Adjusted projections for COVID-19 in Kenya

Update 9th July 2020 | Area: Nairobi County

Summary: Our adjusted model suggests 1.4m people in Nairobi County will be infected with COVID-19 during its life-cycle peaking in September 2020. According to the adjusted model, 102,742 will show symptoms. 8,755 people will need hospitalization over the next year, with 1,505 passing through ICU facilities. 928 will die.

Infections and immunity status

Infection and disease

Prevalence of infections by age groups


Throughout the COVID-19 pandemic, we have shared projections as to the impact and severity of the virus and gaps in healthcare capacity in Kenya. The goal of these models is to help prepare our fleet of first responders and help other frontline health workers prepare for potential outcomes and needed capacity.

One key piece of feedback we have received is that our previous model has not been localised to Kenya. This is definitely true as we used standard global COVID-19 models readily available at the time. Indeed the virus has not played out in Kenya in the way projected by global models and research based on its spread in China, Europe, and North America.

For our projections, we used open-sourced epi-models which we tailored with the best available Kenya-specific information. Three things have now changed that warrant revisiting this model and publishing an update: 

  1. There is now substantive peer-reviewed evidence as to the localised factors in Sub-Saharan Africa which have/will impact the severity and conditions of COVID-19 in countries like Kenya.
  2. On 7th July 2020, Kenya announced an end to the partial lockdown of the country and the ban on inter-county travel. This, coupled with the clear increase in confirmed cases of COVID-19 in recent days in the country suggests that the country may be entering a new phase of local transmission, it is the right time to revisit our model and address concerns. 
  3. There are now significantly better open-sourced models we can tailor to Kenya. These models take important localised information – like age breakdowns – into account before projecting expected infections.

In light of these three things, we are revisiting our COVID-19 projections. This time, we are including unique observations and conditions observed in Kenya specifically. These include:

  1. Age distribution: Our initial projection did not take into account demographics within Kenya, it has since become clear that age is a critical factor in determining mortality outcomes during CV-19. Kenya’s young population needs factoring into our model.
  2. Higher % of Asymptomatic patients: The Kenyan Ministry of Health has been reporting an average of 90% asymptomatic cases in recent weeks. This is significantly higher than what has been observed in other countries and clearly needs to be factored into our model. By comparison in an analysis of all confirmed cases across the EU, 25-50% of cases were found to be asymptomatic or minimally symptomatic.
  3. Lower CFR: The observed CFR (Case Fatality Rate, or the percentage of cases that lead to death) in Kenya is currently around 2%. This may partly be explained by the lower testing levels than in other countries, but the finding has been consistent over several weeks. It is also consistent with the high asymptomatic rate and younger population in Kenya. According to the WHO globally, about 3.4% of reported COVID-19 cases have died with others estimating the global CFR may be as high as 7%.

In order to make our findings as precise as possible, we will be limiting our analysis at this time to Nairobi County, where there are already medium-high levels of community transmission and where the majority (47%) of cases have been found.

Our scenario

With the lifting of lockdown measures, we expect inter-county transmission of the virus to increase but not to significantly increase the transmission of CV19 into Nairobi. This is because cases outside of Nairobi have remained low throughout the previous two weeks. Whilst many are expected to migrate into Nairobi over the coming weeks, it is assumed that few will be carriers of the virus.

We believe that the government will enforce continued curfews and/or restrictions on social contact for a further 100 days, before increased pressure and economic necessities reduce the measures. This is akin to what we have seen in a broad-mix of countries which have imposed lockdown restrictions on their populations.

Model implications

Kenya moved quickly to implement lockdown measures that significantly reduced local transmission of COVID-19. These measures slowed the spread of the virus, giving healthcare workers additional time to prepare for the outbreak. They are a testament to the country’s capacity to implement positive and far-reaching measures to combat the virus.

Some of these measures are now coming to an end and we expect community transmission to increase throughout the country. Nairobi, as an urban area, is in a weak position to stop the spread of the virus: population density is high and economic necessity means that social distancing will become increasingly challenging over the course of the next few weeks. In light of this, our model suggests that community transmission will rapidly increase and that a large number of Nairobi County residents will be infected from now until November 2020. It is important to note that we do think conditions like seasonality, weather or temperature will only play a very marginal role in the virality of COVID-19 in Kenya. Evidence to support such theories is rudimentary and inconclusive.

Despite this, The young population of Nairobi County, high asymptomatic rate and low case-fatality rates suggest that the country may not see the same level of mortality and hospitalisations seen elsewhere. Nevertheless, we believe that Nairobi County hospitals should be prepared for a significant increase in hospitalisations over the coming weeks. At its peak there may be as many as 3,358 people hospitalised across Nairobi with COVID-19 and up to 404 ICU beds should be made available. Nairobi currently only has 278 beds. We believe there is an urgent need to increase the number of ICU beds in the city.

These projections estimate that 928 Nairobi County residents will die from COVID-19 over the next year with the majority of fatalities coming before the new year. This mortality rate is based on a number of assumptions, which still lack important Kenya-specific data points that we will continue to collect to refine our model.   

To access the full logs of this model, please click here

Model Documentation

Disease parameters

Population size

We shall be using a total population size for Nairobi of 4,397,037 people (2019 census).


We have applied specific Kenyan demographics to this projection. This is based on the 2019 census of counties in Kenya. We have broken age groups into 20 5-year groups with a 100+ age bracket.

Initial infections

We will be using the confirmed cases of COVID-19 in Kenya as reported by the Ministry of Health on July 4th 2020, 3,968

Infections from outside of the population (per day)

Since the first case of COVID-19 was confirmed in Kenya, the government restricted movement within the country, putting in place a lockdown on inter-county travel and on international-domestic flights a few weeks later. This should have effectively slowed the rate of inter-county transmission. As of July 6th 2020, only 1,800 of the total 8,000 cases were outside of Nairobi & Mombasa.

It is hard to speculate exactly how many infected people will travel from outside Nairobi into the County following the lowering of lock-down restrictions. According to the Surgo Foundation, all the surrounding Counties to Nairobi have a high mobility index and we can assume that a significant percentage of people will begin travelling into and out of Nairobi County over the next few weeks. At a conservative estimate, we have assumed that at least 10% of the people travelling into Nairobi each month will be infected, which at current rates equates to 360 cases per month.

Another important dimension is international arrivals into Nairobi, particularly from infected countries such as the UK and US. Such travel will be made available from August 2020. Whilst tourism figures are expected to be significantly lower than the 119,670 who travelled to the country in January, we can project based on other tourism markets which have reopened in recent weeks that tourism arrivals will be at 5% of pre-COVID levels: 5,983 tourist arriving into Nairobi each month. Based on randomised studies of infection rates in several of the countries who supply the majority of tourists to Kenya, we can assume that 15% of new arrivals will carry the virus. This equates to 897 new infections arriving each month from overseas.

Combined (domestic and international), our estimate is that 40 infected individuals will travel into Nairobi each day as a result of lifting the lockdown measures.

It is important to note that this will be significantly higher than the number of confirmed imported cases into Nairobi to date (as of 6th of July 2020), listed as 170, a rate of less than 2 per day.


Simulation duration

We have simulated for 365 days which is more than enough to cover the expected peak and gradual reduction of infection rates. Projections over 1 year have tended to rely on increasingly large assumptions and may be more inaccurate than those over shorter time periods.

Transmission, progression, symptom onset and recovery duration of the virus

To assess the progression durations of COVID-19 we used the analysis of available literature compiled in a widely cited study of the virus. This analysis collates research from a wide range of geographies and regions. There is no current evidence that COVID-19 in Kenya has different timings for transmission, progression, symptom onset and recovery than cases observed in these studies.

Hospitalisation (days)

Studies of the total number of days that people need to be hospitalised for COVID-19 range significantly around the world. An aggregated view of 51 studies of hospitalisation found that the median length of stay ranged from 4 to 53 days within China, and 4 to 21 days outside of China.

No study of Kenyan hospital length of stay has yet been published so this information is not yet available to us. Therefore we have made a hypothesis. Due to the potentially low level of hospitalisation and the severity of cases that end up being hospitalised we expect the length of stays at a hospital to average towards the upper bound of this range and have settled on 16 days. We will continue to adjust these figures as new evidence becomes available.

ICU admission (days)

An analysis of ICU length of stay from the same analysis of global COVID-19 research  found a wide discrepancy. Length of stay was reported by eight studies – four within and four outside China – with median values ranging from 6 to 12 and 4 to 19 days, respectively. 

Again, due to the lack of analysis so far in Kenya we need to make a hypothesis. Due to the potentially low level of hospitalisation and the severity of cases that end up being hospitalised we expect ICU length of stays to average towards the upper bound of this range and have settled on 10 days.

Severity by age group

% of infections that will lead to sickness

This is a key input in our model and is significantly different in Kenya than in other countries. The % of COVID-19 tests that have come back positive and asymptomatic are extremely high in Kenya. In the 4 weeks proceeding this projection, the % of asymptomatic cases was 88%, 88%, 92% and 93% respectively. That is an average of 90% asymptomatic cases. By comparison, in an analysis of all confirmed cases across the EU, 25-50% of cases were found to be asymptomatic or minimally symptomatic.

It is important to note that reported asymptomatic cases in Kenya are only an analysis of tested cases where the patient submits themselves voluntarily for testing, rather than randomized sampling. This means that we actually expect the 90% of cases that are asymptomatic is an underestimate of the true relationship. There are likely many more asymptomatic cases that don’t get tested which would be shown in more randomised tests. 

Given this we have used a flat 10% rate for this input. We have kept this consistent as there is currently no evidence to suggest that asymptomatic rates vary between ages in Kenya.

Sick patients who seek medical help

This determines what % of sick cases go to the doctor to seek medical help. Amongst studies of influenza viruses in high-income countries, this has tended to range from 60-80%.

There are two key factors that suggest that this rate will be significantly lower in Kenya than in many observed countries:

  1. COVID-19 treatments are largely uninsured and there is a perception that healthcare is expensive and inaccessible. Average medical savings are $5 in Kenya and it is expected that many will forgo treatment unless their case is severe. According to a national household survey released by Kenya National Bureau of Statistics, 57% of the population seeks treatment from traditional healers and herbalists compared to 28% who access care from health facilities.
  2. There is widespread fear of testing positive for COVID-19 in Kenya as positive cases are isolated at Government facilities. Few patients want to be isolated in this manner and we expect a high level of avoidance.

An analysis of the distribution of presenting symptoms amongst COVID-19 patients shows that 28% exhibit difficulty in breathing with 42% showing signs of fever. We expect both these groups to potentially need/want to seek medical help. However, given the financial burden of COVID-19 treatment, we have set our expectation that 20%. This implies that only 20% of all sick CV-19 patients will seek out medical help if they are under 50 years old and 30% will seek out medical help if they are above 50 years old.

% of those who seek medical help who are hospitalised

A widely cited study of Hospitalisations and ICU admissions around the world by Imperial College London has looked at the percentages of symptomatic COVID-19 cases that actively seek medical help and who are subsequently hospitalised. Crucially they provide an age breakdown which helps us apply this to Kenya’s specific demographics: 

Age groupSymptomatic hospitalisation rate
0-9 years0.1%
10-19 years0.3%
20-29 years1.2%
30-39 years3.2%
40-49 years4.9%
50-59 years10.2%
60-69 years16.6%
70-79 years24.3%
80+ years27.3%

There is no evidence to suggest that hospitalisation rates are different in the UK to those seen in other countries. Therefore we have adopted the above in its entirety in our model.

Hospitalised cases that need intensive care (ICU)

A widely cited study of Hospitalisations and ICU admissions around the world by Imperial College London shows that of those taken to hospital for COVID-19 only a certain percentage will require ICU critical care. These are provided on an age basis:

Age groupHospitalistion to ICU rate
0-9 years5%
10-19 years5%
20-29 years5%
30-39 years5%
40-49 years6.3%
50-59 years12.2%
60-69 years27.2%
70-79 years43.2%
80+ years70.9%

There is no evidence to suggest that hospitalisation rates are different in the UK to those seen in other countries. Therefore we have adopted the above in its entirety in our model.

Sick patients who die from COVID-19

The same UK study provides age breakdowns which are widely accepted in epi-modelling.

Age groupHospitalistion to ICU rate
0-9 years0.002%
10-19 years0.006%
20-29 years0.03%
30-39 years0.08%
40-49 years0.15%
50-59 years0.6%
60-69 years2.2%
70-79 years5.1%
80+ years9.3%

There is no evidence to suggest that hospitalisation rates are different in the UK to those seen in other countries. Therefore we have adopted the above in its entirety in our model.


Amplitude of the seasonal fluctuation of the R number

A study estimating the basic case reproduction number (R0, a measure of the rate of infection) across provinces in China found no correlation with temperature or humidity. This finding is consistent with a further study of 224 cities across China, which observed no clear link between ambient temperature and rates of new infections. However, there is some preliminary data that seasonality may have as much as a 15% amplitude effect on the R number of COVID-19 we have decided to implement a small seasonal fluctuation amplitude of 8%.

Case isolation

Probability that a sick patient is isolated

A key factor when determining local transmission is the probability that sick patients are able to remain isolated. At the moment 100% of patients confirmed to have COVID-19 with symptomatic symptoms are isolated in Government facilities and the remainder are encouraged to isolate at home. We expect this to continue for the duration of the virus.

Maximum capacity of isolation wards (per 10,000 people)

Our best estimation is that there are 278 ICU beds in Nairobi, suggesting that there are 0.6 ICU beds per 10,000 people in Nairobi. 

Contact reduction for cases in home isolation

According to the ministry of Health patients assessed by a Health Care Worker who has a confirmed COVID-19 case and are asymptomatic, free of co-morbidities and have access to a suitable space are encouraged to home isolate for 14 days. Based on analysis of contact reduction for people in home isolation 90% adhere to strict home isolation leading to a 90% reduction in contact reduction.

Contact reduction

Contact reduction duration (days)

This factor determines how long the contact reduction measures put in place in Kenya last. After the end of this period, contacts are set back to the original 100% value. As we have seen from other countries such as the US or UK, contact reduction is only viable for a limited period of time until economic activity is forced to increase. We have only seen a few examples where lockdown has lasted over 100 days and we have used this upper bound as our input for this measure.

Contact reduction in the home (%)

This measure determines what percentage of contacts at home are prevented by social distancing. Unlike in some high-income countries where homes are large enough to facilitate low social contact during an intra-household outbreak, this is not possible in the majority of Kenyan homes. Therefore we have set this reduction at 10%, this is a very loose assumption as there has not been any definitive studies of contact reduction.

Contact reduction in schools (%)

Schools are expected to have a phased reopening from January 2021 in Kenya. This means that over the course of the year we are modeling, we estimate a 100% contact reduction for the first 6 months followed by a gradual increase in contact for the 6 months thereafter.

Contact reduction at work (%)

Small and medium-sized enterprises are the lifeblood of Kenya’s economy, constituting about 98% of all the businesses in the country. Given this we expect low social contact at work to be extremely challenging in Kenya and have set this limit at 50%. Again, this is an assumption that would benefit from empirical analysis.


How Kenya is tracking against our COVID-19 model

Update 5th May 2020

We believe that the infection rate is tracking at worst against our ‘70% reduction in social contact’ scenario, and at best against our ‘90% reduction in social contact’ scenario. This means the peak will hit sometime between 1st August 2020 and 1st October with somewhere between 416k – 8.72m Kenyans infected. However, the virus seems to be less fatal than in other countries. Due to this we are lowering our estimation of health assets needed to overcome the virus.

Best case outcome
90% reduction in social contact
Worst case outcome
70% reduction in social contact
Infections as of 5th May3,70079,181
Deaths as of 5th May24288
Infections at peak416k8.75m
ICU beds needed6169,285
Ventilators needed4576,866 dispatches1,727114

Predicting which scenario Kenya is tracking against

Key terms used

1. Cases: This is what is confirmed as a positive COVID19 case through PCR testing.

2. Infections: The number of total people who have the virus (symptomatic & asymptomatic). This number will always be much bigger than cases. 

3. Case Fatality Rate (CFR): The % of confirmed cases who die.

4. Infection Fatality Rate (IFR): The % of total people infected who die.

Part 1: A look at confirmed cases and how we could estimate infections

As of 5th May 2020, Kenya had 490 confirmed cases of COVID-19. Cases are only a small subset of total infections. This is for two reasons:

  1. Many infections of COVID-19 are mild or even asymptomatic and unlikely to be detected. This rate seems to be extremely high in Kenya. According to the daily situation report (May 2nd) from the Ministry of Health of 435 cases, 71% were asymptomatic.
  2. Lack of testing is also a challenge. Testing is only taking place for those with heightened symptoms, within quarantine and isolation facilities, or those who have come into contact with positive cases. As of 5th May 2020, Kenya has tested 22,897 people, an extremely low per-capita rate. KEMRI, the principal test laboratory facility has warned that it lacks the technical personnel and resources to continue testing and has asked for KSH 790 million in immediate financial support to restock.
CountryApproximate tests per million people (end of April)

Due to the high level of asymptomatic cases and low level of testing in Kenya we are not able to infer much from the confirmed cases as to how many actual infections exist in Kenya. It is very likely the real number of infections in Kenya may be significantly higher than the confirmed number of cases. 

Here are two alternative ways to try to estimate total number of infections: 

  1. Kennedy Odede, a grassroots organiser in Kibera, Nairobi recently undertook a controlled randomised testing of residents. Of 400 results, 3 tested positive for COVID-19 (0.75%). If we believe this is true for the whole of Kibera – a settlement with a population of around 2 million people – we can estimate that 15,000 people are currently infected within this specific area. Projected for Mombasa and Nairobi at the same rate, we can estimate that 42,037 have been infected. This research was conducted around 28th April 2020 and we project total infection rates have since risen by 88% based on the growth in reported cases at a national level. This means that total current infections could be around 79,181 in Kenya. Please note: As has been pointed out by our readers, there is high potential for ‘false-positives’ in this study due to the low results size, therefore we would encourage caution when extrapolating these results.
  1. According to Verity et al., the infection-fatality-rates (IFR) of observed COVID-19 infections in China was 0.66%. This means that 0.66% of infections in China resulted in death. Projecting to Kenya this means the country should have approximately 3,700 COVID-19 infections as of 5th May 2020. 
CountryInfection-fatality-rateRecorded cases% of pop. infectedIf applied to Kenya on May 5th. No. infected
1 case = 1 infection490490
Italy1.29% (Rinaldi et al.)4901,900
UK1.20% (Imperial College)4902,000
Germany1.17% (Imperial College)4902,200
China0.66% Verity et al.4903,700
Kibera randomised study4900.75%79,181

Given this we believe that infections range from 490 (confirmed cases) to 79,181 (infected infections from Kibera study). Clearly there is a wide discrepancy here. Our best estimate is that Kenya’s infection rate will be in-line with China’s and the infection-fatality-rate proposed by Verity et al. However, we cannot discount the possibility that the Kibera study’s findings are in-line with the current status, this therefore forms our outlier result.

Best case estimate no. current infections: 3,700 / Worst case estimate no. current infections: 79,181

Please note: At this stage this IFR is just a hypothesis. Early data from South Africa suggests that natural cause deaths are level with the same period in 2019. But, this data is rudimentary and does not account for specific mortalities within respiratory diseases and we are choosing not to over utilize this data at this time. Data on total mortality rates in Kenya has not been made available to us. If you have this information and would like to help us in refining these assumptions please get in touch.

Part 2: What we know about case fatalities and how we can better estimate the likely current and future fatality rates in Kenya

As of 5th May 2020 there were 24 confirmed COVID-19 deaths in Kenya. In our best estimate explained above we take this number as 100% correct. However, there is a strong possibility that this number omits some of the deaths that are actually occurring from COVID-19. It is hard to reliably track mortality rates in Kenya, particularly during periods of heightened mortality. According to the World Economic Forum, as many as 500 million people living in Sub-Saharan Africa have no official documentation. This makes tracking official mortality rates challenging. 

There are a few ways we can estimate deaths in Kenya: 

  1. Look at other countries and the rate of under-reporting: Many analysts in Nigeria believe the mortality rate in that country is significantly underestimated. Whilst the country has only recorded 1,182 cases and a national death toll of 35, local media reports from Kano province of hundreds of deaths in recent days have raised the possibility of major outbreaks of the lethal disease going undetected for weeks across Africa. Brazil, is one of the first countries to publish a peer reviewed analysis of the extent of COVID-19 mortality underreporting. Like Kenya it too has struggled with testing and has similar issues of mortality registration. Researchers there are arguing the true number of COVID-19 deaths may be 1200% higher than that being officially reported. As Kenya has performed 4x fewer tests than Brazil it seems likely that Kenya is underreporting the true number of deaths to a roughly similar extent. Given this Kenya should have 288 deaths by now (reported cases of 24 x underreporting rate of 1200%).

2. Look at other countries infection-fatality rates: 

CountryInfection-fatality-ratioIf applied to Kenya (no. deaths) on May 5th
Italy1.29% (Rinaldi et al.)1,021
UK1.20% (Imperial College)950
Germany1.17% (Imperial College)926
China0.66% Verity et al.522

The discrepancy between reported fatalities and expected fatalities based on other country’s Infection-fatality-rates is extremely large. Because of this we will use peer reviewed analysis of mortality underreporting to hypothesise that potentially up to 288 people have died in Kenya from COVID-19 in our worst case scenario.

Best case estimate no. current deaths: 24 / Worst case estimate no. current deaths: 288

Please note: It is hard to say with certainty why this rate is so much lower than that oversever in European and Asian countries. As we previously discussed it may be that Kenya has a significantly lower mortality risk profile for the virus due to its small ageing population when compared with countries who have already experienced the peak of the virus. Kenya 6.7x fewer people aged over 65 than Italy. Our projected infection-fatality-rate is around 4x times smaller than that recorded in Italy. It may also be that the high level of malnutrition and underlying health issues in Kenya such as existent levels of pneumonia do not seem to be correlating with higher COVID-19 mortalities. Both of these ideas are hypotheses and peer reviewed analysis is necessary.

Which scenarios are we currently tracking against?

Now, back to our model. How do we use these ranges of cases and deaths to best understand what scenario we’re tracking against? 

First, let’s make sure to review how our model works. In early April we published a projection for how many health assets would be needed to combat a potential outbreak of COVID-19 in Kenya. This projection looks at the potential needs of hospital beds, ICU beds and ventilators and was based on a Discrete Time SIR model of infection based on best known inputs for Kenya at the time. The model is projected on different scenarios which describe how much the country is able to reduce social contact through preventative measures.

We can now compare the real infections and expected infections at these different scenarios.

ScenarioNo. infections as of 5th May
90% reduction in social contact (NEW)3,000
Best case estimate no. current infections3,700
80% reduction in social contact15,022
70% reduction in social contact79,000
Worst case estimate no. current infections79,181
60% reduction in social contact384,636
50% reduction in social contact1,696,428

Clearly our worst case scenario is tracking against our original 70% reduction in social contact scenario. However, our best case scenario is tracking far lower than our original 80% reduction in social contact scenario, what we considered our best possible outcome at the time of drawing up the model. To cater for this we have created a new scenario that correlates to 90% reduction in social contact. 

Our ‘70% reduction in social contact’ scenario is one of the least severe of all possible scenarios for Kenya. It shows a peak in those infected with the virus on 1st August 2020 before a rapid decline. This scenario shows a severely delayed and diminished spike of the virus compared with scenarios where the Government puts in place fewer and less restrictive preventive measures. In total 8.72 million Kenyans will be infected with the virus at its peak.

Please note: We have adjusted this model based on how many deaths we now project to have happened (288) and how many should have been observed and which we previously based our model on (522).

Our ‘90% reduction in social contact’ scenario is extremely reduced . It shows a peak in those infected with the virus on 1st October 2020 before a slow decline. This scenario shows a severely delayed and diminished spike of the virus. In total 416,390 Kenyans will be infected with the virus at its peak.

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A response to some questions about our COVID-19 model

On the 2nd April we published the results of a projection exercise to estimate the potential level of healthcare resources needed to deal with the Novel Coronavirus (COVID-19) in Kenya. This model was primarily created to help those at the frontline of the public health response to COVID-19 in Kenya estimate and seek resourcing for extra hospital beds, ICU facilities, ventilators and EMS assets. We have been extremely excited by the level of interest in our model and wanted to take a few moments to address some of the questions, concerns and potential alternative inputs that you have raised.

Click here for the full predictions.

If you have any questions about this model or would like to access the raw logs, please email

Why have you chosen these scenarios, what do they mean in real terms?

It is hard to accurately determine the effect of quarantining efforts put in place on epidemiological outcomes. Our model quantifies these outcomes in terms of the % reduction in social contact in several different scenarios. This is based on the model built by researchers at the Penn Medical Predictive Healthcare Unit called the CHIME model.

This article attempts to quantify the % reduction in social contact in terms of specific measures. These reductions are on par with research by Imperial College which produced specific scenarios of social contact reduction, these results can be seen below and are roughly similar to the levels presented within our scenarios.

Kenya has responded faster than most countries to the virus, how is that integrated into your model?

So far the Kenyan response to COVID-19 has been extensive. As of the 7th April (at the time of writing) Kenyan airspace was severely restricted, travel across county lines illegal and curfews preventing intra-county travel implemented between certain times. The country is yet to impose a complete quarantine or ‘lockdown’ restricting movement to all but essential travel, but it will likely do so within days. This is despite the country having significantly lower mortality figures than comparable countries when they imposed their lockdowns. Given this it is clear that the Kenyan government has acted significantly faster and with more severity than countries such as the UK or US.

Days since first confirmed case reported

CountryDate of first caseBan on large gatheringsInternational travel control/banInternational travel banEnforced mask useTotal lockdown
Kenya13 Mar0112523N/A
Italy31 Jan22020N/A39
Spain1 Feb343537N/A37
UK31 Jan43N/A53N/A53
US15 Jan6165N/AN/AN/A
South Korea20 Jan810303030
Wuhan, China31 Dec2024242324

For more detailed breakdown of the speed of government preventive measures around the world, please consult this blog at Brookings.

This excellent and widely cited blog demonstrates the need for early, draconian social distancing measures akin to those undertaken in China or Singapore. It clearly shows the impact of immediate social distancing measures which significantly reduce the peak of the viral curve. If Kenya is successful in enforcing this, not only should the country track against our 80% reduction in social contact scenario, but will be potentially manageable from a health response perspective.  

As we have mentioned above there are significant challenges for Kenya to successfully enforce it’s quarantining policies, not least the need to subsidise a significant proportion of the populations salaries for at least 1-2 months, but it is possible with adequate planning and creative solutions for basic service distribution and access.

Our model does not use guessed R rates, but instead uses real data about the spread of the virus and recorded cases/mortality within Kenya to constantly update it’s R factor. We believe this is a better system than estimating the potential R factor based on predicted protective measures. We will continue to review the R factor based on the strength and effectiveness of Kenya’s protective measures.

What % reduction in social contact is actually happening in Kenya?

There are various interesting data-sets being developed to help quantify the reduction in social contact in contained environments around the world. Perhaps the most often cited currently is the Google Global mobility Index, a rich data-set that uses Google Maps data to determine the reduction in ‘normal’ levels of mobility post COVID-19 protective measures.

This index currently shows the following reductions:

  • Retail & recreation: – 45% on baseline levels
  • Grocery & pharmacy: – 33% on baseline levels
  • Parks: – 20% on baseline levels
  • Transit stations: – 39% on baseline levels
  • Workplaces: – 22% on baseline levels

This data is hard to interpret in terms of overall reduction in social contact. Perhaps a good indication of overall contact reduction is the reduction in transit stations, which are used by a cross section of society. When comparing the reduction in this metric with other countries experiencing COVID-19 it seems that Kenya has not had significant success in reducing social contact. By comparison the UK has reduced transit station mobility by 75%. 

Based on this we estimate the current % reduction in social contact to be tracking between 20 and 50% in Kenya.

Kenya has a warm climate, will COVID-19 react similarly to countries with colder climates? Is the reproductive factor the same?

A common question amongst readers has been whether the warm year-round weather in Kenya will play a role in slowing the reproductive rate of the virus. Many of the largest outbreaks have been in regions where the weather is cooler, leading to speculation that the disease might begin to tail off with the arrival of summer. 

The simple truth is that because COVID-19 has not been present for a full seasonal passage we do not yet know the endemic qualities of the virus. The closely related Sars virus that spread in 2003 was contained quickly, meaning there is little information about how it was affected by the seasons.

There are some early hints that Covid-19 may also vary with the seasons. An unpublished analysis comparing the weather in 500 locations around the world where there have been Covid-19 cases seems to suggest a link between the spread of the virus and temperature, wind speed and relative humidity. The study described how the virus seemed to spread fastest at an ‘optimal’ temperature range of 8.07 degrees centigrade, this is similar to that found by Wang et al. which is 8.72 degrees centigrade. In comparison Kenya’s daily temperature in April ranges from 13.4 to 25 degrees centigrade. This is outside of the optimal range proposed by the researchers. Another unpublished study has also shown higher temperatures are linked to lower incidence of Covid-19. However, the effect of temperature on COVID-19 reproductive rate was meagre. The R values with and without temperature were 0.44 and 0.39, respectively, indicating that the inclusion of the temperature effect only provided a relatively modest slowing of transmission rate. This study also notes a notable outlier in the Daegu province of South Korea which has a high average temperature and has experienced an extremely high rate of transmission

However, it is important to be cautious about this research. Endemic viruses are seasonal for a number of reasons that might not currently apply to the Covid-19 pandemic. Pandemics often don’t follow the same seasonal patterns seen in more normal outbreaks.

There is also research to the contrary. A recent analysis of the spread of the virus in Asia by researchers at Harvard Medical School suggests that this pandemic coronavirus will be less sensitive to the weather than many hope. The reality of all this research is that COVID-19 is so recent that there is not a substantive enough research base to definitively prove a relationship between environmental factors and transmission rate. Once such information becomes available we will adjust the inputs in our model to reflect it. 

What is the level of testing in Kenya and what does that mean for the model?

As of Monday 6th April there have been 4,277 tests taken place in Kenya. Of these 158 people have tested positive. It is clear that there is a substantial challenge for testing for COVID-19 in Kenya. Kenya has performed one of the lowest levels of tests in the world, a result partly attributable to the late on-set of the virus in the country, but also due to the limited number of labs currently approved to test. The Kenyan Government is currently estimating it has the total capacity to do 300 tests per day at the national lab and 5000 per day at the KEMRI labs via a Cobas 880 automated testing machine. To improve this rate the government must urgently construct additional labs with PCRs and introduce RDTs.

Kenya is currently testing patients within official quarantine facilities, at designated facilities such as Mbagathi and Aga Khan, this means the country is probably missing a significant % of infections occurring in the wider community.

It is never possible to measure the exact number of infections within an area, but accuracy increases with increased testing. Researchers can use data from places that conduct a significant amount of testing to estimate the total number of infections based on confirmed COVID-19 cases, deaths, and recoveries. Researchers from Imperial College have produced perhaps the most comprehensive analysis of infection rates in Europe. They estimate that on March 28th, 5.9 million people in Italy had been infected with the virus and 16,523 had died, meaning that 0.28% of infections resulted in death. Using this methodology, and assuming the mortality rate is the same in Kenya as in Italy, we can extrapolate that on April 6th, when 4 people had died in Kenya from the infection, there would have been a total of 2,150 infections in the country. Limited testing and asymptomatic carriers would account for the fact that only 158 cases had been officially confirmed on that date.

Kenya has a high disease profile, how will that impact fatalities?

A very important factor in the Case Fatality Rate (CFR) with COVID-19 and other coronaviruses is the comorbidities in the underlying population. Researchers in China have found a significant adjustment, after adjusting for age and smoking status for a variety of comorbidities including diabetes, hypertension and malignancy. Comobitieis of specific concern in Kenya include:

  1. Conditions that can cause a person to be immunocompromised, including cancer treatment, smoking, bone marrow or organ transplantation, immune deficiencies, poorly controlled HIV or AIDS

Kenya has an average HIV prevalence rate of 6% and with about 1.6 million people living with HIV infection, it is one of the six HIV ‘high burden’ countries in Africa. This will put significant and specific stress on Kenya’s healthcare system during the COVID-19 crisis.

Along with other low-middle income countries the prevalence of cancers is lower than in higher income countries as a proportion of the population. According to the WHO there are currently 86,592 cancer patients in Kenya with 19,199 added each year. This population is at significant risk of morbidity/complications.

  1. Chronic kidney disease and who are undergoing dialysis

Amid rapid urbanisation, the HIV epidemic, and increasing rates of non-communicable diseases, people in sub-Saharan Africa are especially vulnerable to kidney disease. 2017 estimates show that 4 million Kenyans have chronic kidney disease with a significant proportion of this population progressing to kidney failure. Out of these, about 10,000 people have end stage renal disease and require dialysis.

  1. Liver disease

According to the latest WHO data published in 2017 Liver Disease Deaths in Kenya reached 5,214 or 1.85% of total deaths. The age adjusted Death Rate is 23.04 per 100,000 of population ranks Kenya #62 in the world, putting it significantly above countries like the US and UK currently experiencing the virus at its peak. 

  1. Respiratory diseases and illnesses

Pneumonia is a serious public health problem in Kenya. Although the percentage of children routinely vaccinated against some strains of the illness is increasing, reaching 84 percent by 2018, it still poses a significant risk in the country. In 2017, pneumonia was responsible for 21,584 deaths according to the Economic Survey 2018, accounting for 22% of deaths, and standing as the leading cause of the death for the third year in a row. In Kenya, about 700,000 cases of pneumonia in children under the age of five are treated every year. Although children are significantly less susceptible to the health implications of COVID-19 this level of pneumonia in the baseline population is concerning.

Tuberculosis remains high in Kenya, and experts say the country lags in the fight against the disease. According to a 2018 TB study there are 558 per 100,000 adult population. While experience on COVID-19 infection in TB patients remains limited, it is anticipated that people ill with both TB and COVID-19 may have poorer treatment outcomes, especially if TB treatment is interrupted.

It is difficult to accurately bake these comorbidity rates into our SIR model, therefore we have excluded at the moment. However, we would recommend that you consider our estimates to exclude, rather than include, this increased risk.

Kenya has a small eldery population, how does that affect the hospitalisation rate?

First, it is worth explaining the basic epidemiological model we have used for this exercise. There are several models to choose from, but we opted for the Discrete Time-Based SIR model of virus transmission in a closed population over time. This is the orthodox epidemiological model and is being used by the majority of modellers around the world to assess the spread of COVID-19 within closed populations. You can read more about the SIR model here and for full documentation of the exact SIR model we used for our projections you can find out more here.

One challenge with a SIR model is that it treats the susceptibility and recovery rate of people with the virus the same across the population. As such it does not take the demographics of a population into account within the dynamics of the model. Instead users of SIR models bake these factors about the population into the inputs of the model: e.g. the % of infections that result in hospitalisation, the % of infections that result in ICU usage and the % of infections that result in the need for mechanical ventilation.

There is significant evidence that the older a population is the higher the mortality rate and utilisation rate of health assets will be. We now have data on over 1.2m confirmed COVID-19 cases and the result is conclusive. The overall death rate from COVID-19 has been estimated at 0.66%, rising sharply to 7.8% in people aged over 80 and declining to 0.0016% in children aged 9 and under. Other studies, such as that focused on the UK by Imperial College researchers have found the rate rising to 27% amongst over 80 year olds.

Many of the countries currently most affected by COVID-19 have eldery populations. Around 23% of Italy’s population is over 65 years of age. In comparison Kenya, as with most other Sub-Saharan countries has a proportionally smaller ageing population, at around 3.4% of the population. This should mean that the overall % of infections requiring hospitalisation will be significantly smaller in Kenya than Italy. 

We have attempted to take this into account in our model. In a comprehensive assessment of the infection-hospitalisation rate in China researchers found that the rate ranged from 0.04% to 18.4% depending on the age of the patient (Verity et al). This study is rigorous and we have used it to weight for Kenya’s small eldery population and have aggregated to assume that 5% of infections will require hospitalisation. Other averaged infection-hospitalisation rates (IHR) include:

  • Imperial college analysis of UK cases and projections: 9.78% IHR
  • Verity et al analysis of China cases and projections: 7.96% IHR
  • analysis of Kenya risk profile and demographics: 5% IHR

One interesting point is that the study by Verity et al, assumes that only ‘severe’ COVID-19 cases will require hospitalisation. We do not yet know whether this will be the case in Kenya or how COVID-19 will react with coexisting medical conditions within the population. Life expectancy at birth in Italy is 83.1 years (2017) whilst Kenya’s is 66 years. Our model does not take these health factors into account and should be viewed with a +/- 5% infection-to-hospitalisation rate variability as a result.

What other factors might influence the R factor in Kenya?

The reproductive or R factor of COVID-19 is determined on far more factors than the virus’ ability to survive different environments. Probably of far greater significance is the level of social contact and density of individuals within a closed environment. On these factors Kenya is potentially in a challenging position. Whilst Kenya has a small population density overall, in it’s cities it is a different matter. 60% of Nairobi’s residents live in informal settlements, with population density often as much as 300,000 people per square kilometer. We believe these conditions will influence the reproductive rate of the virus far faster than any possible temperature factors.

It is also important to note that the SIR model we have used to compute the R factor of the disease computes based on all known data on different reproductive rates and compares that against the known history and growth of cases within Kenya. Because of this we will continue to adjust the model’s outcomes over time, but believe this offers us a better estimation than manual input. The current doubling time of confirmed cases in Kenya compared with other countries in the region is as follows:

  1. Guinea: 2 days
  2. Cameroon: 3 days
  3. Congo: 4 days
  4. Kenya: 5 days
  5. Ethiopia: 7 days
  6. Nigeria: 7 days
  7. South Africa: 11 days
  8. Tanzania: 15 days

Source (Our world in Data)

Is this model conservative or overly fatalistic?

As discussed above there are several reasons why our model might be overly fatalistic. It is possible that the disease will indeed react to warm climates significantly differently, it is also possible that Kenya’s small eldery population will have a more significant effect on hospitalisation than expected. 

But, there are also several reasons why we should view these figures as the best case situation. Containment and quarantine in Kenya will be extremely challenging. To properly slow a virus people must either be forced or highly incentivised to self-isolate. In Kenya neither of these options are possible:

To force those to stay inside people must have accessible basic services. 41% of Kenyans still rely on unimproved water sources, such as ponds, shallow wells and rivers. Simply put they must leave the home for essential services. To incentivize Kenyans to stay inside the government would need to pay the wages of furrowed workers on an unprecedented scale for several months. Estimates of the UK scheme to pay 85% of furrowed workers estimates costs for that scheme could be as high as $96 billion dollars. Assuming Kenya were to furlough workers and pay 85% of monthly wages akin to the UK and assuming that 60% of workers are furloughed under the scheme we believe that Kenya will need a KSH 2,248,763,060,000 ($21.2bn) support package for workers. 

What does your model say about mortality?

Our model does not compute mortality rates. This is deliberate. Instead, the model shows the peak hospitalisations, infections, ICU admissions and ventilations required to stabilize the infected population on a given day. This helps public health agencies and hospitals to plan under several case-load scenarios. 

Our model therefore is not measuring the TOTAL number of people that need ventilation or ICU facilities over time (from which one could infer mortality), but rather the PEAK number of people who will require ventilation on a certain day. This is a more useful number when predicting the number of health assets that need to be purchased, but clearly prevents us from making an inference about mortality.

At a very crude level you can infer from this information the potential mortality based on the number of health assets within the health system. For example, if Y people need mechanical ventilation on a given day, and only X ventilators exist within the population, it is reasonable to suggest that Y-X might die. But, this isn’t correct, as many people require ventilation for multiple to multiple subsequent days (the COVID-19 average being 9 days). The Y people requiring ventilation on any given day includes those who are already on ventilators from previous days and need continued ventilation. Because of this, many or all of the ventilators may be continuously occupied by existing patients for several days.

When estimating the potential mortality rate in Kenya during COVID-19 we recommend referring to other models such as this extremely helpful Case Fatality Rate calculator.

What’s next?

We will continue to review our model and will continue to input real data about cases/mortality in Kenya to maintain its accuracy. Any major revisions or outcomes will be posted on this blog and disseminated to members.

We are working to ensure Kenya’s EMS system has capacity and is prepared for the week’s ahead. We are purchasing PPE equipment in bulk ensuring our front-line health workers are protected and are able to do their job. If you are interested in donating to these efforts please click here.

As the largest digitally connected ambulance network in sub-saharan Africa we have the best and most extensive real-time data on emergency response resources, the widest network of first responders (private and public) on a single hotline and a comprehensive set of COVID-19 training materials and programs in Kenya. We are looking to contribute as much as we can to national planning and response efforts. Please get in touch with any relevant connections or interest at


Predicting the health requirements of Coronavirus (COVID-19) in Kenya

Last updated: 2 April 2020

Please note: This model is based on a widely used Discrete Time-Based SIR model that computes the theoretical number of people infected with a contagious illness in a closed population over time. This model is adaptive and we may change inputs and outcomes over the coming week. A model is only as strong as its methodology and the validity of it’s inputs, therefore we recommend using this only as indicative of overall patterns and outcomes in Kenya.

Model Documentation

We have received significant interest in this model, for answers to common questions click here.

Model summary

Peak active infections: 2 – 30 million people

Hospital beds needed: 9k – 199k

ICU beds needed: 3k – 77k

Ventilators needed: 2k – 55k

Ambulances needed: 57 – 1,496

COVID-19 in Kenya

The current global coronavirus (COVID-19) pandemic is a major public health challenge. Almost every country in the world is currently in some stage of quarantining in an attempt to limit the spread of the virus. The first case of COVID-19 was confirmed in Kenya on 13th March 2020. is the largest network of first responders in Kenya and has been preparing the last month to respond to a very likely outbreak in the country. As part of this preparation, we modelled the disease outbreak in Kenya to understand the gap in resources. 

As well as helping us prepare for a spike in the usage of (# of increased ambulance trips), we are making this model publicly available to investors, others working within the healthcare space, hospitals and the government to assess expected challenges and galvanize efforts to respond.

Beyond modelling we are working in a number of ways to prepare: 

1.Training ambulance partners in best practice for infection control and case management of COVID19 patients.

2. Purchasing and equipping ambulance partners with best-in-class personal protective equipment (PPE).

3. Ramping up efforts to quickly add more ambulances to our platform.

4. Mapping ICU beds, hospital bed and ventilators, keeping a close tab on availability and advocating for additional funding.

Coronavirus (COVID-19) is a new challenge for Kenya; information about the speed and conditions of it’s spread are fast-changing. Our model provides our best estimates based on the current data, it should be used only as advisory information.

Our inputs

Choosing a model

There are several epidemiological models to choose from when mapping COVID-19 in Kenya. However, perhaps the most orthodox is the SIR model that computes the theoretical number of people infected with a contagious illness in a closed population over time. We have used this model with Kenya specific inputs and environmental constraints.

Doubling time or R factor of COVID-19

A crucial factor when determining the spread of a virus is the time it takes for the number of infected patients to double. Research World Health Organisation data shows that the disease is highly infectious but it varies significantly depending on the conditions of reproduction (population density, poor sanitation etc). Data from European Center for Disease Control and Prevention shows that during the height of infection it takes between 3 and 7 days for the disease to double. We believe that Kenya has significant challenges to slow the spread of the infection. 60% of Nairobi’s residents live in informal settlements, with population density often as much as 300,000 people per square kilometer. Under these conditions we believe the doubling time of COVID-19 in Kenya will be at the low end of the scale and have used 3 days in our model. So far this is trending correctly.

Other inputs

Hospitalisation: Verity et al. suggests that around 5% of infected people with Coronavirus (COVID-19) will need hospitalisation. This figure is lower than in other studies of hospitalisation which have noted rates as high as 15%. However, given Kenya’s low elderly population and risk profile we have used a low-level rate.

ICU usage: Similarly referenced by Verity et al. and assumed to be about 30% of hospitalisations or 1.5% of infections. 

Ventilator usage: Similarly referenced by Verity et al. and assumed to be about 30% of hospitalisation or 1.5% of infections. Other studies have found lower rates of total infections and we believe that due to the slightly lower aeging population than in Verity et al Kenya will have a 1% period.

Hospital length of stay: In a 2020 study of a single-center case series involving 138 patients with Novel Coronavirus (click here for more info), the average hospital stay was 10 days. THis pars with other research e.g. The value is based on observed full hospital length of stay for ~10,000 Respiratory Failure patients at four Penn Medicine facilities over a 5 year period.

ICU length of stay: 9 days is based on observed full hospital length of stay for ~4,000 Respiratory Failure patients at four Penn Medicine facilities over a 5 year period requiring ICU support. 

Ambulance usage: At the writing of this report there were no studies of how many COVID-19 hospital admissions and transfers required an ambulance. based on the WHO joint mission data we believe 10% of hospitalised cases were admitted by hospital and based on exceeding bed capacity we believe that 5% of hospitalised patients will need transferring per day.


Our model computes the different outcomes of COVID-19 in Kenya depending on the % decrease in social contact in Kenya as a result of quarantine and self-isolation measures put in place in the country.

These scenarios are:

Scenario 1: 10% reduction in social contact:There is little to no social quarantining in place. Most people continue with their daily lives unchanged. There is no financial aid for disruption and most Kenyans are forced to continue working.

Scenario 2: 30% reduction in social contact: There is limited social quarantining. The government imposes a curfew between certain hours, which has a limited effect, but does not enforce tough quarantining and does not provide financial aid for Kenyans, forcing most to continue working.

Scenario 3: 50% reduction in social contact: The government attempts to impose nation-wide curfews and fines for anyone breaking social isolation. The measures imposed by government are partially adhered to and 50% of people continue moving around as normal.

Scenario 4: 60% reduction in social contact: The government starts to put in place more comprehensive financial aid and salary replacements for a majority of Kenyans. A majority of Kenyans adhere to the social quarantining measures and a majority limit their movement outside the home to essential travel only.

Scenario 5: 70% reduction in social contact: The government comprehensively steps in and provides financial aid to most affected industries and demographics. The government builds pupose build mass accomodation for high-density citizens and the police ensure near 100% compliance for quarantining efforts.

Scenario 6: 80% reduction in social contact: The government stops all movement of people for at least 60 days and provides a comprehensive salary replacement scheme accessible by millions of Kenya. The government reduces all non-critical movement and travel with 80% effectiveness.


Peak active infections

Active infections in Kenya are predicted to peak at anywhere between 2m and 30m people, depending on the Government actions taken. Please note, this is the peak active infections, we are not currently able to compute the total cumulative number of people infected.

Duration and peak of COVID-19

Depending on the range of actions taken by the Government, COVID-19 will take between 1 and 10 months to peak and will last between 290 and 800 days.

Hospital beds needed

We have used the number of hospitalisations to infer the number of hospital beds that will be needed in Kenya, this may not in reality be a 1:1 relationship and some hospitalisations may not require a hospital bed.

Based on analysis of Level 4-6 facilities in Kenya we believe there are around 48,000 hospital beds in the country. In the US, 68% of hospital beds are used at one time. Extrapolating this to Kenya we believe there are 7,680 available hospital beds for COVID-19 patients, this number may, in reality, range anywhere from around 4,000 to 24,000.

Depending on the range of actions taken by the Government, Kenya will need between 9,000 and 199,000 hospital beds to cater for the range of outcomes during the infection.

ICU beds needed

Based on our mapping of Kenya’s ICU facilities we believe there are around 290 ICU beds in Kenya. We will assume that 90% are currently in use for normal critical patients. Therefore, we believe Kenya has 29 ICU beds for COVID-19 cases.

Depending on the range of actions taken by the Government, Kenya will need between 3,000 and 77,000 ICU beds to cater for the range of outcomes during the infection.

Ventilators needed

Based on contact with all major public and private healthcare facilities in Kenya we believe there are between 114 and 290 ventilators within the country.  It is assumed that 90% of these assets are used currently at any one time for other non-coronavirus related incidents. 

Depending on the range of actions taken by the Government, Kenya will need between 2,000 and 55,000 ventilators to cater for the range of outcomes during the infection.

Ambulances needed

Depending on the range of actions taken by the Government, Kenya will need between 57 and 1,496 ambulances to cater for the range of outcomes during the infection. currently has 80 ambulances on or offline.

Cost of purchasing assets

Volume needs and the cost of purchasing health assets

Reduction in social contactHospital beds to buyAmbulances to buyICU beds to buyVentilators to buyApproximate cost


It is clear from this exercise that Kenya needs to purchase significant numbers of ventilators and construct ICU facilities as an urgent priority. At even the best-case scenario modelled here ventilator usage will 100x current maximum capacity and ICU beds will be exceed by 200x the current capacity maximum. A less urgent, but nonetheless important requirement is the construction of mass, quarantined hospital bed facilities. Lessons should be learnt from other countries in how to construct these facilities at scale in limited time. Ambulances will need to be procured and we recommend a minimum order of 50 to be placed immediately.

Overall this model presents in stark terms the important of social quarantining and tough government led measures that ensure a 80%+ reduction in social contact for at least 3-5 months. With these measures in place Kenya’s heath system, with approximately $150m of investment, will be able to cope with predicted cases.

Get in touch

For those tackling the outbreak is capturing important proprietary information during the coronavirus (COVID-19) outbreak in Kenya. We are keen to support public health organisations and companies tackling this outbreak. If you are interested in learning more about this model and/or our coronavirus cases and data, please contact

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