Agent-Based Computational Epidemiological Modeling

Effect of rotavirus vaccination on the burden of rotavirus disease and associated antibiotic use in India: A dynamic agent-based simulation analysis

BACKGROUND

Rotavirus is a leading cause of diarrhea in infants and young children in many low- and middle-income countries. India launched a childhood immunization program for rotavirus in 2016, starting with four states and expanding it to cover all states by 2019. The objective of this study was to estimate the effects of the rotavirus vaccination program in India on disease burden and antibiotic misuse.

METHODS

We built a dynamic agent-based model of rotavirus progression in children under five within each district in India. Simulations were run for various scenarios of vaccination coverage in the context of India's Universal Immunization Programme. Population data were obtained from the National Family Household Surveys and used to calibrate the models. Disease parameters were obtained from published studies. We estimated past and projected future reduction of disease burden and antibiotic misuse due to full vaccination nationwide, by state, and by wealth quintile.

RESULTS

We estimate that rotavirus vaccination in India has reduced the prevalence of rotavirus cases by 33.7% (prediction interval: 30.7-36.0%), total antibiotic misuse due to rotavirus by 21.8% (18.6-25.1%), and total deaths due to rotavirus by 38.3% (31.3-44.4%) for children under five. We estimate total antibiotic misuse due to rotavirus infection to be 7.6% (7.5-7.9%) of total antibiotic consumption in this demographic versus 9.6% (9.4-9.9%) in the absence of vaccination. We project rotaviral prevalence to drop to below one case for every 100,000 individuals in those below five if vaccination coverage is increased by 50.3% (45.2-58.5%) to 68.1% (63.1-76.4) nationwide.

CONCLUSION

Universal coverage of childhood rotavirus vaccination can substantially reduce inappropriate antibiotic use in India.

Generating synthetic population for simulating the spatiotemporal dynamics of epidemics

Agent-based models have gained traction in exploring the intricate processes governing the spread of infectious diseases, particularly due to their proficiency in capturing nonlinear interaction dynamics. The fidelity of agent-based models in replicating real-world epidemic scenarios hinges on the accurate portrayal of both population-wide and individual-level interactions. In situations where comprehensive population data are lacking, synthetic populations serve as a vital input to agent-based models, approximating real-world demographic structures. While some current population synthesizers consider the structural relationships among agents from the same household, there remains room for refinement in this domain, which could potentially introduce biases in subsequent disease transmission simulations. In response, this study unveils a novel methodology for generating synthetic populations tailored for infectious disease transmission simulations. By integrating insights from microsample-derived household structures, we employ a heuristic combinatorial optimizer to recalibrate these structures, subsequently yielding synthetic populations that faithfully represent agent structural relationships. Implementing this technique, we successfully generated a spatially-explicit synthetic population encompassing over 17 million agents for Shenzhen, China. The findings affirm the method's efficacy in delineating the inherent statistical structural relationship patterns, aligning well with demographic benchmarks at both city and subzone tiers. Moreover, when assessed against a stochastic agent-based Susceptible-Exposed-Infectious-Recovered model, our results pinpointed that variations in population synthesizers can notably alter epidemic projections, influencing both the peak incidence rate and its onset.

Antibody-dependent enhancement and neutralization against CVB4 investigated in vitro and in silico through an agent-based model

The infection with coxsackievirus B4 (CVB4) can be enhanced in vitro by antibodies directed against the viral capsid protein VP4. In peripheral blood mononuclear cells, antibody-dependent enhancement (ADE) of CVB4 infection leads to the production of interferon alpha (IFN-α). To investigate ADE of CVB4-induced production of IFN-α, an agent-based model was constructed with enhancing and neutralizing antibodies. The model recapitulates viral neutralization and ADE in silico. The enhancing and neutralizing activities of serum samples were evaluated in vitro to confront the model predictions with experimental results. Increasing the incubation time of CVB4 with serum samples improves virus neutralization in silico as well as in vitro. It also results in ADE at lower antibody numbers in silico, which is confirmed in vitro with IFN-α production at lower serum concentrations. Furthermore, incubation of CVB4 with serum at a low temperature does not induce IFN-α production in vitro. Thus, taken together our results suggest that enhancing antibodies bind cryptic epitopes, more accessible with longer incubation time and at higher temperature due to changes in capsid conformation, consistent with previous results indicating that enhancing antibodies are anti-VP4 antibodies.

A survey on agents applications in healthcare: Opportunities, challenges and trends

BACKGROUND AND OBJECTIVE

The agent abstraction is a powerful one, developed decades ago to represent crucial aspects of artificial intelligence research. The meaning has transformed over the years and now there are different nuances across research communities. At its core, an agent is an autonomous computational entity capable of sensing, acting, and capturing interactions with other agents and its environment. This review examines how agent-based techniques have been implemented and evaluated in a specific and very important domain, i.e. healthcare research.

METHODS

We survey key areas of agent-based research in healthcare, e.g. individual and collective behaviours, communicable and non-communicable diseases, and social epidemiology. We propose a systematic search and critical review of relevant recent works, introduced by an exploratory network analysis.

RESULTS

Network analysis enables to devise out 5 main research clusters, the most active authors, and 4 main research topics.

CONCLUSIONS

Our findings support discussion of some future directions for increasing the value of agent-based approaches in healthcare.

An LBS and agent-based simulator for Covid-19 research

The mobility data of citizens provide important information on the epidemic spread including Covid-19. However, the privacy versus security dilemma hinders the utilization of such data. This paper proposed a method to generate pseudo mobility data on a per-agent basis, utilizing the actual geographical environment data provided by LBS to generate the agent-specific mobility trajectories and export them as GPS-like data. Demographic characteristics such as behavior patterns, gender, age, vaccination, and mask-wearing status are also assigned to the agents. A web-based data generator was implemented, enabling users to make detailed settings to meet different research needs. The simulated data indicated the usability of the proposed methods.

Mobile human ad hoc networks: A communication engineering viewpoint on interhuman airborne pathogen transmission

A number of transmission models for airborne pathogens transmission, as required to understand airborne infectious diseases such as COVID-19, have been proposed independently from each other, at different scales, and by researchers from various disciplines. We propose a communication engineering approach that blends different disciplines such as epidemiology, biology, medicine, and fluid dynamics. The aim is to present a unified framework using communication engineering, and to highlight future research directions for modeling the spread of infectious diseases through airborne transmission. We introduce the concept of mobile human ad hoc networks (MoHANETs), which exploits the similarity of airborne transmission-driven human groups with mobile ad hoc networks and uses molecular communication as the enabling paradigm. In the MoHANET architecture, a layered structure is employed where the infectious human emitting pathogen-laden droplets and the exposed human to these droplets are considered as the transmitter and receiver, respectively. Our proof-of-concept results, which we validated using empirical COVID-19 data, clearly demonstrate the ability of our MoHANET architecture to predict the dynamics of infectious diseases by considering the propagation of pathogen-laden droplets, their reception and mobility of humans.