key: cord-0867727-pfa3h2e8 authors: Knudsen, N. S.; Thomasen, M. M. D.; Andersen, T. B. title: Spread of virus during soccer matches date: 2020-05-01 journal: nan DOI: 10.1101/2020.04.26.20080614 sha: 03004ea35d81a50d48176d1f1bf22a84009e2c89 doc_id: 867727 cord_uid: pfa3h2e8 In the present study, the exposure to virus during soccer matches is calculated. Tracking data from 14 elite matches was used. One player in each match was carrying a virus. The exposure score (measured in seconds) was calculated as time spent closer than 1.5m from the infected player or time spent in an exponentially declining zone where the infected player was positioned earlier. The results reveal that, on average, each player was exposed for 87.8s per match. Following spread of the Covid-19 disease, most countries in the world and the World health Organization (WHO) have emphasized social distancing among the protective measures. This means that sports, in which the athletes are in close contact, are no longer possible. Soccer is one of these sports. The SARS-CoV-2 virus, as other viruses, is generally believed to spread through contact or indirectly through the air (Weber and Stilianakis 2008) . The spread through contact occur when particles from an infected person attach to a surface or an object and other people then touch that surface or object before touching their own eyes, nose or mouth. Furthermore, virus is transmitted through direct contact. The spread through the air occur when an infected person expire particles which are then inspired by other persons. Particles are expired when an infected person coughs, sneezes or through heavy breathing, talking or shouting (Tang 2015) . However, it is not clear how far the particles travel through the air or how long they are viable in the air. A general guideline regarding social distancing is that persons should keep a distance of no less than between 1m (WHO) and 2m (National Health Service, United Kingdom). It is well established that people playing sports are healthier and have a lower risk of numerous diseases (Pedersen and Saltin 2015) . Accordingly, it can be argued, that the lack of possibilities for doing sports is detrimental to the public health. Hence, when a virus spreads through the population, health benefits from social distancing should be weighed against the decline in physical activity through sports. However, the probability of virus spread in soccer is not yet established. Accordingly, in the present study we try to describe the exposure to virus during soccer matches. Data was retrieved from one random match at each stadium in the Danish football league (The Danish Superliga) in the 2018/2019-season. Accordingly, data from 14 matches was used. Player position data was collected using a semiautomatic multiple-camera tracking system (Tracab, ChyronHego®). Data was captured at 25Hz. Player position data was tracked in 2 dimensions (x and y coordinate). Data was filtered using a Butterworth fourth-order low-pass filter with a cut-off frequency of 0.24 Hz filtered the x and y coordinates of the player, using a build in MatLabfunction (The MathWorks, inc., New York, USA). In order to calculate the risk of being infected we calculate a Danger Zone (DZ). We use a distance of 1.5m and calculate the time a player is within this distance from an infected player. Furthermore, we let a tail follow the infected player; a zone where the infected player was positioned time, t, ago. The danger value of this tail is exponentially declining All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 1, 2020. . https://doi.org/10. 1101 /2020 with a half-life of 2s. This means that if a player is positioned within a radius of where an infected player was positioned 2 seconds ago, then the danger value is 0.5 (see figure 1 for an illustration). For every point in time throughout the match, the DZ and the tail of an infected player is drawn and players who are within one of these zones are given a score; a score of 1 if they are within a distance of 1.5m (DZ-score) and a score of 0.5 if they are within 1.5m of the position an infected player was at 2s ago etc. (tail-score). If a player is within more zones at the same time (ie. stationary player) the score is then determined as the maximal score of the zones. Accordingly, the maximal score at any time and position is 1. An exposure-score is then calculated as the sum of all scores divided by the sample frequency (25Hz). This can be translated as how much time a player spent in a risk zone through a match. For example, a score of 55 corresponds to the player standing within a distance of less than 1.5m from the infected player for 55 seconds. The calculations were performed with one infected player in each match and repeated until every player had been the infected one. In 14 matches, a total of 15750 exposure-scores were calculated. Data from the exposure scores were correlated to the time played and a linear regression expresses the fit, and the exposure-score as a function of time played in one half of a match. Furthermore, when presenting mean and confidence intervals, the exposure score is normalised to the duration of a whole match (90 minutes). (15,25) . The color-grading shows the exposure score, both close to the player (<1.5m) and in a tail following the player. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 1, 2020. . The playing time correlates to the exposure score (p<0.001) (Figure 1 ). The mean score per 90 minutes (one match) for each player was 87.8s, with a confidence interval 87.0s -89.6s. The highest score was 656.9s and the lowest score was 0s. Our results show that, on average, a player is positioned within an exposure zone for 1 minute and 28 seconds (87.8s) during a soccer match. We have not been able to find any data on the minimum exposure time before infection. Furthermore, the exposure time corresponds linearly to the playing time. This means that if matches are shorter, then the exposure will be smaller. Both results can be used in the ongoing discussion on the potential re-opening of sports facilities and the playing of soccer matches. Our analysis does not include transmission of virus through contact. This phenomenon will mainly occur when performing throw-ins or during tackles. On the other hand, the analysis does include the celebrations after scoring. Players from the scoring team usually get in close contact when celebrating a goal. In the analysed season an average of 2.6 goals were scored per match. The exposure score can be smaller if players keep their social distance when the ball is not in play. Our analysis only include one infected player. If more players are infected the results can simply be multiplied by the number of infected players. In the present study we used data from elite soccer matches. It is obvious that players at a different level or in different age groups do not move as elite All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 1, 2020. . https://doi.org/10. 1101 /2020 players. Accordingly, it is uncertain how the exposure scores will be for other groups. Based on our analyses, we are not able to conclude whether or not the soccer players are in a high risk of being infected during matches. Our calculated exposure time corresponds to standing within 1.5m of an infected person for less than 1½ minute. Exercise as medicine -evidence for prescribing exercise as therapy in 26 different chronic diseases Investigating the airborne transmission pathway -different approaches with the same objectives Inactivation of influenza A viruses in the environment and modes of transmission: a critical review The authors would like to thank the Danish League (Divisionsforeningen) for letting us access tracking data.