Predominance of Beta and Delta Coronavirus variants in Harare, Zimbabwe — A tale of natural selection in an African City.
Harare is the capital city of Zimbabwe where most of the urban population is located. I downloaded some patient data concerning SARS CoV 2 and COVID19 from GISAID. I do not own any of the Patient data that I used in this study, its freely available on GISAID. This should be an article that has to aim at predominance of the Delta plus variant but I felt some issues related to COVID19 in an African city should also be discussed in our journey there. This study was done using Data Science Python packages NumPy, Pandas, Matplotlib and Seaborn, check the notebook version on my Github.
From the patient data I downloaded there is 96 samples which were listed as originating from Harare. This data included data about each patient sample’s virus name, Accession ID, Collection date, Location, Host, Additional location, Sampling strategy, Gender, Patient age, Patient status, Date of last vaccination, SARS CoV 2 lineage, Clade and amino acid Substitutions among other categories. However, for this study I specialised on a few of these Categories to point out my objective of the study. Some of the categories contained unknown information/values, where necessary such rows were removed from the dataset to decrease noise.
Sampling Gender Disparity and Age Range
From the diagram above, there is a little difference in the gender which went for COVID19 testing with Males 7% more than females. However this could be a weak statistics considering almost half of the participants in the study have unknown gender. But still we can get some insights from the little we could see from the pie chart above. In most cases when people went for COVID19 testing it’s not because they have the intention but because the patient showed symptoms of COVID19, also as we will see in the following sections this can be used as supporting information.
Using Pie Chart A we can see that most people who were sampled are between ages 25–45 years of age (exploded slice) with higher frequency in Age range from 31–40 years of age. One of the inferences we can make from this information is that the working age is the one which visited testing centres probably because companies they were working for, had requested them for COVID19 tests. This is also the age group that is very mobile in the country which has the greatest probability of resisting COVID19 regulation like social distances and wearing masks resulting in prevalence of the Coronavirus. Additionally, it is supported by the trend shown in the Gender disparity pie chart where most Men as main providers of their families had undergone for COVID19 sampling and testing.
From my Medical studies background it is a trend that for Clinical sampling there isa likelihood to have more females who opt for volunteering as research subjects rather than Men, but in this dataset the opposite is observed where Men are the gender that volunteered more. This means there is a pushing factor which like I mentioned could be the issue of employers demanding COVID19 tests results for an individual to continue working at the organisation.
Harare is a city which has a lifestyle that mostly the young enjoy, so most of the population is not the elderly. Unfortunately, the older age which is also the COVID19 vulnerable group contributed about 6% of samples collected. This could be due to misinformation about COVID19 testing and also it could be that, this age group is located in the rural areas where the COVID19 testing areas are a distance from where they live. Also, as the sedentary age group, there wouldn’t be much socialising because of spacious communal lands and mostly because of the long distance ban as a COVID19 lockdown regulation which protected the elderly from physical contact with the young from the less spaced urban areas like Harare.
Samples collection dates
The sample collection can be sub-divided into February and July (July is represented by exploded slices) portions with July contributing ~ 60% to the lot. Different slices of the pie chart represent different dates of sample collection in in the format ‘year/month/day’.
SARS CoV 2 Variants distribution in Harare, Zimbabwe
The variant that caused the second wave of COVID 19 in Zimbabwe originated from SA, Zimbabwe was in a second Level 4 lockdown for the first weeks of the year 2021. It can be inferred that advent of the B.1.351 (or Beta variant) which was first reported in South Africa was due to immigration into the country for the holidays. It even dominated the Zimbabwean variant distribution with its two clades G and GH showing perhaps its higher level of transmissibility thus having an added advantage to pass its genes to the next generation. The third wave of COVD19 in Zimbabwe was due to the Delta variant that was first reported in India in May 2021. AY4 and B.1.617.2 are lineages which can also be classified as the Delta variant.
Using the Pie chart above (February 2021), the predominant SARS CoV 2 variant in February 2021 was the Beta variant (B.1.351) which was likely caused by immigration of Zimbabwean nationals based in South Africa coming for the Christmas holidays. The Beitbridge boarder post is one of the busiest boarder on the African continent giving access to road transport from Southern Africa into Central Africa and beyond. When the lockdown was issued in March 2020 it was very difficult for transportation of non-essential goods and services to gain access into the country. However, when the border post was opened in December 2020, all the pending goods plus influx of SA-based Zim nationals visiting for Christmas holiday contributed to introduction of the Beta variant in the country. Again, Harare being the hotspot for business and surrounded by plenty of leisure centres makes it the favoured choice for immigrants to visit during holidays.
5 months down the line, not even one sample from the Harare, Zimbabwe samples was observed to have the B.1.351 variant. The most prevalent variants observed in July are AY4 and B.1.617.2 which are listed can also be called Delta variants. The lineages under Delta variant which include AY lineages and B.1.617.2 contributed ~ 90% of the observed variant in the Harare sampled population with AY4 having ~70% contribution.
Even though the Delta variant was first reported in India, there is a lesser chance the predominance of the Delta variant in Zimbabwe is chiefly due to immigration like we observed with the Beta variant. The Delta variant was reported to be 2 X more contagious than the previous variants, which means the variant is dominating the world because it gained an advantageous of trait of transmissibility and Zimbabwe is no outlier to this.
This is a model of how evolution works in nature, that certain mutations can lead to advantageous traits which can lead to extinction of those which cannot adapt. Both the Beta and Delta variants acquired more transmissibility by intensifying symptoms like coughing which ensures the Coronavirus can be easily passed to the next person hence having more host bodies with which they can multiply themselves.
This can sound skeptical coming from a Virologist but if human beings were humble enough to learn a lot from Nature, we could progress even faster to where we want to go. What do I mean? There is a need for African countries to join the world in progressing towards Utopia, we need to adapt and not cry for what we once had 20 years ago. Its that time where Africa has to ditch most of the retrograde principles which once worked but is no longer working in this era. If data is the new oil then lets mine as much as we can to enrich not only the African continent but our individual lives.
References
- Tracking SARS-CoV-2 variants (who.int)
- Delta Variant: What We Know About the Science | CDC
- Sheikh A, McMenamin J, Taylor B, Robertson C. SARS-CoV-2 Delta VOC in Scotland: demographics, risk of hospital admission, and vaccine effectiveness. The Lancet. 2021;397(10293):2461–2462. doi:10.1016/s0140–6736(21)01358–1
- Nasreen S, Chung H, He S, et al. Effectiveness of COVID-19 vaccines against variants of concern in Ontario, Canada. medRxiv. 2021 Jul 16;doi:doi.org/10.1101/2021.06.28.21259420external icon
