Revealing the True Incidence of Pandemic A(H1N1)pdm09 Influenza in Finland during the First Two Seasons : An Analysis Based on a Dynamic Transmission Model

The threat of the new pandemic influenza A(H1N1)pdm09 imposed a heavy burden on the public health system in Finland in 2009-2010. An extensive vaccination campaign was set up in the middle of the first pandemic season. However, the true number of infected individuals remains uncertain as the surveillance missed a large portion of mild infections. We constructed a transmission model to simulate the spread of influenza in the Finnish population. We used the model to analyse the two first years (2009-2011) of A(H1N1)pdm09 in Finland. Using data from the national surveillance of influenza and data on close person-to-person (social) contacts in the population, we estimated that 6% (90% credible interval 5.1 – 6.7%) of the population was infected with A(H1N1)pdm09 in the first pandemic season (2009/2010) and an additional 3% (2.5 – 3.5%) in the second season (2010/2011). Vaccination had a substantial impact in mitigating the second season. The dynamic approach allowed us to discover how the proportion of detected cases changed over the course of the epidemic. The role of time-varying reproduction number, capturing the effects of weather and changes in behaviour, was important in shaping the epidemic.Peer reviewe

Revealing the True Incidence of Pandemic A (H1N1)pdm09 Influenza in Finland during the First Two Seasons - An Analysis Based on a Dynamic Transmission Model

The threat of the new pandemic influenza A(H1N1)pdm09 imposed a heavy burden on the public health system in Finland in 2009-2010. An extensive vaccination campaign was set up in the middle of the first pandemic season. However, the true number of infected individuals remains uncertain as the surveillance missed a large portion of mild infections. We constructed a transmission model to simulate the spread of influenza in the Finnish population. We used the model to analyse the two first years (2009-2011) of A(H1N1)pdm09 in Finland. Using data from the national surveillance of influenza and data on close person-to-person (social) contacts in the population, we estimated that 6% (90% credible interval 5.1 - 6.7%) of the population was infected with A(H1N1)pdm09 in the first pandemic season (2009/2010) and an additional 3% (2.5 - 3.5%) in the second season (2010/2011). Vaccination had a substantial impact in mitigating the second season. The dynamic approach allowed us to discover how the proportion of detected cases changed over the course of the epidemic. The role of time-varying reproduction number, capturing the effects of weather and changes in behaviour, was important in shaping the epidemic.</p

Bayesian Inference for Spatio-Temporal Models

The dissertation presents five problem-driven research articles, representing three research domains related to micro-organisms causing infectious disease. Articles I and II are devoted to the A(H1N1)pdm09 influenza (`swine flu') epidemic in Finland 2009-2011. Articles III and IV present software tools for analysing experimental data produced by Biolog phenotype microarrays. Article V studies a mismatch distribution as a summary statistic for the inference about evolutionary dynamics and demographic processes in bacterial populations. All addressed problems share the following two features: (1) they concern a dynamical process developing in time and space; (2) the observations of the process are partial and imprecise. The problems are generally approached using Bayesian Statistics as a formal methodology for learning by confronting hypothesis to evidence. Bayesian Statistics relies on modelling: constructing a generative algorithm mimicking the object, process or phenomenon of interest

Bayesian Inference for Spatio-Temporal Models

The dissertation presents five problem-driven research articles, representing three research domains related to micro-organisms causing infectious disease. Articles I and II are devoted to the A(H1N1)pdm09 influenza (`swine flu') epidemic in Finland 2009-2011. Articles III and IV present software tools for analysing experimental data produced by Biolog phenotype microarrays. Article V studies a mismatch distribution as a summary statistic for the inference about evolutionary dynamics and demographic processes in bacterial populations. All addressed problems share the following two features: (1) they concern a dynamical process developing in time and space; (2) the observations of the process are partial and imprecise. The problems are generally approached using Bayesian Statistics as a formal methodology for learning by confronting hypothesis to evidence. Bayesian Statistics relies on modelling: constructing a generative algorithm mimicking the object, process or phenomenon of interest

Evidence Synthesis for Stochastic Epidemic Models.

In recent years, the role of epidemic models in informing public health policies has progressively grown. Models have become increasingly realistic and more complex, requiring the use of multiple data sources to estimate all quantities of interest. This review summarises the different types of stochastic epidemic models that use evidence synthesis and highlights current challenges

Influenza virus susceptibility to antiviral drugs : drug susceptibility profiling, whole-genome mutational landscape and selective pressure footprints

Tese de doutoramento, Ciências e Tecnologias da Saúde (Microbiologia), Universidade de Lisboa, Faculdade de Medicina, 2018Antivirals play an important and decisive role in the clinical management of influenza and in the underlying reduction of related morbidity and mortality. The emergence of antiviral resistance, and particularly of transmissible resistance, poses a serious threat to public health as it could render influenza antivirals useless against circulating viruses. This is even more worrying when considering the current paucity of alternative antiviral therapy choices. This PhD research project aimed at disclosing the susceptibility of human influenza viruses circulating in Portugal to nationally approved antivirals, and at improving the knowledge on the evolutionary dynamics underlying the emergence and/or spread of influenza variants resistant or with decreased susceptibility to neuraminidase inhibitor (NAI) drugs. To this end, the project focused on three main areas: antiviral susceptibility testing; whole-genome sequencing; and selective pressure (SP) footprints on human influenza neuraminidase (NA)(NAI target). Antiviral susceptibility testing was performed on human influenza viruses circulating in both community and hospital settings from 2004/2005 to 2012/2013, after establishing a technological platform for comprehensive evaluation of virus susceptibility to M2 protein inhibitors and the NAIs oseltamivir (OS) and zanamivir (ZA) (objective 1). Important findings were made on: the circulation of drug-resistant A(H3N2) (M2 inhibitors) and former seasonal (H1N1) (OS) viruses; the cut-off for potentially clinically relevant sub-populations of drug-resistant virus; a potential novel amino acid substitution conferring slightly decreased susceptibility to ZA (N2 NA) and a novel source for a variant with decreased susceptibility; and, the virus type or subtype specificity of two amino acid substitutions conferring reduced susceptibility to the drug. Overall susceptibility data contributed at a better understanding of the relationship between virus NAI susceptibility phenotype and genotype and of the natural variations in the in vitro NAI susceptibility of circulating viruses over time. The emergence of new drift variants (former seasonal A(H1N1), A(H3N2)), the co-circulation of distinct virus lineages (influenza B) and the increase in OS drug use (A(H1N1)pdm09) were found to potentially play a role in this latter. Influenza viruses exhibiting resistance or decreased susceptibility to OS and/or ZA were further evaluated through whole-genome sequencing to identify and characterize the amino acid substitutions specific of their genome (objective 2). No genetic support was found for the fitter NA H275Y OS resistant former seasonal A(H1N1) viruses, but mutations known to or that based on its structural location or functional impact may play a role in the overall viral fitness, were identified in the genome of single or few viruses resistant or with decreased susceptibility to the drug. Large datasets of full-length NA gene sequences of worldwide circulating viruses were created to estimate the global and site-specific SP acting on influenza NA, particularly on the sites associated with NAI resistance or reduced susceptibility and/or contacting with the drug (objective 3a). Further temporal splitting of NA gene sequences allowed to investigate for the first time the impact of NAI introduction into clinic (1999) and/or its increased use during 2009 A(H1N1) pandemic on the SP acting on NA (objective 3b). Major findings include: the potential role of positive SP (PSP) in the low-level and locally variable spread of NA H275Y OS-resistant A(H1N1)pdm09 viruses that has been observed in the community; a potential risk of spread of a synergistic drug-resistant (H275Y/S247N) or a RI (S247G) variant in A(H1N1)pdm09 subtype and a RI variant (A395E) in B/Victoria lineage (positive diversifying selection); and the potential lack of impact of both NAI introduction into clinic and its increased use during 2009 A(H1N1) pandemic on the global and site-specific SP acting on influenza NA, with the single exception of site 154 of B/YAM-lineage NA (framework active site residue). Overall mapping of site-specific SP across the different NA subtypes or lineages allowed for further identify 7 potential new regions for drug targeting. This project marked the beginning of influenza antiviral susceptibility testing and monitoring activities in Portugal. It not only established the technological capacity and capability required to perform such activities but also generated comprehensive information on the susceptibility of circulating human influenza viruses, essential to contribute to both global and European influenza surveillance on antiviral susceptibility. The project also contributed at finding potential determinants of viral fitness in the genome of influenza virus resistant or with decreased susceptibility to NAIs, based on its location onto the protein structure; and at elucidating the role of PSP in the evolutionary pathways to NAI resistance or reduced susceptibility.Fundação Calouste Gulbenkian, projetos FCG 76676 e SDH49; Administração Central do Sistema de Saúde, I.P. (ACSS), projeto SDH4

Efficient real-time monitoring of an emerging influenza pandemic: How feasible?

A prompt public health response to a new epidemic relies on the ability to monitor and predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK revealed pandemic data as noisy, contaminated, potentially biased and originating from multiple sources. This seriously challenges the capacity for real-time monitoring. Here, we assess the feasibility of real-time inference based on such data by constructing an analytic tool combining an age-stratified SEIR transmission model with various observation models describing the data generation mechanisms. As batches of data become available, a sequential Monte Carlo (SMC) algorithm is developed to synthesise multiple imperfect data streams, iterate epidemic inferences and assess model adequacy amidst a rapidly evolving epidemic environment, substantially reducing computation time in comparison to standard MCMC, to ensure timely delivery of real-time epidemic assessments. In application to simulated data designed to mimic the 2009 A/H1N1 epidemic, SMC is shown to have additional benefits in terms of assessing predictive performance and coping with parameter nonidentifiability

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

The Epidemiology and Control of Human Influenza in Vietnam

Understanding the epidemiology of human influenza in Viet Nam is important for developing local policies and also for understanding the dynamics of influenza in tropical and subtropical southeast Asia. I have analysed an 18 year time-series of influenza-like-illness (ILI) surveillance data, and assessed the relationship of this time-series with climate variables and with sentinel influenza virus surveillance data. I also conducted a study of influenza A/H1N1 transmission within households. ILI notifications in Viet Nam show a latitudinal gradient, with seasonality in the north but no seasonal pattern observed in low lying areas of central and southern Viet Nam. Seasonality is however observed in the elevated provinces of central Viet Nam, suggesting that the seasonal patterns are driven by climate. Principal component analysis finds that temperature and absolute humidity (AH) are positively correlated and together explain around 59% of total climatic variance, and that there is a strong latitudinal gradient in these variables. Regression tree analysis shows that provinces with strong seasonality of AH have strong ILI seasonality. Although virological surveillance data are limited, increases in ILI notifications are associated with an increase in the proportion of upper respiratory tract swabs that are influenza positive. In a prospective study of H1N1/2009 transmission in a household-based cohort, 11 of 59 household contacts were infected, giving a household secondary infection risk of 18.6% (95%CI 10.7-30.4%), but 5 (45%) did not develop symptoms. Virus genetic sequencing indicated that 10 of the 11 secondary cases (91%) were probably infected within the household rather than from the community. This research provides new insights into the seasonality and climatic determinants of ILI and influenza epidemiology in Viet Nam, and on the transmission of influenza within households. The findings are valuable for national influenza control policies and also add to the current state of knowledge of influenza epidemiology