Challenges and Future Directions in Pandemic Control

In this letter, we describe some of the most important objectives and needs in pandemic control. We identify the main open problems in the different stages of the decision making process, as well as the most significant challenges to overcome them, leading to promising future research di rections. We provide a concise review of the most recent literature describing such challenges, highlighting the main results, achievements and methodologies that can be employed to address them. In particular, we discuss some promising recent techniques that have been successfully applied to the Covid-19 pandemic and could be very valuable in the design of novel methodologies to face future pandemic

Dynamic modelling of hepatitis C virus transmission among people who inject drugs: a methodological review

Equipment sharing among people who inject drugs (PWID) is a key risk factor in infection by hepatitis C virus (HCV). Both the effectiveness and cost-effectiveness of interventions aimed at reducing HCV transmission in this population (such as opioid substitution therapy, needle exchange programs or improved treatment) are difficult to evaluate using field surveys. Ethical issues and complicated access to the PWID population make it difficult to gather epidemiological data. In this context, mathematical modelling of HCV transmission is a useful alternative for comparing the cost and effectiveness of various interventions. Several models have been developed in the past few years. They are often based on strong hypotheses concerning the population structure. This review presents compartmental and individual-based models in order to underline their strengths and limits in the context of HCV infection among PWID. The final section discusses the main results of the papers

Stochastic programming and agent-based simulation approaches for epidemics control and logistics planning

This dissertation addresses the resource allocation challenges of fighting against infectious disease outbreaks. The goal of this dissertation is to formulate multi-stage stochastic programming and agent-based models to address the limitations of former literature in optimizing resource allocation for preventing and controlling epidemics and pandemics. In the first study, a multi-stage stochastic programming compartmental model is presented to integrate the uncertain disease progression and the logistics of resource allocation to control a highly contagious infectious disease. The proposed multi-stage stochastic program, which involves various disease growth scenarios, optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals due to an epidemic. Two new equity metrics are defined and formulated, namely infection and capacity equity, to explicitly consider equity for allocating treatment funds and facilities for fair resource allocation in epidemics control. The multi-stage value of the stochastic solution (VSS), demonstrating the superiority of the proposed stochastic programming model over its deterministic counterpart, is studied. The first model is applied to the Ebola Virus Disease (EVD) case in West Africa, including Guinea, Sierra Leone, and Liberia. In the following study, the previous model is extended to a mean-risk multi-stage vaccine allocation model to capture the influence of the outbreak scenarios with low probability but high impact. The Conditional Value at Risk (CVaR) measure used in the model enables a trade-off between the weighted expected loss due to the outbreak and expected risks associated with experiencing disastrous epidemic scenarios. A method is developed to estimate the migration rate between each infected region when limited migration data is available. The second study is applied to the case of EVD in the Democratic Republic of the Congo. In the third study, a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental stochastic programming model is developed to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through deriving a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. In the fourth study, a simulation-optimization approach is introduced to address the vaccination facility location and allocation challenges of the COVID-19 vaccines. A detailed agent-based simulation model of the COVID-19 is extended and integrated with a new vaccination center and vaccine-allocation optimization model. The proposed agent-based simulation-optimization framework simulates the disease transmission first and then minimizes the total number of infections over all the considered regions by choosing the optimal vaccine center locations and vaccine allocation to those centers. Specifically, the simulation provides the number of susceptible and infected individuals in each geographical region for the current time period as an input into the optimization model. The optimization model then minimizes the total number of estimated infections and provides the new vaccine center locations and vaccine allocation decisions for the following time period. Decisions are made on where to open vaccination centers and how many people should be vaccinated at each future stage in each region of the considered geographical location. Then these optimal decision values are imported back into the simulation model to simulate the number of susceptible and infected individuals for the subsequent periods. The agent-based simulation-optimization framework is applied to controlling COVID-19 in the states of New Jersey. The results provide insights into the optimal vaccine center location and vaccine allocation problem under varying budgets and vaccine types while foreseeing potential epidemic growth scenarios over time and spatial locations

A decision-support framework to optimize border control for global outbreak mitigation

The introduction and spread of emerging infectious diseases is increasing in both prevalence and scale. Whether naturally, accidentally or maliciously introduced, the substantial uncertainty surrounding the emergence of novel viruses, specifically where they may come from and how they will spread, demands robust and quantifiably validated outbreak control policies that can be implemented in real time. This work presents a novel mathematical modeling framework that integrates both outbreak dynamics and outbreak control into a decision support tool for mitigating infectious disease pandemics that spread through passenger air travel. An ensemble of border control strategies that exploit properties of the air traffic network structure and expected outbreak behavior are proposed. A stochastic metapopulation epidemic model is developed to evaluate and rank the control strategies based on their effectiveness in reducing the spread of outbreaks. Sensitivity analyses are conducted to illustrate the robustness of the proposed control strategies across a range of outbreak scenarios, and a case study is presented for the 2009 H1N1 influenza pandemic. This study highlights the importance of strategically allocating outbreak control resources, and the results can be used to identify the most robust border control policy that can be implemented in the early stages of an outbreak. Document type: Preprin