ABC | Volume 114, Nº6, June 2020

Editorial Bittencourt Why We Build Models Arq Bras Cardiol. 2020; 114(6):992-994 of other clinical characteristics. In such case, the patient would have been sent to the Cath lab upon the initial presentation and would not have had MI. Finally, if the patient made into the Cath lab before clinical worsening , he could have been adequately managed medically with aspirin, statins, betablockers and other therapies. In such scenario, this patient could have lived another 10 years without any other clinical manifestation of ASCVD. Would his initial FRS be then interpreted as right or wrong? Models should not be judged right or wrong long after they are developed. The appropriate question is whether the model had been adequately designed to the situation where it is being used, whether the outcome it aims to predict is of interest and whether the information is incremental to what is currently known. When such premises are met, models can lead to more well-informed decisions that may have a meaningful impact. In the case above, the adequate use of the ASCVD score in the initial presentation or a different interpretation of the ST-segment depression could have led to changes in management and completely modify the history of the disease for this patient. While even the most experienced clinical cardiologists are unsurprised by such peculiarities of risk prediction models, they are not always well understood by lay people. A similar problem is now seen with the raising relevance assigned to epidemiological models for the prediction of the COVID-19 outbreak. An initial model published by the Imperial College London suggested that the outbreak could have a major impact across the world, 5 leading to millions of deaths related to COVID-10 in the United States and the United Kingdom. The model also estimated the impact of potential interventions leading to a colossal reduction in deaths. Other models followed, with much lower numbers, sometimes orders of magnitude lower than prior worst-case scenarios leading to several voices in the scientific community, the lay press and the general public to raise strong criticism against those initial models, most of which using current or newer projections to illustrate how “wrong” the initial model was. The development of epidemiological models for COVID-19 have little resemblance to the simpler models used for risk prediction in cardiology. Yet, both use current and prior data to project a future scenario trying to estimate the value of interventions to reduce the risk of negative outcomes. However, due to the limited time since COVID-19 was discovered, several parameters related to the behavior of the virus are estimated based on restricted preliminary data. Sometimes, when no data is available, parameters are only best guesses based on related conditions or prior comparable situations. Additionally, such models are dependent on the viral transmission, a complex process that may involve hard-to-estimate parameters, such as the average number of social interactions each individual has or demographic density in each area. Some of those inputs might not be available and, once again, the best- informed guess is used by modelers. An example is the use of some data from Peru in an ICL model for the Brazilian case for pieces of data that were unavailable for Brazil. With such limited data inputs, it should come as no surprise that such models include immense variability. Yet, this is only part of the issue when interpreting post-outbreak models. Although specific changes in the interventions can be considered in the model, it is impossible to predict how the government or the population will behave in the future, just like one cannot predict if the patient will start smoking when the cardiovascular risk is initially calculated. Even if social distancing is considered in the model, its true impact depends on how much the population follows such measures. For example, while strict measures to increase social distancing had been proposed for the city of São Paulo, the government recognizes they did not achieve more than half of the expected effect. Thus, its impact is also expected to be lower. Yet, even if models are successful, they might be interpreted as being wrong in the future. For example, the aforementioned model from the ICL presented such a catastrophic scenario that it led to substantial policy changes across the world. If those changes led to a substantially lower death rate due to its early and effective implementations, such reduction in deaths could lead to claims that the model was “wrong” because it overestimated deaths. Another important aspect of models in an epidemic such as COVID-19 is that the earlier they are developed, the less information is available, leading to a less precise model. However, the earlier the model is developed, the larger the impact of interventions derived from it. In a world of perfect information, COVID-19 could have been extinct if the information we currently have were available when the first case was diagnosed and the first case and its contacts were isolated. On the other extreme, perfect details of transmission and viral spread would be of little social impact after the outbreak ended. Hence, we are left to live with the uncertainty and imprecisions derived from models, particularly if we expect that such models will appear in time to guide effective policy interventions. Thus, to have successful models, we need to accept, understand and acknowledge such limitations. Additionally, we need to be humble enough to adjust the sails to the ever-changing wind conditions update and improve our models along the way. Each model should only be judged bearing in mind the time when it was developed, including the limited knowledge available back then. In the end, it would be just like Francisco’s case: we could have improved the initial risk prediction and the course of his life with a better initial model to estimate his cardiovascular risk. Yet, after his myocardial infarction, even perfect information on his cardiovascular risk would be of little value. Just like in clinical medicine, when those epidemiological models are evaluated, we should refrain from being next day’s doctors who are always right after the diagnosis is known. Instead of aggressively pointing fingers at models that are known to be uncertain, let us be humble and practical when evaluating them. Was the model able to better inform interventions at its time and was it able to reduce, even by a little, the imprecisions we had? If yes, then the models were useful, even if wrong. 993