Wednesday, June 16 at 10:30pm (PDT)Thursday, June 17 at 06:30am (BST)Thursday, June 17 02:30pm (KST)
SMB2021 FollowWednesday (Thursday) during the "CT08" time block.
Department of Mathematics, Soongsil University
" Mathematical modeling and optimal intervention strategies to control COVID-19"
The ongoing outbreak of the novel coronavirus disease (COVID-19) has considerably affected public health and the economy worldwide.Optimizing control measures is urgent given the substantial societal and economic impacts associated with infection and interventions. We established mathematical models to determine the optimal strategies. We used game theory to identify the individually optimal strategy, and optimal control theory to find optimal strategies that minimize the costs associated with infection and intervention. When social distancing and testing with contact tracing are considered as intervention strategies, the results demonstrate that testing should be maintained at a maximum level in the early phases and after the peak of the epidemic, whereas social distancing should be intensified when the prevalence of the disease is greater than 15%. After the peak of the pandemic, it would be optimal to gradually relax social distancing and switch back to testing. Additionally, we identified the individually optimal strategy based on the Nash strategy when social distancing and vaccination are available as control strategies. We determined the relative costs of control strategies at which individuals preferentially adopt vaccination over social distancing (or vice versa).
University of Auckland
"Likelihood-based estimation and prediction for misspecified epidemic models: an application to measles in Samoa"
Prediction of the progression of an infectious disease outbreak in a population is an important task. Differential equations are often used to model an epidemic outbreak's behaviour but are challenging to parametrise. Furthermore, these models can suffer from misspecification, which biases the estimates. In this talk we present our recent work on an explicitly likelihood-based variation of the generalised profiling method as a tool for prediction and inference under model misspecification. Our approach allows us to carry out identifiability analysis and uncertainty quantification using profile likelihood-based methods without the need for marginalisation. Rather than introduce an explicit stochastic process model, generalised profiling uses a deterministic model as an approximately enforced `smoothing penalty' term and uses maximisation rather than integration to handle nuisance parameters. We provide additional justification for this approach by introducing a novel interpretation of the model approximation component as a stochastic constraint, and preserves the rationale for using profiling rather than integration to remove nuisance parameters while still providing a link back to explicitly stochastic models. We present results applying our approach to data from an outbreak of measles in Samoa and show that fast, accurate prediction is possible.
School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, Republic of Korea
"Cost-benefit analysis of tafenoquine for the relapse prevention of Plasmodium vivax malaria in South Korea"
Plasmodium vivax malaria has not been eradicated in South Korea since 1993 and the government is aiming to grant certification for malaria elimination from the WHO in 2024. P. vivax malaria has a dormant liver-stage, and this can cause relapse. Tafenoquine has been proven to effectively prevent relapse as an alternative to primaquine. In this study, we developed a model for P. vivax malaria using delay differential equations to estimate the impact of tafenoquine introduction on malaria burden. We also conducted a cost-benefit analysis of tafenoquine from the payer's perspective based on the cost and benefit extracted from the national health insurance data and performed probabilistic sensitivity analysis. The results showed that the introduction of tafenoquine could prevent 77.78% of relapse and 12.27% of total malaria cases over 10 years compared to primaquine. And the cost-benefit analysis provided an incremental cost of $13,115 and an incremental benefit of $165,520 resulting in an incremental benefit-cost ratio of 12.26. Furthermore, the sensitivity analysis showed a consistent result with a probability of 98.3%. Hence, the introduction of tafenoquine can reduce the malaria burden and is beneficial over primaquine. These findings support the introduction of tafenoquine to step toward malaria elimination in South Korea.
University of Warwick
"Analysing early warning signals of disease elimination by approximating the potential surface"
The theory of critical slowing down states that a system displays increasing relaxation times as it approaches a critical transition. Such changes in relaxation times can be seen in statistics generated from data timeseries, which can be used as early warning signals of a transition. While analytic equations have been derived for various early warning signals in a variety of epidemiological models, there is frequent disagreement with the general theory of critical slowing down, with some indicators performing well when used in prevalence data but not when applied to incidence data. We investigate this effect in an SIS model by reconstructing the potential surface for different types of data. By modelling prevalence, incidence and the rate of infection as stochastic differential equations, then using an equation-free method to approximate their drift functions from simulated timeseries, we reconstruct the potential surface for each data type. Slowly varying parameters provides insight into how the shape of the potential surface changes. Analytic equations for the drift functions are also derived for comparison with simulated results, showing that the potential surface for all data types becomes shallower upon the approach to a critical transition from either direction, as predicted by critical slowing down.