The epidemic is intensifying in Hungary, more and more people are staying at home

To successfully break the epidemic, people’s mobility and opportunities to meet must be reduced, says the classic epidemiological rule, which everyone could experience on their own skin during the first wave of spring, during access restrictions. At that time, Google’s mobility index in the categories studied fell 30-50% compared to the average mobility for the same days in the first five weeks of January. Then, when the epidemic and the restrictions eased, life began to return to normal levels of mobility, although not in all categories. The figure below clearly shows that time spent at work and perceived mobility in public transport hubs did not return to pre-epidemic levels even in summer.

Unfortunately, Google has yet to announce changes to some of its mobility sub-indices since August 20, as it revises its data to more accurately relate actual population mobility to a location marked on its own map, such as a restaurant. However, it provides mobility indices at work and in places considered for housing and at the end of the time series (September 27). the rate of time spent at home appears to have increased slightly. The mobility rate for time spent at work is permanently 15% lower than in the pre-epidemic period. This suggests that the phenomenon of the home office – true not as intensely as in spring, but – really lives permanently with us, as you can see in a CSO data post:

According to the infection data, the German epidemic has already placed Budapest, Győr-Moson-Sopron county, Pest county, Csongrád-Csanád and Vas on a “ban list”. This means that it does not recommend its citizens to visit it, and special quarantine rules apply to people traveling from here to Germany. The fact that more and more counties can be added to the list was already written last weekend, and this week it happened in the case of 3 counties. Because of all this, we’ve now seen how these two mobility time series, which Google continues to report, have evolved in these counties. As we can see in the following figures:

In each area, the rate of time spent at home increased, while the rate of time spent at work did not show a uniform picture.

At the time of our analysis last weekend, Békés, Zala and Heves counties were considered the least infected counties, so we also looked at how the two types of mobility subscripts develop. Here we also see the same pattern as in the counties said to be more infected, suggesting that the population may begin to be a little more cautious in the second wave of the epidemic, spending more time at home at worse times, and therefore the number of contacts may decrease. In addition, obviously, the near-zero mobility of people in home quarantine can also affect mobility rates.

These graphs do not show a drastic change in mobility, suggesting that the second wave of the epidemic has yet to have a marked slowdown effect on economic activity.

We emphasize that all this only reflects the mobility that can be extracted from the cellular information of the cellular phone (and for some important subscripts there are no recent time series), which may include measurement errors, so we cannot extract further statements. solid data from these data. However, its great advantage is that this data is available almost in real time, so we will continue to pay special attention to if they can show a substantial change in the behavior of the population due to the intensification of the epidemic. Because if they are shown, it also indicates a change in economic activity, so it has informative value that is relevant to us.

Cover image source: MTI / Tamás Sóki