Modeling the COVID-19 outbreaks and the effectiveness of the containment measures adopted across countries

Edward De Brouwer; Daniele Raimondi; Yves Moreau

Effectiveness of containment measures. Containment measures in China significantly reduced COVID-19 spreading. The first lockdown resulted in a 65% decrease of the reproduction factor R 0 and the second, stricter wave of measures eventually managed to bring it to close to 0. We do however observe that while the model was able to fit the Italian, Belgian, and Spanish data relatively well, its fit of the data from China was rather mediocre. We are unsure about what could have caused this discrepancy. Containment measures in Italy appear to have had a more gradual effect. The reason for this is not entirely clear, but data from the Lombardy region, based on anonymous cell phone tracking (not showed here), suggests that almost 40% of the population of Lombardy were still commuting and moving around notwithstanding quarantine measures, although the trend from February 26, 2020 and March 16, 2020 clearly indicates a progressive reduction of displacements. This progressive trend is also consistent with the Community Mobile Reports provided by Google. Such large percentage of the population moving across Lombardy might in part be explained by the fact that factory closures were only partial until March 23, 2020, when the Italian government issued a decree mandating the immediate halt of all non-essential production, industries, and businesses across the country. Data from the Italian Ministry of Interior (17) also indicates that during the lockdown 1.7 million police controls were carried out with infractions to the containment registered in 4% of controls. The analysis of the data from Belgium suggests at first that the enforcement of the first lockdown measures were counterproductive. This is very unlikely. Instead, a plausible explanation may lie in the variation of number of tests during the the transition period between the two belgian lockdowns. As show on Figure 10 in the Supplementary, the number of tests significantly dropped during this period, leading to underreporting of new infections. When, 5 days later, the number of tests increases again, so does the number of newly infected patients. Under the assumptions of our model, this rapid but rather artificial sudden increase of infected patients is supposed to have been contaminated by patients infectious during the between lockdowns period. As this number was underestimated, the R 0 automatically increases to account for the too low number of infectious patients. More generally, the compliance of the population to the measures was progressive as suggested by seismic data (18) and community mobility reports of Google.