The design and use of an agent-based model to simulate the 1918 influenza epidemic at Norway House, Manitoba

What's luck got to do with it? A generative model for examining the role of stochasticity in age-at-death, with implications for bioarchaeology

INTRODUCTION

The role of "luck" in determining individual exposure to health insults is a critical component of the processes that shape age-at-death distributions in mortality samples but is difficult to address using traditional bioarcheological analysis of skeletal materials. The present study introduces a computer simulation approach to modeling stochasticity's contribution to the mortality schedule of a simulated cohort.

METHODS

The present study employs an agent-based model of 15,100 individuals across a 120 year period to examine the predictive value of birth frailty on age-at-death when varying the likelihood of exposure to health insults.

RESULTS

Birth frailty, when accounting for varying exposure likelihood scenarios, was found to account for 18.7% of the observed variation in individual age-at-death. Analysis stratified by exposure likelihood demonstrated that birth frailty alone explains 10.2%-12.1% of the variation observed across exposure likelihood scenarios, with the stochasticity associated with exposure to health insults (i.e., severity of health insult) and mortality likelihood driving the majority of variation observed.

CONCLUSIONS

Stochasticity of stressor exposure and intrinsic stressor severity are underappreciated but powerful drivers of mortality in this simulation. This study demonstrates the potential value of simulation modeling for bioarchaeological research.

Stochastic model to assess bioeconomic impact of PRRS on pig farms in Costa Rica

Despite the economic importance of PRRS and its high prevalence in Costa Rica, there are no studies on the bioeconomic impact of the disease in the country or, even, in Central America. Such studies are essential in finding cost-effective preventive measures tailored for different production circumstances. Therefore, the objective of this study was to evaluate economic and production parameters of a PRRSV-infection for a medium-sized farrow-to-finish pig farm system in Costa Rica with a farm-level stochastic Monte Carlo simulation model. The effect of PRRS was assessed by scenario analysis, in which a baseline PRRS-free situation was compared against three alternative scenarios that assumed low, medium and high PRRS effects. The PRRS effects were based on data from local farms, scientific literature and expert opinion. Sensitivity analyses were performed to assess the impact of key input parameters on output variables. Results show that at the animal level, changes between the baseline and the PRRS-high scenario were estimated as: + 25 d in age to slaughter, - 9.9 pigs to slaughter (per breeding sow/yr), + 6% annual replacement rate, - 255 d in sow productive lifetime, - 6.9 mo in age at culling of sows, and + 24 non- productive days. For a medium size local farm (n = 588 sows), a reduction of 5826 fat pigs to slaughter per farm/yr from baseline compared to PRRS-high scenario was observed. PRRS-induced loss per farm per year was estimated at -US $142,542, US $180,109 and -US $524,719 for PRRS-low, medium and high scenarios, respectively. Revenues/costs ratio changed from 1.12 in the baseline to 0.89 in the PRRS-high scenario. The production cost per kg carcass weight increased from US $2.63 for the baseline to US $3.35 in the PRRS-high scenario. PRRS-induced loss was estimated at US $77.1 per slaughtered pig/yr and US $892 per breeding sow/yr for the PRRS-high scenario. Results from the model indicate that pig farms with medium to high prevalence of PRRS will require optimal market conditions in order to have positive economic outcomes. These results can be helpful in the design of better control strategies for PRRS.

Pandemics, past and present: The role of biological anthropology in interdisciplinary pandemic studies

Biological anthropologists are ideally suited for the study of pandemics given their strengths in human biology, health, culture, and behavior, yet pandemics have historically not been a major focus of research. The COVID‐19 pandemic has reinforced the need to understand pandemic causes and unequal consequences at multiple levels. Insights from past pandemics can strengthen the knowledge base and inform the study of current and future pandemics through an anthropological lens. In this paper, we discuss the distinctive social and epidemiological features of pandemics, as well as the ways in which biological anthropologists have previously studied infectious diseases, epidemics, and pandemics. We then review interdisciplinary research on three pandemics–1918 influenza, 2009 influenza, and COVID‐19–focusing on persistent social inequalities in morbidity and mortality related to sex and gender; race, ethnicity, and Indigeneity; and pre‐existing health and disability. Following this review of the current state of pandemic research on these topics, we conclude with a discussion of ways biological anthropologists can contribute to this field moving forward. Biological anthropologists can add rich historical and cross‐cultural depth to the study of pandemics, provide insights into the biosocial complexities of pandemics using the theory of syndemics, investigate the social and health impacts of stress and stigma, and address important methodological and ethical issues. As COVID‐19 is unlikely to be the last global pandemic, stronger involvement of biological anthropology in pandemic studies and public health policy and research is vital.

A Chess and Card Room-Induced COVID-19 Outbreak and Its Agent-Based Simulation in Yangzhou, China

Objective

To evaluate epidemiological characteristics of the COVID-19 outbreak that resurged in Yangzhou and to simulate the impact of different control measures at different regional scales.

Methods

We collected personal information from 570 laboratory-confirmed cases in Yangzhou from 28 July to 26 August 2021, and built a modified susceptible-exposed-infected-removed (SEIR) model and an agent-based model.

Results

The SEIR model showed that for passengers from medium-high risk areas, pre-travel nucleic acid testing within 3 days could limit the total number of infected people in Yangzhou to 50; among elderly persons, a 60% increase in vaccination rates could reduce the estimated infections by 253. The agent-based model showed that when the population density of the chess and card room dropped by 40%, the number of infected people would decrease by 54 within 7 days. A ventilation increase in the chess and card room from 25 to 50% could reduce the total number of infections by 33 within 7 days; increasing the ventilation from 25 to 75% could reduce the total number of infections by 63 within 7 days.

Conclusions

The SEIR model and agent-based model were used to simulate the impact of different control measures at different regional scales successfully. It is possible to provide references for epidemic prevention and control work.

Investigating Dynamics of COVID-19 Spread and Containment with Agent-Based Modeling

Governments, policy makers, and officials around the globe are working to mitigate the effects of the COVID-19 pandemic by making decisions that strive to save the most lives and impose the least economic costs. Making these decisions require comprehensive understanding of the dynamics by which the disease spreads. In traditional epidemiological models, individuals do not adapt their contact behavior during an epidemic, yet adaptive behavior is well documented (i.e., fear-induced social distancing). In this work we revisit Epstein’s “coupled contagion dynamics of fear and disease” model in order to extend and adapt it to explore fear-driven behavioral adaptations and their impact on efforts to combat the COVID-19 pandemic. The inclusion of contact behavior adaptation endows the resulting model with a rich dynamics that under certain conditions reproduce endogenously multiple waves of infection. We show that the model provides an appropriate test bed for different containment strategies such as: testing with contact tracing and travel restrictions. The results show that while both strategies could result in flattening the epidemic curve and a significant reduction of the maximum number of infected individuals; testing should be applied along with tracing previous contacts of the tested individuals to be effective. The results show how the curve is flattened with testing partnered with contact tracing, and the imposition of travel restrictions.

One-Health Simulation Modelling: A Case Study of Influenza Spread between Human and Swine Populations using NAADSM

The circulation of zoonotic influenza A viruses including pH1N1 2009 and H5N1 continue to present a constant threat to animal and human populations. Recently, an H3N2 variant spread from pigs to humans and between humans in limited numbers. Accordingly, this research investigated a range of scenarios of the transmission dynamics of pH1N1 2009 virus at the swine-human interface while accounting for different percentages of swine workers initially immune. Furthermore, the feasibility of using NAADSM (North American Animal Disease Spread Model) applied as a one-health simulation model was assessed. The study population included 488 swine herds and 29, 707 households of people within a county in Ontario, Canada. Households were categorized as follows: (i) rural households with swine workers, (ii) rural households without swine workers, and (iii) urban households without swine workers. Forty-eight scenarios were investigated, based on the combination of six scenarios around the transmissibility of the virus at the interface and four vaccination coverage levels of swine workers (0-60%), all under two settings of either swine or human origin of the virus. Outcomes were assessed in terms of stochastic 'die-out' fraction, size and time to peak epidemic day, overall size and duration of the outbreaks. The modelled outcomes indicated that minimizing influenza transmissibility at the interface and targeted vaccination of swine workers had significant beneficial effects. Our results indicate that NAADSM can be used as a framework to model the spread and control of contagious zoonotic diseases among animal and human populations, under certain simplifying assumptions. Further evaluation of the model is required. In addition to these specific findings, this study serves as a benchmark that can provide useful input to a future one-health influenza modelling studies. Some pertinent information gaps were also identified. Enhanced surveillance and the collection of high-quality information for more accurate parameterization of such models are encouraged.

Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model

world has not faced a severe pandemic for decades, except the rather mild H1N1 one in 2009, pandemic influenza models are inherently hypothetical and validation is, thus, difficult. We aim at reconstructing a recent seasonal influenza epidemic that occurred in Switzerland and deem this to be a promising validation strategy for models of influenza spread.

METHODS

We present a spatially explicit, individual-based simulation model of influenza spread. The simulation model bases upon (i) simulated human travel data, (ii) data on human contact patterns and (iii) empirical knowledge on the epidemiology of influenza. For model validation we compare the simulation outcomes with empirical knowledge regarding (i) the shape of the epidemic curve, overall infection rate and reproduction number, (ii) age-dependent infection rates and time of infection, (iii) spatial patterns.

RESULTS

The simulation model is capable of reproducing the shape of the 2003/2004 H3N2 epidemic curve of Switzerland and generates an overall infection rate (14.9 percent) and reproduction numbers (between 1.2 and 1.3), which are realistic for seasonal influenza epidemics. Age and spatial patterns observed in empirical data are also reflected by the model: Highest infection rates are in children between 5 and 14 and the disease spreads along the main transport axes from west to east.

CONCLUSIONS

We show that finding evidence for the validity of simulation models of influenza spread by challenging them with seasonal influenza outbreak data is possible and promising. Simulation models for pandemic spread gain more credibility if they are able to reproduce seasonal influenza outbreaks. For more robust modelling of seasonal influenza, serological data complementing sentinel information would be beneficial.

Agent-based modeling of the spread of the 1918-1919 flu in three Canadian fur trading communities

OBJECTIVES

Previous attempts to study the 1918-1919 flu in three small communities in central Manitoba have used both three-community population-based and single-community agent-based models. These studies identified critical factors influencing epidemic spread, but they also left important questions unanswered. The objective of this project was to design a more realistic agent-based model that would overcome limitations of earlier models and provide new insights into these outstanding questions.

METHODS

The new model extends the previous agent-based model to three communities so that results can be compared to those from the population-based model. Sensitivity testing was conducted, and the new model was used to investigate the influence of seasonal settlement and mobility patterns, the geographic heterogeneity of the observed 1918-1919 epidemic in Manitoba, and other questions addressed previously.

RESULTS

Results confirm outcomes from the population-based model that suggest that (a) social organization and mobility strongly influence the timing and severity of epidemics and (b) the impact of the epidemic would have been greater if it had arrived in the summer rather than the winter. New insights from the model suggest that the observed heterogeneity among communities in epidemic impact was not unusual and would have been the expected outcome given settlement structure and levels of interaction among communities.

CONCLUSIONS

Application of an agent-based computer simulation has helped to better explain observed patterns of spread of the 1918-1919 flu epidemic in central Manitoba. Contrasts between agent-based and population-based models illustrate the advantages of agent-based models for the study of small populations.

Internet resources for agent-based modelling

The use of agent-based models (ABMs) is steadily increasing in all the disciplines including environmental chemistry and toxicology. This growth is mainly driven by their ability to address problems that conventional modelling techniques cannot, such as the change of scale or the emergence of unanticipated phenomena resulting from interactions between their constitutive goal-directed agents. After a brief introduction on the basic principles of agent-based modelling and the presentation of selected case studies, the main software resources available on the Internet are presented. An attempt is made to estimate the complexity of these tools versus their potentialities and flexibility.