The relatively young and rural population may limit the spread and severity of Covid-19 in Africa: a modelling study
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Abstract
Introduction
A novel coronavirus disease 2019 (COVID-19) has spread to all regions of the world. There is great uncertainty regarding how countries characteristics will affect the spread of the epidemic; to date, there are few studies that attempt to predict the spread of the epidemic in African countries. In this paper, we investigate the role of demographic patterns, urbanization and co-morbidities on the possible trajectories of COVID-19 in Ghana, Kenya, and Senegal.
Methods
We use an augmented deterministic SIR model to predict the true spread of the disease, under the containment measures taken so far. We dis-aggregate the infected compartment into asymptomatic, mildly symptomatic, and severely symptomatic to match observed clinical development of COVID-19. We also account for age structures, urbanization, and co-morbidities (HIV, tuberculosis, anemia).
Results
In our baseline model, we project that the peak of active cases will occur in July, subject to the effectiveness of policy measures. When accounting for the urbanization, and factoring-in co-morbidities, the peak may occur between June 2 nd and June 17 th (Ghana), July 22 nd and August 29 th (Kenya), and finally May 28 th and June 15 th (Senegal). Successful containment policies could lead to lower rates of severe infections. While most cases will be mild, we project in the absence of policies further containing the spread, that between 0.78 and 1.03%, 0.61 and 1.22%, and 0.60 and 0.84% of individuals in Ghana, Kenya, and Senegal respectively may develop severe symptoms at the time of the peak of the epidemic.
Conclusion
Compared to Europe, Africa’s younger and rural population may modify the severity of the epidemic. The large youth population may lead to more infections but most of these infections will be asymptomatic or mild, and will probably go undetected. The higher prevalence of underlying conditions must be considered.
Summary
What is known?
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While most COVID-19 studies focus on western and Asian countries, very few are concerned with the spread of the virus in African countries.
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Most African countries have relatively low urbanization rates, a young population and context-specific co-morbidities that are still to be explored in the spread of COVID-19.
What are the new findings?
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In our baseline predictions 33 to 50% of the public will be actively infected at the peak of the epidemic and 1 in 36 (Ghana), 1 in 40 (Kenya) and 1 in 42 (Senegal) of these active cases may be severe.
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With rural areas, infection may be lowered to 65-73% (Ghana), 48-71% (Kenya) and 61-69% (Senegal) of the baseline infections.
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Comorbidities may however increase the ratio of severe infections among the active cases at the peak of the epidemic.
What do the new findings imply?
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Rural areas and large youth population may limit the spread and severity of the epidemic and outweigh the negative impact of HIV, tuberculosis and anemia.
Article activity feed
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SciScore for 10.1101/2020.05.03.20089532: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: Our model does not incorporate changes in the survival rate of the virus due to weather or humidity, and in that regard, our simulations are a worst-case scenario.[36,37] Additionally, the model assumes homogeneous mixing of individuals within rural areas and urban areas which is an unlikely assumption. In a future iteration …
SciScore for 10.1101/2020.05.03.20089532: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: Our model does not incorporate changes in the survival rate of the virus due to weather or humidity, and in that regard, our simulations are a worst-case scenario.[36,37] Additionally, the model assumes homogeneous mixing of individuals within rural areas and urban areas which is an unlikely assumption. In a future iteration of our model, we plan to use a spatially-structured model in order to relax the homogeneous mixing assumption by leveraging phone data.[38–40] The model also excludes international population flows. All countries in our sample have closed their international borders — airports and roads — before or a few days after their first confirmed imported case (see figure 2). However, it is possible that COVID-19 was spreading undetected for days in the respective countries. If that is true, the peak of active cases might be delayed in comparison to the true peak. Furthermore, the spread of this disease is highly dependent on the reproduction number Rt. Since this number is contingent upon many factors (policies, individuals’ behavior etc.); its value in the long-run is subject to large uncertainties. The projected number of infections in the medium to long term could thus be considerable overestimates (or underestimates) of the true number of infections (depending on the scenarios). These predictions aggregate infections in rural and urban areas, however, in practice, the peaks in urban areas, due to higher reproduction rates, will occur earlier. In r...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
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