Reading the COVID-19 Data
As of April 2020, the number of COVID-19-positive cases in the world has exceeded the figure of 1.7 million, with a mortality rate of around 6.1 percent
As of April 2020, the number of COVID-19-positive cases in the world has exceeded the figure of 1.7 million, with a mortality rate of around 6.1 percent (Johns Hopkins data, 11 April 2020).
The rate of spread and death rate that continues to increase is troubling many. Efforts to find vaccines, treatments, and to curb the spread of the coronavirus outbreaks need to be continuously carried out. The discovery of vaccines or treatments of the coronavirus is the most desired thing at the moment. However, that does not mean it is easy to do. In the course of history, the discovery of drugs and vaccines often requires a research process and a long time.
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In the case of HIV/AIDS, for example, treatment was only discovered a decade after it became epidemic in the 1980s. Even that was not in the form of a vaccine (increasing immunity), but rather reducing the risk of a death judgment to a crisis condition. As of 2018 37.9 million HIV positive cases were still found (WHO data, 2018). A similar case arises in malaria.
The disease began to become an epidemic in the middle age, but only in 2016 did WHO recommended a vaccine that was believed to be effective (WHO, 2016). Reflecting on the two experiences, efforts to deal with Covid-19 will be difficult if only awaiting the discovery of vaccines/drugs. Strategic steps and interventions that can reduce the spread of the epidemic significantly in the short term are needed.
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Coronavirus curve and the missing details
Many countries refer to what is known as the "coronavirus curve" in reading the dynamics of epidemic development. This curve is the result of a long study conducted by scientists on the pattern of disease outbreak spread -- which was then contextualized in the Covid-19 case. This curve basically shows the prediction of the number of cases in the passage of time with a gaussian pattern that resembles the letter "n".
The predicted natural pattern on the curve is that the number of coronavirus patient cases will increase sharply and is beyond the capacity of a country\'s health services. This is certainly very terrible and needs to be avoided. Therefore, one of the recommendations that arises is that countries need to take steps or interventions that can stop/reduce the spread, at least until it is under the control of the country\'s health service capacity. The manifesto of this recommendation can be seen in various policies taken by numerous countries at present, ranging from social distancing to lockdown.
Although many countries carry out similar policies, the implications that arise can be different. There are countries that are still experiencing a surge in cases and there are also countries that are getting stable (reduction in the spread). However, this cannot be interpreted immediately that policies in one country are more effective than others. There are many details that are missing in the curve and need to be read deeper. Some of them are when and under what conditions a policy is implemented, issues of citizens\' order in complying with regulations, population density, frequency of human movement, and most importantly, data on the number of tests being carried out and the scope of the tests.
Data on the number of tests actually has a crucial role in ensuring the accuracy of the coronavirus curve. Frequently, this data is read separately from the increasing number of cases. In fact, the correlation between the two is difficult to separate. As an illustration, the number of coronavirus cases in country A will be stable (no additions) if no tests are performed that day. Likewise, the number of coronavirus cases will increase drastically if the test is done on a massive way on one day.
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In that context, the interpretation of the number of additional cases (without being accompanied with data on the number of tests per day/week) may be inaccurate and give a "wrong message". For example, country A believes that the number of cases has declined. However, it turns out, that number falls because tests are no longer carried out, and not because the number of the spread decreases in real terms.
Likewise, for example, in the case of country B which believes its country is still "safe" because the rate of the spread is only around 100 cases per day, not as massive as country C which reaches 800 cases per day. In fact, country B conducts "only" 150 tests per day, while country C carries out 1,000 tests per day.
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Such mistake in reading certainly needs to be watched out by every government because it can have fatal consequences: encouraging over-reacted or under-reacted steps. This note is also important for the Government of Indonesia, especially considering that Indonesia has only tested as many as 19,500 or around 0.007 percent of the population as of 10 April 2020.
With the "loss" of this data from the curve, it will be difficult for the government to read the dynamics and patterns of epidemic development in a real and comprehensive manner.
The number and percentage are still very low compared to South Korea, which had conducted 510,479 tests or the equivalent of 1 percent of the population; and Italy 963,473 tests or 1.6 percent of the total population, on the same date (data 11 April 2020, ourworldindata.org). Data on the number of tests per day/week is important to include and to be analyzed together.
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Another thing that also needs to be watched out in the reading of the curve is the scope of the tests. Most countries only test people under monitoring (ODP) or people who have symptoms and/or have had interactions with coronavirus-positive patients. This is not wrong, but there are implications for the way of reading, namely the graph that appears on the relative curve will only represent the ODP "group" and not the entire population in the country. This kind of reading can be said to be partial.
The fact is that the spread of coronavirus cases is even more widespread because of the large number of human groups that carry the virus, but do not feel any symptoms. This group is part of the population, but is not part of the ODP or the group that is undergoing testing. With the "loss" of this data from the curve, it will be difficult for the government to read the dynamics and patterns of epidemic development in a real and comprehensive manner.
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Reflecting on the previous discussion, it is important for the government to be proactive in conducting tests on all citizens (not only ODP), transparent about the number of tests conducted per day/week, and analyzing the test data together with the number and rate of coronavirus cases. It has to be admitted that the urgency of the government\'s proactive stance is only recently realized by many stakeholders. For example, the Chinese Government only began to expand the scope of the tests (not only ODP) in April.
Besides providing the reading in an accurate and comprehensive way on the development of the epidemic, the government\'s proactive stance in conducting the tests can actually also minimize the mortality rate. This can happen because the sooner someone affected by the virus is handled professionally, the risk of death will be smaller.
Germany is one country that can be used as an example in this context. Although the number of positive cases of coronavirus in Germany are among the five highest in the world, reaching 125,452 on 11 April, the number of deaths in Germany is very low compared to other countries. The comparison: Germany is around 2.2 percent; US 3.8 percent; China 4 percent; Indonesia 8.5 percent; Spain 10 percent; Italy 12.7 percent. Germany constantly conducts tests on all citizens (both on those with symptoms and without symptoms), covering as many as 350,000 tests per week (New York Times, 4/4).
Finally, the government\'s proactive stance in dealing with Covid-19 is indeed the key in stopping the spread of the outbreak. Therefore, efforts to make early detection (increase the number of the tests) as well as how to read accurately and comprehensively are important in order that the policies being taken are accurately on target and measurable.
Richard Mengko, Retired lecturer in biomedical engineering at the Bandung Institute of Technology (ITB).