Epidemic Modeling, Prediction and Control скачать в хорошем качестве

Epidemic Modeling, Prediction and Control

The COVID-19 pandemic has disrupted almost every facet of life globally and has posed new challenges to policymakers and questions to researchers. In this article, we discuss some of the key considerations and challenges in modeling epidemics, predicting their diffusion within and across populations and evaluating their control policies. Epidemic prediction is challenging due to the uncertain nature of its spatial and temporal diffusion, co-evolution of latent confounding factors, sparsity of signals particularly during the initial stages of a pandemic and the complex interactions of the individual- and group-level behaviors with mitigating policy interventions. We explain, illustrate and comment on the strengths and weaknesses of the commonly used epidemic models. We classify the existing models on methodologies used such as compartmental models versus agent-based models, nature of model uncertainties considered such as deterministic versus stochastic models and factors included in the models such as network effects, disease characteristics and control actions. We highlight some of the common behavioral traits exhibited by individuals and discuss the theoretical sources of such behavior. Based on our work, we illustrate the formulation of a specific compartmental model that accounts for asymptomatic spread of COVID-19 and the effect of control actions such as testing and lockdowns. We also demonstrate the nature of optimal actions based on analytical and agent-based simulation methodology. Finally, we conclude by discussing lessons learned from the COVID-19 pandemic to better manage any future pandemic. Speakers: Ujjal Kumar Mukherjee, Sridhar Seshadri ⭐️Join INFORMS: https://bit.ly/3BkR66X ⭐️YouTube channel: https://bit.ly/3zeAsEl Social Media: ➡️Instagram:   / informs_orms   ➡️Twitter:   / informs   ➡️LinkedIn:   / info.  . ➡️Facebook:   / informspage