What the corona virus numbers reveal?
Category: General
By: Annah-Grace Kemunto, Maureen Barasa,
By: Annah-Grace Kemunto & Maureen Barasa.
“Fellow Kenyans…”
These have been the opening remarks of the daily reminder of how much this pandemic has eaten into our lives as Kenyans. With the highest number of infections and mortalities in a single day so far being 960 and 23 respectively out of a population of 47 million Kenyans, is this pandemic as lethal as we think? This blog post seeks to find out whether there is a Monday effect in Kenya like other countries across the world , how the testing sequence informs the positivity rate and what the reproduction rate means.
To begin with, what is the Monday effect? In Behavioral Economics, the Monday effect is when the stock market returns on Mondays are usually low compared to other days of the week. Contrarywise, in terms of Covid-19, the Monday effect is a regularity where the number of coronavirus infections on Mondays are the highest throughout the week. To investigate this, we collected data on Covid-19 daily infections from the onset of the pandemic in Kenya on Thursday 12th March 2020 for the first 28 weeks; therefore, making the first day (1) of what we call the Kenya Corona week a Thursday. We recorded the daily infections as a proportion of the week’s total coronavirus infections. Our analysis revealed to us that on average, Wednesdays (7) record the highest number of cases compared to the other 6 days of the week.
Figure 1: An Illustration of the Wednesday Effect for the first 28 weeks
Source: Ministry of Health Kenya
Given that the daily Covid-19 statistics are presented at a press release at around 3 pm every day, when the announcer says “within the past 24 hours”, they mean the period between the time of the previous day’s brief and that day’s brief. Hence, the Wednesday effect is in reality a Tuesday effect since the results announced on Wednesday are a reflection of the tests conducted on Tuesday. This is however pegged on the assumption that all the test results were reported faithfully within these 24 hours of the test as stated by the Ministry of Health.
Next, we look at the trend in tests done by the day of the week. This would help us understand whether the Wednesday effect is influenced by an influx of tests done on Tuesday. Based on our analysis in Figure 2, on average, the highest proportion of tests done in a week were done on Thursday (1). This, therefore, tells us that the Wednesday effect is indeed a phenomenon that could be explained by behavioral patterns. One of which is evident in Figure 2 below where we have seen that testing during towards the end of the week, during the weekend and through till Monday is very low (from day 2 to 5) leaving a bulk of tests to be done mid-week thus the higher proportion falling on Tuesday (6) probably resulting in the Wednesday effect.
Figure 2: An Illustration of the Testing Trend During the Week
Source: Ministry of Health Kenya
Unfortunately, the scope of Kenyans being tested cumulatively is just above 1% of the total population. The total number of tests done so far (until week 28) comes to about 1.03% of the population with the highest number of tests done in a day covering 0.018% of 47 million Kenyans. Compared to a country like India which is testing about 0.085% of its population daily, about 1.17 million tests a day[1], Kenya is doing poorly. India started off with 104 tests a day at the beginning of the pandemic whereas Kenya began with over 1003 tests in the first week. India was able to increase its sample size by expanding the testing lab networks both private and public.
The low number of tests done per day in Kenya gives us insufficient information as to how far the pandemic has spread and overstate the positivity rate. The positivity rate is simply the number of infections as a proportion of the tests conducted. For perspective, on 31st July 2020, there were 723 infections out of 8679 tests done yielding a positivity rate of 8.3% whereas on 1st September 2020 there were 727 infections out of 6371 tests done yielding a positivity rate of 11.4%. Also considering that according to statistical theory, a smaller sample size may indicate lower accuracy overall especially for a disease that can grow so fast, a smaller sample size could miss localized infections and the possibility of new clusters of infections[2]. Such high positivity rates have been broadcast on the media and make the situation look even more terrifying than should be. Therefore, highlighting the need for the government to expand the testing capacity to get a clearer picture. From Figure 3 below, we see that the number of tests done per week has significantly reduced since a peak in week 22 thus creating a false sense of the curve flattening (see Figure 4) from week 22 onwards.
Figure 3: Illustration of Slow Down of Testing from week 22
Source: Author’s own
Figure 4: Infections Peak at week 22
Source: Author’s own
The basic reproduction ratio is an epidemiologic metric used to describe the contagiousness or transmissibility of infectious agents. If the positivity rate is not a good indicator of the shape of the curve, then what is? We look at the reproduction rate as a valid indicator of the flattening of the curve. According to the reported cases in Kenya, projections have been made on the increase in reproduction rate depending on the number of cases. The reproduction rate of Covid-19 was estimated to be between 1.78 and 3.46 in Kenya. [1]If the reproduction rate is >1 the pandemic is likely to end because the infection rates between persons would be low while the reproduction rate being <1 the infection rates are likely to decrease.
Giving an example of a school with a population of 100 pupils, we project how long it would take to infect 50% and 100% of the school’s population using Kenya’s estimated reproduction rate range of between 1.78 and 3.46, and an ideal rate less than one – in this case half of the lower end estimate – 0.89 (Figure 5). Using the range of 0.89, it will take 6 days to infect half the population and the other half will be infected in 1 day. Using the 1.78 rate, half of the population will be infected in 4 days while the other half will be infected in half a day. On the other hand, looking at the extreme rate of 3.46, it will take 2.5 days to infect half of the population and the other half will be infected in less than half a day. Therefore, the recommended reproduction rate of <1 is essential as the rate of infection from person to person is lower.
Figure 5: Different reproduction rates scenario
Source: Author’s own
Basing the reproduction rate on the number of cases, it is quite uncertain to project the rate. The Kenya reproduction rate case projected a high number of cases in the shortest time possible. The cases have been rising and decreasing due to insufficient randomized mass testing. The difficulties arise for several reasons making it hard to make projections. The basic symptoms of the coronavirus were yet to be known at the onset of the pandemic. The asymptomatic cases might be the largest spreaders of the disease and might have been missed by the testing. The recoveries are likely to get Corona Virus making it hard to project the reproduction rate or if a second wave of the virus might occur. [1]
Looking at the number of cases that possibly offer projection on the reproduction rate. It brings about uncertainty given the reasons mentioned above. The mass testing has been below the required threshold and thus the uncertainty in predicting the future number of cases. This also makes it hard to adopt better policies on flattening the curve, resumption of economic activities as well as proof of the current measure being sufficient in flattening the curve. Lack of transparency on the whole pandemic management might be a contributing factor in making projections of reproduction rate inaccurate[2]. We, therefore, advise for increased testing and better transparency in terms of reported statistics.
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What the corona virus numbers reveal?
| Post date: Fri, Oct 9, 2020 |
| Category: General |
| By: Annah-Grace Kemunto, Maureen Barasa, |
By: Annah-Grace Kemunto & Maureen Barasa.
“Fellow Kenyans…”
These have been the opening remarks of the daily reminder of how much this pandemic has eaten into our lives as Kenyans. With the highest number of infections and mortalities in a single day so far being 960 and 23 respectively out of a population of 47 million Kenyans, is this pandemic as lethal as we think? This blog post seeks to find out whether there is a Monday effect in Kenya like other countries across the world , how the testing sequence informs the positivity rate and what the reproduction rate means.
To begin with, what is the Monday effect? In Behavioral Economics, the Monday effect is when the stock market returns on Mondays are usually low compared to other days of the week. Contrarywise, in terms of Covid-19, the Monday effect is a regularity where the number of coronavirus infections on Mondays are the highest throughout the week. To investigate this, we collected data on Covid-19 daily infections from the onset of the pandemic in Kenya on Thursday 12th March 2020 for the first 28 weeks; therefore, making the first day (1) of what we call the Kenya Corona week a Thursday. We recorded the daily infections as a proportion of the week’s total coronavirus infections. Our analysis revealed to us that on average, Wednesdays (7) record the highest number of cases compared to the other 6 days of the week.
Figure 1: An Illustration of the Wednesday Effect for the first 28 weeks
Source: Ministry of Health Kenya
Given that the daily Covid-19 statistics are presented at a press release at around 3 pm every day, when the announcer says “within the past 24 hours”, they mean the period between the time of the previous day’s brief and that day’s brief. Hence, the Wednesday effect is in reality a Tuesday effect since the results announced on Wednesday are a reflection of the tests conducted on Tuesday. This is however pegged on the assumption that all the test results were reported faithfully within these 24 hours of the test as stated by the Ministry of Health.
Next, we look at the trend in tests done by the day of the week. This would help us understand whether the Wednesday effect is influenced by an influx of tests done on Tuesday. Based on our analysis in Figure 2, on average, the highest proportion of tests done in a week were done on Thursday (1). This, therefore, tells us that the Wednesday effect is indeed a phenomenon that could be explained by behavioral patterns. One of which is evident in Figure 2 below where we have seen that testing during towards the end of the week, during the weekend and through till Monday is very low (from day 2 to 5) leaving a bulk of tests to be done mid-week thus the higher proportion falling on Tuesday (6) probably resulting in the Wednesday effect.
Figure 2: An Illustration of the Testing Trend During the Week
Source: Ministry of Health Kenya
Unfortunately, the scope of Kenyans being tested cumulatively is just above 1% of the total population. The total number of tests done so far (until week 28) comes to about 1.03% of the population with the highest number of tests done in a day covering 0.018% of 47 million Kenyans. Compared to a country like India which is testing about 0.085% of its population daily, about 1.17 million tests a day[1], Kenya is doing poorly. India started off with 104 tests a day at the beginning of the pandemic whereas Kenya began with over 1003 tests in the first week. India was able to increase its sample size by expanding the testing lab networks both private and public.
The low number of tests done per day in Kenya gives us insufficient information as to how far the pandemic has spread and overstate the positivity rate. The positivity rate is simply the number of infections as a proportion of the tests conducted. For perspective, on 31st July 2020, there were 723 infections out of 8679 tests done yielding a positivity rate of 8.3% whereas on 1st September 2020 there were 727 infections out of 6371 tests done yielding a positivity rate of 11.4%. Also considering that according to statistical theory, a smaller sample size may indicate lower accuracy overall especially for a disease that can grow so fast, a smaller sample size could miss localized infections and the possibility of new clusters of infections[2]. Such high positivity rates have been broadcast on the media and make the situation look even more terrifying than should be. Therefore, highlighting the need for the government to expand the testing capacity to get a clearer picture. From Figure 3 below, we see that the number of tests done per week has significantly reduced since a peak in week 22 thus creating a false sense of the curve flattening (see Figure 4) from week 22 onwards.
Figure 3: Illustration of Slow Down of Testing from week 22
Source: Author’s own
Figure 4: Infections Peak at week 22
Source: Author’s own
The basic reproduction ratio is an epidemiologic metric used to describe the contagiousness or transmissibility of infectious agents. If the positivity rate is not a good indicator of the shape of the curve, then what is? We look at the reproduction rate as a valid indicator of the flattening of the curve. According to the reported cases in Kenya, projections have been made on the increase in reproduction rate depending on the number of cases. The reproduction rate of Covid-19 was estimated to be between 1.78 and 3.46 in Kenya. [1]If the reproduction rate is >1 the pandemic is likely to end because the infection rates between persons would be low while the reproduction rate being <1 the infection rates are likely to decrease.
Giving an example of a school with a population of 100 pupils, we project how long it would take to infect 50% and 100% of the school’s population using Kenya’s estimated reproduction rate range of between 1.78 and 3.46, and an ideal rate less than one – in this case half of the lower end estimate – 0.89 (Figure 5). Using the range of 0.89, it will take 6 days to infect half the population and the other half will be infected in 1 day. Using the 1.78 rate, half of the population will be infected in 4 days while the other half will be infected in half a day. On the other hand, looking at the extreme rate of 3.46, it will take 2.5 days to infect half of the population and the other half will be infected in less than half a day. Therefore, the recommended reproduction rate of <1 is essential as the rate of infection from person to person is lower.
Figure 5: Different reproduction rates scenario
Source: Author’s own
Basing the reproduction rate on the number of cases, it is quite uncertain to project the rate. The Kenya reproduction rate case projected a high number of cases in the shortest time possible. The cases have been rising and decreasing due to insufficient randomized mass testing. The difficulties arise for several reasons making it hard to make projections. The basic symptoms of the coronavirus were yet to be known at the onset of the pandemic. The asymptomatic cases might be the largest spreaders of the disease and might have been missed by the testing. The recoveries are likely to get Corona Virus making it hard to project the reproduction rate or if a second wave of the virus might occur. [1]
Looking at the number of cases that possibly offer projection on the reproduction rate. It brings about uncertainty given the reasons mentioned above. The mass testing has been below the required threshold and thus the uncertainty in predicting the future number of cases. This also makes it hard to adopt better policies on flattening the curve, resumption of economic activities as well as proof of the current measure being sufficient in flattening the curve. Lack of transparency on the whole pandemic management might be a contributing factor in making projections of reproduction rate inaccurate[2]. We, therefore, advise for increased testing and better transparency in terms of reported statistics.
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