Premortem of the post-pandemic period, postmortem of the pre-pandemic phase
Now is the great reset time, a time for proactive leaders to conduct a premortem of the emerging post-pandemic period and a postmortem of the pre-pandemic phase.
When the whole story of the disruptive global pandemic which is COVID-19 will be fully told, great students of history will be the greatest beneficiaries of its lessons. To understand the development and effects of COVID-19 is to understand countries and cooperation, digitalisation and distance learning, lives and livelihoods, markets and manufacturing, platform business models and pipeline business models, proactiveness and procrastination, production and politics, recovery and resilience, research and reinvention, safety and security, social discrimination and social distancing. Under the right lens of understanding and reasoning, the ravaging pandemic can be a lesson in disease and disaster governance, invoking curiosity and thought-provoking questions on how to leverage productive collaborations on all fronts to deliver a better post-pandemic world. Nowhere is such collaboration needed more than the borderless and democratic space of scientific research and creative genius.
Country experiences with COVID have been rich in lessons on the penalty of waging linear wars against a wily, wavy warrior. The time for a liberating reset is now. It is the time for a reset of mindsets to rediscover the original virgin and unbridled curiosity about discoveries that transform lives. Henceforth, post-pandemic leadership must be proactive, evolving from reactionary responses using isolated disciplinary tools to predictive risk management by applying integrated and multi-stakeholder approaches.
Transdisciplinary and beyond health sciences
Though the axioms of fighting COVID-19 lend their provenance to the domain of health sciences, solving the attendant long-term challenges must draw on political, technological, social and economic decisions that transcend the province of medical practice. COVID-19 has made more compelling the case for transdisciplinary collaboration towards finding solutions to the common and urgent problems facing humanity today.
Cross-fertilisation of disciplines is critical to progressive research and innovation. Unlike mechanical problems that are amenable to deterministic models, disease governance is a stochastic challenge in the turbulent sea of complex, resurgent and adaptive social environments. Governments need to put in place well-constituted disease and disaster governance teams, and the teams must draw on a multiplicity of disciplinary expertise and roles in society. The fast lane to doomed teams is to constitute skewed taskforces that are only heavy on health research experts and keep excluding policy analysts, historians, psychologists, economists, geographers, engineers, legal experts, business and data managers, behaviour-change agents, and community opinion leaders.
Tracing the roots of a borderless knowledge culture and education
History is rich in examples of personalities who applied their legerity of mind to transform the world by defying the limiting and self-imposed boundaries of formal school subjects. Johannes Kepler enthusiastically described mathematics as the language of rational order in creation. Michael Faraday, among the few who Albert Einstein recognised, only received 13 years of formal education then accomplished the rest under homeschooling and apprenticeship as a bookbinder. Thomas Bayes was a Presbyterian pastor, but to date, he is better known for Bayes Theorem in statistics. Isaac Newton was a professor in natural philosophy, an expanded study of nature and the physical universe; he is today better known for breakthroughs in mathematics and physics. Gottfried Wilhelm Leibniz, a polymath, spoke many languages and made key contributions in mathematics, physics, logic, ethics, and theology. Johann Carl Friedrich Gauss, a wunderkind — child prodigy — was a prominent mathematician and physicist.
Love for knowledge as a virtue was the common denominator for these timeless legends. They did not identify themselves as “exclusive subject owners” who erect boundaries and are reluctant to seek or entertain opinion from other disciplines as we do witness among today’s intelligentsia. To almost equal depths, they were immersed in philosophy, mathematics, astronomy, history, and the service of humanity through various occupations. Who are their equivalent today? They must be the liberated minds which embrace systems thinking to understand the non-linear, dynamic and irreducible interactions in nature. Education in the post-pandemic era needs to reclaim its lost glory and intrinsic meaning as a lifelong commitment to the pursuit of progressive knowledge and liberating truth.
The health-geography interface
Promoting transdisciplinary research at the interface of geography and health sciences has become more crucial than ever. The multidisciplinary field of geomatics has evolved to become one of the most compelling subjects applying information technology to leverage health-related research outcomes. Geomatics combines traditional and modern aspects of surveying and mapping including airborne and spaceborne technologies, essentially using location-based data (spatial data) to deliver accurate and precise metrics for decision support.
Have you ever described a dream as distant, a claim as far-fetched, or a judgement as sloping? Life is full of such spatial metaphors. Daily, we perceive and interpret distance and direction in our physical and mental worlds. The quality of a nation’s healthcare system is a critical strand in the fabric of life and livelihoods. The 1854 discovery by Dr John Snow in London of the cause of cholera was a triumph of mapping techniques. The 2014 Nobel prize in Medicine or Physiology recognised the key finding that the brain has grid cells and place cells, an “inner GPS” which maps out space by encoding coordinates to guide memory and navigation. The German naturalist and explorer, Alexander von Humboldt, applied mapping to the erstwhile non-traditional areas of climate and distribution of biota, laying the foundation for modern biogeography.
Like the presently ravaging COVID-19 pandemic, many diseases demonstrate a close nexus between people, place, and time. 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, there exist certain chronic health conditions that are far removed from genotype and lifestyle, leaving environmental factors as the most convincing explanation. Effective disease governance, therefore, has strong spatio-temporal dimensions. To live up to their raison d’être as the foremost disease detectives, modern medical epidemiologists should draw actionable intelligence from Geographic Information Systems (GIS) to combat infectious diseases and protect communities against exposure risks.
Geomedicine particularly emerges strongly in this case, being a new field that utilises the spatial intelligence extracted from the environment using terrestrial, airborne and satellite-based navigation and mapping technologies to enhance solutions to individual and public health. Medical diagnostic experience has traditionally been an enterprise rich in keeping the records of a patient’s medical history. To date, the medical records have been lean on the health-geography interface. This state of affairs denies clinicians 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.
Various kinds of diagnosis and prognosis, as well as preventive and predictive healthcare, stand to gain from geospatial technologies. Using modern information technology to map at scale and deliver geomedical informatics and intelligence on a patient’s potential exposure risks to diseases in the living environment, geomedicine can empower modern clinicians to improve the quality and quantity of diagnostic results and strategic interventions, which is a key requirement for combating disease outbreaks.
The highly infectious COVID-19 pandemic has, therefore, illuminated the important topic of disease governance as well as the research and policy issues accompanying it. Effective disease governance has strong spatio-temporal dimensions. A premortem of the emerging post-pandemic era and a postmortem of the pre-pandemic phase is not just a good idea, but the irreducible minimum which progressive world leaders must face up to. These three areas remain critical to achieving the desired transformation: education, research, and healthcare.
The penalty of waging linear wars against a wily, wavy warrior
Experiences with reopening around the world and repeat attacks have now confirmed that COVID-19 is an elusive and resurgent wave. The true nature and systemic effects of the novel disease are still under investigation. Advanced economies that reopened earlier have had to resort to occasional reclosures as the pandemic lives up to its wily and wavy attack mode. The pandemic adds to the century’s challenge of disease governance across geographies amidst evolving data and insights in the face of a new virus storing up secrets and surprises. Traditional, linear and reactionary responses have been challenged the world over. No country has been spared the push to improve healthcare systems and integrate digitalisation into education, governance, business and formal work routines.
The World Health Organization (WHO) has specified to countries a threshold of 5% COVID-19 infection rate, sustained for two weeks, before declaring any confidence in flattening the curve. In East Africa, Kenya has lately been on the spot for making a claim, though cautiously, to a flattening curve. In the absence of well-reasoned debates supported by transparent data trends and models, such claims can only invite offensive and defensive responses in equal measure. An immediate declaration of victory can only feed the demands of linear thinking, which cannot fully comprehend the non-linear dynamics of COVID-19.
A model of COVID-19 scenarios across the globe
On September 1, 2020, COVID-19 cases had topped 25.8 million globally. Africa had topped 1.25 million cases. The USA had topped 6 million cases as Brazil and India followed, each with more than 3.6 million cases. Kenya had confirmed 34,315 cases. The data-driven mathematical prediction model used since March in this series for estimation of confirmed COVID-19 cases had established that India, riding on the back of a high population and increasing mean testing rates, would eventually displace Brazil from the second position globally. The model estimated that India was likely to reach 4 million cases on September 5, 2020. The actual figure came to 4.11 million cases in India. The model further projects that India’s exponential curve, if sustained, would top 5 million cases by September 14, 2020.
By September 1, 2020, the established trend was already a gradual increase in recovery rates, averaging 70% globally, 79% in Africa, and 59% in Kenya. The case fatality rate in Kenya was still steady at 1.7%, at 2.4% in Africa, and 3.4% globally. The cases in Africa still remained within the model’s bandwidth of projections which estimated the cases to reach between 1.17 and 1.41 million on September 2, 2020. The actual figure came to 1,259,770 cases in Africa. The same modelling trajectory has simulated a declining COVID-19 curve for Russia, an optimistic scenario that should peak at 1.137 million cases on November 20, 2020 if the current trend maintains. Russia has hit the headlines for the Sputnik V COVID-19 candidate vaccine.
By September 8, 2020, Africa’s recovery rate had risen to 80% with a case fatality rate of 2.4%. This performance was better than the global average of 71% recovery rate and 3.3% case fatality rate. Africa had by this date registered 1.3 million cases against the global total of 27.5 million cases. With 4.7% of the global COVID-19 cases, Africa was by this date claiming only 3.5% of the global case fatalities and 5.3% of the global recoveries, still a sterling performance skewed in favour of Africa based on these reported cases. Kenya’s recovery rate was by this date 61% with a case fatality rate of 1.7%.
Africa’s testing capacity and COVID-19 curves
The low COVID-19 testing capacity in Africa has been a key topic of discussion. Assumptions underpin the basic rationale of any modelling exercise. The model used in this series assumes a continuation of the observed trend in country mean testing rates, calculated using a population-normalised index as a key variable in projecting future cases based on an observed regime of infection rates. The adopted infection or positivity rate is an average value observed over a period. By September 1, 2020, most African countries were still registering a normalised testing index of below 50 tests per million people per day except a few such as Mauritius, South Africa, Morocco, Rwanda, and Ghana. Israel, Russia, the USA, and many countries in Europe had sustained indices of 900 tests per million people per day and above.
Based on the experience gained from applying the models at various scales globally, in Africa and to individual country cases, updating the models every three to four weeks has been necessary to capture new trends in growth rates. These rates are influenced by several moderating and intervening variables such as community behaviour and changes in testing capacity, sampling efficiency, and positivity rates.
Given the established community transmission trends, a change in testing rates influences the number of cases significantly. The pervasive community transmission mode is further evidenced within the models in how the easing of movement restrictions across Kenya on July 6 led to a visible increase in the number of reported COVID-19 cases within two days. The reopening of international flights on August 1 was, however, not accompanied by any visible change in the trend of confirmed cases.
Spotlight on Kenya’s COVID-19 curve
There was observed a significant increase in Kenya’s normalised testing index by 10 for the period between the end of June and the end of July, and the same increase between the end of July and the end of August. Theoretically, this change in normalised testing index leads to a total increase of 15,000 over one month — after multiplying 10 by 50 (to reflect Kenya’s current population of about 50 million) and again by 30 for the days in a month. The actual end-month differences in cases for June/July came to 14,270 cases (i.e., 20,636 minus 6,366) and 13,565 cases for July/August (i.e., 34,201 minus 20,636). This observation shows that the total change in the testing index acts strongly with other factors to augment the confirmed cases over time.
The observed all-time average population-normalised testing rate had been incremental at 0.5 tests per million people per day before August 16, a trend whose maintenance would have seen Kenya reach a normalised mean of 55.0 tests per million people per day by September 1, 2020. On September 1, however, Kenya’s mean testing rate was still stunted at 49.1 tests per million people per day. By this date, Ethiopia for her high population which is twice Kenya’s had come a long way in increasing her testing rate to 46.8 tests per million people per day, close to Kenya’s. Ethiopia has since overtaken Kenya, Ghana, and Nigeria in the ranking of total COVID-19 cases.
The observed drastic shortfall in Kenya’s testing capacity is the difference between the September 1 closing index of 49.1 and the business-as-usual model expectation of 55.0, which comes to 5.9. Multiplying this factor by 50 and again by the number of days from August 16 to September 1 (16 days) gives a total drop of 4,720 from the trend before August 16, 2020. If the implication of the 15,000 increase in total testing index observed above were to be applied to this new figure of 4,720 for the August 16 — September 1 period, then it is arguable that the decline in testing rate would reduce Kenya’s closing number of cases by about 4,400. This would mean a figure of 38,715 cases on September 1, which is close to the model prediction of 39,050 cases on the same date.
Even on September 7, Kenya’s normalised testing rate was still stunted at 49.4 tests per million people per day, far below the projected September 1 index of 55.0 tests per million per day that could have been achieved had the trend in testing rates observed before August 16 been maintained. The cases reported in Kenya have mostly been in the Nairobi metropolitan region, which has claimed 75% of the national tally between July and August. September has introduced a trend whereby most of the new cases have been shifting from the metropolitan area to the countryside and exceeding 12% of the national tally, unlike the single-digit percentages that they used to claim before July.
Four key leadership lessons
COVID-19 draws us to a point of stillness and silence. A silence dedicated to active listening is liberating, but a silence that quietly ignores lessons only feeds ignorance. This philosophical discourse and cross-country review against the backdrop of the century’s hitherto unchallenged disruptor which is the COVID-19 global pandemic distils the following key lessons for listening leaders.
1.Adaptive crisis management
Despite the uncertainties of the behaviour of the novel coronavirus and constraints on testing capacities, there is already a glimmer of hope across the world that the COVID-19 curve is gradually declining. There are exceptions to this rule because of structural, regional, cultural and demographic disparities, with India exemplifying a curve that was still determined to rise and surpass Brazil not long after September 5, and further likely to top 5 million cases by September 14, 2020. Adaptive management of the crisis informed by incessant research remains critical.
2. Data integrity for monitoring and concrete conclusions
Data integrity is critical to reaching any concrete conclusions on a flattening curve, hence the effectiveness of testing and sampling. Otherwise, the flattening would remain apparent based on limited data only. Though Africa compares favourably with the global average in COVID-19 case fatality and recovery rates, African countries still lag far behind the rest of the world in the mean testing capacity as measured across the board using an all-time daily average normalised by country populations. There is, therefore, no room for laxity in caution, containment action, and generating key data for continuous monitoring of the pandemic.
3. Strengthening local governance and community intervention structures
The art of thinking and rethinking is at the core of long-term solutions to complex problems. “System as Cause Thinking or Endogenous Thinking” is a key principle of systems thinking which comes in handy at these times. It challenges leaders to interrogate to what extent they, as internal actors, are responsible for the vulnerabilities and exposure risks of their countries to the pandemic and similar shocks before blaming any external factors.
Kenya fittingly represents Africa’s case of community transmission trends that have had their established nuclei in the capital cities and metropolitan areas but are now opening new battlefronts in the countryside. The largely rural population share in Africa means that the progression of COVID-19 to rural areas should be a wake-up call to the local governments in these regions to escalate measures to contain the pandemic’s elusive and resurgent wave. Even more critical is the implication for a phased reopening of schools, with the majority of learners in rural areas deprived of space and key facilities for maintaining hygiene.
4. Revamping the health-geography interface in the post-pandemic 21st century
Emerging research areas such as geomedicine acknowledge the key geographical fact that where one lives determines one’s health outcomes. As early as 1854, mapping was a major part of the solution to containing the spread of cholera. Advances in computer-aided mapping and data analytics have created more opportunities for enhancing the health-geography interface using geomedical informatics, all obtainable from modern geospatial and sensor technologies.
Location-based intelligence acquired through sound spatial mapping and GIS technologies is needed more than ever for geographically representative sampling and spatial modelling. This crucial step will leverage decision support systems to deliver on the complete, dynamic and big picture of the key metrics and geodemographics required for effective disease governance in the post-pandemic 21st century.
By Nashon Adero
The author is a member of the System Dynamics Society, has more than ten years of experience in applied dynamic modelling and policy research. He is a lecturer in the School of Mines and Engineering at Taita Taveta University, Kenya. Email: firstname.lastname@example.org