Particle filtering in compartmental projection models

Simulation models are important tools for real-time forecasting of pandemics. Models help health decision makers examine interventions and secure strong guidance when anticipating outbreak evolution. However, models usually diverge from the real observations. Stochastics involved in pandemic systems, such as changes in human contact patterns play a substantial role in disease transmissions and are not usually captured in traditional dynamic models. In addition, models of emerging diseases face the challenge of limited epidemiological knowledge about the natural history of disease. Even when the information about natural history is available -- for example for endemic seasonal diseases -- transmission models are often simplified and are involved with omissions. Availability of data streams can provide a view of early days of a pandemic, but fail to predict how the pandemic will evolve. Recent developments of computational statistics algorithms such as Sequential Monte Carlo and Markov Chain Monte Carlo, provide the possibility of creating models based on historical data as well as re-grounding models based on ongoing data observations. The objective of this thesis is to combine particle filtering -- a Sequential Monte Carlo algorithm -- with system dynamics models of pandemics. We developed particle filtering models that can recurrently be re-grounded as new observations become available. To this end, we also examined the effectiveness of this arrangement which is subject to specifics of the configuration (e.g., frequency of data sampling). While clinically-diagnosed cases are valuable incoming data stream during an outbreak, new generation of geo-spatially specific data sources, such as search volumes can work as a complementary data resource to clinical data. As another contribution, we used particle filtering in a model which can be re-grounded based on both clinical and search volume data. Our results indicate that the particle filtering in combination with compartmental models provides accurate projection systems for the estimation of model states and also model parameters (particularly compared to traditional calibration methodologies and in the context of emerging communicable diseases). The results also suggest that more frequent sampling from clinical data improves predictive accuracy outstandingly. The results also present that assumptions to make regarding the parameters associated with the particle filtering itself and changes in contact rate were robust across adequacy of empirical data since the beginning of the outbreak and inter-observation interval. The results also support the use of data from Google search API along with clinical data

Avoiding Tough Policy Choices in an Influenza Pandemic: The Role of Kettl\u27s Rocket Science Model in Public Health

The security and social inequality approaches to public health present distinct answers to policy objectives relative to a pandemic. However, each approach leaves us with tough choices between the most valued objectives. I demonstrate how the networked approach, which Kettl\u27s Rocket Science Model (RSM) exemplifies, does not leave us with such choices. Furthermore, I connect the epidemiological concepts public health practitioners apply toward communicable disease pandemics to RSM concepts. Finally, drawing on the disease parameters of a worst-case scenario influenza pandemic, I demonstrate how the networked approach helps public health practitioners expand capacity such that tough choices are unnecessary

REASSORTMENT AND GENE SELECTION OF INFLUENZA VIRUSES IN THE FERRET MODEL AND POTENTIAL PLATFORMS FOR IN VIVO REVERSE GENETICS

Influenza A virus is a highly infectious agent that cause seasonal epidemics affecting 5-15% of the world population with mild to severe illness and possibly death. While this pathogen represents a significant disease burden to the human population, it can also infect a wide range of animals including swine and land-based poultry, which are thought to serve as intermediate hosts between the human and natural wild aquatic bird reservoir. Here, two viruses, a swine-origin pandemic H1N1 and a seasonal human H3N2 are examined for segment fitness during co-infection of in vivo animal models. In three independent co-infections, reassortment between seasonal and pandemic viruses resulted in the selection of an H1N2 virus with a seasonal PB1 with an otherwise pandemic internal gene constellation. Selection for the seasonal PB1 and NA as well as the pandemic M segment was observed to occur rapidly during segment resolution. As pandemic M gene reassortant strains are being consistently identified in the field, studies were performed to identify the genetic determinants in pandemic M gene selection. Research here shows that both the M1 capsid protein and M2 ion channel from the pandemic virus are sufficient to drive the selection of the entire M segment. As swine represent an important intermediate host for the adaptation of potentially pandemic viruses, including pandemic M gene reassortant strains, alternative DNA and recombinant baculovirus-based platforms are investigated for their ability to generate influenza viruses from porcine polymerase I promoters and serve as potential vaccine candidates. Research here shows that influenza A virus can be rescued de novo using the porcine polymerase I promoter in an eight plasmid system. Furthermore, a single bacmid can be constructed that rescues influenza virus or baculovirus encoding the influenza reverse genetic system in mammalian tissue culture or Sf9 cells, respectively. These represent a new generation of species-tailored vaccine platforms

Networks of inter-organisational coordination during disease outbreaks

Multi-organisational environment is demonstrating more complexities due the ever-increasing tasks’ complications in modern environments. Disease outbreak coordination is one of these complex tasks that require multi-skilled and multi-jurisdictional agencies to coordinate in dynamic environment. This research discusses theoretical foundations and practical approaches to suggest frameworks to study complex inter-organisational networks in dynamic environments, specifically during disease outbreak. We study coo¬¬rdination as being an interdisciplinary domain, and then uses social network theory to model it. I have surveyed 70 health professionals whom have participated in the swine influenza H1N1 2009 outbreak. I collected both qualitative and quantitative data in order to build a comprehensive understanding of the dynamics of the inter-organisational network that evolved during that outbreak. Then I constructed a performance model by use three main components of the network theory: degree centrality, connectedness and tie strength as the independent variables, and disease outbreak inter-organisational performance as the dependent one. In addition, we study both the formal networks and the informal ones. Formal networks are based on the standard operating structures, and the informal ones emerge based on trust, mutual benefits and relationships. Results suggest that the proposed social network measures have positive effect on coordination performance during the outbreak in both formal and informal networks, except centrality in the formal one. In addition, none of those measures influence performance before the outbreak. Practically, the results suggest that increasing the communication frequency and diversifying the tiers of the inter-organisational links enhance the overall network’s performance in formal coordination. In the informal one, links are created with the intention to improve performance; hence, all suggested network measures improve performance

Networks of inter-organisational coordination during disease outbreaks

Multi-organisational environment is demonstrating more complexities due the ever-increasing tasks’ complications in modern environments. Disease outbreak coordination is one of these complex tasks that require multi-skilled and multi-jurisdictional agencies to coordinate in dynamic environment. This research discusses theoretical foundations and practical approaches to suggest frameworks to study complex inter-organisational networks in dynamic environments, specifically during disease outbreak. We study coo¬¬rdination as being an interdisciplinary domain, and then uses social network theory to model it. I have surveyed 70 health professionals whom have participated in the swine influenza H1N1 2009 outbreak. I collected both qualitative and quantitative data in order to build a comprehensive understanding of the dynamics of the inter-organisational network that evolved during that outbreak. Then I constructed a performance model by use three main components of the network theory: degree centrality, connectedness and tie strength as the independent variables, and disease outbreak inter-organisational performance as the dependent one. In addition, we study both the formal networks and the informal ones. Formal networks are based on the standard operating structures, and the informal ones emerge based on trust, mutual benefits and relationships. Results suggest that the proposed social network measures have positive effect on coordination performance during the outbreak in both formal and informal networks, except centrality in the formal one. In addition, none of those measures influence performance before the outbreak. Practically, the results suggest that increasing the communication frequency and diversifying the tiers of the inter-organisational links enhance the overall network’s performance in formal coordination. In the informal one, links are created with the intention to improve performance; hence, all suggested network measures improve performance

An international study of the use of pandemic vaccines during the 2009-10 influenza A(H1N1) pandemic: a qualitative methodological study

Background: The 2009-10 influenza A(H1N1) pandemic was the first pandemic influenza of the twenty-first century and presented the first major opportunity for the use of influenza vaccines en-masse during a pandemic scenario. National anticipatory policies of pandemic influenza vaccine preparedness were implemented, and vaccine guarantee agreements were activated. Large quantities of vaccines were purchased and made available to identified citizens over the course of the pandemic. The use of pandemic influenza vaccines has been examined in this research. Methods: A comparative health policy approach in five study countries (Sweden, New Zealand, Japan, Singapore, and Canada) was conducted. Qualitative interviews (n= 36) were undertaken in each country with key pandemic influenza response personnel (n = 39). Participants included public health officials, policy makers and clinicians engaged at national country response level. Interviews facilitated discussions surrounding the 2009-10 influenza A(H1N1) pandemic response and use of vaccines. Documentary examination of available records supplemented the analysis of the interview data. Results: Several interview themes were identified following data analysis of the use of pandemic vaccines in the study countries. Themes of the vaccine use included: single or multiple vaccine supplier routes; hemisphere variation; historical pandemic legacy; targeted populations; setting vaccination priorities; side effect concerns; perceived effectiveness of vaccines during the pandemic influenza response. The themes which were most prominent comprised the sourcing and distribution of the vaccines during the response and the associated communication challenges. The necessary prioritisation of vaccines caused extensive discussions and uneasiness by the pandemic influenza response personnel as the initial vaccines arrived in small quantities and required allocation, especially in circumstances where country’s intended for all/most citizens to eventually have access to the vaccine. The variation in timing of the vaccination campaigns and disease activity would suggest that subsequent influenza wave morbidities and mortalities could have been reduced if vaccines had been available more promptly. The southern hemisphere country, New Zealand, exemplified the circumvention of vaccine safety concerns through the use of a trivalent vaccine inclusive of H1N1. Conclusions: Pandemic vaccines were the cornerstone of two countries responses and were associated with high uptake rates. Vaccine discussions, such as prioritisation and essential workers estimates, can be established during interpandemic phases by pandemic influenza response personnel. The use of annual seasonal influenza vaccines that are inclusive of the novel pandemic influenza strain should play a greater role in future pandemic influenzas, should the vaccination campaign timing be appropriate, as this may reduce public anxiety concerning the perceived safety of novel vaccines. The use of the 2009-10 influenza A(H1N1) pandemic vaccines had varied in success and the lessons learnt from this event have important implications for future policy. Pandemic influenza response personnel are recommended to prepare as fully as possible during this interpandemic period

Engineering of virus-like particles for alternative vaccine candidate targeting a hypervariable peptide antigen element

A promising alternative to replace the current egg- or cell culture-based technology for vaccine production from live viruses is virus-like particle (VLP) technology based on a microbial platform. VLPs are macromolecular assemblies of viral capsid proteins, which have been shown to tolerate insertion of antigen modules via genetic recombinant technology, yielding modular VLPs. Many studies on modular VLPs presume that when a peptide antigen element is taken out from the intact proteins and then modularised on VLPs, it is unable to fold into its native structure. However, until now, presentation of a peptide antigen element on a VLP and the impact of the display strategy to present the antigen element on the quality of the resulting antibodies (i.e. the ability of the antibodies to recognise the intact protein) are not fully understood. This thesis aims to understand the underlying fundamentals regarding modularisation of peptide antigen elements on VLPs for induction of high-quality antibodies. A hypervariable receptor-binding domain, Helix 190 (H190), from the hemagglutinin protein of influenza A virus was used as a model for modularisation on VLPs from murine polyomavirus (MuPyV) VP1 protein. Four major findings are presented. Firstly, two display strategies, i.e. arraying of H190 in tandem repeats and the use of helix promoter elements, were shown to display H190 in its immunogenic form equally. However, modularisation using tandem repeat display induced antibodies of a higher quality than modularisation using helix promoter elements. Secondly, the quality of antibodies induced by the tandem repeat display bearing two copies of H190 was optimum, thus no significant improvement was observed following the use of adjuvant or increasing the copy number of H190. Additionally, the increase in the copy number of H190 was shown to reduce the assembly capability and solubility of modular VP1 in an environment that was optimised for wild-type VP1. Thirdly, this thesis shows the novel finding in the use of flanking ionic elements to stabilise VLP precursors, termed as capsomeres, bearing two copies of H190 containing a hydrophobic stretch, which caused aggregation. Fourthly, the first steps towards obtaining the atomic crystal structure of presented H190 on a modular protein were performed, i.e. a mild and satisfactory laboratory process was developed to achieve high-purity modular VP1 capsomeres, unattainable using previously established expression and purification process of wild-type MuPyV VP1. This thesis shows a step forward towards understanding the presentation of a peptide antigen element on a VLP that enables induction of highquality antibodies, and towards VLP engineering to manipulate the aggregation and solubility of modular VP1. VLP technology based on a microbial platform presented here is a potentially safe and effective alternative vaccine candidate that targets a hypervariable peptide antigen element. The speed of the microbial platform allows a rapid response to the hypervariability of the peptide antigen element, which otherwise may be unachievable using the egg- and cell culture-based technologies

Forecasting influenza in Europe and globally: the role of absolute humidity and human travel, and the potential for use in public health decision making

Influenza causes substantial morbidity and mortality yearly in both temperate and tropical regions, as well as sporadic and potentially severe pandemics. Although vaccines for seasonal influenza exist, most options for controlling influenza outbreaks are reactive in nature. Sufficiently accurate and well-calibrated forecasts, on the other hand, could allow public health practitioners, medical professionals, and the public to respond to unfolding influenza outbreaks proactively. For example, hospitals could prepare additional beds for a predicted surge, and public health experts could redouble vaccination efforts. Recently, skillful forecasts have been developed for a range of infectious diseases, including influenza, but this work has been limited to only a few countries. In this dissertation, we explore the potential for generating accurate influenza forecasts using a publicly-available dataset of country-level epidemiologic and virologic surveillance data. In Chapter 2, we use a combined model-inference system to generate retrospective forecasts for 64 countries in both temperate and tropical climates. We show that forecast accuracy is significantly better in countries with temperate climates, and that inclusion of environmental forcing, specifically modulation of viral transmissibility due to variability of absolute humidity conditions, also improves forecast accuracy in temperate climates. In Chapter 3, we develop a metapopulation model of twelve European countries using data on international air travel and commuting. We find that this model is unable to produce more skillful forecasts than those produced for individual countries in isolation. We make recommendations for improvements in data collection and reporting that may increase the success of similar modeling efforts in the future. In Chapter 4, we assess the performance of real-time forecasts generated for 37 countries over two influenza seasons and discuss the potential for their use in public health decision making. Finally, in Chapter 5 we describe the results of a small survey of public health practitioners in the United States. We find that the majority of respondents desire more effective communication between modelers and public health practitioners, and we discuss the importance of regular and improved communication in advancing the practical use of forecasts as public health decision making tools. This dissertation advances the science of influenza forecasting by demonstrating that skillful retrospective and real-time forecasts can be generated for many countries where previous forecasting efforts are either minimal or absent. However, it is vital that data quality issues be addressed if further progress is to be made. Future work should focus in particular on climatic drivers of influenza in the tropics and subtropics, on the role of human travel at various spatial scales, and on the development of regional and local forecasting capacity. Additionally, dedicated collaboration between modelers and public health practitioners will be instrumental for motivating and informing the use of forecasts in combating influenza outbreaks