Engineering Effective Response to Outbreaks of Influenza

Objective. Allocation of vaccines and deployment of non-pharmaceutical interventions (NPIs) are critical to controlling influenza. We examine how these policies can minimize the societal impact. Methods. An engineering systems framing and modeling approach incorporates theories and data on the spread of influenza. Models employed data from the CDC and state governments on cases and vaccine administered during the 2009 H1N1 outbreak, and published literature on how to reduce human-to-human contacts. Results. During the outbreak, barely half of all states received proportional allotments of vaccine in time to protect any citizens, while fewer sought vaccine after the peak. While individuals prone to contract and spread infection drive the progression, diligent hygiene practices and social distancing measures can drive down the number of cases. Conclusions. NPIs are highly effective in reducing the spread of influenza before, but also after vaccine is administered. Policies to allocate vaccine in direct proportion to population should be replaced and larger stocks sent to regions where greater numbers of persons stand to be protected

Differentiable agent-based epidemiology

Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of complex, dynamic infections under varying conditions and navigate uncertain environments. Agent-based models (ABMs) are an increasingly popular simulation paradigm that can represent the heterogeneity of contact interactions with granular detail and agency of individual behavior. However, conventional ABM frameworks not differentiable and present challenges in scalability; due to which it is non-trivial to connect them to auxiliary data sources. In this paper, we introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources. This provides an array of practical benefits for calibration, forecasting, and evaluating policy interventions. We demonstrate the efficacy of GradABM via extensive experiments with real COVID-19 and influenza datasets

Dynamic pricing strategy to optimally allocate vaccines

In the United States, the Advisory Committee on Immunization Practices (ACIP) makes recommendations as to which cohorts (identified groups of individuals) ought to have higher priority access to vaccines when their supply is insufficient to immunize all susceptible individuals in the country. Typically, cohorts are determined based on susceptibility to contracting seasonal influenza and on the resulting consequences of infection for different age groups. For seasonal influenza, high-risk cohorts commonly include children, teenagers, pregnant women and people with different chronic diseases. This study proposes the application of revenue management theory to better allocate seasonal influenza vaccines among different risk-based population cohorts. Our model maximizes the number of immunized individuals by dynamically adjusting the price per dose in each cohort as to discourage vaccination in low-risk cohorts and preserve more supply for high-risk cohorts. Experimental results show that up to 12% of infections and deaths due to seasonal influenza could be avoided by implementing this price discrimination policy in hypothetical yet realistic scenarios

Modeling reduction of pandemic influenza using pharmaceutical and non pharmaceutical interventions in a heterogeneous population

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.Cataloged from PDF version of thesis.Includes bibliographical references.In an event of a pandemic influenza outbreak such as the great "Spanish Flu" of 1918 and the more recent 2009-2010 H1N1 "Swine Flu" scare, pharmaceutical as well as non-pharmaceutical resources are limited in availability and effectiveness. In this thesis we apply OR methods to evaluate the effectiveness of such resources and the strategies for reducing the number of infections resulting from an outbreak. In the first half of this work, we focus on epidemiological analysis of influenza modeling in a heterogeneous population. The majority of existing epidemiological literature models influenza spread in a statistically homogeneous population, but the model-based inclusion of heterogeneity by contact rate, susceptibility, and infectivity introduces significant effects on disease progression. We introduce a new discrete-time influenza outbreak model for a heterogeneous population and use it to describe the changes in a population's flu-related characteristics over time. This information allows us to evaluate the effectiveness of different vaccine targeting techniques in achieving herd immunity, that is, the point at which there is no further growth in new infections. In the second half of this work we switch to a practical application of OR methods in a pandemic situation. We evaluate the effectiveness of vaccines administered to US states during the 2009-2010 H1N1 pandemic. Since the US is geographically diverse and large, the outbreak progressed at different rates and started at different times in each individual state. We discuss dynamic, multi-regional, vaccine allocation schemes for large geographical entities that take into account the different conditions of the epidemic in each region and maximize the total effect of available vaccines. In addition, we discuss effective strategies for combining vaccines with non-pharmaceutical interventions such as hand-washing and public awareness campaigns to decrease the strain of an outbreak on the population.by Anna Teytelman.Ph.D

An Early Warning System for Detecting H1N1 Disease Outbreak - A Spatio-temporal Approach

The outbreaks of new and emerging infectious diseases in recent decades have caused widespread social and economic disruptions in the global economy. Various guidelines for pandemic influenza planning are based upon traditional infection control, best practice and evidence. This article describes the development of an early warning system for detecting disease outbreaks in the urban setting of Hong Kong, using 216 confirmed cases of H1N1 influenza from 1 May 2009 to 20 June 2009. The prediction model uses two variables – daily influenza cases and population numbers – as input to the spatio-temporal and stochastic SEIR model to forecast impending disease cases. The fairly encouraging forecast accuracy metrics for the 1- and 2-day advance prediction suggest that the number of impending cases could be estimated with some degree of certainty. Much like a weather forecast system, the procedure combines technical and scientific skills using empirical data but the interpretation requires experience and intuitive reasoning.postprin

Literature Review - the vaccine supply chain

Vaccination is one of the most effective ways to prevent the outbreak of an infectious disease. This medical intervention also brings about many logistical quest