Just How Many Asymptomatic Cases Are There In Malaysia ? Here Are A Few Potential Estimation Methods

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MOH has updated its Tahfiz school cluster-info to report that 85% of those tested positives were asymptomatic.  This large % of asymptomatic is consistent with earlier reports of 80% to 90% in other parts of the country.  Given that the public is not likely to get into contact with those hospitalized and/or under EMCO, the risk then is being in contact with the asymptomatic cases. If so, then the questions boil down to

• How many asymptomatic cases are there in the country? 
• Can we identify the district with such cases?

My hypothesis is that we can infer the number of asymptomatic cases from the actual number of cases identified by MOH.  I think there are at least 3 ways to get such an estimate

1. In my 1 May posting, using a backward-looking mortality analysis, I estimated that the number of asymptomatic cases is 2.9 times the cases reported by MOH (refer to Note 1) 
2. Another approach is to factor the current number of cases reported with RO where RO is inferred by looking at the past few weeks' trends of cases. This gives a ballpark estimate of the same number of asymptomatic cases as that reported by MOH. (refer to Note 2)
3. I also developed a maths model as per Note 3 that suggests that we may have 3.1 times as many asymptomatic cases c/w with the numbers reported by MO

The main point is that even if we take the highest factor of 3, we have (based on 20 cases per day due to community spread) an average of 60 new daily asymptomatic cases.  Assuming a 14 days incubation period, we have 840 potential sources.   I would think that this is a low probability event. 

BTW according to the model of Note 3 we can have a ‘steady” number of cases every period if we have RO = 1.4.  The main inference from the model is that we can have continuing infections in the country without having exponential growth even with RO = 1.4 because of MOH protocol to hospitalize all cases whether mild, asymptomatic or serious.  This is of course a simple mathematical model and only considers one path of infection.  The question is whether it reflects reality. 

Irrespective of your view on the validity of the various estimation approaches, I think we cannot run away from the logic that there must be some relationship between the number of asymptomatic cases and the number of community cases reported.  The infections must come from some source.  Only the Skywalker family and maybe Yoda have the midichlorian to create cases out of thin air.  

As to the question of whether we can identify the districts with the asymptomatic cases, logically if there are no cases in a particular district during the 14 days incubation period, then we can safely assume that there will not be any asymptomatic cases.  In Malaysia, we use the green zone to define those districts that do not have any cases for the past 2 weeks.  While those yellow and red zones are those cases, I would argue that if the cases in these zones are not due to community spread, it would be safe to assume that there is no asymptomatic case.  Conclusion?

• If you are in the green zone, it is safe to do your Raya thing
• If you are in the other coloured zones and if the number of cases is not due to sporadic/community spread, it is also safe to do your Raya thing
• Unfortunately, the above does not apply to the Klang Valley as people can cross districts easily so the zoning does not provide much comfort. 

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Notes

1) I inferred that there were 23,072 cases c/w the reported 5,851 cases implying that 17,221 were asymptomatic.  Using this ratio of asymptomatic to reported cases, we have 2.94.

  

2) This 1:1 ratio is based on the total number of cases reported by MOH over the past 4 weeks that does not show any uptrend or downtrend implying RO = 1

3) One feature of our Covid 19 cases over the past few weeks is that the number of cases from sporadic/community transmission is “steady”.  I wanted to see whether I could create a mathematical model where there is a steady number of cases every period given the following conditions:

• The general public will only be infected from sporadic/community spread i.e. the public will avoid those hospitalized and/or the EMCO areas.  Accordingly, the general public will be infected by the asymptomatic cases

• MOH will hospitalize all cases from mild, asymptomatic to the serious.  Due to MOH targeted testing and contact tracing programme once a serious case has been identified, those asymptomatic cases that caused the infection will also be identified and hospitalized

• About 85 % of the cases in Malaysia are asymptomatic

Mathematically the virus is spread from one generation to the next as shown in the decision tree and based on the following assumptions/rules:

1) Virus from one generation to the next can be transmitted to either Asymptomatic (A) or     Infected (I) with symptoms i.e. serious cases.  Technically there will be those      Not Infected (O) but this will be ignored in the transmission to the next generation
2) RO = the number transmitted. The number of (A) and (I ) cases are computed by      multiplying R0 by the % of asymptomatic and % serious cases respectively
3) All (I) cases are treated ie removed from spreading further
4) When an (I) case is treated, there will be contact tracing to identify the portion of     (A) case which is subsequently removed
5) Because of  item 4, the number of (A) case that can spread the virus to the next      generation will be reduced
6) Because of the above, the next generation can only be infected by the balance (A)

For each generation, the model will compute

• The number of serious infections (I)
• The number of asymptomatic cases (A)
• The number of asymptomatic cases identified by MOH and hence removed from infecting the next generation = (A )multiplied by % serious cases

Disclaimer:  I am not an epidemiologist, healthcare worker, pharmacist or staff in the Ministry of Health, but rather is someone with a strong interest in numerical analysis.  The content is an attempt to understand what is happening in the battle against Covid 19 from a data-based perspective. The opinions expressed here are based on information extracted from readily available public sources but I do not warrant its completeness or accuracy and should not be relied on as such. 

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