Individual-based modeling of COVID-19 transmission in college communities.

The ongoing COVID-19 pandemic has created major public health and socio-economic challenges across the United States. Among them are challenges to the educational system where college administrators are struggling with the questions of how to mitigate the risk and spread of diseases on their college campus. To help address this challenge, we developed a flexible computational framework to model the spread and control of COVID-19 on a residential college campus. The modeling framework accounts for heterogeneity in social interactions, activities, environmental and behavioral risk factors, disease progression, and control interventions. The contribution of mitigation strategies to disease transmission was explored without and with interventions such as vaccination, quarantine of symptomatic cases, and testing. We show that even with high vaccination coverage (90%) college campuses may still experience sizable outbreaks. The size of the outbreaks varies with the underlying environmental and socio-behavioral risk factors. Complementing vaccination with quarantine and mass testing was shown to be paramount for preventing or mitigating outbreaks. Though our quantitative results are likely provisional on our model assumptions, sensitivity analysis confirms the robustness of their qualitative nature.

Is annual vaccination best? A modelling study of influenza vaccination strategies in children.

INTRODUCTION: Annual vaccination of children against influenza is a key component of vaccination programs in many countries. However, past infection and vaccination may affect an individual's susceptibility to infection. Little research has evaluated whether annual vaccination is the best strategy. Using the United Kingdom as our motivating example, we developed a framework to assess the impact of different childhood vaccination strategies, specifically annual and biennial (every other year), on attack rate and expected number of infections. METHODS AND FINDINGS: We present a multi-annual, individual-based, stochastic, force of infection model that accounts for individual exposure histories and disease/vaccine dynamics influencing susceptibility. We simulate birth cohorts that experience yearly influenza epidemics and follow them until age 18 to determine attack rates and the number of infections during childhood. We perform simulations under baseline conditions, with an assumed vaccination coverage of 44%, to compare annual vaccination to no and biennial vaccination. We relax our baseline assumptions to explore how our model assumptions impact vaccination program performance. At baseline, we observed less than half the number of infections between the ages 2 and 10 under annual vaccination in children who had been vaccinated at least half the time compared to no vaccination. When averaged over all ages 0-18, the number of infections under annual vaccination was 2.07 (2.06, 2.08) compared to 2.63 (2.62, 2.64) under no vaccination, and 2.38 (2.37, 2.40) under biennial vaccination. When we introduced a penalty for repeated exposures, we observed a decrease in the difference in infections between the vaccination strategies. Specifically, the difference in childhood infections under biennial compared to annual vaccination decreased from 0.31 to 0.04 as exposure penalty increased. CONCLUSION: Our results indicate that while annual vaccination averts more childhood infections than biennial vaccination, this difference is small. Our work confirms the value of annual vaccination in children, even with modest vaccination coverage, but also shows that similar benefits of vaccination may be obtained by implementing a biennial vaccination program. AUTHOR SUMMARY: Many countries include annual vaccination of children against influenza in their vaccination programs. In the United Kingdom (UK), annual vaccination of children aged of 2 to 10 against influenza is recommended. However, little research has evaluated whether annual vaccination is the best strategy, while accounting for how past infection and vaccination may affect an individual's susceptibility to infection in the current influenza season. Prior work has suggested that there may be a negative effect of repeated vaccination. In this work we developed a stochastic, individual-based model to assess the impact of repeated vaccination strategies on childhood infections. Specifically, we first compare annual vaccination to no vaccination and then annual vaccination to biennial (every other year) vaccination. We use the UK as our motivating example. We found that an annual vaccination strategy resulted in the fewest childhood infections, followed by biennial vaccination. The difference in number of childhood infections between the different vaccination strategies decreased when we introduced a penalty for repeated exposures. Our work confirms the value of annual vaccination in children, but also shows that similar benefits of vaccination can be obtained by implementing a biennial vaccination program, particularly when there is a negative effect of repeated vaccinations.

Estimating the effect of non-pharmaceutical interventions to mitigate COVID-19 spread in Saudi Arabia.

BACKGROUND: The Kingdom of Saudi Arabia (KSA) quickly controlled the spread of SARS-CoV-2 by implementing several non-pharmaceutical interventions (NPIs), including suspension of international and national travel, local curfews, closing public spaces (i.e., schools and universities, malls and shops), and limiting religious gatherings. The KSA also mandated all citizens to respect physical distancing and to wear face masks. However, after relaxing some restrictions during June 2020, the KSA is now planning a strategy that could allow resuming in-person education and international travel. The aim of our study was to evaluate the effect of NPIs on the spread of the COVID-19 and test strategies to open schools and resume international travel. METHODS: We built a spatial-explicit individual-based model to represent the whole KSA population (IBM-KSA). The IBM-KSA was parameterized using country demographic, remote sensing, and epidemiological data. A social network was created to represent contact heterogeneity and interaction among age groups of the population. The IBM-KSA also simulated the movement of people across the country based on a gravity model. We used the IBM-KSA to evaluate the effect of different NPIs adopted by the KSA (physical distancing, mask-wearing, and contact tracing) and to forecast the impact of strategies to open schools and resume international travels. RESULTS: The IBM-KSA results scenarios showed the high effectiveness of mask-wearing, physical distancing, and contact tracing in controlling the spread of the disease. Without NPIs, the KSA could have reported 4,824,065 (95% CI: 3,673,775-6,335,423) cases by June 2021. The IBM-KSA showed that mandatory mask-wearing and physical distancing saved 39,452 lives (95% CI: 26,641-44,494). In-person education without personal protection during teaching would have resulted in a high surge of COVID-19 cases. Compared to scenarios with no personal protection, enforcing mask-wearing and physical distancing in schools reduced cases, hospitalizations, and deaths by 25% and 50%, when adherence to these NPIs was set to 50% and 70%, respectively. The IBM-KSA also showed that a quarantine imposed on international travelers reduced the probability of outbreaks in the country. CONCLUSIONS: This study showed that the interventions adopted by the KSA were able to control the spread of SARS-CoV-2 in the absence of a vaccine. In-person education should be resumed only if NPIs could be applied in schools and universities. International travel can be resumed but with strict quarantine rules. The KSA needs to keep strict NPIs in place until a high fraction of the population is vaccinated in order to reduce hospitalizations and deaths.

Integrating Human Mobility into the Epidemiological Models of COVID-19: Progress and Challenges

The spread of infectious diseases is usually a highly nonlinear space-time diffusion process. Epidemiological models can not only be used to predict the epidemic trend, but also be used to systematically and scientifically study the transmission mechanism of the complex processes under different hypothetical intervention scenarios, which provide crucial analytical and planning tools for public health studies and policy-making. Since host behavior is one of the critical driven factors for the dynamics of infectious diseases, it is important to effectively integrate human spatiotemporal behavior into the epidemiological models for human-hosted infectious diseases. Due to the rapid development of human mobility research and applications aided by big trajectory data, many of the epidemiological models for Coronavirus Disease 2019 (COVID-19) have already coupled human mobility. By incorporating real trajectory data such as mobile phone location data at an individual or aggregated level, researchers are working towards the direction of accurately depicting the real world, so as to improve the effectiveness of the model in guiding actual epidemic prevention and control. The epidemic trend prediction, Non-pharmaceutical Interventions (NPIs) evaluation, vaccination strategy design, and transmission driven factors have been studied by the epidemiological models coupled with human mobility, which provides scientific decision-making aid for controlling epidemic in different countries and regions. In order to systematically understand this important progress of epidemiological models, this study collected and summarized relevant literatures. First, the interactions between the COVID-19 epidemic and human mobility were analyzed, which demonstrated the necessity of integrating the complex spatiotemporal behavior, such as population-based or individual-based mobility, activity, and contact interaction, into the epidemiological models. Then, according to the modeling purpose and mechanism, the models integrated with human mobility were discussed by two types: short-term epidemic prediction models and process simulation models. Among them, based on the coupling methods of human mobility, short-term epidemic prediction models can further be divided into models coupled with first-order and second-order human mobility, while process simulation models can be divided into models coupled with population-based mobility and individual-based mobility. Finally, we concluded that epidemiological models integrating human mobility should be developed towards more complex human spatiotemporal behaviors with a fine spatial granularity. Besides, it is in urgent need to improve the model capability to better understand the disease spread processes over space and time, break through the bottleneck of the huge computational cost of fine-grained models, cooperate cutting-edge artificial intelligence approaches, and develop more universal and accessible modeling data sets and tools for general users. 2021, Science Press. All right reserved.

Exploring the Local Determinants of SARS-CoV-2 Transmission and Control via an Exposure-Based Model.

A simulation model was developed aimed at assisting local public health authorities in exploring strategies for the suppression of SARS-CoV-2 transmission. A mechanistic modeling framework is utilized based on the daily airborne exposure of individuals defined in terms of inhaled viruses. Comparison of model outputs and observed data confirms that the model can generate realistic patterns of secondary cases. In the example investigated, the highest risk of being newly infected was among young adults, males, and people living in large households. Among risky occupations are food preparation and serving, personal care and service, sales, and production-related occupations. Results also show a pattern consistent with superspreading with 70% of initial cases who do not transmit at all while 13.4% of primary cases contribute 80% of secondary cases. The impacts of school closure and masking on the synthetic population are very small, but for students, school closure resulted in more time at home and increased secondary cases among them by over 25%. Requiring masks at schools decreased the case count by 80%. We conclude that the simulator can be useful in exploring local intervention scenarios and provides output useful in assessing the confidence that might be placed on its predictions.

June: open-source individual-based epidemiology simulation.

We introduce June, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. June provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply June to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.

Effectiveness of non-pharmaceutical interventions against local transmission of COVID-19: An individual-based modelling study.

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused global transmission, and been spread all over the world. For those regions that are currently free of infected cases, it is an urgent issue to prevent and control the local outbreak of COVID-19 when there are sporadic cases. To evaluate the effects of non-pharmaceutical interventions against local transmission of COVID-19, and to forecast the epidemic dynamics after local outbreak of diseases under different control measures, we developed an individual-based model (IBM) to simulate the transmission dynamics of COVID-19 from a microscopic perspective of individual-to-individual contacts to heterogenous among individuals. Based on the model, we simulated the effects of different levels of non-pharmaceutical interventions in controlling disease transmission after the appearance of sporadic cases. Simulations shown that isolation of infected cases and quarantine of close contacts alone would not eliminate the local transmission of COVID-19, and there is a risk of a second wave epidemics. Quarantine the second-layer close contacts can obviously reduce the size of outbreak. Moreover, to effectively eliminate the daily new infections in a short time, it is necessary to reduce the individual-to-individual contacts. IBM provides a numerical representation for the local transmission of infectious diseases, and extends the compartmental models to include individual heterogeneity and the close contacts network. Our study suggests that combinations of self-isolation, quarantine of close contacts, and social distancing would be necessary to block the local transmission of COVID-19.

'Accidents waiting to happen'-Insights from a simple model on the emergence of infectious agents in new hosts.

This study evaluates through modelling the possible individual and combined effect of three populational parameters of pathogens (reproduction rate; rate of novelty emergence; and propagule size) on the colonization of new host species-putatively the most fundamental process leading to the emergence of new infectious diseases. The results are analysed under the theoretical framework of the Stockholm Paradigm using IBM simulations to better understand the evolutionary dynamics of the pathogen population and the possible role of Ecological Fitting. The simulations suggest that all three parameters positively influence the success of colonization of new hosts by a novel parasite population, but contrary to the prevailing belief, the rate of novelty emergence (e.g. mutations) is the least important factor. Maximization of all parameters results in a synergetic facilitation of the colonization and emulates the expected scenario for pathogenic microorganisms. The simulations also provide theoretical support for the retention of the capacity of fast-evolving lineages to retro-colonize their previous host species/lineage by ecological fitting. Capacity is, thus, much larger than we can anticipate. Hence, the results support the empirical observations that opportunity of encounter (i.e. the breakdown in mechanisms for ecological isolation) is a fundamental determinant to the emergence of new associations-especially Emergent Infectious Diseases-and the dynamics of host exploration, as observed in SARS-CoV-2. Insights on the dynamics of Emergent Infectious Diseases derived from the simulations and from the Stockholm Paradigm are discussed.

SARS-CoV-2 transmission and control in a hospital setting: an individual-based modelling study.

Development of strategies for mitigating the severity of COVID-19 is now a top public health priority. We sought to assess strategies for mitigating the COVID-19 outbreak in a hospital setting via the use of non-pharmaceutical interventions. We developed an individual-based model for COVID-19 transmission in a hospital setting. We calibrated the model using data of a COVID-19 outbreak in a hospital unit in Wuhan. The calibrated model was used to simulate different intervention scenarios and estimate the impact of different interventions on outbreak size and workday loss. The use of high-efficacy facial masks was shown to be able to reduce infection cases and workday loss by 80% (90% credible interval (CrI): 73.1-85.7%) and 87% (CrI: 80.0-92.5%), respectively. The use of social distancing alone, through reduced contacts between healthcare workers, had a marginal impact on the outbreak. Our results also indicated that a quarantine policy should be coupled with other interventions to achieve its effect. The effectiveness of all these interventions was shown to increase with their early implementation. Our analysis shows that a COVID-19 outbreak in a hospital's non-COVID-19 unit can be controlled or mitigated by the use of existing non-pharmaceutical measures.

Potential impact of individual exposure histories to endemic human coronaviruses on age-dependent severity of COVID-19.

BACKGROUND: Cross-reactivity to SARS-CoV-2 from exposure to endemic human coronaviruses (eHCoV) is gaining increasing attention as a possible driver of both protection against infection and COVID-19 severity. Here we explore the potential role of cross-reactivity induced by eHCoVs on age-specific COVID-19 severity in a mathematical model of eHCoV and SARS-CoV-2 transmission. METHODS: We use an individual-based model, calibrated to prior knowledge of eHCoV dynamics, to fully track individual histories of exposure to eHCoVs. We also model the emergent dynamics of SARS-CoV-2 and the risk of hospitalisation upon infection. RESULTS: We hypothesise that primary exposure with any eHCoV confers temporary cross-protection against severe SARS-CoV-2 infection, while life-long re-exposure to the same eHCoV diminishes cross-protection, and increases the potential for disease severity. We show numerically that our proposed mechanism can explain age patterns of COVID-19 hospitalisation in EU/EEA countries and the UK. We further show that some of the observed variation in health care capacity and testing efforts is compatible with country-specific differences in hospitalisation rates under this model. CONCLUSIONS: This study provides a "proof of possibility" for certain biological and epidemiological mechanisms that could potentially drive COVID-19-related variation across age groups. Our findings call for further research on the role of cross-reactivity to eHCoVs and highlight data interpretation challenges arising from health care capacity and SARS-CoV-2 testing.

A simple, SIR-like but individual-based epidemic model: Application in comparison of COVID-19 in New York City and Wuhan.

In this study, an individual-based epidemic model, considering latent-infectious-recovery periods, is presented. The analytic solution of the model in the form of recursive formulae with a time-dependent transmission coefficient is derived and implanted in Excel. The simulated epidemic curves from the model fit very well with the daily reported cases of COVID-19 in Wuhan, China and New York City (NYC), USA. These simulations show that the transmission rate of NYC's COVID-19 is nearly 30% greater than the transmission rate of Wuhan's COVID-19, and that the actual number of cumulative infected people in NYC is around 9 times the reported number of cumulative COVID-19 cases in NYC. Results from this study also provide important information about latent period, infectious period and lockdown efficiency.

Statistical Implementations of Agent-Based Demographic Models.

A variety of demographic statistical models exist for studying population dynamics when individuals can be tracked over time. In cases where data are missing due to imperfect detection of individuals, the associated measurement error can be accommodated under certain study designs (e.g. those that involve multiple surveys or replication). However, the interaction of the measurement error and the underlying dynamic process can complicate the implementation of statistical agent-based models (ABMs) for population demography. In a Bayesian setting, traditional computational algorithms for fitting hierarchical demographic models can be prohibitively cumbersome to construct. Thus, we discuss a variety of approaches for fitting statistical ABMs to data and demonstrate how to use multi-stage recursive Bayesian computing and statistical emulators to fit models in such a way that alleviates the need to have analytical knowledge of the ABM likelihood. Using two examples, a demographic model for survival and a compartment model for COVID-19, we illustrate statistical procedures for implementing ABMs. The approaches we describe are intuitive and accessible for practitioners and can be parallelised easily for additional computational efficiency.