Abstract: Close proximity interactions (CPI) between individuals, as measured by electronic wearable sensors, have been increasingly used as a proxy to contacts leading to disease transmission. However, there is little evidence that CPIs are indeed a good proxy to transmission. However, this issue is difficult to analyze for want of data and because of different timescales in data collection.Here, we study this issue in a French hospital, where 85000 CPI were recorded in a network of 590 participants over a 5-months long period, both in staff and patients, jointly with 4700 pharyngeal swabs for carriage of bacteria (Staphylococcus aureus). We first define a measure of association based on path-lengths in dynamic networks, and show, using simulations, that it is a statistically powerful approach. Then, we show that paths connecting bacteria carriers to incident carriers have characteristics supporting the use of CPI as proxy for contacts leading to transmission. Transmission events are further characterized according to type and duration of contacts, especially in staff and patients.We conclude that CPI indeed inform on the network of contacts responsible for pathogen transmission. We examine the possibility of using such CPI to inform detection of incident carriage and optimize hygiene measurements in hospitals.

Abstract: Highly pathogenic avian influenza H5N1 remains a persistent public health threat, being capable of causing infection in humans with a high mortality rate, in addition to negatively impacting the livestock industry. A central question is to determine regions that are likely sources of newly emerging influenza strains with pandemic causing potential. A suitable candidate is Bangladesh, due to being one of the most densely populated countries in the world and having an intensifying farming system. It is therefore vital to establish the key factors, specific to Bangladesh, that enable both continued transmission within poultry and spillover across the human-animal interface. We apply a modelling framework to H5N1 epidemics in the Dhaka region of Bangladesh occurring from 2007 onwards, which resulted in large outbreaks in the poultry sector and a limited number of confirmed human cases. This model consisted of a poultry transmission component and a zoonotic transmission component. Utilising poultry farm spatial and population information a set of competing nested models of varying complexity were fitted to the observed case data, with parameter inference carried out using Bayesian methodology and goodness-of-fit verified by stochastic simulations. We found successfully identifying a model of minimal complexity that enabled the size and spatial distribution of cases in H5N1 outbreaks to be predicted accurately was dependent on the administration level being analysed, while a consistent outcome of non-optimal reporting of infected premises materialised in each poultry epidemic of interest. Our zoonotic transmission component found the main contributor to spillover transmission of H5N1 in Bangladesh was found to differ from one poultry epidemic to another. These results indicate that shortening delays in reporting of infected poultry premises alongside reducing contact between humans and poultry will help reduce risk to human health.

Abstract: The complex and slow nature of the development process of new vaccines is specially concerned in the case of tuberculosis. One of the main reasons for such difficulties comes from the current absence of reliable immunological correlates of protection, which makes the only approach feasible to estimate vaccine efficacy to be the enrolment and subsequent follow-up of large cohorts of susceptible individuals in extremely challenging and costly efficacy clinical trials. After these trials, vaccine efficacy is commonly estimated from straightforward comparisons of the fraction of subjects at each different trial's endpoint --healthy, infected or diseased--, or as the ratio between the rates of the transitions, in vaccine vs control cohorts, either at the level of protection against infection (VE_inf) or against progression to disease (VE_dis). In this work, we identify a conceptual limitation of this basic approach to estimate vaccine efficacy, which consists on a degeneracy in the different mechanisms through which a vaccine can disrupt the natural cycle of the disease that are in turn compatible with a single trial observation of VE_dis. In this sense, once measured VE_dis , we identify an entire family of compatible vaccines in which the mechanism of action is arbitrarily distributed between 1) a reduction of the fraction of the individuals with experiencing fast progression after infection and 2) a deceleration of the rate at which that fast progression process take place. Furthermore, using disease spreading models, we find that the mentioned --and so far neglected-- degeneracy encompasses the introduction of critical levels of uncertainty when it comes to estimate the expected vaccine's impact in terms of reduction of cases and casualties; compromising our very ability to make meaningful predictions for statistically significant vaccine impacts. Finally, we propose an alternative approach to solve the degeneracy problem, which succeeds at providing independent estimations for the vaccine effects on both reducing the fraction of rapid progressors and restraining the rate at which they develop disease. Our method involves the analysis of the individual transition times of the individuals between the different end-points in the trial, an observable whose retrieval is compatible with state-of-the-art protocols. By doing so, the new method contributes to a more detailed and precise description of vaccines' features and unlocks more precise impact forecasts.

Abstract: Spontaneous human behavioral responses triggered by a pandemic/epidemic threat have the potential to shape the dynamics of infectious diseases. Nonetheless, detecting and quantifying their contribution in the spread of an epidemic remains challenging. In this work we make use of an individual-based model of influenza transmission, calibrated on age-specific serosurvey data in England regions, in order to identify the main determinants of heterogeneities in the spatial spread of 2009 H1N1 pandemic at the sub-national scale. In fact, the 2009 pandemic spread in England was characterized by two major waves: the first wave spread (almost only) in London, while the second one mainly spread in a highly homogenous way across the country. Our modeling results suggest that this dynamics was mainly attributable to a significant change in the effective distance at which potential infectious contacts have occurred. In particular, we estimated a remarkably lower force of infection at large distances in the first wave compared to the second one. Such decrease may be interpreted as a behavioral adaptation to the perceived risk of infection, which was particularly high in the initial phase of the pandemic, and may have resulted in a decrease of mobility and/or number of potentially infectious contacts at high distances. Such findings contribute to shed light on the role played by (spontaneous) precautionary behaviors in the spread of epidemics, especially under the pressure posed by a pandemic threat, and highlight the need to take human behavior into account for planning effective mitigation strategies.

Abstract: In the era of international traveling and with frequent occurrence of global events across the world, there is an increasing concern to study the impact of these mass gatherings (MGs) on a global level. The massive influx of spectators from different regions presents serious health threats and challenges for hosting countries and the countries where participants originate. Global MGs such as the Olympics, FIFA World Cup, and Hajj (Muslim pilgrimage to Makkah, Saudi Arabia), cause the mixing of various infectious pathogens due to the mixture of disease exposure history and the demographics of the participants. The travel patterns at the end of global events could cause a rapid spread of infectious diseases affecting large number of people within a short period of time. Mathematical and computational models provide valuable tools that help public health authorities to estimate, study, and control disease outbreaks at challenges settings such as MGs. In this study, we present a computational framework to model disease spread at the annual global event of the Hajj, where over two million pilgrims from over 189 countries. We used the travel and demographic data of five Hajj seasons (2010-2014), and spatial data of the holy sites where the rituals are performed. As 92% of the international pilgrims arrive by air, we used the daily flights profiles of the five Hajj seasons to model the arrival of pilgrims. We simulate the interactions of pilgrims using agent-based model where each agent represents a pilgrim and maintains related demographic attributes (gender, age, country of origin) and health information (infectivity, susceptibility, immunity, date of infection, number of days being exposed or infected). The proposed model includes several simulations of the stages of Hajj with hourly or daily time steps. At each stage, the agent-based model of pilgrims is integrated to simulate their interactions within the space and time frames of that stage.

Abstract: Large measles epidemics represent a persisting public health issue for both developing and developed countries. In order to avoid the occurrence of repeated outbreaks, it is crucial to identify the age segments that have not been adequately immunized by vaccination programs and to investigate the influence of local demographic conditions in shaping measles epidemiology. To do this, we consider ten countries with distinct demographic and vaccination history and develop a transmission model explicitly accounting for a dynamic population age structure and immunization activities performed since 1980. The model is calibrated at a country level with a Markov chain Monte Carlo approach exploring the likelihood of measles serological data. The model is used to identify the determinants of the observed age-specific immunity profiles, highlighting the contribution of different immunization programs, fertility and mortality trends. Our estimates suggest that, in most countries, routine first dose administration produced over 80% of the successfully immunized individuals, whereas in African countries catch-up campaigns played a critical role in mitigating the effects of sub-optimal routine coverage. Remarkably, our results suggest that consequences of past immunization activities are expected to persist longer in populations with older age structures. Consequently, countries with high fertility rates, where residual susceptibility is mostly concentrated in early childhood, should optimize their routine vaccination program. Conversely, catch-up campaigns targeting adolescents are essential to achieve measles elimination in populations characterized by low fertility levels, where we found relevant fractions of susceptibles in all age groups.

Abstract: We present an outline of two software frameworks being used together to simulate disease spread in the large urban metropolitan area around Chicago, USA: the Chicago Social Interaction Model (chiSIM) framework and the Repast Framework for Agent-based Compartmentalized Epidemiological Models (ReFACE). For a disease that is spread through interpersonal contact, the rate of spread through a population is in part a function of the topology of the network of contacts within that population. This contact network emerges from the movement of people through their daily activities and the locations in which they come into contact with each other. The chiSIM framework allows populations of agents move through daily activities at high chronological resolution (hourly). As agents move through their daily schedules they arrive at locations (work, home, school, etc.) where they interact with other agents; in the context of disease modeling, these interactions include disease transmission, which can be conditionally based on the type of location and/or the activities in which the agents engage. The chiSIM framework allows large-scale simulation of these events: we present examples in which ~5 million agents move among ~2 million places across Chicago and surrounding areas at hourly resolution for durations as long as 10 years. The ReFACE framework allows the construction of compartmentalized epidemiological models such as SIR and SEIR models in ways that can be incorporated into agent-based approaches. In accord with standard compartmentalized models, the disease is represented as a collection of states and the rates of transition from state to state. The ReFACE framework permits these specifications to be explored directly using traditional analytical techniques (i.e. via differential equation solvers). Additionally, however, the framework also allows these to be translated into states and state transitions for use within individual agents in an agent-based model. In epidemiological agent-based applications, each agent is considered to be in one of many possible disease states, and transitions from one state to the next are driven by the disease specification. In the agent-based approach a number of mitigating factors may also play a role in these transitions. For example, a state that represents one treatment pathway may be available only to a subset of agents, and this subset may change during certain periods in the simulation. In the ABM, these contingent pathways can be considered in light of the other actions that the agent is undertaking and the conditions of simulation, such as school closings or hospital overcrowding. We apply these two frameworks to study the relationship between the topological structure of the network of interactions provided by chiSIM and the disease progression dictated by the compartmentalized model. The topological structure may be dynamically impacted by agent decisions representing behavioral changes. We discuss the value added in using agent-based approaches, and focus on the ability to capture rich agent differences and dynamic, responsive agent behavior, to represent a dynamic topology of an interaction network, and to combine these to analyze the real impacts of possible interventions. We present analyses of specific test cases that illustrate these advantages.

Abstract: Introduction: This project was conceptualized in order to bring data and modern analytical techniques to the problem of identifying the source of large scale, multi-state outbreaks of foodborne illness. Determining the spatial origin of a contaminated food causing an outbreak of foodborne disease is a challenging problem due to the complexity of the food supply and the absence of coherent labeling and distribution records. Current investigative methods are time and resource intensive, and often unsuccessful. New tools and approaches that take advantage of data available to investigators are needed to efficiently identify the source of an outbreak while contamination-caused illnesses are still occurring, thereby resolving investigations earlier and averting potential illnesses. Approach: In this work, a network-theoretical approach for rapid identification of the source of foodborne contamination events is developed. The objective is to locate the source of an outbreak of foodborne disease, given a known food distribution network and a set of observed illness times and network locations, under a few practical constraints: that only a small fraction of illnesses are reported, and that the reported times are highly imprecise. Additionally, we assume that the presence of contamination at locations within the distribution network is unknown or hidden; thus, the source of contamination can be recovered only from the information associated with the reported illnesses. We tackle this problem by developing a two-stage framework for source localization, solving the problem from two perspectives. In the first stage, we assume the reported illness times are accurate within a given uncertainty. Assuming a continuous time diffusion model of contamination from each feasible initiation node, we identify the most likely contamination source and initiation time by maximizing the likelihood of the observations given each diffusion model. In the second stage, we disregard the reported illness times, assuming that their signal is dominated by the imprecision in their measurements. We take a topological approach that identifies the source of contamination as the node maximizing the joint likelihood of the collection of paths to the observed contaminated nodes. The approach here is to view the problem from the perspective of a probabilistic graphical model that represents, through a set of conditional probability distributions, how the observation of a contamination at a given node increases the probability that the contamination has traveled through adjacent upstream and downstream nodes. Accuracy and Evaluation: We subject both techniques to an extensive study to evaluate their performance and robustness across multiple outbreak scenarios and network structures. Analytical expressions are derived to determine a lower bound on the accuracy achievable for specific multi-partite network structures. A probabilistic simulation approach involving generalized food distribution network models and diffusion models of contamination was developed to (1) analyze the robustness of the traceback methodology across multiple outbreak scenarios, (2) determine the relationship between accuracy and network structural parameters, (3) analyze the performance of the algorithms under strategic interventions introduced to improve traceback, and (4) to quantify benefits of the approach through comparison to heuristics, which can be viewed as representative of the kind of ?reasonably smart? investigation strategies one might apply in practice, and state of the art theoretical methods. From the results of this study, we recommend a combined approach for outbreak investigation that combines both algorithms to maximize the probability of localizing the source to a well-defined region or a single node. Findings: In extensive simulation testing across a variety of distribution network structures, we find that the methodology is highly accurate and efficient: the actual outbreak source is robustly ranked within the top 5% (1%) of feasible locations after 5% (25%) of the cases had been reported, thereby reducing by up to 45% (25%) the eventual total number of illnesses in the simulated outbreaks, greatly outperforming not only heuristics but also state of the art methods. We determine that large improvements in traceback accuracy (up to 50%) are possible if routine sampling is implemented at a small number (5%) of strategically chosen nodes, and find that it is possible to determine which supply chain actors should be investigated next during an investigation, given the currently available information, in order to increase the probability of identifying the source. We identify specific properties of distribution network structures that both limit propagation and facilitate more accurate tracebacks, thresholds for specific parameters above which traceback is trivial, and the reversal. Conclusions: This project has contributed an entirely novel approach to outbreak traceback investigations: a network-theoretic framework for efficient spatio-temporal localization of the source of a large scale, multi-state outbreak of foodborne illness. Our analytical and simulation results suggest that this methodology that can form the basis of a ?tool? to supplement real-time traceback procedures by identifying high probability sources of an ongoing outbreak and making strategic recommendations regarding allocation of investigative resources. It is important to stress, however, that live use of these techniques has yet to occur and may demonstrate features of the real problem inadvertently omitted from the modeling. Extensive testing of our tool across multiple historical cases, followed by real-time application during outbreak emergencies, will ultimately be necessary to determine the utility to public health in terms of how much earlier an investigation can be resolved and how many illnesses averted as a result.