laminated terrestrial sequence from Pula maar, Hungary, containing about 320,000 years in annual layers between 3.05 and 2. 60 million years ago, provides a detailed record of rates of climatic change across this dramatic transition. An analysis of the record implies that climatic variations at sub-Milankovitch frequencies (less than or equal to 15,000 years) were an important driving force during this transitional interval and that, as the threshold was approached, these increased in frequency and amplitude, possibly providing the final trigger for the amplification of Northern Hemisphere ice sheets.

Spatial heterogeneity, nonlinear dynamics and chaos in infectious diseases.

There is currently considerable interest in the role of nonlinear phenomena in the population dynamics of infectious diseases. Childhood diseases such as measles are particularly well documented dynamically, and have recently been the subject of analyses (of both models and notification data) to establish whether the pattern of epidemics is chaotic. Though the spatial dynamics of measles have also been extensively studied, spatial and nonlinear dynamics have only recently been brought together. The present review concentrates mainly on describing this synthesis. We begin with a general review of the nonlinear dynamics of measles models, in a spatially homogeneous environment. Simple compartmental models (specifically the SEIR model) can behave chaotically, under the influence of strong seasonal 'forcing' of infection rate associated with patterns of schooling. However, adding observed heterogeneities such as age structure can simplify the deterministic dynamics back to limit cycles. By contrast all current strongly seasonally forced stochastic models show large amplitude irregular fluctuations, with many more 'fadeouts' of infection that is observed in real communities of similar size. This indicates that (social and/or geographical) spatial heterogeneity is needed in the models. We review the exploration of this problem with nonlinear spatiotemporal models. The few studies to date indicate that spatial heterogeneity can help to increase the realism of models. However, a review of nonlinear analyses of spatially subdivided measles data show that more refinements of the models (particularly in representing the impact of human demographic changes on infection dynamics) are required. We conclude with a discussion of the implication of these results for the dynamics of infectious diseases in general and, in particular, the possibilities of cross fertilization between human disease epidemiology and the study of plant and animal diseases.