COVID-19 Status Map: Third Wave?

Is Kenya staring at the third wave? Projections from tried and tested modelling experience

For reasons that this research has attributed largely to time-delayed response and low sampling and testing efficacies, the surge in COVID-19 cases in Kenya this modelling series simulated to happen after the December festivities and subsequent school reopening in January 2021 was 21% higher than the confirmed total of 100,773 cases as at January 31, 2021. This was the highest difference between the simulated and confirmed end-month totals for Kenya since this modelling series started in early 2020. Noteworthy however, the period January — February 2021 saw a significantly reduced testing rate in Kenya, leading to a stagnation in the long-term daily average population-normalised testing rate at 66–67 tests per million people per day. It was not until March 7, 2021, that this rate increased to 68.

Updated COVID-19 Statistics as at March 8, 2021

Brazil is already bearing the brunt of another serious wave of the pandemic as new and more contagious coronavirus variants invade the globe without sparing the younger demographic on their destructive missions. With new variants already confirmed in several African countries including Kenya, the metrics on the share of active and critical COVID-19 cases must take precedence in policy and strategic decisions. Kenya’s active cases on March 8, 2021 made up 6% of Africa’s total active cases, punching above her 4% continental population share on this measure. On the same day, the sum of serious and critical cases in Kenya made up 3% of Africa’s total serious and critical cases.

The share of Kenya’s confirmed COVID-19 cases by County, mapped below, has consistently been higher in the main urban centres, the Nairobi metropolitan area, and along the major transportation routes. This spatial profiling could inform the level of strictness of containment measures by tiers whenever radical measures have to be advanced.

The Decisive Parameters

First though, some insights into the key parameters guiding this research series.

Why normalise the COVID-19 tests? Data blindness?

Put another way, what colour blindness is to visual accuracy, data blindness is to making informed policy and strategic decisions.

This modelling series has been calculating and applying the long-term daily average population-normalised COVID-19 tests for countries, in terms of tests per million people per day. This normalised measure of COVID-19 tests by countries removes the bias of wide variations in country population sizes and the dates on which they started testing for COVID-19. The resulting metric gives a fair performance indicator for cross-country comparison. Such metrics helping decision makers to better gauge performance disparities and inform adjustments to foster real progress and forestall slow but sure devastating consequences of the laxity inherent in ‘data blindness’. On this metric doesn’t matter that Rwanda has a quarter of Kenya’s population and Kenya’s population is a quarter Nigeria’s, or that they started testing on different dates. Put another way, what colour blindness is to visual accuracy, data blindness is to making informed policy and strategic decisions.

Key country examples on testing capacity

Sampling and geodemographic diversity

Is it Kenya’s Third Wave?

Going by the experience gained from the previous wavy styles of the COVID curve, expecting the third wave and preparing to contain it adequately is not just a good idea, but the pragmatic minimum for a country that has lost a whole school calendar to the pandemic and widened the existing inequalities in access to education amidst crippling structural divides in human and infrastructural resources.

From mid-February 2021 as the graph shows, the positivity rates have been on an upward trend, mostly staying above 5% from early March. Going by the experience gained from the previous wavy attack styles of the COVID curve, expecting the third wave and preparing to contain it adequately is not just a good idea, but the pragmatic minimum for a country that has lost a whole school calendar to the pandemic and widened the existing inequalities in access to education amidst crippling structural divides in human and infrastructural resources. Young learners are expected to be back home for holiday in March, hence the unavoidable movements across the country. It is, therefore, not the time to relax the routine COVID-19 containment measures or for citizens to lower their guard.

Probable Scenarios within the Model Boundaries

Learning from the past trends, two model scenarios have been generated in this COVID-19 model:

  1. Business as usual (BAU) assuming the trend from January 14 — February 26 would maintain, simulating 115,896 cases on March 31, 2021
  2. Upper trajectory assuming the rising trend from February 26 continued, simulating 137,675 cases on March 31, 2021

Conclusions and Implications for Kenya

Facing the third wave — better safe than sorry

The wavy nature of the pandemic, studied over the last one year in this IBD modelling series, makes an upcoming third wave a highly likely yet inconvenient fact to face and adequately prepare to deface using all the proven containment measures: mask wearing, hygiene, social distancing, and adequate population-targeted sampling, tracing and testing. If the latest emerging trend in Kenya’s COVID-19 cases continues, it is possible to record a third wave rising to between 115,000 and 138,000 cases by March 31, 2021.

Testing efficacy — data integrity is critical

The recent increase in Kenya’s COVID-19 testing rate from late February 2021 is laudable. Adequate sampling, tracing and testing produces the data essential to knowledge-based and effective calibration of containment policies and strategies. Data integrity is critical, as is the building of public trust in the containment processes.

Spatial justice — are urban centres the hotspots or simply the most tested?

The COVID-19 map of Kenya has for long displayed the key urban centres as the hotspots of the rising number of cases. This outcome could inform extra care to help reduce transmissions in these areas, but it could also challenge the testing protocol adopted with respect to its spatial representativeness over the year. Data integrity remains critical to accurately timing and calibrating the containment measures against the resurgent pandemic.

Visualisation — how to influence the modern demographic

Communication is increasingly becoming visual, making visualisation techniques key to influencing behaviour change among the dominant demographic of this era. It is important to leverage COVID-19 data collection techniques and processes with location-based intelligence from spatial mapping. This step will help improve public engagement using mapped visual evidence at scale.

Curfew extension — a reasonable middle-ground for health and economy

For an economy recovering from the shocks of COVID-19 while facing the possible resurgence of its curve, softer movement restrictions such as relaxed curfew hours deserve to be retained as stricter enforcement of public health protocols take precedence. Reviving the economy is key, but even more so is protecting health for all with a keen awareness of the likely choices of the nation’s young and socially active citizens if all restrictions are removed.

School holidays — parental and community care to improve

Besides controlling mass gatherings as witnessed in many political rallies across Kenya, a big positive difference is expected if parents and the local community will assist their children, who are supposed to be back from school for holiday, to ensure compliance with all COVID-19 containment measures. A surge in cases is likely to grow worse if children and young learners engage in super-spreader activities while out of school. Complete removal of the curfew will be a highly tempting trigger of such activities.

A geospatial and systems modelling expert, lecturer, youth mentor and trained policy analyst, who applies system dynamics to model complex adaptive systems.

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