How much is social outbreak

There may be limits on how many people can gather, both outdoors and indoors, including at places of worship. Be aware of how many people can visit your home. If you want to gather in larger numbers, you may be able to do this in an outdoor public space such as a park or the beach.

If you attend a mass gathering — such as a protest or sporting event — and do not feel unwell, you don't need to self-isolate or be tested afterwards. If you have any symptoms at all, do not go to a mass gathering. Get tested and self-isolate until you receive your result.

Stay home until the result is available. This will help with contact tracing , which may limit any outbreak that occurs. When you're out in public, it's important to maintain good hygiene and physical-distancing practices:. Information is also available in Aboriginal languages NT. Learn more here about the development and quality assurance of healthdirect content.

If you have children or teens with disability, autism or other conditions, our tips can help you with physical distancing and staying at home during COVID Read more on raisingchildren. Physical distancing can be challenging and positive for families. Support family wellbeing with quality time, routines, connections and conflict management. Staying home and social distancing is an important way to reduce the risk of catching COVID coronavirus.

Learn how to survive social distancing with your baby or small children. Kids need good information, plus opportunities to talk about feelings. Here's what you should know about COVID, including the symptoms, how to self-isolate or practise social distancing, and how to explain the virus to children.

Information for pregnant women and parents on how to keep you and your family safe during the coronavirus COVID pandemic. The rollout of the COVID vaccine has begun, but if you are pregnant or breastfeeding, you might be wondering whether it is safe for you to get vaccinated.

About COVID vaccines and vaccination programs to protect yourself and others and help stop the spread of coronavirus in South Australia. Read more on SA Health website. Read more on Gidget Foundation Australia website. Pharmacotherapy, in the form of opioid replacement therapy ORT , is the replacement of a drug of dependence, such as heroin, codeine and OxyContin, with a legally prescribed substitute. Read more on Alcohol and Drug Foundation website.

Read more on Australian Prescriber website. With the global COVID pandemic having a severe impact on all aspects of society and the health of people worldwide, it is now more important than ever to update your knowledge on the spread and containment of infectious diseases, and what you can do to help break the chain of infection.

Social norms reflect commonly shared beliefs about appropriate actions, with the expectation that others will follow them, too. These appropriate actions then become prominent among all possible behaviours. They stand out. Take, for example, greetings. Shaking hands is normally a prominent action that is used to greet others in many cultures. By extending your arm, you expect the other person to do the same.

If this belief is shared, the two of you shake hands. For preventive measures to become prominent and gain footing as a new norm, common knowledge needs to be provided by health authorities to instill common beliefs. Will public messages be efficient enough and sufficient to achieve consensus around new behaviours?

No one knows. However, there are lessons to be learned from the H1N1 influenza outbreak, which emerged in the spring of , and has some similarities to and important differences from the coronavirus outbreak. The H1N1 influenza was less contagious and less fatal than COVID, and its social and economic impacts were not as severe as that those we are likely facing today. In , authorities recommended self-isolation only to people with flu-like symptoms and asked them to seek medical care only if their symptoms worsened.

This was the extent of efforts needed to stop virus transmission and to avoid overcrowding emergency rooms. By late November a targeted H1N1 influenza vaccine had become widely available , adding one more thing to the list of prevention measures. However, the main public messages about prevention in overlapped with those we hear today: wash your hands, cough or sneeze into your sleeve, and stay home if you have symptoms. Tailored to create common beliefs, these messages were highly visible in public spaces, in the media and online.

To understand how Canadians responded to these messages, we conducted an economic experiment with volunteers in Montreal, measuring changes in their common beliefs about prominent health actions before and after the H1N1 outbreak.

Our findings imply that exposure to common information, including messages from public health authorities, was responsible for these changes in common beliefs. First, public messaging that strongly emphasizes the health of others is even more important today than it was for H1N1 influenza, because COVID can be transmitted by nearly asymptomatic people who may be unwilling to self-isolate for weeks. In , the focus of official messages on the public health benefits rather than personal needs appeared to work.

In , young adults were more vulnerable to the H1N1 influenza than were older individuals. My estimates highlight the importance of interdependencies and social networks in the transmission of coronavirus cases, in the increase of risk perceptions, and in social distancing behavior.

Domestic variables, for the vast majority of countries, are significantly affected by foreign aggregates, constructed with weights based on the strength of social connections across countries. Given the role played by Italy and the USA as centers of the outbreak in different phases of the epidemic, I study how variables in the rest of the world respond to coronavirus shocks originating in these countries.

I document strong and significant responses of risk perceptions and social distancing to the Italy COVID shock almost everywhere in the world. Countries also respond to the subsequent US shock, although with a smaller magnitude. Spillovers from Spain and the UK also play a sizable role. I can, however, reveal some common patterns. The countries that respond with social distancing do so with a delayed and sluggish adjustment. They seem to learn from the experience of other countries, but they display an adaptive behavior: they do not adjust their habits instantly; instead, they gradually reduce their social mobility, which reaches a negative peak almost a week after the shock.

In the opposite direction of causality, changes in social distancing lead to a decline in the growth rate of COVID cases. The implications of the pandemic for unemployment also vary significantly by country. Labor markets in the USA and Spain are the most negatively affected, with large expected increases in unemployment rates. But large spikes in unemployment are not inevitable since most other countries seem to experience much more contained fluctuations. The results suggest that different institutional features can partly insulate the corresponding populations from the worse effects of large exogenous shocks.

Due to the historical importance of the COVID pandemic, research related to the disease and its effects has been growing swiftly. In economics, a number of recent papers have adopted a similar framework and developed the theory further by adding relevant trade-offs between health and economic costs e.

This paper, instead, takes a different route by providing empirical evidence related to the social response to the outbreak, and using an alternative framework. In contrast to studies using the SIR model, I do not aim to predict the evolution of the number of infected individuals in a population; my focus lies more on explaining the social responses to the original health shocks around the world.

Other recent works investigate the determinants of different approaches to social distancing. Gupta et al. Painter and Qiu and Adolph et al. Andersen finds evidence of substantial voluntary social distancing, and he also shows that it is affected by partisanship and media exposure. In light of these results, my approach does not use data on mandates, but it exploits, instead, the actual decline in mobility, as measured using location tracking technologies.

Qiu et al. They provide empirical evidence on the transmission of coronavirus cases across cities in China between January and February.

They estimate how the number of new daily cases in a city is affected by the number of cases that occurred in nearby cities and in Wuhan, over the previous 2 weeks. They show that social distancing measures reduced the spread of the virus, whereas population flows out of Wuhan increased the risk of transmission.

My paper stresses the importance of modeling cross-country interrelationships to understand the evolution of the next phase of the pandemic. A recent work by Zimmermann et al.

They find that countries that are more globalized are affected by the pandemic earlier and to a larger extent. Therefore, they discuss how early measures that temporarily reduce inter-country mobility would be beneficial.

Most papers in the literature consider macroeconomic applications and study the global spillovers of policy and other shocks e. Others have studied interdependencies in housing markets Holly et al. The effect of foreign variables is usually assumed to depend on trade balances across countries. My framework, instead, introduces a different connectivity matrix, based on social networks, which can be promising for a different set of applications.

Therefore, my paper is also connected to recent papers that propose the use of Facebook connections to measure social networks across locations Bailey et al. Finally, I measure risk perceptions and fears of unemployment using Google Trends data.

Askitas and Zimmermann discuss how Internet data can be useful for empirical research in a variety of social science applications and, in particular, for research about human resource issues Askitas and Zimmermann and Simionescu and Zimmermann provide evidence directly related to the unemployment rate. I investigate the connections among these variables both within countries, and across borders, by studying contagion and spillovers internationally. The data are collected on a sample of 41 countries.

The estimations use either the growth rate or, as a robustness check, the number of daily cases. The epidemiology literature stresses the importance of social distancing to contain the spread of the virus, by reducing the basic reproduction number R 0 the expected number of secondary infections produced by a single infection in a population where everybody is susceptible and flattening the curve of infected individuals.

The response has been different across countries, either in terms of policies, enforcement, or voluntary reductions in mobility. Therefore, it is important to have accurate data on actual social distancing by different populations to track the implied health and economic effects. To this scope, I use daily time series indicators on social mobility made available by Google. The data measure the change in the number of visits and length of stay at different places compared with a baseline.

For each day of the week, mobility numbers are compared with an historical baseline value, given by the median value for the corresponding day of the week, calculated during the 5-week period between January 3 and February 6, The data are reported for five place categories: grocery and pharmacies, parks and beaches, transit stations, retail and recreation, and residential.

The risk perception is measured using daily data on Web searches from Google Trends. Finally, I similarly use an indicator of unemployment to measure the initial economic effects of the outbreak. Given that actual unemployment data are typically available only at monthly frequency and that their release is lagged by more than a month, I also exploit Google Trends data about unemployment as a variable that can be used to have early and real-time indications of the official variable.

Askitas and Zimmermann and Choi and Varian , among others, show that unemployment searches can help predict initial unemployment claims and the unemployment rate.

More recently, Askitas and Zimmermann and Simionescu and Zimmermann document how Internet data can be useful for nowcasting and forecasting the unemployment rate in a diverse set of countries. Footnote 5. The index uses active Facebook users and their friendship networks to measure the intensity of connectedness between each pair of locations. The measure of Social Connectedness between two locations i and j is given by:. The Social Connectedness index, therefore, measures the relative probability of a Facebook connection between any individual in location i and any individual in location j.

The data used in this paper refer to the measure calculated for March Bailey et al. Other current papers are uncovering the link between social networks and the diffusion of COVID e. The measure can be preferred to alternatives based simply on inverse geographic distance, since it can provide a more accurate account of business relations, tourism patterns, and family or friendship ties, across different areas.

I argue here that the strength of social connections can also affect information about the outbreak and social distancing responses. As Bailey et al.

They regress social connectedness on geographic distance, the number of residents with ancestry in the foreign country as an indicator of past migration , and on the number of residents born in the foreign country indicating current migration , and show that all three are strongly significant. Friendship connections also lead to statistically significant increases in both exports and imports between the USA and the foreign country. Figures 1 and 2 show the likelihood of social connections across countries, with Italy and the USA chosen as examples and, therefore, shown in red in their corresponding figure.

Social connections between Italy and the rest of countries in the sample. The reference country Italy is shown in red; social connections are measured with different tonalities of blue, with darker tones denoting stronger connections; countries that are not considered in the estimation are in gray.

Social connections between the USA and the rest of countries in the sample. The reference country the USA is shown in red; social connections are measured with different tonalities of blue, with darker tones denoting stronger connections; countries that are not considered in the estimation are in gray.

Distance is clearly a determinant of social networks, but not the only determinant. For the USA, as expected, the most socially connected countries are Mexico and Canada, followed, at lower levels, by Ireland and Israel.

The USA have strong connections with Australia and New Zealand, which would be downplayed based on a pure measure of distance. Figure 3 shows, instead, the social distancing response across a sample of major countries in the sample for easiness of exposition, I show the experiences of 15 out of 41 countries in the figure.

While in some countries, the adjustment was abrupt e. Sweden is an outlier in Europe, as it maintained only small fluctuations of mobility around the historical mean. Japan and Korea observed their first cases earlier; therefore, their social distancing responses during this period appear more limited. In many European countries and in the USA, mobility returns to its historical average by the beginning of June.

Assume that there are N units, representing countries in this case, and for each unit, the dynamics is captured by k i domestic variables. Footnote 6 The vector of domestic variables is modeled as:.

For each country i , therefore, domestic variables are a function of their p i lagged values, possibly of the contemporaneous values and q i lagged values of foreign, or global, variables. My approach uses the extent of social connections across country borders from the Facebook Social Connectedness index dataset to measure the weights.

This assumption can be easily tested for all the variables. For cases in which a domestic variable has an unduly large effect on global variables, weak exogeneity will not be invoked there and the foreign variable, instead, will not be included in that VAR. The estimation works in two steps. Second, the estimated country models are stacked to form a large GVAR system, which can be solved simultaneously.

Footnote 8. The model in Eq. Substituting into Eq. The GVAR solution can be used to trace the impact of shocks on the variables of interest, both domestically and globally. The vector of GIRFs is given by:. Spatial VARs are very strongly connected.

They also assume a connectivity matrix, which is usually based on geographic distance. The main difference between the two approaches lies with the structure of correlations: as discussed at length in Elhorst et al. The approach can similarly be seen as a particular form of panel VAR. The main advantage here is that, through the weight matrix W i , this approach exploits knowledge about social networks and uses that knowledge to inform the magnitude of cross-country interdependencies.

Panel VARs often impose the same coefficients for each unit, shutting down static and dynamic heterogeneity, as well as neglecting cross-country interdependencies. An exception is provided by Canova and Ciccarelli : they introduce a factor structure in the coefficients to solve the curse of dimensionality. Their approach is particularly useful when there is no a priori knowledge that can be exploited about the spillovers.

In this case, the extent of social networks can be, instead, exploited to provide some information about the relative strength of interdependencies. Finally, the GVAR has relations with dynamic factor models. As Chudik and Pesaran show, the GVAR specification approximates a common factor across units, and it extracts common factors using structural knowledge. The model is particularly suited to account for potentially complex patterns of interdependencies across countries.

At the same time, the GVAR specification does so while maintaining simplicity and parsimony. The dimensionality issue is resolved by decomposing a large-scale VAR into a number of smaller scale VARs for each unit, which can be estimated separately, conditional on the dynamics of weakly exogenous foreign variables. The interdependencies are not left entirely unrestricted, since it would be unfeasible to estimate all the parameters, but they are given a structure based on knowledge of the data.

The dates are chosen based on availability of Google social mobility data at the time the paper was written. Those countries, at different times, have accounted for a large share of global cases. I also allow risk perceptions in each country, as well as social distancing outcomes, to be affected even contemporaneously by corresponding variables in different countries.

Finally, I assume that domestic unemployment perceptions are affected by foreign unemployment perceptions, but not within the same day. This assumption is not important for the results which are robust , but it is motivated by the idea that the unemployment data are driven more by country-specific, than across-the-border, factors.

I test the weak exogeneity assumptions for all foreign variables, and they are never rejected in the data. Recently, some studies have emphasized the importance of superspreaders in the transmission of the virus e. Beldomenico discusses how SARS-CoV-2 appears to start by spreading gradually in a region, until transmission is triggered by a possible cascade of superspreader events, and cases explode.

As a result, the pattern of transmission can become highly heterogeneous. Here, I focus on numbers of cases aggregated at the country level. My framework can account for heterogeneous responses across countries. However, even if the weak-exogeneity tests suggest that domestic countries do not affect global variables in a statistical sense, it is conceivable that, with superspreaders, COVID infections can transmit very quickly, and do so even between country pairs with a limited degree of social connections.

My identification assumption, however, requires that the impact of a superspreader from country i on the total number of global cases remains small enough.

First, to study the magnitude of global interdependencies, Table 1 shows the contemporaneous effects of foreign variables on domestic variables, for each country. The table reports the estimated coefficients, alongside the associated standard errors.

Domestic variables are significantly affected by the country-specific foreign aggregates, computed using the matrix of social connections as country-by-country weights.

The results indicate that the international spread of COVID cases can be, in part, explained by existing social networks across country borders. Moreover, the contagion not only relates to the number of cases and the spread of the disease, but it also affects the spread of perceptions and social behavior.

Both the measure of risk perceptions about coronavirus and the social distancing responses are significantly influenced by developments in the rest of the world. Only few countries do not show a statistically significant response to global conditions. Risk perceptions do not rise in response to increasing international distress only in Brazil, South Africa, and Turkey.