An individual-based model of hepatitis A transmission

Status of Immunity Against the Hepatitis A Virus in Healthy Population: A Report From Southeastern Iran

Background: Recently, epidemiological studies on hepatitis A virus (HAV) infection showed the seroprevalence has been changing due to changes in lifestyle. To the best of our knowledge, there have been no published data on the seropositivity of HAV in Zahedan, southeastern Iran. Objectives: This study aimed to investigate the seroprevalence of HAV immunoglobulin G (IgG) antibody in Zahedan, southeastern Iran, to provide the required information for better planning in preventive strategies. Methods: In this cross-sectional study, using the available sampling method, a total of 250 serum samples (18 years and above) in both the urban and rural areas of Zahedan were evaluated for anti-HAV IgG by enzyme-linked immunosorbent assay. Results: Based on the results, it was observed that 228 out of 250 (91.2%) serum samples were positive for HAV IgG antibody. Male gender, family size, parents’ education, mother’s occupation, and history of jaundice before the age of 12 years were associated with positive HAV antibody (P < 0.001). The seroprevalence HAV rates were not statistically different between the residents of urban and rural regions. Conclusions: The seropositivity of HAV is high in both the urban and rural areas of Zahedan, Iran. Therefore, the HAV vaccination of the general population is not necessary. It is recommended to monitor HAV seroprevalence in the general population to determine high-risk groups, including anti-HAV seronegative individuals, for HAV vaccination in the residents of the southeast border.

A Macroeconomic SIR Model for COVID-19

The COVID-19 pandemic and subsequent lockdowns highlight the close and delicate relationship between a country’s public health and economic health. Models that combine macroeconomic factors with traditional epidemic dynamics to calculate the impacts of a disease outbreak are therefore extremely useful for policymakers seeking to evaluate the best course of action in such a crisis. We developed a macroeconomic SIR model that considers herd immunity, behavior-dependent transmission rates, remote workers, and the indirect externalities of lockdowns. It is formulated as an exit time control problem where a social planner is able to prescribe separate levels of the lockdown low-risk and high-risk portions of the adult population. The model predicts that by considering the possibility of reaching herd immunity, high-risk individuals are able to leave lockdown sooner than in models where herd immunity is not considered. Additionally, a behavior-dependent transmission rate (which represents increased personal caution in response to increased infection levels) can lower both output loss and total mortality. Overall, the model-determined optimal lockdown strategy, combined with individual actions to slow virus transmission, is able to reduce total mortality to one-third of the model-predicted no-lockdown level of mortality.

Spatio-temporal simulation of the novel coronavirus (COVID-19) outbreak using the agent-based modeling approach (case study: Urmia, Iran)

The ongoing outbreak of the COVID-19 as the current global concern threatens lives of many people around the world. COVID-19 is highly contagious so that it has infected more than 1,848,439 people until April 14, 2020 and killed more than 117,217 people. The main aim of this study is to develop an agent-based model (ABM) that simulates the spatio-temporal outbreak of COVID-19. The main innovation of this research is investigating the impacts of various strategies of school and educational center closures, heeding social distancing, and office closures on controlling the COVID-19 outbreak in Urmia city, Iran. In this research, the outbreak of COVID-19 disease was simulated with the help of ABM so that all agents considered in the ABM along with their attributes and behaviors as well as the environment of the ABM were described. Besides, the transmission of COVID-19 between human agents was simulated based on the SEIRD model, and finally, all control strategies applied in Urmia city along with corresponding actions of each control strategy were implemented in the ABM. The results of the ABM indicated that school and educational center closures in Urmia city, reduced the number of infected people by 4.96% each week on average and 49.61% in total from February 21 until May 10. Heeding social distancing by 30% and 70% of people of Urmia city from March 27, led to decrease the number of infected people by 5.24% and 10.07% each week, on average and 31.46% and 60.44% in total, respectively, and if 30% and 70% of civil servants of Urmia city did not go to work, the number of infected people would be decreased by 3.30% and 5.25% each week, on average and 32.98% and 52.48% in total from February 21 until May 10, respectively. As a result of this research, heeding social distancing by the majority of people is recommended for Urmia city in the current situation. The main advantages of disease modeling are to investigate how the disease is likely to evolve amongst the population of society and also assess the impacts of control strategies on controlling the outbreak of disease.

Detecting critical slowing down in high-dimensional epidemiological systems

Despite medical advances, the emergence and re-emergence of infectious diseases continue to pose a public health threat. Low-dimensional epidemiological models predict that epidemic transitions are preceded by the phenomenon of critical slowing down (CSD). This has raised the possibility of anticipating disease (re-)emergence using CSD-based early-warning signals (EWS), which are statistical moments estimated from time series data. For EWS to be useful at detecting future (re-)emergence, CSD needs to be a generic (model-independent) feature of epidemiological dynamics irrespective of system complexity. Currently, it is unclear whether the predictions of CSD-derived from simple, low-dimensional systems-pertain to real systems, which are high-dimensional. To assess the generality of CSD, we carried out a simulation study of a hierarchy of models, with increasing structural complexity and dimensionality, for a measles-like infectious disease. Our five models included: i) a nonseasonal homogeneous Susceptible-Exposed-Infectious-Recovered (SEIR) model, ii) a homogeneous SEIR model with seasonality in transmission, iii) an age-structured SEIR model, iv) a multiplex network-based model (Mplex) and v) an agent-based simulator (FRED). All models were parameterised to have a herd-immunity immunization threshold of around 90% coverage, and underwent a linear decrease in vaccine uptake, from 92% to 70% over 15 years. We found evidence of CSD prior to disease re-emergence in all models. We also evaluated the performance of seven EWS: the autocorrelation, coefficient of variation, index of dispersion, kurtosis, mean, skewness, variance. Performance was scored using the Area Under the ROC Curve (AUC) statistic. The best performing EWS were the mean and variance, with AUC > 0.75 one year before the estimated transition time. These two, along with the autocorrelation and index of dispersion, are promising candidate EWS for detecting disease emergence.

Predicting the effects of polychlorinated biphenyls on cetacean populations through impacts on immunity and calf survival

The potential impact of exposure to polychlorinated biphenyls (PCBs) on the health and survival of cetaceans continues to be an issue for conservation and management, yet few quantitative approaches for estimating population level effects have been developed. An individual based model (IBM) for assessing effects on both calf survival and immunity was developed and tested. Three case study species (bottlenose dolphin, humpback whale and killer whale) in four populations were taken as examples and the impact of varying levels of PCB uptake on achievable population growth was assessed. The unique aspect of the model is its ability to evaluate likely effects of immunosuppression in addition to calf survival, enabling consequences of PCB exposure on immune function on all age-classes to be explored. By incorporating quantitative tissue concentration-response functions from laboratory animal model species into an IBM framework, population trajectories were generated. Model outputs included estimated concentrations of PCBs in the blubber of females by age, which were then compared to published empirical data. Achievable population growth rates were more affected by the inclusion of effects of PCBs on immunity than on calf survival, but the magnitude depended on the virulence of any subsequent encounter with a pathogen and the proportion of the population exposed. Since the starting population parameters were from historic studies, which may already be impacted by PCBs, the results should be interpreted on a relative rather than an absolute basis. The framework will assist in providing quantitative risk assessments for populations of concern.

Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015)

Background

Individual-based models (IBMs) are useful to simulate events subject to stochasticity and/or heterogeneity, and have become well established to model the potential (re)emergence of pathogens (e.g., pandemic influenza, bioterrorism). Individual heterogeneity at the host and pathogen level is increasingly documented to influence transmission of endemic diseases and it is well understood that the final stages of elimination strategies for vaccine-preventable childhood diseases (e.g., polio, measles) are subject to stochasticity. Even so it appears IBMs for both these phenomena are not well established. We review a decade of IBM publications aiming to obtain insights in their advantages, pitfalls and rationale for use and to make recommendations facilitating knowledge transfer within and across disciplines.

Methods

We systematically identified publications in Web of Science and PubMed from 2006-2015 based on title/abstract/keywords screening (and full-text if necessary) to retrieve topics, modeling purposes and general specifications. We extracted detailed modeling features from papers on established vaccine-preventable childhood diseases based on full-text screening.

Results

We identified 698 papers, which applied an IBM for infectious disease transmission, and listed these in a reference database, describing their general characteristics. The diversity of disease-topics and overall publication frequency have increased over time (38 to 115 annual publications from 2006 to 2015). The inclusion of intervention strategies (8 to 52) and economic consequences (1 to 20) are increasing, to the detriment of purely theoretical explorations. Unfortunately, terminology used to describe IBMs is inconsistent and ambiguous. We retrieved 24 studies on a vaccine-preventable childhood disease (covering 7 different diseases), with publication frequency increasing from the first such study published in 2008. IBMs have been useful to explore heterogeneous between- and within-host interactions, but combined applications are still sparse. The amount of missing information on model characteristics and study design is remarkable.

Conclusions

IBMs are suited to combine heterogeneous within- and between-host interactions, which offers many opportunities, especially to analyze targeted interventions for endemic infections. We advocate the exchange of (open-source) platforms and stress the need for consistent “branding”. Using (existing) conventions and reporting protocols would stimulate cross-fertilization between research groups and fields, and ultimately policy making in decades to come.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-017-2699-8) contains supplementary material, which is available to authorized users.

Modeling the hepatitis A epidemiological transition in Thailand

BACKGROUND

In most low- and middle-income countries, hepatitis A virus (HAV) is shifting or expected to shift from high endemicity to intermediate or low endemicity. A decreased risk of HAV infection will cause an increase in the average age at infection and will therefore increase the proportion of infections that results in severe disease. Mathematical models can provide insights into the factors contributing to this epidemiological transition.

METHODS

An MSLIR compartmental dynamic transmission model stratified by age and setting (rural and urban) was developed and calibrated with demographic, environmental, and epidemiological data from Thailand. HAV transmission was modeled as a function of urbanization and access to clean drinking water. The model was used to project various epidemiological measures.

RESULTS

The age at midpoint of population immunity remains considerably younger in rural areas than in urban areas. The mean age of symptomatic hepatitis A infection in Thailand has shifted from childhood toward early adulthood in rural areas and is transitioning from early adulthood toward middle adulthood in urban areas. The model showed a significant decrease in incidence rates of total and symptomatic infections in rural and urban settings in Thailand over the past several decades as water access has increased, although the overall incidence rate of symptomatic HAV is projected to slightly increase in the coming decades.

CONCLUSIONS

Modeling the relationship between water, urbanization, and HAV endemicity is a novel approach in the estimation of HAV epidemiological trends and future projections. This approach provides insights about the shifting HAV epidemiology and could be used to evaluate the public health impact of vaccination and other interventions in a diversity of settings.

The effects of demographic change on disease transmission and vaccine impact in a household structured population

The demographic structure of populations in both more developed and less developed countries is changing: increases in life expectancy and declining fertility have led to older populations and smaller households. The implications of these demographic changes for the spread and control of infectious diseases are not fully understood. Here we use an individual based model with realistic and dynamic age and household structure to demonstrate the marked effect that demographic change has on disease transmission at the population and household level. The decline in fertility is associated with a decrease in disease incidence and an increase in the age of first infection, even in the absence of vaccination or other control measures. Although large households become rarer as fertility decreases, we show that there is a proportionate increase in incidence of disease in these households as the accumulation of susceptible clusters increases the potential for explosive outbreaks. By modelling vaccination, we provide a direct comparison of the relative importance of demographic change and vaccination on incidence of disease. We highlight the increased risks associated with unvaccinated households in a low fertility setting if vaccine behaviour is correlated with household membership. We suggest that models that do not account for future demographic change, and especially its effect on household structure, may potentially overestimate the impact of vaccination.

Local and global epidemic outbreaks in populations moving in inhomogeneous environments

We study disease spreading in a system of agents moving in a space where the force of infection is not homogeneous. Agents are random walkers that additionally execute long-distance jumps, and the plane in which they move is divided into two regions where the force of infection takes different values. We show the onset of a local epidemic threshold and a global one and explain them in terms of mean-field approximations. We also elucidate the critical role of the agent velocity, jump probability, and density parameters in achieving the conditions for local and global outbreaks. Finally, we show that the results are independent of the specific microscopic rules adopted for agent motion, since a similar behavior is also observed for the distribution of agent velocity based on a truncated power law, which is a model often used to fit real data on motion patterns of animals and humans.

Computational modeling of interventions and protective thresholds to prevent disease transmission in deploying populations

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.

An agent-based model driven by tropical rainfall to understand the spatio-temporal heterogeneity of a chikungunya outbreak

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An agent-based model is used to describe the spatio-temporal spread of chikungunya.

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A chikungunya epidemic with symptomatic and asymptomatic classes is described.

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Restricting the movement of symptomatic individuals cannot halt disease spread.

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Probability of symptomatic people moving has minimal impact on disease incidence.

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Identification of asymptomatic people would help control a chikungunya epidemic.

Vector-borne diseases, such as dengue, malaria and chikungunya, are increasing across their traditional ranges and continuing to infiltrate new, previously unaffected, regions. The spatio-temporal evolution of these diseases is determined by the interaction of the host and vector, which is strongly dependent on social structures and mobility patterns. We develop an agent-based model (ABM), in which each individual is explicitly represented and vector populations are linked to precipitation estimates in a tropical setting. The model is implemented on both scale-free and regular networks. The spatio-temporal transmission of chikungunya is analysed and the presence of asymptomatic silent spreaders within the population is investigated in the context of implementing travel restrictions during an outbreak. Preventing the movement of symptomatic individuals is found to be an insufficient mechanism to halt the spread of the disease, which can be readily carried to neighbouring nodes via sub-clinical individuals. Furthermore, the impact of topology structure vs. precipitation levels is assessed and precipitation is found to be the dominant factor driving spatio-temporal transmission.

Hepatitis A in Puglia (South Italy) after 10 years of universal vaccination: need for strict monitoring and catch-up vaccination

Background

Raw seafood consumption was identified as the major risk factor for hepatitis A during the large epidemic of 1996 and 1997 in Puglia (South Italy). In Puglia, vaccination for toddlers and preadolescents has been recommended since 1998.

The aim of the study was to evaluate the incidence, seroprevalence, molecular epidemiology, and environmental circulation of hepatitis A virus (HAV) in Puglia more than ten years after the introduction of anti-HAV vaccination in the regional immunization program.

Methods

Data on the incidence of acute hepatitis A in Puglia were analyzed. Characteristics and risk factors of 97 acute hepatitis A cases occurring in 2008–2009 were analyzed. Serum samples from 868 individuals aged 0 to 40 years were tested for anti-HAV antibodies. Fecal samples from 49 hepatitis A cases were analyzed by sequence analysis in the VP1/P2A region. In 2008, 203 mussel samples and 202 water samples from artesian wells were tested for HAV-RNA.

Results

Between 1998 and 2009, the incidence of acute hepatitis A declined from 14.8 to 0.8 per 100,000. The most frequent risk factors reported by cases in 2008–2009 were shellfish consumption (85%) and travel outside of Puglia or Italy (26%). Seroepidemiologic survey revealed high susceptibility to HAV in children and adults up to age 30 (65%-70%). None of the mussel or water samples were HAV-positive. Phylogenetic analysis revealed co-circulation of subtypes IA (74%) and IB (26%) and clustering of strains with strains from Germany and France, and those previously circulating in Puglia.

Conclusion

Vaccination and improved sanitation reduced the incidence of hepatitis A. Strict monitoring and improved vaccination coverage are needed to prevent disease resurgence.

Inferring the structure of social contacts from demographic data in the analysis of infectious diseases spread

Social contact patterns among individuals encode the transmission route of infectious diseases and are a key ingredient in the realistic characterization and modeling of epidemics. Unfortunately, the gathering of high quality experimental data on contact patterns in human populations is a very difficult task even at the coarse level of mixing patterns among age groups. Here we propose an alternative route to the estimation of mixing patterns that relies on the construction of virtual populations parametrized with highly detailed census and demographic data. We present the modeling of the population of 26 European countries and the generation of the corresponding synthetic contact matrices among the population age groups. The method is validated by a detailed comparison with the matrices obtained in six European countries by the most extensive survey study on mixing patterns. The methodology presented here allows a large scale comparison of mixing patterns in Europe, highlighting general common features as well as country-specific differences. We find clear relations between epidemiologically relevant quantities (reproduction number and attack rate) and socio-demographic characteristics of the populations, such as the average age of the population and the duration of primary school cycle. This study provides a numerical approach for the generation of human mixing patterns that can be used to improve the accuracy of mathematical models in the absence of specific experimental data.

The dynamics of infectious diseases caused by pathogens transmissible from human to human strongly depends on contact patterns between individuals. High quality observational data on contact patterns, usually presented in the form of age-specific contact matrices, are difficult to gather and are currently available only for few countries worldwide. Here we propose a computational approach, based on the simulation of a virtual society of agents, allowing the estimation of contact patterns by age for 26 European countries. We validate the estimated contact matrices against those obtained by the most extensive field study on contact patterns, with data collected in eight European countries. We show that our contact matrices share some common features, e.g. individuals tend to mix preferentially with individuals their own age, and country-specific differences, which can be partly explained by differences in population structures due to different demographic trajectories followed after WWII. Our analysis highlights well defined correlations between epidemiological parameters and socio-demographic features of the populations. This study provides the first estimates of contact matrices for many European countries where specific experimental data are still not available.

Modeling socio-demography to capture tuberculosis transmission dynamics in a low burden setting

Evidence of preferential mixing through selected social routes has been suggested for the transmission of tuberculosis (TB) infection in low burden settings. A realistic modelization of these contact routes is needed to appropriately assess the impact of individually targeted control strategies, such as contact network investigation of index cases and treatment of latent TB infection (LTBI). We propose an age-structured, socio-demographic individual based model (IBM) with a realistic, time-evolving structure of preferential contacts in a population. In particular, transmission within households, schools and workplaces, together with a component of casual, distance-dependent contacts are considered. We also compared the model against two other formulations having no social structure of contacts (homogeneous mixing transmission): a baseline deterministic model without age structure and an age-structured IBM. The socio-demographic IBM better fitted recent longitudinal data on TB epidemiology in Arkansas, USA, which serves as an example of a low burden setting. Inclusion of age structure in the model proved fundamental to capturing actual proportions of reactivated TB cases (as opposed to recently transmitted) as well as profiling age-group specific incidence. The socio-demographic structure additionally provides a prediction of TB transmission rates (the rate of infection in household contacts and the rate of secondary cases in household and workplace contacts). These results suggest that the socio-demographic IBM is an optimal choice for evaluating current control strategies, including contact network investigation of index cases, and the simulation of alternative scenarios, particularly for TB eradication targets.

Modelling the effects of population structure on childhood disease: the case of varicella

Realistic, individual-based models based on detailed census data are increasingly used to study disease transmission. Whether the rich structure of such models improves predictions is debated. This is studied here for the spread of varicella, a childhood disease, in a realistic population of children where infection occurs in the household, at school, or in the community at large. A methodology is first presented for simulating households with births and aging. Transmission probabilities were fitted for schools and community, which reproduced the overall cumulative incidence of varicella over the age range of 0-11 years old.Moreover, the individual-based model structure allowed us to reproduce several observed features of VZV epidemiology which were not included as hypotheses in the model: the age at varicella in first-born children was older than in other children, in accordance with observation; the same was true for children residing in rural areas. Model predicted incidence was comparable to observed incidence over time. These results show that models based on detailed census data on a small scale provide valid small scale prediction. By simulating several scenarios, we evaluate how varicella epidemiology is shaped by policies, such as age at first school enrolment, and school eviction. This supports the use of such models for investigating outcomes of public health measures.

Little Italy: an agent-based approach to the estimation of contact patterns- fitting predicted matrices to serological data

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.

Data on social contact patterns are fundamental to design adequate control policies for directly transmissible infectious diseases, ranging from a flu pandemic to tuberculosis, to recurrent epidemics of childhood diseases. Most countries in the world do not dispose of such data. We propose an approach to generate synthetic contact data by simulating an artificial society that integrates routinely available socio-demographic data, such as data on household composition or on school participation, with Time Use data, which are increasingly available. We then validate the ensuing simulated contact data against real epidemiological data for varicella and parvo-virus. The results suggest that the approach is potentially a very fruitful one, and provide some insights on the biology of transmission of close-contact infectious diseases.

Spatiotemporal dynamics of viral hepatitis A in Italy

Viral hepatitis A is still common in Italy, especially in Southern regions. In this study, a metapopulation model for hepatitis A virus (HAV) transmission is proposed and analyzed. Analytical results on the asymptotic and transient behaviors of the system are carried out. Based on the available Italian movement data, a national spatial contact matrix at the regional level, which could be used for new studies on the transmission dynamics of other infectious diseases, is derived for modeling fluxes of individuals. Despite the small number of fitted parameters, model simulations are in good agreement with the observed average HAV incidence in all regions. Our results suggest that the mass vaccination program introduced in one Italian region only (Puglia, the one with the highest endemicity level) could have played a role in the decline of HAV incidence in the country as a whole. The only notable exception is represented by Campania, a Southern region showing a high endemicity level, which is not substantially affected by HAV dynamics in Puglia. Finally, our results highlight that the continuation of the vaccination campaign in Puglia would have a relevant impact in decreasing long-term HAV prevalence, especially in Southern Italy.

Control of hepatitis A by universal vaccination of children and adolescents: an achieved goal or a deferred appointment?

Temporal trends of Hepatitis A cases and vaccination coverage data against Hepatitis A Virus have been investigated to analyse the impact of the universal routine vaccination strategy more than 10 years from its introduction in Puglia (region of Southern Italy). The basic reproductive number (R(0)) before vaccination introduction and the effective reproductive number (R(e)) after introduction have been calculated. A progressive decrease in incidence has been recorded in Puglia during last 10 years. Vaccination coverage is actually 64.8% (95% CI: 52.7-76.9%) for children aged 12-24 months and of 67.6% (95% CI: 58.4-76.8%) for 12-year-old adolescents. R(0) estimated in 1996 was 2.01; actually R(e) is 0.651. Theoretical age at infection is 31.82 years. Universal routine vaccination aimed at the control of direct transmission remains the milestone in the strategy for the containment of the disease in settings at an intermediate level of endemicity.

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

Background

In 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.

Methods

We 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.

Results

The 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.

Conclusions

The 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.