An individual-based model of hepatitis A transmission

Ajelli, Marco; Merler, Stefano

doi:10.1016/j.jtbi.2009.03.038

Cited by 30 publications

(28 citation statements)

References 38 publications

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“…If these hypotheses are not met, seroprofiles can become non monotonic. Recent work [40]–[41] has aimed at considering infection dynamics in non-steady populations, or non steady contact networks. This work has suggested the importance of population structures in shaping contact patterns, and therefore the intrinsic instability of contact matrices over time.…”

Section: Discussionmentioning

confidence: 99%

Little Italy: An Agent-Based Approach to the Estimation of Contact Patterns- Fitting Predicted Matrices to Serological Data

et al. 2010

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Knowledge of social contact patterns still represents the most critical step for understanding the spread of directly transmitted infections. Data on social contact patterns are, however, expensive to obtain. A major issue is then whether the simulation of synthetic societies might be helpful to reliably reconstruct such data. In this paper, we compute a variety of synthetic age-specific contact matrices through simulation of a simple individual-based model (IBM). The model is informed by Italian Time Use data and routine socio-demographic data (e.g., school and workplace attendance, household structure, etc.). The model is named “Little Italy” because each artificial agent is a clone of a real person. In other words, each agent's daily diary is the one observed in a corresponding real individual sampled in the Italian Time Use Survey. We also generated contact matrices from the socio-demographic model underlying the Italian IBM for pandemic prediction. These synthetic matrices are then validated against recently collected Italian serological data for Varicella (VZV) and ParvoVirus (B19). Their performance in fitting sero-profiles are compared with other matrices available for Italy, such as the Polymod matrix. Synthetic matrices show the same qualitative features of the ones estimated from sample surveys: for example, strong assortativeness and the presence of super- and sub-diagonal stripes related to contacts between parents and children. Once validated against serological data, Little Italy matrices fit worse than the Polymod one for VZV, but better than concurrent matrices for B19. This is the first occasion where synthetic contact matrices are systematically compared with real ones, and validated against epidemiological data. The results suggest that simple, carefully designed, synthetic matrices can provide a fruitful complementary approach to questionnaire-based matrices. The paper also supports the idea that, depending on the transmissibility level of the infection, either the number of different contacts, or repeated exposure, may be the key factor for transmission.

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Section: Discussionmentioning

confidence: 99%

Little Italy: An Agent-Based Approach to the Estimation of Contact Patterns- Fitting Predicted Matrices to Serological Data

et al. 2010

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“…In recent years, two major classes of methodologies emerged in the simulation of influenza-like illnesses (ILIs) and other emerging infectious diseases. The first one is the very accurate epidemic description with agent-based models, which keep track of each individual in the population in an extremely detailed way [3-14]. The second scheme relies on metapopulation structured models that consider in a detailed way the long range mobility scheme at the inter-population level while using coarse-grained techniques at the intra-population level [15-25].…”

Section: Introductionmentioning

confidence: 99%

“…Both models have been used in realistic scenarios [14,27] and incorporating actual data in relation to the H1N1 pandemic [24,28]. However, comparing simulation results with real data would require a thorough discussion and analysis of the disease parameters, the identification of the initial conditions, the assessment of the reliability of reporting and notification systems that are the sources of the empirical data.…”

Section: Introductionmentioning

confidence: 99%

Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models

et al. 2010

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BackgroundIn recent years large-scale computational models for the realistic simulation of epidemic outbreaks have been used with increased frequency. Methodologies adapt to the scale of interest and range from very detailed agent-based models to spatially-structured metapopulation models. One major issue thus concerns to what extent the geotemporal spreading pattern found by different modeling approaches may differ and depend on the different approximations and assumptions used.MethodsWe provide for the first time a side-by-side comparison of the results obtained with a stochastic agent-based model and a structured metapopulation stochastic model for the progression of a baseline pandemic event in Italy, a large and geographically heterogeneous European country. The agent-based model is based on the explicit representation of the Italian population through highly detailed data on the socio-demographic structure. The metapopulation simulations use the GLobal Epidemic and Mobility (GLEaM) model, based on high-resolution census data worldwide, and integrating airline travel flow data with short-range human mobility patterns at the global scale. The model also considers age structure data for Italy. GLEaM and the agent-based models are synchronized in their initial conditions by using the same disease parameterization, and by defining the same importation of infected cases from international travels.ResultsThe results obtained show that both models provide epidemic patterns that are in very good agreement at the granularity levels accessible by both approaches, with differences in peak timing on the order of a few days. The relative difference of the epidemic size depends on the basic reproductive ratio, R0, and on the fact that the metapopulation model consistently yields a larger incidence than the agent-based model, as expected due to the differences in the structure in the intra-population contact pattern of the approaches. The age breakdown analysis shows that similar attack rates are obtained for the younger age classes.ConclusionsThe good agreement between the two modeling approaches is very important for defining the tradeoff between data availability and the information provided by the models. The results we present define the possibility of hybrid models combining the agent-based and the metapopulation approaches according to the available data and computational resources.

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“…Hepatitis A has an incubation period lasting from two to seven weeks [8, 41, 42], and an infectious period lasting approximately three weeks [22, 43]. Once infected, 85% [44] of cases are assumed to be symptomatic, while the remaining 15% are asymptomatic and experience lower levels of infectivity.…”

Section: Results and Discussion: Case Studiesmentioning

confidence: 99%

“…Inactivated hepatitis A vaccine has an efficacy of 99% [42, 43, 45, 47]; however there is a 30-day lag period before full immunity is conferred, during which these newly-vaccinated individuals may become infected. In the US, hepatitis A vaccine is recommended for 1-year-old children[48].…”

Section: Results and Discussion: Case Studiesmentioning

confidence: 99%

Computational Modeling of Interventions and Protective Thresholds to Prevent Disease Transmission in Deploying Populations

Burgess¹,

Peace²,

Everett³

et al. 2014

Computational and Mathematical Methods in Medicine

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Military personnel are deployed abroad for missions ranging from humanitarian relief efforts to combat actions; delay or interruption in these activities due to disease transmission can cause operational disruptions, significant economic loss, and stressed or exceeded military medical resources. Deployed troops function in environments favorable to the rapid and efficient transmission of many viruses particularly when levels of protection are suboptimal. When immunity among deployed military populations is low, the risk of vaccine-preventable disease outbreaks increases, impacting troop readiness and achievement of mission objectives. However, targeted vaccination and the optimization of preexisting immunity among deployed populations can decrease the threat of outbreaks among deployed troops. Here we describe methods for the computational modeling of disease transmission to explore how preexisting immunity compares with vaccination at the time of deployment as a means of preventing outbreaks and protecting troops and mission objectives during extended military deployment actions. These methods are illustrated with five modeling case studies for separate diseases common in many parts of the world, to show different approaches required in varying epidemiological settings.

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An individual-based model of hepatitis A transmission

Cited by 30 publications

References 38 publications

Little Italy: An Agent-Based Approach to the Estimation of Contact Patterns- Fitting Predicted Matrices to Serological Data

Little Italy: An Agent-Based Approach to the Estimation of Contact Patterns- Fitting Predicted Matrices to Serological Data

Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models

Computational Modeling of Interventions and Protective Thresholds to Prevent Disease Transmission in Deploying Populations

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