Coronavirus Curves Across Africa: Telling Trends from Data-Driven Models Insights, inspiration and implications for charting a recovery roadmap

Quick containment action and targeted mass testing of critical hotspots and clusters, supported by spatial intelligence, are key to forestalling the danger of rapid explosion of COVID-19 cases within community transmission clusters.

Global simulation curve predicting a hopeful start in June 2020, but…

Starting April 5th, 2020, a new global equation governing the growth trend of COVID-19 cases has emerged, heralding hope in the horizon beginning June 2020. The new curve is showing a respite along its projected downward path, a departure from the exponential curves we have been treated to in the last two updates here. As shown in the graph, the emerging equation becomes:

Basic calculus, using April 5th as the origin, reveals that this quadratic curve reaches a climax at 3,728,604 cases by May 31st, 2020. After this point, the curve doesn’t rise further. The implication is that there is likely to be a flattening in the global numbers and hence a rapid reduction in active COVID-19 cases. This projection assumes that the cases in Africa, which as at April 16th still remained low, wouldn’t surge with more tests – especially the more comprehensive targeted mass testing which African countries like Kenya have reported to start embarking on.

The average global recovery rate and death rate have been on the rise week to week. Over the last three weeks under review, the global recovery rates have increased from 21% by April 3rd, 22% by April 10th, to 25% by April 17th. Similarly, by the same dates, death rates have increased from 5% (7 deaths per million), 6% (12.5 deaths per million), to 7% (18.5 deaths per million).

A government that trivialises science and evidence-based models in decision making invites stealthy, mostly slow, but exponentially damaging consequences in the long run.

Germany and France projected to flatten the curve soon

The same mathematical modelling approach projects interesting fates for Germany and France, assuming the trends maintain the business-as-usual (BAU) scenario as observed in this study. The model reveals the flattening of the curve for Germany to occur from April 27th with some estimated 149,257 cases and from May 4th for France with some estimated 185,883 cases. These peaks are not surprising outcomes because France has lately overtaken Germany in the reported cases of COVID-19.

Africa’s COVID-19 narrative: A plot of ambivalence?

The unfolding statistics on COVID-19 cases in Africa, a continent visited by the novel coronavirus later than the rest of the world, generate more questions than answers. Could the evidently lower figures be the lull before the storm or an early messenger of good news to a hardened continent graciously spared the brunt of a pandemic which has ravaged the mighty nations and disrupted all sectors of the economy? Any country leader in Africa has a genuine cause for both worry and cautious optimism.

In the face of such ambivalence, data-driven models and scientific thinking make the difference and boldly unearth trends worthy of urgent and progressive interventions, however unpopular. No honest narrative of COVID-19 patterns across Africa can be told without digging deeper to generate sound metrics of prevalence and performance. As emphasised earlier, knowledge-led influence is the source of sound governance. A wellspring of well-researched and well-reasoned models is a rich resource for proper policy formulation and planning. A government that trivialises science and evidence-based models in decision making invites stealthy, mostly slow, but exponentially damaging consequences in the long run.

The importance and urgency of quality data and models

Data forms a key strategic asset in disaster governance, big data being of high value in predictive modelling. Models aid in visualising assumed scenarios which are influential in supporting intelligent strategies, policy action, planning, and behaviour change. The tracing of a COVID-19 transmission cluster to a saltshaker in Bavaria, in the case of Germany, is a suitable example of how quality data and models can lead to economical and precise solutions.

Realising the urgent need for informed discussions and evidence-based models for policy action in Africa in the face of this pandemic, this third release in the series of data-driven mathematical models has focused on Africa’s COVID-19 scenarios. It has used COVID-19 statistics from eleven representative African countries to model the trends and scenarios that should inform and help shape policy direction and strategic interventions. It also builds on the two previously shared articles on a global model of COVID-19 scenarios informed by fourteen leading country cases and a critical interpretation of Kenya’s coronavirus curve with an outlook to the aspirational future “flattening of the curve” (you can read the previous articles here: Article 1 and Article 2). In the first article, high daily growth rates of 20% and above in the critical exponential growth phases were noted in the USA, Spain, and the UK. As expected of such a high exponential rate, the three countries have reported a rapid rise in the number of cases.

Telling metrics from the coronavirus curves across Africa

The following eleven African countries in the table below were selected for this study to represent different regional blocs. Over the study period covering from February 14th to April 16th, it can be seen that South Africa, Ghana, and Rwanda scored better than the rest on most of the indicators of good performance.

Rising recovery rates

Burkina Faso, Rwanda, South Africa, and Algeria have been posting relatively high recovery rates on the continent — all above 30%. By April 16th, Ghana’s recovery rate at 13% was the lowest in this study group of eleven countries, followed by Niger (17%), Egypt (22%), and Kenya (24%).

Death rates also rising

Particularly, Rwanda has reported no COVID-19 death over this study period. The other notable low death rates have been recorded in Ivory Coast (0.9%), Ghana (1.2%), and South Africa (1.8%). Accompanying rising COVID-19 cases in the population have been rising death rates as well, with Algeria recording the highest rate of 15.3% in the group over the study period ending April 16th. The observed death rates in Egypt (7.3%), Burkina Faso (5.9%), and Kenya (4.9%) have also been telling, going by similar rates shared earlier on the cases of the battered countries in Europe, Asia, and America.

Analysis of COVID-19 cases for eleven African countries (14 Feb.- 16 April 2020) showing higher death rates in Algeria (15.3%), Egypt (7.3%), and Burkina Faso (5.9%). Lower testing metrics are evident in Nigeria, Algeria, Egypt, and Kenya. So far, Cameroon, Ivory Coast, and Burkina Faso had not reported any figures on the number of tests conducted by April 16th, 2020

Disappointing testing rates from a scientific perspective

The number of COVID-19 tests a country has performed is a necessary but not sufficient metric of effectiveness in sampling the populations. In this study, therefore, the number of tests has been normalised by the country populations and the number of days since the first cases of COVID-19 were confirmed. Using tests per million people per day, the figures in Africa fade in comparison to the metrics so far reported elsewhere. By April 16th, Spain had a high score on this metric of 258.4, ahead of Germany (254.7), Italy (249.9), the USA (118.5), the UK (79.9), and France (60.9). The highest score in Africa by then among the eleven countries was only 46.6, in Ghana, followed a distant second by South Africa (38.2) and Rwanda (14.6). Nigeria (0.5), Algeria (1.5), Egypt (3.9), and Kenya (5.9) were still performing dismally on this normalised performance metric.

Lesson 1: The efficiency and effectiveness of testing for COVID-19 depends on understanding and applying normalised metrics that can simultaneously take care of the variables of time, space, and the demographics unique to each African country. As such, citing the gross number of tests conducted is not the answer to containing COVID-19. Improving on the measure of tests per million people per day directed by location-based intelligence to target the most strategic clusters and hotspots is the winning approach. This finding must be instructive to the rolling out of targeted mass testing in Kenya.

What the curves narrate with mathematical precision

The eleven countries presented in this study narrate the story of COVID-19 in Africa with mathematical precision, taking an average of 16 days to start the fast-rising phase of the growth curves. This figure is lower than the average of 33 days the previous study established for the 14 countries with leading cases in Europe, Asia, and America.

Lesson 2: Taking a shorter time to the fast-rising phase could be a harbinger of tougher times ahead in controlling the fast spread of infections within community clusters. Quick containment action and targeted mass testing of critical hotspots and clusters, supported by spatial intelligence, are key to forestalling the danger of rapid explosion of COVID-19 cases within community transmission clusters.

Six out of the eleven countries have displayed exponential growth phases with the highest exponential rates in the group seen in South Africa (26%), Cameroon (23%), Kenya (18%), and Egypt (14%). The rest have been displaying simulation curves which fit a rising quadratic trend (Niger) or a rising linear trend (Rwanda, Nigeria, Burkina Faso, and Algeria — in the order of increasing gradients).

Lesson 3: The previous analysis (Article 1determined similar rates in the critical exponential growth phases for the USA (26%), Spain (20%), and the UK (20%). Given the battering experience these three countries have lately been experiencing, the fitting lesson for African countries (especially South Africa, Cameroon, Kenya, and Egypt in this group) is to ensure they either maintain or enhance tight measures to prevent the explosive growth in COVID-19 cases that accompanies exponential trends.

Assuming routine testing of COVID-19 continues in the pace and fashion witnessed by April 16th, a data-driven model of selected scenarios across Africa develops as shown below. As shown in the equations, the countries have experienced different growth phases using the data shared from the mostly limited samples so far tested. The projections shown assume that no drastic changes would cause a departure from past trends. Mass testing, however, is likely to change the numbers as already realised in Kenya on April 18th, when the testing capacity got doubled. Compared to the projections below, Kenya’s actual reported cases in April were: 16th (234); 17th (246); 18th (262) — figures which are within 1.5% difference from the displayed graph of short-term projections. Similar projections in April 2020 show that Ghana is likely to experience an increase in total cases to over 1200 by 20th and over 1800 by 24th; South Africa over 3000 by 20th and over 3500 by 24th; and Egypt over 3500 by 20th and over 4700 by 24th.

Lesson 4: Mathematical models are an essential part of the multifaceted approach required to combat COVID-19 in Africa, otherwise misleading metrics can create complacency while disguising a slow but sure final explosion. Prudence in COVID-19 crisis management, therefore, summons government goodwill in promoting and utilising scientific research as well as the collective citizen responsibility in containing the pandemic.

Nashon J. Adero

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.