A Covid-19 vaccine will need equitable, global distribution say Rebecca Weintraub, Prashant Yadav and Seth Berkley in a Harvard Business Review article published today.
The article highlights the tremendous work that the GAVI organization has been doing for years when it comes to worldwide equality in the accessibility of vaccines. Considering the financial, logistical, healthcare system and population mobilization constraints, GAVI has developed tools and expertise in providing guidance on what a global vaccination program could look like.
As part of the efforts in the past, GAVI has been leveraging data to predict vaccination adherence behavior. “<As part of defining an effective vaccination strategy> we need to understand risk of transmission at the hyperlocal level and the likelihood of adherence for specific geographies and sub-populations. In settings with limited individual-level health data, we will need to leverage available sources. For example, artificial intelligence company Macro-Eyes uses satellite imagery, digital conversations, and publicly available data to predict with 76% accuracy which child will drop out of routine immunization programs.”
GAVI calls for a global coordination will be required. “At least for the first eight to 12 months after the Covid-19 vaccine becomes available, it is likely that there will be only a limited supply to meet global demand. Consequently, there needs to be a global agreement on allocating stocks to countries around the world. If that doesn’t happen, the result will be political tensions like those we are currently experiencing over the allocation of personal protective equipment, ventilators, and test kits.
Although the poorest countries have in place systems that have been well honed over 20 years through the Vaccine Alliance, middle-income countries ineligible for Gavi’s assistance do not. We need to decide how to support them — whether to extend Gavi assistance to them or provide other mechanisms.”
With supplies not meeting global demand and in support of global coordination, we would have to define models and predict what areas would be in most need for the vaccines and where vaccines can have most impact, as well as understand the physical capability of distributing the vaccines effectively, taking into consideration, amongst others, temperature control challenges and the capacity of the health system to effectively administer the vaccines.
Can we define effective predictive models, where the physical capabilities of executing a vaccination programme, can be combined with its actual supply, community resistance levels reached, the actual prognosis of the curve by geographical location, thus defining a pinpointed vaccination strategy? We are currently partaking in a group consisting of resources from universities form Canada and the UK, data scientist and technologists to see how we can evolve such predictive models. If you feel you can contribute, reach out to us.