Published in Economic & social issues · 30 May 2020
Release date: May 2020
What might have happened without lockdowns?
Coronavirus (Covid-19) forecast slide decks are provided for the first wave below, to explore what might have happened under a ‘no lockdown’ scenario; where voluntary social distancing occurs, but without a forced lockdown. Included below are detailed assumption notes and a forecast per country 365 days ahead (starting with the day of the first case).
Why forecast under a no lockdown scenario?
Allows a more accurate estimate of the eventual number of official cases, actual infections and fatalities; since the data is not distorted by the impact of an artificial lockdown (which in turn distorts estimates of model parameters). Only data available pre-lockdown is used to fit the model.
Provides an indication of when the number of new daily cases would have peaked in the first wave without a lockdown, allowing a comparison with the eventual reality.
Early indications are that the forecast model provided a good approximation to reality. The 365 day forecast released on the 18th of May 2020 (for SA) forecast fatalities between 12,074 and 48,287 by the end of the 365 day window (ending March 2021). As of 25 August 2020, actual fatalities equal 13,159, which is within the forecast boundaries. This number is expected to increase, but not exceed the forecast upper boundary of 48,287 within a first wave. However, if a vaccine is not available in time, a second wave may exceed this estimate.
Forecast slide decks by country
The primary impact, under the no lockdown scenario, is that the speed at which infections spread is greater, and so the peak is reached more quickly.
The forecasts below give an indication of what might have happened if voluntary social distancing had been the only non-medical intervention. The models are estimated based on actual data prior to a lockdown. The ‘official’ daily new case data was pre-processed. To improve the accuracy of the forecasts the official daily new case data was adjusted to estimate the actual number of new infections each day. This was further adjusted to account for the number who are currently infectious (contagious) using a moving window. The two days prior to the lockdown were excluded from the data used to fit models (in each country) since some may have changed behaviour in anticipation of a lockdown.
Original code from Learning Machines. Acentric adapted the original code to improve accuracy. The original code input 'cumulative cases' which is not suitable for a SIR model. A new module was created to estimate daily new 'infectious' (as opposed to 'infections' or 'cases' which are entirely different metrics) to provide the correct input and to automate data gathering and reporting.