Managing the pandemic in Africa has a strong component in improving the testing capacity, behaviour and recovery rates
Changing Phases of Africa’s COVID-19 Curve
Under the Impact Borderless Digital (IBD) research articles, we have since March 13 sustained regular and adaptive modelling of COVID-19 scenarios globally, across Africa, and for some of the 47 counties of interest in Kenya. Africa had topped 1.07 million COVID-19 cases as at August 13, 2020. The daily exponential rise in cases has been dwindling from 4.16% between late April and early June, 3.3% between early June and July 30, and later down to 1.3% up to August 13, 2020. Weeks before July 27, the model trend of growth in Africa’s COVID-19 cases predicted between 871,452 and 1,118,495 cases in Africa come July 31. The actual figure came to 909,712 or 4.2% above the model projection on the optimistic curve.
As shown in the graph below, a more accurate estimation model of the emerging trend in Africa’s confirmed cases after July 30 is a polynomial trajectory which theoretically peaks at 1.17 million cases on September 2, 2020, below the upper projection range of 1.41 million cases by that date. The dwindling country testing capacities explain a major part of the slowing of confirmed cases, a case in point illustrated by the 50% fall in Kenya’s daily tests over the period of August 16–17, 2020. Uganda’s average testing rate, impressive most of March — July, has recently stagnated. This has enabled Kenya’s mean performance on the population-normalised testing rates to overtake Uganda’s by August 14, 2020.
The Hurdles of Testing Capacity
The hurdles of testing capacity and contact tracing have been key concerns in Africa as the shortage of reagents and crucial equipment hits hard. The testing rates have generally increased from the low figures in April, as shown in the radar plot below. On a population-normalised index of daily average rates as at August 14, 2020, South Africa by registering an all-time daily average testing rate of 350 tests per million people per day has dwarfed most African countries: Egypt (7), Nigeria (10), Madagascar (12), Ethiopia (32), Uganda (45), Kenya (46), Senegal (46), Ghana (88), Rwanda (164), among others. Mauritius has been ahead on this index, explicable by her low population of only 1.2 million in a region whose country populations mostly top tens of millions. On this scale, Ethiopia’s recent mean performance has been illustrious for her high population of over 100 million, only comparable to the population of Egypt. Nigeria, the continent’s most populous nation, has a long way to go in improving on this measure. It should, however, be noted that despite their high populations, the USA and Russia have registered very high scores on this index of above 900 tests per million people per day. Through the lens of development disparities and technological capacity, explaining these vast differences reduces to an easy task.
Key Modelling Insight: Though omitted in most reports, the number of days is a key rate parameter for ensuring a normalised, fair and unbiased comparison of the COVID-19 tests conducted across countries. This is because not all countries started testing the same day. This also explains why the rates posted widely across the media which miss the “per day” parameter are much larger numbers. This is similar to comparing the performance of athletes on the distance covered per race without a mention of their speed differentials/rates— unfair!
Improving Recovery Rates
There has been a gradual and progressive improvement in recovery rates. Before August, Africa’s recovery rate remained below the global average. As at August 14, 2020, Africa’s average COVID-19 recovery rate of 72% and the case fatality rate of 2% had exceeded the global average of 66% and 3.6% by then, respectively. Kenya by then had improved her recovery rate to 52% as the case fatality rate maintained at 1.6%. Reported case fatality rates were particularly lower in Rwanda (0.3%), Uganda (0.5%), Ghana (0.5%), and Madagascar (1.1%). Sudan (6.5%) and Egypt (5.3%) had registered Africa’s high case fatality rates by this date. Africa’s leading recovery rates as at August 14 had been registered in Mauritius (97%), Ghana (95%), Uganda (87%), Madagascar (83%), and South Africa (80%). Ethiopia had not reached 50% recovery rate by then, posting 43% on August 14, 2020.
Adaptive Country-level Model of Compact Scenarios: Kenyan Example
Kenya’s Ministry of Health has confirmed that about 90% of the confirmed COVID-19 cases are asymptomatic. According to the data posted by the Ministry of Health, the infection rateincreased from single-digit percentages in June to double digits over July — August: July 22 — August 17 registered a mean positivity rate of 11%, ranging 7–20.5%.
Through a data-driven mathematical approach with model calibration, testing and updates as new data on COVID-19 cases trickle in, it has been possible to evaluate the predictive power of the models that have been shared in this series (known as IBD Series). For Kenya, the difference between the confirmed and model-predicted cases has been less than 1%. The current updated model shown below assumes that if Kenya could sustain an upward trend in her mean testing rate, which before August 14 was improving incrementally by 0.5 tests per million people per day, then the country would register more than 39,000 cases in a business-as-usual scenario by September 1, 2020. The confirmed cases in Kenya have been highly responsive to the gradual increase in the normalised testing rates, as shown below.
The green dotted line below simulates a declining trend in confirmed cases that theoretically peaks on April 10, 2021. The sharp fall in testing rates, a whole 50% drop, has been reflected in the dipping of the black line graph of confirmed COVID-19 cases (see the figure below). The opening of international flights on August 1, as shown in the model graphs, did not visibly affect the trend of confirmed cases in Kenya. The July 6 reopening of Kenya’s county borders to allow free movement, however, had a visible effect in the form of a surge in confirmed cases within 48 hours. This observation confirms that the non-linear and pervasive community transmission of COVID-19 is the currently main driver of the rising numbers.
Metropolitan Regions and the Spread of COVID-19: Kenyan Example
The significance of the tight functional relationships between centres in metropolitan regions — expressed in employment, housing, and commercial activities, has been evident in Kenya. The COVID-19 cases reported from June to July showed that the Nairobi Metropolitan Region was claiming a mean share of 77% of the national tally. Nairobi City County alone was commanding a mean share of 61%. The three other counties in the metropolitan region are Kiambu, Machakos, and Kajiado. As shown, the COVID-19 curves of the various scales of analysis have been mimicking the curves of the principal contributors to the statistics, as expected. Outside the metropolitan area, the other rising curves have been of the border regions (e.g. Migori and Busia) and key transit towns (e.g. Nakuru), where travelling and social interactions are substantial and frequent. Mombasa, Kenya’s prime tourist destination, has lately been displaying a declining curve, heralding hope for the country’s vibrant tourism sector. The community-based approach to raising awareness and behaviour change has been cited as a key success factor. The observed June — July trends are illustrated below.
A repeat of the county-level comparison for the period July 22 — August 17, 2020, confirmed again the supremacy of the contribution of the metropolitan region to the national aggregate COVID-19 statistics. Nairobi City County still commanded a mean share of 60% while the greater Nairobi Metropolitan Region contributed a mean share of 78% over the study period. Mombasa’s share continued falling as the other counties such as Murang’a, Narok, Nyeri, Uasin Gishu, and Nakuru increased their share of the national tally. Despite Kenya’s reduced testing rates over this period, the mean positivity rate from the samples reported maintained at 11% over this period too. The following graphs illustrate the county-level trends over this second phase.
The multi-scale modelling and analyses of the emerging COVID-19 scenarios across Africa provide the following key insights.
Enhancing the accuracy and coverage of COVID-19 testing across Africa is key to providing the actionable intelligence needed to manage the pandemic effectively. Innovations in testing procedures, as well as scientific and location-based technological leverage for sampling and contact tracing, are essential to meeting the growing challenge of testing the exposed populations. The non-homogeneity of populations in African countries justifies such keen and intelligent geodemographic sampling procedures that can lessen cost without sacrificing the overall integrity of the scientific exercise.
The focus and priorities of COVID-19 testing and containment strategies need to be calibrated against the weight of evidence in the key centres that act as the trendsetters.
The dwindling testing rates in many African countries, for any reason, are likely to push the peaks to later dates in 2021. Compliant community-based approaches and behaviour change are, however, capable of reversing this trend.
Given that to date the case fatality rate in Africa has remained generally low, just half the global average, the main focus should shift to boosting recovery rates through affordable means, home-based care being key, and establishing accurate percentages of the serious and critical cases that will warrant hospital-based care. This strategy will help to significantly lower the future active COVID-19 cases, which may overwhelm the already strained health systems in African countries. Indirect and usually unaccounted cases such as excess deaths are also worthy of increased attention going forward.
Nashon, a geospatial expert, lecturer and trained policy analyst applies dynamic models to complex adaptive systems. He is a youth mentor on career development and the founder of Impact Borderless Digital.