How can Science and Technology Help Mitigate the Effects of Pandemics on SDG 3?

Covid-19 has severely impacted global progress towards reaching the UN’s 3rd Sustainable Development Goal (regarding ‘Good health and Well-being’). How can the science and tech industries lessen the impacts of future disease outbreaks on global health? 


The emergence of COVID-19 has proved catastrophic for good health and wellbeing worldwide. Amidst sharp rises in unemployment, over 25 million recorded coronavirus cases, and a predicted 5.2% shrinkage in the global economy, it is difficult to see how nations can recover their efforts towards achieving the UN’s Third Sustainable Development Goal (SDG 3). Not only has coronavirus directly endangered lives from all around the globe, it has also resulted in disruptions which could ultimately reverse decades of health improvements. For example, cancellations of prevention campaigns and severe disruptions in treatment are estimated to increase malaria deaths in Sub-Saharan Africa by 100%, while childhood immunisation programmes have been interrupted in around 70 countries.  

In damaging the significant progress made towards achieving SDG 3, Covid-19 has highlighted vast areas of crisis both within governments and global healthcare systems. As disease outbreaks of this type are expected to have a much higher incidence rate from now on, it is necessary to change global systems and responses, so that effects of future pandemics on healthcare are minimised. When considering how to combat such rapidly disseminating health crises in this way, much could be learnt from the science and tech sectors. This is because these sectors, having driven forward much incremental research and innovation during the pandemic, could provide solutions to mitigate the impacts of the future disease outbreaks on SDG 3. 

As disease outbreaks of this type are expected to have a much higher incidence rate from now on, it is necessary to change global systems and responses, so that effects of future pandemics on healthcare are minimised.

Firstly, in the race to produce coronavirus vaccines at unprecedented speeds, the scientific sector (including the Biotechnology industry and university-affiliated research labs) has made crucial progress in the research and development of experimental vaccines. One example of such vaccines are mRNA vaccines, which prove advantageous as they are both faster and less expensive to produce than traditional vaccines. In addition, they have no chance of being infectious, since the virus itself never enters the body. While a vaccine production process of this type could be standardised and upscaled relatively quickly, there are also many unknowns to overcome since a commercially-available mRNA vaccine has never been made before. For example, researchers will need to establish which injected quantities of mRNA will elicit a sufficiently protective immune response. They will also need determine how to minimise or eliminate any possible side-effects produced by the vaccine. 

With many companies working on mRNA vaccines (such as Moderna in partnership with National Institutes of Health, and BioNTech collaborating with Pfizer and Fosun Pharma), huge developments have undoubtably been made in their production, with some already in phase 3 of clinical trials. Whether such experimental vaccines end up becoming approved for full use against Covid-19 or not, the incremental steps forward taken by the Biotech industry to address Covid-19 and provide such vaccines will undoubtably aid the process of vaccine production when future pandemics occur. Not only will such vaccine research directly help the effort to save lives, but such efforts will also work towards SDG target 3.8– to achieve ‘access to safe, effective, quality and affordable essential medicines and vaccines for all’. 

Another scientific effort, which could mitigate the impacts of future pandemics on global health (thus minimising any adverse secondary impacts on SDG 3), has been the research generated on the efficacy of different types of face-covering. This research became of particularly high importance during the current pandemic after new recommendations from the WHO advised the general public to wear non-medical masks when physical distancing is unfeasible. 

In this study, conducted by researchers at Duke University, it was found that the majority of types of face covering significantly reduce droplet transmission, although there are notable exceptions. Masks made of neck fleece, for example, were shown to disperse large droplets into smaller droplets (which are airborne for longer), rendering them ineffective and harmful. In contrast, surgical masks reduced droplet transmission by at least 90% (ranking them second-most effective following the fitted N-95 masks), while a three-layered mask (made of polypropylene fabric laid in between two cotton layers), and a two-layered (polypropylene fabric) mask also performed similarly well. 

While the study noted that the research was conducted on a very small scale (involving 14 types of commonly available masks and a small sample of participants), the results proved significant in terms of suggesting which face-coverings would reduce the spread of Covid-19 most effectively. In the future, more advanced research (which improves upon the limitations outlined in the aforementioned study) could serve as a definitive basis upon which organisations and governments could unfold suitable guidelines. This would strengthen the protective barrier against airborne transmission, helping to contain diseases spread by droplet infection – thus alleviating pressure on hospitals and health workers worldwide, while having significant and positive implications for SDG 3.

In addition to scientific research, technological innovation has also played an invaluable role in combatting Covid-19. For example, Alibaba Group (a Chinese tech and e-commerce giant) made use of image recognition to develop an artificial intelligence (AI) system for the effective diagnosis of coronavirus cases. While it usually takes doctors up to 15 minutes to identify Covid-19 from CT scans (since there can be over 300 images to assess), Alibaba’s AI system can diagnose the disease in an average of 20 seconds with 96% accuracy. Moreover, the technology has proven highly useful in quantifying the proportions of lesion sites on patients’ lungs, helping to determine the severity of each case. Such technology could have fundamental importance to achieving ‘good health and wellbeing’ in terms of treating cases from similar future diseases as quickly and effectively as possible. Reducing the impacts of pandemics on global healthcare systems using AI would prevent the disruption of other medical services, thus mitigating any negative consequences to SDG 3.  

Lastly, technology has also proved highly valuable in terms of both monitoring and preventing the spread of diseases. Taiwan, in particular, has been highly proficient at utilising big data to track individual coronavirus cases, helping to minimise infection among its citizens. For example, every citizen’s two-week travel history was integrated into the national health insurance database on January 27th, enabling officials to identify individuals who had travelled through high-risk areas, and order them to quarantine. Such use of data collection undoubtably raises privacy concerns, especially since cellular-based location tracking by telecom companies is used to alert the authorities whenever a quarantining resident departs from their address, or switches off their mobile phone. However, while these measures may seem exceedingly strict, especially to the western world, Taiwan has managed the outbreak of Covid-19 immensely well according to the WHO, which praised the country for its public health response. 

while Covid-19 has brought about severe worldwide disruptions towards reaching SDG 3, the science and tech sectors have provided useful solutions and advanced understanding on how to combat the problems presented by pandemics.

Such practical use of technology to monitor coronavirus cases demonstrates the critical role that big data and surveillance can have on minimising the consequences of pandemics on public health. While some would highly disagree with the monitoring nature of Taiwan’s coronavirus response, it is irrefutable that their employment rates, economy, and health sector have suffered limited disruption- something which all countries worldwide could benefit from. If more nations took greater steps to mirror Taiwan’s big data approach, then the outbreak of coronavirus could have much fewer disruptions on the progress towards achieving universal ‘good health and well-being’.

In conclusion, while Covid-19 has brought about severe worldwide disruptions towards reaching SDG 3, the science and tech sectors have provided useful solutions and advanced understanding on how to combat the problems presented by pandemics. The research and innovation driven forward during this time has significantly advanced our progress towards controlling Covid-19, and the responses and breakthroughs experienced now will make us better equipped for reducing similar disruptions to ‘good health and wellbeing’ in future disease outbreaks. 

Art by Oliver Walter

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