Pandemic Forecasting: Between Astrology and Science
By: Asfa Asfeeya
Tags: Coronavirus, Pandemic, Science, Forecasting, Indonesia

Global data states that victims exposed to COVID19 are those from high-income groups. To this pandemic, several predictions related to COVID19 were made by various institutions, for example, University of Indonesia (March 24, 2020) with the Center for Mathematical Modeling of Infectious Disease model, stating that there are about 70,000 undetected cases and as many as 250,000 potential cases in Indonesia.

Then research from Eijkman / Oxford Clinical Research Unit (March 20, 2020) stated COVID19 cases in Indonesia could reach 71,000 cases by the end of April. Bandung Institute of Technology (March 15, 2020) stated an increase of 600 cases per day until April, and the State Intelligence Agency (March 2020 13,) stated that the peak of COVID19 in Indonesia would be in May 2020 with the number of positive cases of 95,451.

Forecast in the economy is something that is commonly done especially to predict economic growth. In forecasting, the form of revision/reprojection is commonplace. Forecasting is guessing what will happen in the future, but that doesn’t mean everyone is a forecaster. For events that have a broad impact, the “forecast” must be left to the experts or what is called a “super forecaster”.

In literature, prediction and forecasting are often considered to have the same meaning. However, the term projection tends to be more “dreamy” or less realistic and is long term. According to Massad et al. (2005) in his paper entitled Forecasting Versus Projection Model in Epidemiology, forecasting is an attempt to predict what will happen, while projection is an attempt to describe what will happen based on assumptions, so that projection is considered weaker than prediction.

Then, the paper by De La Barrera, CA & Reyes Teran (2005) entitled Influenza: forecast for a Pandemic, the author states that a new pandemic appears with three prerequisites:

A new virus, Can enter the human body and cause extreme pain. The virus can transmit from one body to another.

So actually from 2005, we have known that there will be a possibility of a pandemic. But uniquely, Bill Gates at the TED Talk in 2015 stated that we never prepared anticipation for this possibility. And this has been proven recently by the condition of western countries like Italy, which are actually overwhelmed and tend to give the impression of giving up due to this pandemic. Moreover, countries in the world tend to budget for preventive action in the form of protection against visible attacks (weaponry systems), rather than improving health systems.

Prediction of a pandemic needs to be done because the handling of a pandemic is extremely dependent on prevention and intervention. Predictions about the impact and duration are needed. Therefore, to be able to answer these needs, accurate predictions are very dependent on a solid scientific basis. So far, mathematical models provide a pretty good template in which theory or model can be tested, refuted, and proven. Mathematical models in health issues aim to scientifically understand the issues faced in the context of current theory, properly identify the types of epidemiological data that need to be collected, formulate a testable hypothesis, and make predictions.

In economics and business, forecasting with a statistical approach consists of several types of techniques:

Qualitative. Example: Delphi, Market Research, Panel Consensus, Visionary Forecast, Historical Analogy Time series. Example: Moving Average, Exponential Smoothing, Box Jenkins, X-11, Trend Projections Causal Method. Example: Regression Model, Econometric Model, Intention To Buy And Anticipations To Survey, and Input-Output Models

In health science, several approaches are taken—for example, deterministic vs probabilistic, comparative methods vs generative methods. However, in general, forecasters rely on two sources of information and data from experts opinion (medical expert researchers) and time-series data (series of pandemic events, outbreaks). Data processing can be done with mathematical or statistical modelling. For example, “flatten the curve”, which uses the term statistics.

Pandemic forecasting analysis: case study H1N1- 2009, in Michele Tizzoni’s paper “Real-time Numerical Forecast of Global Epidemic Spreading: Case Study of 2009 A / H1N1pdm.”

Begin with an explanation of the basic theory of the spread of the flu virus starting from the time of infection (susceptible), latency period (latent), infectious period (symptomatic, infectious (no travel and travel), asymptomatic infectious), and recovered.

Then an explanation of the global epidemic and mobility model, using demographic data, mobilization and epidemic models. Furthermore, the data were processed using 2 main research questions, namely intra-population transmission dynamic and inter-population mobility. And the research output is a forecast.

In the forecasting process, parameters are the keywords (the model’s mainstay). In the case of this paper, several parameters were chosen, among others: Reference reproduction number in the topic (R0), Generation Time (GT), and so on. The more precise the parameters, the better (accurate), the resulting forecasting is. It is also necessary to pay attention to the error rate to support forecasting accuracy.

Judging from the facts that occurred in Indonesia, the shortage in Indonesia in the delivery of communication-related to the pandemic is the absence of coherence information so that overlaps appear between the messages conveyed. Also, the information conveyed regarding the pandemic is more of an astrological nature than forecast with a scientific model whose impact actually degrades the level of people’s consciousness so that they tend to make their own decisions and to be inattentive.

(Summarized from Tamanni, 2020)