Modeling and Simulations of COVID-19 impact and mitigation strategies

Monday, June 14 at 5:45pm (PDT)
Tuesday, June 15 at 01:45am (BST)
Tuesday, June 15 09:45am (KST)

SMB2021 SMB2021 Follow Monday (Tuesday) during the "MS03" time block.
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Preeti Dubey and Christopher Hoover (Francis I. Proctor Foundation, UCSF, USA)


COVID-19 pandemic has caused record number of infections around the globe in a year. The mitigation strategies (vaccination and non-pharmaceutical interventions (NPIs) seem not to be as effective as were anticipated with the rapid emergence of novel SARS-COV-2 mutations and asymptomatic transmission. This minisymposium will cover three types of mathematical models: (i) compartmental, (ii) stochastic, and (iii) agent-based model (ABM). The topics will include methods for modeling simulations, model calibration, parameter estimation, time-varying effective reproduction number, inclusion of heterogeneity of populations and compliance and non-compliance of NPIs, efficacy of NPIs and vaccine, place and equity-based distribution of vaccine. They will be illustrated using different datasets of COVID-19 cases from different regions of the USA such as California, San Francisco, St. Louis, optimal contact tracing and vaccination prioritization, asymptomatic transmission and mitigation strategies.

Ranjit Upadhyay

(Indian Institute of Technology (ISM) Dhanbad, India)
"Modeling the recent outbreak of COVID-19 in India and its Control strategies using NPIs and vaccination"
Robust testing and tracing are key to fighting the menace of coronavirus disease 2019 (COVID19). This outbreak has progressed with tremendous impact on human life, society and economy. In this work, we propose few models (an age-structured SIQR model, SEQIR model and an SEICR model with comorbidity) to track the progression of the pandemic in India taking into account the different age structures of the country. We have made predictions about the disease dynamics, identified the most infected age groups and analysed the effectiveness of social distancing measures taken in the early stages of infection. This encompasses modeling the dynamics of invaded population, parameter estimation of the model, study of qualitative dynamics, and optimal control problem for non-pharmaceutical interventions (NPIs) and vaccination events such that the cost of the combined measure is minimized. The investigation reveals that disease persists with the increase of exposed individuals having comorbidity in society. The extensive computational efforts show that mean fluctuations in the force of infection increase with corresponding entropy. Further, an increasing trend to the mean force of infection has been indicated through the composite effect. Higher Shannon entropy production, i.e., more disorder in mean force of infection has indicated more strengthen the force of infection according to dynamical perspective. This might cause a dangerous situation in the population. This is a piece of evidence that the outbreak has reached a significant portion of the population. However, optimal control strategies with combined measures provide an assurance of effectively protecting our population from COVID-19 by minimizing social and economic costs. From an epidemiological perspective, comorbidity individuals get gradually infection due to lack of precautions and surveillance and like, social distancing, proper sanitation wearing masks, etc. In this situations, susceptible individuals become infected and turned to exposed individuals. Indeed, exposed individuals can prevent COVID-19 infection due to strong immunity in this connection. Based on the observed data, 7-days moving average curves are plotted for pre-lockdown, lockdown and unlock 1 phases. Following the trend of the curves for the infection, a generalized exponential function is used to estimate the data and corresponding 95% confidence intervals are simulated to estimate the parameters. The effect of control measures, such as quarantine and isolation are discussed and systematically explore the impact of lockdown strategy in order to control the recent outbreak of COVID-19 transmission in India.

Christopher Hoover

(Francis I. Proctor Foundation, UCSF, USA)
"Targeted allocation of testing and vaccination reduces transmission of SARS-CoV2 and improves health equity: An agent-based modeling study"
Effectively allocating scarce resources to combat infectious disease outbreaks is essential to reducing their impact. The ongoing pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) has been characterized by extreme heterogeneity in individual spread and higher spread among underserved sub-populations such as the Latinx community in San Francisco and New York City, and similarly marginalized racial and ethnic minority groups across the U.S. This heterogeneity in transmission creates challenges for control efforts when those experiencing the highest rates of transmission also have limited access to resources to mitigate transmission. However, even imperfect interventions that are efficiently targeted to individuals or groups driving transmission can have a far greater impact than interventions that are evenly distributed in the population. Here we describe an agent-based model (ABM) developed to simulate the transmission of SARS-CoV2 in the City and County of San Francisco (SF). The ABM incorporates a synthetic population developed to capture drivers of racial and ethnic disparities in transmission observed in SF over the course of the pandemic. Challenges in ABM calibration are discussed in light of the large parameter space and limited reliable data sources on which to calibrate. We then describe how the ABM can be used to explore counterfactual scenarios in which resources such as testing, work from home support, and vaccination are targeted towards population strata with the highest rates of infection. Using the ABM, we demonstrate the benefit of these targeted strategies on both reducing the spread of SARS-CoV2 and improving metrics of health equity.

Morganne Igoe

(University of Tennessee, Knoxville, USA)
"ZCTA-level Predictors of COVID-19 Hospitalization Risk in the St. Louis Area"
COVID-19 has overwhelmed U.S. healthcare systems, with almost 30 million cases and over 500,000 deaths as of March 28, 2021. Older age, male gender, race, and underlying medical conditions have been identified as factors among hospitalized patients. There are also geographic disparities in COVID-19 hospitalization risk that are at least partly driven by geographic differences in sociodemographic, economic, and co-morbid factors. If these factors could be identified, they could help inform control efforts. The aim of this study is to identify ZCTA-level predictors of COVID-19 hospitalization risk in the St. Louis Area using sociodemographic, economic, and chronic disease factors. ZCTA-level sociodemographic, economic, and chronic disease factors were evaluated for correlation with each other and univariable associations with the age-adjusted number of COVID-19 hospitalizations. The ZCTA population was used as the offset. A multivariable negative binomial regression model was then fit using a backwards elimination process. Percent black population, percent of the population with some college education, number of diabetes discharges per 100 population, and population adjusted cases were all positively associated with COVID-19 hospitalization risk. A number of sociodemographic and chronic conditions are important determinants of disparities in COVID hospitalization risk. These findings will inform health care systems of where large numbers of patients may occur to reduce overburdening of hospitals and to guide vaccination efforts.

Preeti Dubey

(Francis I. Proctor Foundation, UCSF, USA)
"Optimal vaccine prioritization for COVID-19 between high-risk and core group in California"
The COVID-19 pandemic has caused record numbers of daily confirmed cases and deaths in USA during the winter of 2020-2021. With the approval of Moderna and Pfizer vaccines, a substantial mitigation of the epidemic is expected by mid-2021. During the initial rollout, it was unclear whether or not vaccine should be targeted at elderly individuals at highest risk of mortality and morbidity, or towards younger individuals responsible for a majority of transmission. In this study, we sought to investigate the optimal prioritization of COVID-19 vaccination in California. We constructed a compartmental model based on the natural history of COVID-19 transmission. The model involved two transmission risk groups (low and high), and two age prioritization groups. We considered the case of random mixing vs. positive assortative mixing, as well as the relative rate of high COVID-19 risk behavior in the high transmission risk group. The model considered incomplete reporting and reporting delays and was calibrated to California case data. All 24 possible orderings of age/risk prioritization were simulated suggesting that age prioritization was optimal when the relative riskiness of the high-risk group was low. The randomness in mixing has also played a role in making the decision of optimality of risk prioritization when the relative riskiness was high. Our results are aligned with the findings of other modeling groups that some risk targeting can be a valuable consideration in controlling the pandemic.

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Virtual conference of the Society for Mathematical Biology, 2021.