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What Covid-19 has taught the world on data

OPINION
By Charles Onyango | February 13th 2021

The pandemic showed how the right product designs can facilitate access.

The Covid-19 pandemic has been more far-reaching and devastating than even loss of lives, jobs and economic growth would suggest. It has worsened poverty, childhood malnutrition, and domestic violence and halted or reversed gains made in nearly half of the 17 UN Sustainable Development Goals (SDGs). Many people, including mothers and young children, have died not from the virus, but because of disruptions and delays in care. Up to a decade of progress against HIV, tuberculosis, malaria has been erased.

Hospitals, clinics, and other health care organisations have faced huge challenges that will persist long after the pandemic. The crisis has created long-term uncertainty in staffing, funding, and operational issues. The World Health Organisation reports that nearly all lower and medium-income countries (LMICs) reported disruptions to essential health services. Its economic consequences, such as rising poverty, increased demand for services, the nature of which shifted as the willingness to travel long distances for medical care plummeted, accompanied by other behaviour changes.

The global health system will not simply snap back to normal, especially in countries like Kenya. As global organisations confront the pandemic, they would benefit from a new approach that builds a stronger foundation for improving public health and meeting the SDGs. Accelerating work on simplifying access and delivery, bringing data-driven global health care to life, changing behaviour to improve health, and creating sustainable and joined-up sources of development funding will allow the global health system to become more efficient and leapfrog into the future.

The pandemic showed how the right product designs can facilitate access. The early Covid vaccines, with their cold-storage requirements and two-dose regimens, could be more difficult to distribute and administer in Africa than in Global North, keeping the world in a health and economic limbo until more convenient alternatives become widely available. Against the backdrop, it’s surprising that only four per cent of clinical trials of Covid vaccines occurred in Africa, according to the US National Library of Medicine.

Global health innovations should focus on simplifying services and designing products around the needs, lifestyles and ability to pay of patients and end-users. The world needs to learn how to overcome constraints such as limited cold-storage supply chains. Increasing the number of clinical trials in LMICs and broadening them to include children and immune-compromised individuals are achievable first steps in the journey to easier and wider access to all medical treatments, not just vaccines.

The virus raced ahead of the systems and processes in place. Its novelty highlighted the need to invest in data analytics in public health that can contribute to improved prevention and treatment strategies. Most governments didn’t excel at creating effective surveillance infrastructure to track and limit community spread. They also fell short in identifying, reaching out to, and protecting the vulnerable. Governments have an opportunity to flip the script in delivering vaccines. They should work with technology and data consulting firms to deploy network optimisation, data visualisation, and analytics tools to allocate doses dynamically based on supply, local prevalence, community receptivity, and social-equity concerns. The communities shouldn’t be merely receiving doses according to total population but disease prevalence or percentage of the elderly people or those with underlying conditions.

Behavioural change proved to be the best way to flatten the curve of infections. It showed that public health isn’t just a challenge of supply, but also that of demand, in the interest of which people are called upon to modify their habits, lifestyles, and choices.

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-The writer is a STEM Scholar and a Global Fellow at MWI

Covid 19 Time Series

 

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