The Government of Ghana has announced a three-prong strategy for comprehensively responding to the COVID-19 crisis: Testing, Tracing & Treatment.

Of those three dimensions, many observers feel that the first two are the most critical in the current phase of the crisis as they are more visible and more closely linked with the prevention, which given the country’s limited resources, is far more critical than curing.

There is no doubt that tracing and testing are critical, but the strategy for doing both well is even more important.

In addition to early detection, effective tracing and testing also enable responders to use the number of *confirmed cases* to project/predict the pattern of *true cases*. In every epidemic, there are always many people with the disease whose condition is not known and has therefore not been recorded by the official health system. Hence, confirmed cases always lag and underrepresent true cases. What is important is for the confirmed cases to track the true situation on the ground reasonably faithfully.

For that to happen though, confirmed cases must constitute a *representative sample* of true cases.

Crudely, C —> xT, where “x” is a common or constant ratio, “C” is a measure of the confirmed cases and “T” is a measure of true cases. If you consider each daily count/announcement of the infection rate/level as a term in a series, that term should be expressed as closely as possible by the crude mathematical mapping relationship indicated above: C —-> xT.

The reason why one needs a roughly stable relationship between confirmed cases and true cases is because that is the only way one can use the official count of confirmed cases for any kind of policy management.

If the trend in confirmed cases does not reflect the underlying trend of true cases, then the official count becomes useless. No one can tell, in those circumstances, if any policy, such as lockdowns, are working or not.

For the confirmed counts to be representative of the true level of prevalence, the total number of tests doesn’t really matter as much as usually supposed unless the number of tests cover a very large proportion of the overall population, i.e. is in the millions. In Ghana’s case, tests underway are about 44,000, of which 15,000 have been completed (Presidential Advisor on Health, April 11th, 2020). The condition of overwhelming proportionality does not therefore apply.

What matters more than anything else then is how public health authorities determine and secure a *representative sample* of the likely exposed populations for testing without overestimating the true extent of the spread.

So far, I haven’t seen any clear analytical logic explained clearly by officialdom in Ghana as to how that high bar is being aimed for.

And given the logistical challenges in pooling samples, running tests, batching results, sending them to the Ministry of Health, which then releases the data to the GHS strongroom, before proceeding to inform the public, the actual data points in any global number announced on any particular day could be coming from any of the preceding days spanning a two- or even three- week period.

So, when the Ghana Health Service (GHS) announces that 30 more infections have been recorded between, say, the 10th and 11th of April, the breakdown of that “30” figure could easily be something like this:

A. 10 out of the 30 people announced as positive for that 24-hour cycle may have been tested 3 weeks ago.

B. 11 people tested 17 days ago.

C. 3 people tested 3 days ago.

D. 6 people tested on 10th April.

Thus, one is not looking at some kind of realtime dashboard of a consistently evolving situation. One is, in fact, looking at a mixed reality, composed of different snapshots across time. A lagging, composite, picture; not a sequential reel.

It is thus meaningless to say that infections are growing, slowing, growing faster or slowing sluggishly etc etc by simply relying on these global numbers. The current structure of data collection and delivery does not really allow a mere observer to say that.

The Government itself, on the other hand, has better insight into which tests came from which batches etc and therefore has better official intelligence to make those determinations. The general public unfortunately does not.

When the Government wishes to change the tone of policy, however, it would need to align its private picture of the epidemic with the public picture it has painted over time. That process, currently, is a work in progress as the authorities are now in the process of bringing more testing capacity on board by activating other laboratories in the veterinary services, the Tamale Teaching Hospital, the CSIR, the Food & Drug Authority and even, as I have recently heard, Korle Bu Teaching Hospital.

This will make the official counts (example: 408 infections as of 11th April) a truly dynamic picture of the true trends.

Aligning the *public trend picture* with the *official trend picture* is however only one of the two critical things that have to happen to make government policy more reflective of the supporting data.

The second task is what I mentioned earlier: aligning the confirmed cases picture with the true cases picture by ensuring something as close to a constant/common ratio in the daily progression of announced counts. That is to say, work must be done to increase confidence that the confirmed case count for day one is roughly consistent with the true, unknown, case count on day one and the confirmed case count for day two is roughly consistent with the true case count for day two.

In simple terms, if on day one, there were 200 confirmed cases but the true number of infected individuals is 4000, then if the number of confirmed infections move to 220 on day two, the true level of infections must also shift close to 4400. Note that this is more critical than the absolute number, whether 200 or 220. And therein lies an important distinction between the alignment point canvassed in this brief note and other concerns swirling around about what the true prevalence level might be.

These two alignments would then enable the Government to make forward-looking policy based on whether previous policies are having a statistically significant effect or not.

Until those alignments are in place, policy is merely provisional.

Naturally, I have had to severely simplify epidemiological statistics to a great degree in order to make the quick point I intended to make here. But the core points are valid. Refinements using standard biostatistical methods and techniques won’t change the fundamental insights too much.

How can these alignments be achieved then?

Aligning the public and official trend pictures would require improved logistics for sampling and increased capacity, which the Government is already working on. The strategy there is quite clear.

Aligning the confirmed case count more uniformly with the true level of prevalence requires serious modeling of the spatial distribution of the COVID-19 burden in Ghana presently using historical data of where people from overseas usually disperse among the population. And then conducting mass randomized testing that omni-axially tracks infection dynamics along certain key radial pathways. But it also requires deliberate validation of “control sites”. One does not want to overestimate prevalence anymore than one wishes to underestimate it.

In connection with this second angle on alignment, the government’s plans are vague. What has been said publicly suggests considerable gaps in process design since the entire enhanced tracing regime has been based on direct tracing of returnees and attempts to identify and test their direct contacts?

At any rate, the distribution of contact tracers in the current process does not follow a statistically rigorous distribution pattern.

Well, noted returnee hotspots like Asante Akim and the Techiman area have seen very limited tracing and limited risk-based sampling for mass testing. Nor is any attempt being made to validate assumptions about “non-hotspots” in order to reduce “data anisotropy”.

Part of the challenge arose from the initial skewing introduced by designing contact tracing around the 1030 international arrivals placed under mandatory quarantine. Depending on which day of the week, the cohort of such arrivals would not be adequately representative of international arrivals since the outbreak intensified.

The Government’s decision to extend the coverage to most of the entirety of March helps matters but does not entirely dispel the data challenges since the training and effective distribution of trackers nationwide takes time to build up, during which period case contact trails become more convoluted.

The most critical issue of all, though, is the lack of public awareness, even at elite levels, of these gaps and the timeline for fixing them. This makes political milestone management lax since critical observers don’t know how to measure the progression of the health authorities towards this all critical point of alignment.

The media, in these times, that civil society is taking a backseat to give Government space to focus on relief, needs to better understand the statistical aspects of the pandemic so that they can nudge the government towards delivering and communicating more effectively on the *twin alignments* discussed here.

It is absolutely imperative that Government assessments of whether the country is doing well or not be sufficiently transparent and logically easy to follow so that the roadmap to success is not hijacked by distrust, morbid partisanship and confusion.

When the time comes for the Government to loosen restrictions and actively kickstart the resumption of economic activities, the collaboration of the citizenry shall be vital. Much better if the logical journey to making those decisions has been made clear from the outset to the larger part of the population for the most part.