Predicting SARS-CoV-2 infection trend using technical analysis indicators

Paroli, Marino; Sirinian, Maria Isabella

COVID-19 pandemic is a global emergency caused by SARS-CoV-2 infection. Without efficacious antiviral drugs or vaccines available, mass quarantine has been the main strategy adopted by governments to contain the virus spread. This has led to a significant reduction in the number of infected people and deaths and to a diminished pressure over the health care system. However, an economic depression is following due to the forced absence of worker from their job and to the closure of many productive activities. For these reasons, governments are lessening progressively the mass quarantine measures to avoid an economic catastrophe. However, most people has not been yet exposed to the virus, so are susceptible to Covid-19. Therefore, the reopening of firms and commercial activities might lead to a resurgence of infection, as already reported in South Korea and in China were epidemic was considered almost gone although in a few of cases. In the worst-case scenario, this might impose the return to strict lockdown measures. Epidemiological models are therefore necessary to forecast possible new infection outbreaks and to inform government to promptly adopt new containment measures. In this context, we tested here if technical analysis methods commonly used in the financial market might provide early signal of change in the direction of SARS-Cov-2 infection trend in Italy, Iran and Brazil, three countries which have been strongly hit by the pandemic but whose present infection trends are substantially different from each other being therefore particularly useful to understand the potential of TA models. We conclude that technical analysis indicators can be usefully adopted to this aim.