Facilitating Understanding, Modeling and Simulation of Infectious Disease Epidemics in the Age of COVID-19

Rubin, David M.; Achari, Shamin; Carlson, Craig S.; Letts, Robyn F. R.; Pantanowitz, Adam; Postema, Michiel; Richards, Xriz L.; Wigdorowitz, B.

doi:10.3389/fpubh.2021.593417

Cited by 12 publications

(8 citation statements)

References 20 publications

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“…The progression or transition model is responsible for simulating the evolution of the state of COVID-19 in infected agents. This model is inspired by the conventional compartment model: Susceptible Infected Recovered (SIR) (Kermack and McKendrick, 1927;Wangping et al, 2020;Rubin et al, 2021). The progression model includes eight compartments and three types of transition rules (Figure 2).…”

Section: Progression Modelmentioning

confidence: 99%

Assessing the Role of Daily Activities and Mobility in the Spread of COVID-19 in Montreal With an Agent-Based Approach

Manout

Ciari

2021

Front. Built Environ.

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Daily activities and mobility dynamics play a central role in the spread of COVID-19. Close physical interactions involved by certain daily activities help transmit the virus. Travel required by the spatial distribution of activities contributes to the propagation of the virus. In order to control and limit this propagation, it is critical to understand the contribution of daily activities to the dynamics of COVID-19. This paper investigates the connection between daily activities, their distribution in space and time, the characteristics of the individuals performing them, and the transmission of the virus. A business-as-usual agent-based simulation scenario of Montreal, Canada is used. To address this research question, we use two agent-based models: MATSIM and EPISIM. MATSIM simulates daily activities and mobility dynamics of the population. EPISIM simulates the spread of the virus in the population using contact networks computed by MATSIM. A synthetic population of Montreal is defined to replicate the main observed sociodemographic characteristics of Montrealers as well as their activity and mobility patterns. The definition of the synthetic population relies on various data sources: household travel survey, census, real estate, car ownership, and housing data. In the business-as-usual scenario, findings underline the significant role of home, work, and school activities in community transmission of COVID-19. Secondary activities, including leisure and shopping, also help spread the virus, but to a lesser degree in comparison with primary activities. The risk of infection in the workplace depends on the economic sector. Healthcare workers are, by far, the most exposed workers to the virus. Workplace infections mirror the gender-biased job market of Montreal. Most infections in the healthcare and educational services are among women. Most infections in the manufacturing, construction, transportation, and warehousing industries are among men. In the business-as-usual scenario where community transmission is high, primary and secondary school-aged children are found to be a major transmission vector of the virus. Finally, simulation results suggest that the risk of infection in the public transportation system is low.

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Section: Progression Modelmentioning

confidence: 99%

Assessing the Role of Daily Activities and Mobility in the Spread of COVID-19 in Montreal With an Agent-Based Approach

Manout

Ciari

2021

Front. Built Environ.

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“…Specifically, we considered three scenarios: releasing 0-64 age groups, releasing only one age group, releasing two age groups. In addition, we assumed that the contact rates had been reduced by 80% during the time of COVID-19 epidemic (30)(31)(32). In particular, compared with the current practice, we estimated that under what level of contact rate, a smaller second wave or a larger second wave of COVID-19 would have been prevented in NYC.…”

Section: Impact Of Release Of Different Age Groupsmentioning

confidence: 99%

How to Reduce the Transmission Risk of COVID-19 More Effectively in New York City: An Age-Structured Model Study

et al. 2021

Front. Med.

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Background: In face of the continuing worldwide COVID-19 epidemic, how to reduce the transmission risk of COVID-19 more effectively is still a major public health challenge that needs to be addressed urgently.Objective: This study aimed to develop an age-structured compartment model to evaluate the impact of all diagnosed and all hospitalized on the epidemic trend of COVID-19, and explore innovative and effective releasing strategies for different age groups to prevent the second wave of COVID-19.Methods: Based on three types of COVID-19 data in New York City (NYC), we calibrated the model and estimated the unknown parameters using the Markov Chain Monte Carlo (MCMC) method.Results: Compared with the current practice in NYC, we estimated that if all infected people were diagnosed from March 26, April 5 to April 15, 2020, respectively, then the number of new infections on April 22 was reduced by 98.02, 93.88, and 74.08%. If all confirmed cases were hospitalized from March 26, April 5, and April 15, 2020, respectively, then as of June 7, 2020, the total number of deaths in NYC was reduced by 67.24, 63.43, and 51.79%. When only the 0–17 age group in NYC was released from June 8, if the contact rate in this age group remained below 61% of the pre-pandemic level, then a second wave of COVID-19 could be prevented in NYC. When both the 0–17 and 18–44 age groups in NYC were released from June 8, if the contact rates in these two age groups maintained below 36% of the pre-pandemic level, then a second wave of COVID-19 could be prevented in NYC.Conclusions: If all infected people were diagnosed in time, the daily number of new infections could be significantly reduced in NYC. If all confirmed cases were hospitalized in time, the total number of deaths could be significantly reduced in NYC. Keeping a social distance and relaxing lockdown restrictions for people between the ages of 0 and 44 could not lead to a second wave of COVID-19 in NYC.

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“…Modelling is particularly useful for conducting assessments at a population level and predicting the long-term impacts of policies. 22 The classical epidemic models, which are known as compartmental models, 23 have been used widely to model the dynamics of many infectious diseases, such as HIV, [24][25][26][27] hepatitis B virus, 28 COVID-19 29 30 and TB. 16 18 Besides, stochastic models like agent-based models offer an alternative approach to project future trends.…”

Section: Open Accessmentioning

confidence: 99%

Impact of the gate-keeping policies of China’s primary healthcare model on the future burden of tuberculosis in China: a protocol for a mathematical modelling study

You

et al. 2021

BMJ Open

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IntroductionIn the past three decades, China has made great strides in the prevention and treatment of tuberculosis (TB). However, the TB burden remains high. In 2019, China accounted for 8.4% of global incident cases of TB, the third highest in the world, with a higher prevalence in rural areas. The Healthy China 2030 highlights the gate-keeping role of primary healthcare (PHC). However, the impact of PHC reforms on the future TB burden is unclear. We propose to use mathematical models to project and evaluate the impacts of different gate-keeping policies.Methods and analysisWe will develop a deterministic, population-level, compartmental model to capture the dynamics of TB transmission within adult rural population. The model will incorporate seven main TB statuses, and each compartment will be subdivided by service providers. The parameters involving preference for healthcare seeking will be collected using discrete choice experiment (DCE) method. We will solve the deterministic model numerically over a 20-year (2021–2040) timeframe and predict the TB prevalence, incidence and cumulative new infections under the status quo or various policy scenarios. We will also conduct an analysis following standard protocols to calculate the average cost-effectiveness for each policy scenario relative to the status quo. A numerical calibration analysis against the available published TB prevalence data will be performed using a Bayesian approach.Ethics and disseminationMost of the data or parameters in the model will be obtained based on secondary data (eg, published literature and an open-access data set). The DCE survey has been reviewed and approved by the Ethics Committee of the School of Public Health, Sun Yat-sen University. The approval number is SYSU [2019]140. Results of the study will be disseminated through peer-reviewed journals, media and conference presentations.

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Facilitating Understanding, Modeling and Simulation of Infectious Disease Epidemics in the Age of COVID-19

Cited by 12 publications

References 20 publications

Assessing the Role of Daily Activities and Mobility in the Spread of COVID-19 in Montreal With an Agent-Based Approach

Assessing the Role of Daily Activities and Mobility in the Spread of COVID-19 in Montreal With an Agent-Based Approach

How to Reduce the Transmission Risk of COVID-19 More Effectively in New York City: An Age-Structured Model Study

Impact of the gate-keeping policies of China’s primary healthcare model on the future burden of tuberculosis in China: a protocol for a mathematical modelling study

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