Optimizing vaccine allocation for cholera epidemics

Oral cholera vaccines (OCV), administered in two doses, have emerged recently as a powerful tool for short-term outbreak control. When resources are limited, single-dose vaccination has been used as a way to stretch the supply, however, previous studies have shown that a single dose of vaccine is less effective, and wanes faster. In addition, studies have shown reduced effectiveness of the OCV in children under five years old, who are disproportionally affected by cholera. Determining who, how, and when to vaccinate are pressing decisions that public health officials usually take during an unfolding epidemic, often resulting in a sub-optimal use of resources. We will use mathematical models paired with optimization algorithms to determine optimal vaccine allocation, thereby providing public health officials with an evidence-based rationale for vaccine distribution.

Our specific aims are:

• To construct data-driven mathematical models of cholera transmission, infection and vaccination.
• To determine optimal vaccine allocation strategies.

This research will benefit countries with scarce resources for which making the best use of each dose of vaccine is a top priority.”

Cholera is an acute diarrheal disease. If left untreated, it can be fatal within hours. Oral cholera vaccines, administered in two doses, are a powerful tool for short-term control. When resources are limited, single-dose vaccination has been used as a way to stretch the supply. However, single-dose vaccination is less effective. In addition, cholera vaccines are less effective in young children, who are disproportionally affected by the disease. Decision makers are left with choices: vaccinating with one or two doses; vaccinating those who will benefit the most or those who are the most at risk. Determining who, how, and when to vaccinate are decisions that public health officials usually take under immense pressure, often resulting in a sub-optimal use of the available resources. We propose to use mathematical models paired with optimization algorithms to determine optimal vaccine allocation, thereby providing public health officials with an evidence-based rationale for vaccine distribution.

This project will provide a quantitative framework for OCV allocation, thereby providing decision makers with evidence-based tools for vaccine prioritization.