Reflections on key insights and implications for disaster governance in Kenya and Africa
Finally, Hope on the Horizon
On a day when Kenya received the first batch of 1.025 million doses of COVID-19 AstraZeneca-Oxford vaccine under the global COVAX initiative, just after Ghana, this IBD multiscale modelling series takes you down memory lane and shares fresh perspectives on the manner and meaning of the COVID-19 curves it has been studying over the last one year. This has been an exciting journey of substance, surprises, and scaling of new heights of knowledge on the scale and character of the global pandemic. Inspiration from the developing success of vaccination in Israel gives hope to a world ravaged by a pandemic. On a cautionary note, the high percentage of citizens that should be vaccinated to ensure herd immunity, about 70% according to current knowledge, raises the stakes for African countries with their large populations and slower rates of testing and vaccination.
For hosting 17% of the global population, Africa’s 3% share of the global COVID-19 cases easily escapes the boundaries of defining a narrow escape. Insights from data and new knowledge may, however, reveal new lessons over time.
From just one million COVID-19 cases globally in early April 2020 with a recorded global case fatality rate of 5% and recovery rate of 21%, to more than 115 million cases by March 3, 2021 and a high global recovery rate of 79%, much has changed. For Africa, the higher recovery rate of 89% on the same date with a case fatality rate of 2.6% (almost at par with the global case fatality rate of 2.2%) has been a remarkable observation. On the same date, Kenya compared favourably with 1.7% case fatality rate and 81% recovery rate. For hosting 17% of the global population, Africa’s 3% share of the global COVID-19 cases easily escapes the boundaries of defining a narrow escape. Insights from data and new knowledge may, however, reveal new lessons over time.
The IBD modelling series started with COVID-19 data from January 2020. The abundant COVID-19 data generated since early 2020 justified the choice of statistical modelling to conduct data-driven mathematical simulations based on the best-fitting equations (R-squared of at least 0.995). The models were calibrated using time-series COVID-19 data for selected countries that were already exhibiting notable trends in their COVID-curves from January 2020 onwards. Sixteen indicator countries were selected from outside Africa and sixteen indicator countries from Africa, Kenya included. Informed by parameters based on modelling theory and assumptions based on the observed COVID-19 growth trends as well as sociocultural characteristics, pessimistic and optimistic growth curves were generated and adjusted adaptively every three to four weeks as new insights came in from the developing COVID-19 scenarios in the countries of interest. Population-normalised daily average COVID-19 testing rates were computed and compared across the countries to inform the likely future scenarios of COVID-19 cases.
As early as the March — April 2020 period, the model results already showed early signs of fast-rising COVID-19 curves with high daily exponential rates in the following countries: USA (26%), South Africa (26%), Cameroon (23%), Spain and the UK (20%), Kenya (18%), and Egypt (14%). The models projected the end-month COVID-19 cases within 10% margin of error for more than 90% of the instances. In the case of Kenya, this range was 0.1% to 13.8%, the highest difference of 13.8% applying to September, after a 50% reduction in the testing rates observed after August 16, 2020. The model further estimated that Kenya’s new COVID-19 curve emerging from November 15, 2020 would theoretically peak at 103,188 cases on January 14, 2021. Between January 1 and January 14, the simulations assuming the curve would peak on January 14 remained within 4.6–5.5% above the actual cases. Kenya confirmed 89,771 total cases on January 14, 2021 (4.6% difference). These projections remained within 10% margin of error, which is within the boundary recommended for most policy and planning purposes.
Flogging the Old Thinking to Birth New Thinking
Scientific thinking is rational, treating models as working hypotheses that should be improved adaptively over time with new knowledge. Knowledge is progressive. Policy should be calibrated with science to provide sound direction, coherence, and continuity.
Before COVID-19, “modelling and simulation” occupied the lonely margins reserved for elite scholars. The disruptive, distractive, destructive and distancing disease has pushed all that to the mainstream of everyday public and political discussions. For this, the pandemic will forever remain a permanent feature in the memory of the present generation and a reference point for future generations. Henceforth, the science-policy interface can only gain prominence in governing global disasters.
Scientific thinking is rational, treating models as working hypotheses that should be improved adaptively over time with new knowledge. Knowledge is progressive. Policy should offer sound direction, coherence, and continuity. The traditional approach to calibrating policies and strategies in response to disasters has for too long assumed reactionary and linear thinking in silos, with little consideration for structured and long-term engagements with researchers, scientists, and think tanks. The urgent and sweeping global effects of the COVID-19 pandemic have challenged this limiting culture, making a compelling case for new governance models informed by systems thinking and a close working collaboration between researchers, scientists, policymakers, citizens, and the business community. The traditional thinking is not sustainable anymore and must give way to new thought leadership in the post-pandemic era.
The 7Ts of Disaster Governance Lessons a Year Later
A premortem of the emerging post-pandemic era and a postmortem of the pre-pandemic period have together provided key lessons. The lessons have been summarised into a list that has been referred to here as 7Ts, as they all start with T.
Timing, Testing, Tracing
Timely responses are key to effective disaster governance, hence the deployment of technology and adequate data. Adequate testing and tracing together provide the much-needed data to generate actionable intelligence for disaster governance. Spatial technologies add the critical layer of location-based intelligence across geographies. The tracing of the first case of COVID-19 in Germany to a saltshaker in a restaurant in Bavaria is exemplary of the pinpointed triumph of applied geospatial technologies. COVID-19 has demonstrated the power of data in supporting quality and evidence-based policy advice.
Using a list of thirty countries to compare COVID-19 testing rates that have been normalised for population differences and the days over which different countries have been testing, 14 from Africa and 16 from outside Africa, the limited COVID-19 testing and tracing capacity in Africa becomes evident in the figure displayed below, representing key testing indicators from July 2020 to February 2021.
Across Africa, increasing COVID-19 testing rates were only noticeable over the June — September 2020 period before mostly stagnating or reducing thereafter towards February 2021. Mauritius and Cameroon are examples here of such a reduction in testing rates over time. African countries with large populations must confront the COVID-19 testing challenge more drastically to score better on the normalised testing indicator, hence the visible case here of Nigeria and Egypt. Ethiopia, for being almost at par with Kenya and Uganda on this normalised measure despite being one of the most populous countries on the continent, deserves some applause .
Israel, the USA, and most of the European countries have demonstrated consistently high and increasing normalised COVID-19 testing rates. Holding positivity rates constant, the significant differences in the population-normalised daily average COVID-19 testing rates should explain a major part of the key differences realised in the total COVID-19 cases reported across African countries by the end of 2020. The recent resurgence of the leading European countries to their former top positions in the global COVID-19 cases has followed their more aggressive testing rates of late.
As observed, the population-normalised COVID-19 testing rates remained much lower and slower in progression in the African countries making up the study group. Mauritius, Morocco, South Africa, and Rwanda were the few African countries that had average scores of more than 185 tests per million people per day by December 12, 2020. Israel, the UK, and the USA had by this date scored above 2000 tests per million people per day, followed by more than 1000 tests per million people per day for most of the European countries in the study group, such as Russia, Italy, Spain, France, and Germany. India, Brazil, and China still had scores of less than 415 tests per million people per day by December 12, 2020, and their testing rates were reducing with time towards the new year. A similar receding trend towards the year 2021 was also observed in Cameroon, Ethiopia, and Egypt. Kenya’s normalised testing rate was only 65 tests per million people per day on December 12, 2020, a rate that had only increased marginally to 66.6 by January 19, 2021. The progression in the normalised testing index between December 12, 2020 and January 19, 2021 was a daily increase of 29 in Israel and 14 in the UK, far ahead of the Africa’s leading incremental case of only 1.5 in South Africa.
Transparency and Trust Building
Data integrity and effective communication with participatory approaches that engage stakeholders in co-owned processes help to enhance the transparency of government efforts in containing disasters. Building public trust in government processes and projects is supreme if citizens are to be motivated and active participants. Disaster taskforces need to ensure inclusivity across the vast stakeholder profiles. Modern scientific concepts, such as citizen science, empower the public to be active participants in generating the crowdsourced data crucial to calibrating decision support models and systems. Raising awareness among sceptical citizens would help turn them into active change agents. Citizen science in this case provides an inclusive and cost-effective route to enhancing participatory scientific research and securing public trust for effective disaster governance.
Countries that have ensured that their COVID-19 taskforces include behaviour-change agents and communication experts may attest to increased levels of citizen compliance with the pandemic containment rules. With the first batch of COVID-19 vaccines reaching African countries in early March 2021, such agents and experts will be critical to addressing the emerging issues related to vaccine hesitancy, pandemic fatigue, and vaccine equity.
Training and Transdisciplinary Thinking
Disaster governance thrives on the quality of produced capital of which critical disaster-management infrastructure is key, and the integrity of life-support systems as reflected in sustainable natural capital. Human capital, including the highly skilled African diaspora, is undisputedly a critical part of Africa’s overall capital without which the other forms of capital cannot be developed and managed in efficient and sustainable ways. This fact makes quality training and transdisciplinary collaborations critical to Africa’s overall progress and the Agenda 2063. In this respect, Africa’s learning institutions have the inescapable duty of nurturing thinkers with the right leadership skills for collaboration on key projects focused on common public good. Government and industry support in this direction will help boost disaster governance as well.
Examples of transdisciplinary thinking will suffice here. Like the presently ravaging COVID-19 pandemic, many diseases and disasters display a close nexus between people, place, and time. Geomedicine utilises the spatial intelligence extracted from the environment using technologies such as terrestrial, airborne and satellite-based navigation and mapping to enhance solutions to individual and public health. Epidemiologists and clinicians should benefit from access to the expanding pool of location-based intelligence they need to tap into for a more precise clinical understanding of the links between patients’ health and where they live, work, and play. Where we live determines the air, water, soil, and the communities we interact with routinely. As already written about widely by Bill Davenhall of Esri on the importance of geomedicine as a transdisciplinary field, there exist certain chronic health conditions that are far removed from genotype and lifestyle, leaving environmental factors as the most convincing explanation. Effective disease and disaster governance, therefore, features strong spatio-temporal dimensions. Modern disaster risk mitigation measures must, consequently, draw actionable intelligence from Geographic Information Systems (GIS) to combat disasters and protect communities against exposure risks. Using modern information technology to map at scale and deliver spatial informatics and intelligence on citizens’ potential exposure risks to disasters in the living environment will improve the quality of strategic interventions to combat the disasters.
Disaster governance is too important an issue to be left out of Africa’s mainstream discussions on post-pandemic policies, programmes, and innovations. COVID-19 only makes this call more compelling. The future of disaster governance on the continent should grow brighter with predictive mapping, supported by the Internet, sensors, big data, automation, robotics, and artificial intelligence (AI). Analysis-ready data from continental Earth Observation services such as Digital Earth Africa (DEA) and cutting-edge space research by space agencies in Africa, such as Kenya Space Agency and its ongoing operational space weather project in collaboration with three Kenyan public universities, are sterling examples of the rich resources that African governments can utilise to improve disaster governance.