 Original article
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Saving lives during the COVID19 pandemic: the benefits of the first Swiss lockdown
Swiss Journal of Economics and Statistics volume 157, Article number: 4 (2021)
Abstract
The implementation of a lockdown to control the spread of the COVID19 pandemic has led to a strong economic and political debate in several countries. This makes it crucial to shed light on the actual benefits of such kind of policy. To this purpose, we focus on the Swiss lockdown during the first wave of COVID19 infections and estimate the number of potentially saved lives. To predict the number of deaths in the absence of any restrictive measure, we develop a novel agestructured SIRDC model which accounts for agespecific endogenous behavioral responses and for seasonal patterns in the spread of the virus. Including the additional fatalities which would have materialized because of the shortage of healthcare resources, our estimates suggest that the lockdown prevented more than 11,200 deaths between March and the beginning of September 2020.
Introduction
Since the end of 2019, all countries in the world have experienced a rapid spread of the COVID19 epidemic, which has required the fast development of appropriate policy responses to face the increasing number of infections, hospitalizations, and deaths. The majority of governments have therefore introduced different types of measures to reduce contacts among people. Such interventions have included bans on public events and gatherings of people and closures of national and regional borders, as well as school closures and the interruption of all nonessential business activities. These policies have been at the center of a heated debate, mainly due to their high economic and social costs.
A lockdown may have substantial negative effects on economic activities, leading to business disruption, job losses, and earnings reductions. Recent surveys reveal that at least 42% of young people experienced a deterioration of their career prospects and serious income losses (ILO, 2020). Such detrimental consequences in terms of learning outcomes and disposable income are also reverberated in lower levels of wellbeing and worse mental health conditions (OECD, 2020b; Cutler and Summers, 2020).
The aim of this paper is to evaluate the number of lives which a lockdown can potentially save. Given the economic costs implied by this policy, a reliable estimate of its benefits is crucial to understand whether its adoption is actually optimal (Gros, 2020). In order to address our research question, we focus on the lockdown implemented in Switzerland in response to the first wave of COVID19 infections. To the best of our knowledge, the existing literature has not provided yet an estimate of the lives saved by the Swiss lockdown in spring 2020.
Taking advantage of a unique dataset about the universe of individuals who tested positive for the disease, we estimate the number of potentially saved lives by developing a novel SIRDC model, which allows to predict the daily amount of infections, hospitalizations and deaths for different age groups in the absence of lockdown. In particular, our model accounts for seasonal patterns characterizing the transmissibility of the virus (Atkeson, 2021) and includes agespecific endogenous behavioral responses (Cochrane, 2020). More specifically, we assume that not only individuals respond to changes in the death rate of their age group, but they are also altruistic and care about the wellbeing of other subjects. A basic SIR model, instead, would lead to overstate the impact of the policy right because it does not consider that citizens spontaneously reduce their contacts even in the absence of government interventions. To obtain a reliable estimate of saved lives, we also take into account potential overflow deaths due to hospital overcrowding. This is particularly relevant if we consider that the impossibility of providing proper hospital treatments, especially in intensive care units (ICU), results in a higher mortality risk also for younger subjects.
Our SIRDC model suggests that the absence of any policy intervention in Switzerland would have resulted into approximately 11,500 deaths by September 1, plus 1500 additional casualties due to the lack of available beds in intensive care units. Relying on a basic SIR model, instead, we would have predicted roughly 65,000 deaths, plus 62,000 fatalities due to the limited availability of healthcare resources. Such estimates would be in line with the simulations performed by the Imperial College COVID19 Response Team. Neglecting hospital overcrowding, behavioral responses and seasonality, indeed, Flaxman et al. (2020) conclude that Switzerland would have reached 54,000 deaths by May 4. Our basic SIR model would deliver higher estimates only because we consider a time horizon which goes beyond May 4 and reaches the end of May, when the contagion fades out.
Our work is related to a growing literature concerning the impact of restrictive measures which limit the spread of an epidemic, especially after the outbreak of COVID19. For instance, Zhang et al. (2020) show that contacts among people were reduced by more than seven times in China thanks to physical distancing policies, while Fang et al. (2020) document that the lockdown in Wuhan reduced the number of potential infections by almost 65%. Some studies have also attempted an evaluation of the monetary benefits associated to the lives saved by the lockdown (e.g., Greenstone and Nigam, 2020; Thunström et al., 2020). However, these analyses often rely on simulations based on early limited data (Verity et al., 2020).
This work contributes to the current literature about the COVID19 pandemic from both a methodological and an empirical point of view. First, we develop a novel agestructured SIRDC model that accounts for seasonal patterns and agespecific endogenous behavioral responses, including both an egoistic and an altruistic component. Second, we provide an estimate of the severity of COVID19 based on rich data concerning the entire period of the first wave of infections in Switzerland. Third, to the best of our knowledge, this is the first estimate of the number of lives saved by the first Swiss lockdown in spring 2020.
The rest of the paper is organized as follows. Section 2 introduces the Swiss context and the policies implemented during the first wave of the COVID19 pandemic, between March and the beginning of September. Section 3 describes the data. Section 4 presents our model and the estimates of the potential number of deaths in the absence of containment measures. Section 5 focuses on overflow deaths due to hospital overcrowding. Section 6 concludes.
Background
After the outbreak of the COVID19 epidemic in China and in several European countries, at the end of February 2020 Switzerland started facing the spread of the virus, with an increasing number of infections. As a consequence, massive public health nonpharmaceutical interventions became the only viable strategy to limit the contagion.
Switzerland is a Confederation made up of 26 independent and sovereign cantons, so interventions can be planned and implemented both at national and cantonal levels. Indeed, some restrictive measures were already introduced, canceling several public events, on February 26 in the cantons at the border with Italy and France, where the first COVID19 cases were reported^{Footnote 1}. Meanwhile, the first containment measure adopted at the national level by the federal government on February 28 was the banning of any event involving more than 1000 participants.
However, because of the rapidly increasing number of infections throughout the country, the Swiss federal government intervened with more stringent measures. In particular, on March 17, schools and nonessential economic activities were closed, while gatherings of more than five people were forbidden starting from March 20. Nevertheless, differently from other countries like Italy, Switzerland did not opt for a strict lockdown, with the general requirement to stay at home.
Although economic losses were expected to be severe also in a country with a high GDP per capita (World Bank, 2020) and Human Development Index score (United Nations, 2020), the the Federal Council (2020) aimed at avoiding an unsustainable burden in terms of infections and lost lives. Such concern was particularly reasonable considering that the Swiss population has increasingly aged over the last decades and more than 20% of people are older than 65, hence far more likely to develop serious illnesses or eventually die from COVID19. In light of the constrained availability of healthcare facilities, moreover, it was necessary to prevent a scenario in which access to lifesaving treatments would have been denied to patients in need.
After reaching a peak during the first half of April, the number of infections and, consequently, deaths started to exhibit a decreasing pattern. As a result, lockdown measures were progressively loosened. On April 27, several shops opened again, while schools restarted on May 11 and the activities in the majority of offices and facilities could take place again from June 8.
Data
Our analysis is based on individuallevel data released by the Federal Office of Public Health (FOPH) about the universe of individuals who tested positive for COVID19 in Switzerland between February 24 and May 15, during the first wave of the epidemic^{Footnote 2}. For each positive case in a specific Swiss canton on a certain day, this dataset contains information about age and gender, as well as the date of the onset of the first symptoms. Furthermore, these data also report whether and when an individual was hospitalized, specifying if intensive care was required and providing the exact days on which the patient entered and left the intensive care unit. Finally, we know whether and when the person eventually died. Table 1 summarizes these data.
In spite of relevant testing efforts, however, during the first wave of the pandemic, asymptomatic cases were largely undetected. Because of the limited availability of resources, only people with severe symptoms were tested. This is the reason why we derive information about seroprevalence from the study conducted in Geneva by Stringhini et al. (2020). In this way, it is possible to understand the extent to which younger subjects, who tend to be underrepresented in the official data, were actually affected by the spread of the disease.
These data are complemented by the yearly cantonal statistics provided by the Federal Statistical Office about the resident population and the weekly number of deaths by age. As we will discuss in Section 4, we also exploit the Value of Statistical Life (VSL) to model the agespecific individual behavioral responses. The average VSL for the Swiss population is derived from the estimates released by the Federal Office for Spatial Development (2019)^{Footnote 3}. To obtain an agespecific VSL^{Footnote 4}, we rescale the estimates obtained by Murphy and Topel (2006) in the USA by means of the Swiss average value.
As far as the healthcare supply in Switzerland is concerned, we rely on several sources. The Organization for Economic Cooperation and Development (OECD) provides indicators about the number of total and acute care hospital beds per 1000 inhabitants, and the latest statistics available for Switzerland are for year 2018 (OECD, 2020a). We also refer to Rhodes et al. (2012), who estimated the number of intensive care beds in several European countries including Switzerland, expressing them as a percentage of total acute care beds. Besides, we rely on the information released by the Swiss Society of Intensive Care Medicine about the percentage of healthcare resources which could be exclusively allocated to COVID19 patients. In order to derive the number of daily available beds, finally, we need statistics about the average length of stay in hospital and intensive care for COVID19 patients. To this purpose, we exploit the FOPH dataset to compute the average number of days spent in ICU by these patients. In the case of individuals who were hospitalized but did not enter ICU, instead, FOPH data provide only the day of entrance, so we take advantage of the statistics available in Pellaud et al. (2020) about hospitalizations related to COVID19 in Fribourg.
To estimate the number of overflow deaths due to hospital overcrowding, we finally need information about the mortality rates associated with being admitted to or rejected from hospital or ICU. While FOPH data allow to compute mortality rates for COVID19 patients who received appropriate care, the corresponding estimates for rejected individuals will be taken from the literature (Greenstone and Nigam, 2020; Rojas, 2020), since Switzerland never faced the problem of overcrowded hospitals during the first wave of the pandemic.
An estimate of potential direct deaths
The present section describes our estimates of the potential number of avoided direct deaths thanks to containment measures in Switzerland. The term “direct” refers to the fact that these estimates do not include the additional potential deaths due to hospital overcrowding, which will be computed in the next section. We now proceed with the following steps. First, we focus on the initial phase of the epidemic, when the growth of infections was not influenced yet by any restriction, to determine the parameters which allow to predict the subsequent spread of the contagion in a counterfactual scenario without mitigation policies. Second, we develop a novel SIRDC model to estimate the potential number of infections and the corresponding deaths between March and the beginning of September. To this purpose, we use an agespecific imputed infection fatality rate derived from the data^{Footnote 5}.
However, before proceeding with our analysis, we need to address a preliminary issue, which requires an adjustment of the data. Indeed, older people, who are more likely to exhibit severe symptoms, tend to be overrepresented among positive cases, while younger (and often asymptomatic) individuals are systematically underreported. Therefore, the total number of predicted infections in the counterfactual scenario cannot be attributed to the different age groups on the basis of the shares retrieved from the original data.
To circumvent this issue, we exploit the results obtained by Stringhini et al. (2020) from the seroprevalence tests conducted in Geneva. They not only estimate the overall seroprevalence in the population in each of the 5 weeks between April 6 and May 9, but they also compute how the relative risk varies depending on age. After computing the average value of seroprevalence over the 5 weeks, using the number of observations in each week as a weight, we exploit the specific relative risks to obtain the shares of people belonging to different age groups who have been actually infected in Geneva.
At this point, for each age group, we compute the ratio between the actual share of infected people in Geneva and the corresponding share of infected individuals in our data. Such ratio represents an age groupspecific factor k_{a} measuring the extent to which each age group in the canton of Geneva is underrepresented in the data (see Table 2). Since testing criteria in Switzerland are defined centrally by the FOPH, it is reasonable to assume that the factor k_{a} computed for Geneva can be applied to all the other cantons. Hence, after multiplying the number of reported cases in each age group by the corresponding adjustment factor k_{a}, the issue of over or underrepresentation of different groups is overcome (Table 3).
Estimating R _{0} during the early stage of the epidemic
As a first step, we estimate the basic reproduction number (R_{0}) of the disease, which reveals the number of individuals who are infected by a single positive person during the initial phase of the epidemic^{Footnote 6}, when the population consists almost exclusively of susceptible individuals and the cumulative number of cases grows exponentially until some containment measures are introduced (Muggeo et al., 2020; Daddi and Giavalisco, 2020; Massad et al., 2005).
The starting date of the epidemic is identified as the first day when an incidence of at least 20 cases of COVID19 per 100,000 people is registered after the adjustment described above. The duration of the initial phase, before the materialization of any effect due to containment measures, is computed by estimating when the linear growth of the logarithm of the cumulative number of infections changes slope. In practice, we estimate a hockey stick regression model that allows to identify the breakpoint date at which the slope of this linear relationship changes^{Footnote 7}, as also displayed in Fig. 1:
where Y_{t} is the cumulative number of infections at day t=1,2,...,n, after we have normalized the first day of the epidemic as day 1.
Table 4 reports the breakpoint dates estimated both for Switzerland and its seven macroregions. Since the federal lockdown was announced on March 16, its effects are expected to be observed at most 10 days later, considering that the incubation period for COVID19 amounts to 5 days and other 4.5 days pass on average between the onset of the first symptoms and the test. This timing is exactly reflected in our estimates, with an anticipated effect in French cantons and in Ticino, where some restrictions were introduced earlier.
In light of these results, it is finally possible to compute the value of R_{0} using the following equation (Massad et al., 2005; Daddi and Giavalisco, 2020):
Here, Y_{b} is the cumulative number of infections on the breakpoint date, Y_{1} is the cumulative number of infections on the first day, while γ represents the resolving rate, so that \(\frac {1}{\gamma }\) is the average infectious period during which an individual can transmit the virus to others. Such period can be expected to be similar to the incubation period and, indeed, according to Almeshal et al. (2020), it amounts to 5.8 days. Exploiting this value, we derive the estimates of R_{0} reported in Table 4. Given that the basic reproduction number R_{0} is defined as the product between the contact rate β and the average infectious period 1/γ, we can finally retrieve the value of β, which captures how the infection is transferred.
Table 4 reveals the existence of remarkable differences across Swiss regions in the intensity of the spread of the epidemic, which can also be explained by cultural heterogeneity (Mazzonna, 2020). A separate analysis of regions, however, would not allow to take into account the possibility that the contagion also spreads from one region to another, an aspect of key importance in a country where the degree of mobility is extremely high. Hence, in order to avoid underestimating the potential effects of lockdown measures, in the following sections of the paper we will rely on the number of infections, hospitalizations and deaths estimated at the national level.
Imputed infection fatality rates
The most widely used measure for the severity of a disease is the infection fatality rate (IFR), which indicates the proportion of deaths among all infected individuals, including those who are asymptomatic or undiagnosed. After adjusting the data in light of seroprevalence results, we can actually estimate the whole number of cases in each age group. Hence, by taking the ratio between the number of reported deaths and the number of cases within each age group, we obtain an age groupspecific imputed infection fatality rate IFR_{a} for COVID19^{Footnote 8}. These estimates will now be exploited to fit our model and derive the potential number of direct deaths in the absence of restrictive measures.
Direct deaths in the absence of restrictions
An agestructured SIRDC model with endogenous behaviors
The values of R_{0} and β determined above can be now exploited to fit a model which allows to simulate the spread of the COVID19 epidemic in Switzerland in the absence of any mitigation policy. In particular, our aim is to improve the estimates which could be derived from a basic SIR model (see Appendix 1) by considering a more realistic counterfactual scenario in which people tend to reduce spontaneously their contacts also in the absence of any government intervention. Furthermore, following Atkeson (2021), we are also including in the model an additional component which accounts for seasonal variation in the spread of the virus. Indeed, as documented by the epidemiological literature (e.g., Park et al., 2020), the transmissibility of the virus changes during the year, reaching a peak towards the end of January.
As far as the time horizon of our predictions is concerned, we focus on the 180 days between March 5 and September 1. Indeed, the present analysis is meant to estimate the benefits associated to the lockdown implemented in response to the first wave of infections. Moreover, such focus allows us to avoid a potential bias in our estimates arising from factors which changed after summer and led to the insurgence of the second wave of infections. However, Appendix 4 also reports the results of our model when the time horizon is not restricted and we consider the entire period over which infections and deaths would occur.
We start from a simple SIRDC model (Villaverde and Jones, 2020), in which individuals can be in one of five possible states: Susceptible (S), Infectious (I), Resolving (R), Dead (D), and reCovered (C). Since we are interested in estimating how the number of potential infections and deaths varies with age, we distinguish eight age groups^{Footnote 9}.
Excluding vital dynamics (i.e., neglecting births and deaths that are unrelated to the epidemic, see Rowthorn and Maciejowski, 2020) and taking into account that the contagion may spread also across age groups, the model is described by the following system of five ordinary differential equations:
with a indicating one of the eight age groups, a∈{1,...,8}. N_{a} represents the total population belonging to a given age group, while N represents the total population, which does not vary over time since vital dynamics are here neglected.
The number of subjects in each compartment varies over time, but the stock across the five states remains constant:
The rate at which susceptible individuals in each age cohort a become infectious is \(\frac {\beta _{0} \sum _{a=1}^{8} I_{a} }{\sum _{a=1}^{8}N_{a}} * S_{a} = \frac {\beta _{0} I S}{N}\). Hence, it depends on the share of infectious subjects in the total population, on the value of the contact rate β_{0}, which mirrors the speed of the transmission of the disease, and on the amount of individuals who are still susceptible. Infectiousness resolves at rate γ. Once individuals are no longer in the state in which they can infect others, they move to the resolving state. In each period t, then, a constant fraction of individuals (θ) in every considered age group leaves the resolving compartment, ending in one of the two final stages: either dead (with probability δ_{a}) or recovered (with probability (1−δ_{a}))^{Footnote 10}. These last two states are permanent, that is, once in them, people can no longer change compartment. We set β_{0}=0.3596 and γ=0.1724, while δ_{a} indicates agespecific mortality rates^{Footnote 11}. Finally, we set θ=0.1. This value reflects the \(\frac {1}{\theta } = 10\) days which on average an individual spends with the disease before it resolves.
The system of differential equations can be recursively estimated to predict the daily number of people in each compartment. Since the analysis is performed at the national level, the initial conditions are represented by the individuals in each age group and compartment on March 5 (see Appendix 2). More in detail, the initial number of susceptible people in each age group is the number of individuals who had not been infected by March 5. Since the infectious period 1/γ is assumed to be 5.8 days on average, the initial number of infectious individuals is represented by the number of new infections occurred during the 5.8 days before March 5^{Footnote 12}. The initial number of people in the resolving state is given by all the subjects who were infected previously^{Footnote 13}. Only one person aged 72 had officially died from COVID19 before March 5, while no subjects had recovered yet on this date. Finally, dividing these values by the total population, we obtain the shares of individuals who initially belong to each age group and compartment^{Footnote 14}.
At this point, following Cochrane (2020), we introduce in this framework an endogenous behavioral response common to all age groups. In other words, we suppose that when individuals start getting infected and dying, the contact rate β becomes lower, as people try to avoid the disease. Hence, we model the behavioral response as a function of the current death rate, according to the following equation:
where \(D_{t} = \sum _{a = 1}^{8} D_{a, t}\) and \(N = \sum _{a = 1}^{8} N_{a}\).
We calibrate α_{D} as in Cochrane (2020). Using Eq. 8, we assign values to β_{0},β_{t} and ΔD_{t} to obtain the parameter α_{D}, which measures people’s sensitivity to changes in the death rates. β_{0} is the baseline contact rate (β_{0}=0.3596), while β_{t} is the lowest value of β which is observed. Thus, the calculations based on our data reveal that β_{t}=0.173^{Footnote 15}. The peak in the variation of the daily number of deaths in Switzerland is 25 deaths, so ΔD_{t}=25. Finally, N is the total Swiss population in 2020. We recover α_{D}=108,697.16.
However, we know that there is striking heterogeneity in mortality rates across age groups. If people’s behavior is affected by their perceived personal risk, behavioral responses could greatly vary by age and imposing a common differential equation for β could be an unrealistic assumption. Thus, we adapt the behavioral differential equation to introduce agespecific responses. We model the behavioral response of each age group as a function of both the death rate for that particular age group and a fraction of the death rates registered for the other age groups (introducing both an egoistic and an altruistic component).
First of all, we assume that individuals care to the maximum possible level (=1) to the death rate of people belonging to their own age group, so we keep a onetoone relationship between \(\frac {d\beta _{a}}{dt} \) and \(\frac {dD_{a}}{dt}\). Second, we assume that individuals are, at least partially, altruistic, and adjust their behavior also in response to changes in the death rates of other age groups. However, they weight other people’s wellbeing less than their own, with an altruism factor equal to 0.27 (Long and Krause, 2017). Third, we assume that people do not give the same importance to the death rates of all the other age cohorts, but rather they adopt a societal perspective. In other words, individuals give more weight to the death rates of those age groups that have a higher VSL. Therefore, if we consider the perspective of age cohorts 0–9, 10–19, 30–39, 40–49, 50–64, 65–79, and 80+, and we normalize their VSL by giving value 1 to the highest VSL (i.e., that of the age group 20–29), we obtain the coefficients reported in column (1) of Table 5. When we adopt the perspective of individuals in group 20–29, we have slightly different normalized coefficients, since, excluding the VSL of that group, the highest VSL becomes that of the cohort aged 30–39. Normalizing it to 1, we obtain the coefficients displayed in column (2) of Table 5.
Following Atkeson (2021), we finally include in Eq. 8 a parameter ψ(t) that captures seasonal patterns affecting the transmissibility of the virus:
where ω measures the amplitude of seasonal fluctuations and is set equal to 1, while τ identifies the peak in the transmission of the virus. In line with Atkeson (2021) and the epidemiological literature mentioned above (e.g., Park et al., 2020), we conventionally set this peak on January 31, thus τ=33^{Footnote 16}.
Putting everything together, we now have agespecific differential equations for the behavioral responses which can be included in the agestructured SIRDC model:
These equations imply an immediate reduction of contact rates for older individuals, while younger people tend to reduce their interactions more slowly since the death rate for their age group is low or even null. Figure 2 shows the evolution over time of the contact rates by age cohort. As before, N is normalized to 1, so that S_{a},I_{a},R_{a},D_{a} and C_{a} represent the shares of population in each age group and compartment. As already mentioned, we consider a time horizon of 180 days. Therefore, instead of looking directly at the results for state D_{a} on September 1, direct deaths are obtained by applying the IFR to the cumulative number of infections predicted in each age group by that day. This allows to take into account the additional deaths which would have materialized in the first weeks of September. Figure 3 reports the evolution over time of the variables considered in our SIRDC model after aggregating the different age groups.
Results
Table 6 shows our estimates of the potential number of direct deaths^{Footnote 17} in the absence of restrictive measures.
According to our SIRDC model which accounts for citizens’ behavioral responses and for seasonal patterns, the spread of the virus in the absence of any government intervention would have caused almost 11,500 deaths within 6 months from the beginning of the pandemic (see also Table 14), especially among older age groups. Robustness evidence will be presented in Appendix 3, where we discuss an alternative approach to derive the infection fatality rates.
An estimate of potential overflow deaths
The present section is dedicated to estimate the overflow deaths which would have occurred in a counterfactual scenario without lockdown measures. These fatalities would have resulted from hospitals reaching their capacity and being unable to serve some COVID19 patients. In order to estimate them, we first need to quantify daily demand for specialized healthcare and daily supply of acute care and intensive care beds in Switzerland. Second, we need to assign mortality probabilities for cases requiring hospitalization or intensive care, both when appropriate care is provided or denied.
Healthcare demand
The SIRDC model presented in Section 4.3.1 (as the basic SIR model in Appendix 1) allows us to compute the daily number of new cases within each age group. On each day t, the share of new cases in age group a can be computed as NC_{a,t}=(I_{a,t}−I_{a,t−1})+(R_{a,t}−R_{a,t−1})+(D_{a,t}−D_{a,t−1})+(C_{a,t}−C_{a,t−1}) in the SIRDC model. The actual number of cases is then obtained multiplying NC_{a,t} by the total Swiss population. In order to derive the demand for healthcare services by COVID19 patients, we exploit our data to compute the share of infected individuals within each age group who were hospitalized or needed intensive care treatment^{Footnote 18}.
Healthcare supply
As the survival probability of COVID19 patients depends crucially on the provision of specialized care, we need precise information about the total number of available hospital and ICU beds in Switzerland. According to the OECD, in 2018, there were 3.6 acute care hospital beds per 1000 inhabitants in the country. Considering the population in 2020, the stock of curative hospital beds over the entire country turns out to be about 30,982 beds. According to Rhodes et al. (2012), then, 3.1% of these acute care beds are for intensive care, giving us a stock of 960 beds in Switzerland. This figure is in line with the estimate provided by the Swiss Society of Intensive Care Medicine, which sets the stock between 950 and 1000 beds in the 82 intensive care units present on the Swiss territory. For the moment, we do not consider the possibility to improve healthcare supply, although there is some evidence that the total stock of ICU beds could be increased by 50% (icumonitoring.ch, 2020).
However, healthcare resources cannot be allocated only to COVID19 patients and, indeed, before the spread of the virus, the daily average occupation rate of hospital and ICU beds was, respectively, 74% and 75% (Federal Statistical Office, 2020; European Society of Intensive Care Medicin, 2020). We assume that 50% of the stock of acute care beds can be allocated to the treatment of COVID19 patients and, following the Swiss Society of Intensive Care Medicine, we fix the available stock of ICU beds for individuals affected by COVID19 at 56%.
The daily availability of beds depends also on the length of stay in hospital and intensive care for the average patient. Hence, we exploit the data released by the FOPH to calculate the average number of days spent by a COVID19 patient in ICU, obtaining an estimate of 11.3 days. Some of the individuals admitted to the ICU spend some time before in acute care beds, for an average of 1.9 days. We notice in the data that when the patients pass through the hospitalization phase before receiving intensive care, the date of the test assessing whether they have contracted the virus or not is subsequent to the hospitalization date. We can speculate that these 120 people are first admitted to the hospital and then moved to ICU once confirmed to be positive for COVID19.
With regard to patients who do not need ICU, instead, we cannot apply the same procedure described above to obtain the figure for hospitalizations, since we know when an individual enters the hospital, but the exit date is not available in the dataset. Therefore, we rely on Pellaud et al. (2020), who calculate several metrics in a retrospective cohort study about 196 hospitalized individuals with confirmed cases of COVID19 in the Fribourg area. The average length of stay for COVID19 patients who require hospitalization but not intensive care is 7 days.
Finally, daily supply is obtained dividing the stock of hospital and ICU beds which could be allocated to COVID19 patients by the respective length of stay, obtaining an estimate of \(0.5 * \frac {(30,982960)}{7} = 2,144.39\) daily hospital beds and \(0.56 * \frac {960}{11.3} = 47.58\) daily places available in ICU.
Mortality rates
In order to estimate the number of overflow deaths, then, we need mortality rates for the cases in which people are admitted or not to the hospital or ICU. The individual data released by the FOPH also allow us to calculate the probability of death when patients receive appropriate care: indeed, the problem of overcrowding was never faced by Switzerland over the period covered by these data. In light of these data, the probability of dying for admitted patients is 17.9% in case of hospitalization and 52% in case of intensive care. These results are in line with those presented in international literature (Rojas, 2020; Greenstone and Nigam, 2020).
Since we cannot directly calculate the corresponding probabilities when the demand for healthcare cannot be accommodated, we follow Rojas (2020), who assumes that mortality increases threefold when a patient is rejected from a hospital (i.e., 53.7% in Switzerland). For ICU cases, we assume a survival probability of 10%, which is derived from the existing literature (Greenstone and Nigam, 2020; Ferguson et al., 2020). It is worth remarking that such assumptions imply that the mortality rates do not change depending on the age of the potential patient. This situation leads to a considerable number of overflow deaths also among younger people, explaining why these overflow deaths, compared to direct ones, are significantly higher in those categories. However, we do expect that reached a certain level of criticality, even younger people will face a significant risk of dying if left without proper healthcare interventions.
Overflow deaths
Exploiting the daily demand and supply of hospital beds computed above, we can now predict the daily number of deaths due to the shortage of healthcare resources. More in detail, on days when demand≤supply, all people in need can receive appropriate care, and, therefore, survival probabilities are those estimated using FOPH data. When, instead, demand>supply and facilities reach their capacity (Greenstone and Nigam, 2020), for the individuals who do not receive healthcare, we apply the mortality probabilities of 53.7% and 90% for hospitalization and intensive care respectively.
Following the literature, we assume that age does not affect the probability of being rejected or admitted to healthcare facilities. In other words, the share of patients in each age group who do not receive appropriate care stays constant. For instance, if 20% of the cumulative number of patients cannot obtain a hospital bed on day t, that day 20% of patients belonging to each age group are assumed not to have received the needed care. We obtain the total number of overflow deaths over the considered time period by summing up across all days.
As reported in Table 7 (see also Table 15), our SIRDC model allows to predict slightly more than 1500 overflow deaths by September 1, all imputable to overcrowded ICUs. Such estimate is significantly lower in comparison to the one obtained by means of a basic SIR model. Endogenous individual responses and seasonal patterns, in fact, lead to a slower spread of the virus, flattening the number of new cases. As a result, since the fraction of new cases requiring hospitalization or intensive care remains constant, hospitals avoid reaching their maximum capacity.
Conclusions
The introduction of lockdown measures to limit the spread of the COVID19 pandemic has been at the center of a heated economic and political debate in the majority of countries. Several studies have therefore attempted an evaluation of the benefits associated to such restrictive measures. Focusing on the lockdown implemented in Switzerland in March 2020, our paper contributes to this extensive literature from both a methodological and an empirical perspective.
In order to estimate the number of potentially saved lives during the first wave of the pandemic in Switzerland, we have developed a new SIRDC model which predicts the evolution of the epidemic in the absence of containment measures. In comparison to a basic model, our version includes additional features which make the counterfactual scenario more realistic. First of all, we incorporate agespecific endogenous behavioral responses. In other words, not only we consider that individuals would spontaneously reduce their contacts even in the absence of a government intervention, but we also account for the fact that this response varies depending on age. Furthermore, by including a seasonality component, we avoid to neglect that the transmissibility of the virus is not constant over time and, after reaching a peak in winter, tends to become very low in summer.
Our predictions about the daily number of infections, hospitalizations and deaths are based on rich individuallevel data concerning COVID19 cases in Switzerland. In particular, we exploit these data to derive the initial conditions and the necessary parameters to fit our model. We also predict the number of additional casualties which would have occurred because of the constrained availability of healthcare facilities. Although Switzerland did not face serious issues of hospital overcrowding during the first wave of the pandemic, in fact, the absence of containment measures would have led to a higher number of deaths because of the lack of hospital beds, especially in intensive care units.
Although the features of our SIRDC model allow to improve the reliability of predictions, results should always be interpreted cautiously. Indeed, they depend on the assumptions concerning the structure of model, the value of its parameters, and the utilization of healthcare resources.
According to our estimates, the absence of any policy intervention would have led to approximately 11,500 direct deaths within 6 months from the beginning of the pandemic, plus 1500 overflow fatalities related to hospital overcrowding. Considering the actual number of COVID19related deaths over the same time period, our results suggest that more than 11,200 lives were saved by the lockdown, as reported in Table 8. This is a largely relevant result, especially if we consider the relatively short time period under analysis (until September 1), which also includes the summer months during which the spread of the disease decreases spontaneously (see Appendix 4 for estimates covering a longer time horizon).
Appendix 1: An agestructured SIR model
The values of R_{0} and β determined in Section 4.1 can be exploited to fit a susceptibleinfectedrecovered (SIR) model which allows to simulate the evolution of the spread of the epidemic in Switzerland if containment measures had not been implemented. Since we are interested in estimating the number of potential infections which would have occurred in each age group, we build an agestructured SIR model following Deforche (2020), but letting the age groups be eight (i.e., 0–9; 10–19; 20–29; 30–39; 40–49; 50–64; 65–79; 80+) rather than only two.
According to this model, which allows contacts between all age groups a, at any time, each individual can be either Susceptible (S), Infectious (I), or Recovered (R). The last compartment not only includes those subjects who are not infectious any more, but also those who died because of the disease. Excluding vital dynamics (i.e., neglecting births and deaths that are unrelated to the epidemic, see Rowthorn and Maciejowski, 2020), the model is described by the following system of ordinary differential equations:
The rate at which susceptible individuals in each age group a become infectious \(\Big (\frac {\beta _{0} \sum _{a = 1}^{8} I_{a}}{\sum _{a = 1}^{8} N_{a}} * S_{a}\Big)\) depends on the share of infectious subjects in the total population, on the value of the contact rate β_{0}, which mirrors the speed of the transmission of the disease, and on the remaining stock of susceptible individuals.
As previously mentioned, γ represents the rate at which infectiousness resolves: individuals who are no longer infectious move to the resolving state and cannot change compartment any more (Eksin et al., 2019; Toxvaerd, 2020). At each point in time, the cumulative stock of individuals across states remains constant: \(\sum _{a = 1}^{8} (S_{a} + I_{a} + R_{a}) = \sum _{a = 1}^{8} N_{a} = N\), where N is the total population. Normalizing N to 1, S_{a},I_{a}, and R_{a} are interpreted as the shares of the population belonging to each compartment.
At this point, the system of differential equations can be recursively estimated to predict the daily number of people in each compartment after the beginning of the epidemic. Since the analysis is performed at the national level, the initial conditions are represented by the individuals in each age group and compartment on March 5. We exploit the values of β_{0} and γ discussed before (β_{0}=0.3596; γ=0.1724).
Figure 4 plots the evolution over time of the predicted share of individuals who belong to each compartment when age groups are aggregated. It is interesting to observe that, when herd immunity is reached^{Footnote 19}, the epidemic continues to spread at a slower rate, since each person infects less than one other person. Thanks to this model, therefore, we can estimate the total number of infected people by the end of the pandemic, who correspond to the amount of people in state R when the number of susceptible individuals does not decrease any more and nobody else contracts the disease^{Footnote 20}. At this point, having predicted the total number of infections in each age group, the corresponding number of potential deaths can be derived through the infection fatality rate computed from the data.
Appendix 3: An alternative estimate of the infection fatality rate
Considering that several approaches have been proposed so far in the literature to estimate the infection fatality rate of COVID19, we now back up the imputed IFR discussed in Section 4.2 by estimating the severity of the disease with an alternative methodology. More specifically, we follow the approach proposed by Rinaldi and Paradisi (2020), which relies on the use of administrative data concerning death counts and demographic information.
A potential concern regarding the imputed IFR reported in Table 6, indeed, is represented by the fact that official data about COVID19 cases may misrepresent the actual number of deaths related to the spread of the virus. FOPH deaths data may present a downward bias because people might die at home (because of COVID19) or in other nonmedical facilities, and remain untested. This situation can be present if individuals decide not to go to the hospital, or they are not in a position to go. At the same time, official COVID19 deaths data can present an upward bias since a fraction of those who died because of the pandemic were already severely ill individuals, who might have died over the following few weeks or months without the virus. Thus, COVID19 has simply slightly anticipated their death.
In the attempt to correct for these biases, we use weekly administrative data about the deaths recorded between 2000 and 2020^{Footnote 21} by the Federal Statistical Office, which also provides demographic information at the cantonal level^{Footnote 22}. We then elaborate these data to identify eight age groups (0–9; 10–19; 20–29; 30–39; 40–49; 50–64; 65–79; 80+) in the seven major Swiss regions (Lake Geneva, Espace Mittelland, NorthWest Switzerland, Zurich Region, Eastern Switzerland, Central Switzerland, and Ticino).
Exploiting such information, we build a Bayesian model which fits agestratified mortality and demographic data for the seven regions between 2000 and 2020 over the weeks 11–19, namely those characterized by the COVID19 outbreak. Specifically, starting from a simple standard binomial mortality mode, we assume that deaths are binomially distributed and in weeks affected by COVID19 the baseline lethality rate is augmented by a factor that indicates the interaction between the IFR and the infection rate of COVID19. Furthermore, we assume that mortality is not correlated between different age groups. The model is described with the following binomial equations:
where i denotes the macroregion, y the year, and a one of the eight age groups (0–9; 10–19; 20–29; 30–39; 40–49; 50–64; 65–79; 80+). D_{i,a,y} and N_{i,a,y} are, respectively, the total deaths and population in macroregion i, year y, and age range a.
The baseline lethality rates δ_{a} are assumed to be constant across macroregions and years, but can vary across age groups. Before 2020, the infection fatality rates \(\delta _{a}^{Covid}\) are assumed to be equal to zero, while in 2020, they are heterogenous across age ranges and fixed in the other dimensions. Finally, the infection rates θ_{i} are regionspecific but constant across age groups.
The identifying assumption is that in the absence of the COVID19 outbreak, the weekly deaths recorded in 2020 would have been the same on average as the ones in the previous 20 years. We provide visual evidence (Fig. 5) about the extent to which this assumption is satisfied. Indeed, over the first 10 weeks of 2020, excess mortality (calculated as the number of deaths in 2020 versus the average value of deaths over the years between 2000 and 2019) is substantially null. However, we cannot check whether the composition of the typologies of deaths changes over time and particularly in 2020, given that statistics on the causes of deaths are not available.
Using Markov Chain Monte Carlo procedures, we estimate an overall infection fatality rate for COVID19 of 1.087123% (95% confidence interval 0.2899833%), with striking heterogeneity across age groups (see Table 11).
As required with a Bayesian model, we specify priors for all the parameters we are interested in monitoring, i.e., \(\delta _{a}, \delta _{a}^{Covid}, \theta _{i}\). We choose uninformative priors for all parameters:
To derive point estimates and respective 95% confidence intervals for the parameters of interest, we employ a Markov Chain Monte Carlo procedure that allows us to calculate the median and the confidence intervals of the posterior distributions of \(\delta _{a}, \delta _{a}^{Covid}\), and θ_{i}, using as model Eqs. (6) and (7)^{Footnote 23}. We draw 100,000 samples from the joint posterior distribution and use 50 independent chains. The burn in interval is fixed at 20,000, and the thinning interval is 30. Convergence is checked (and satisfied) visually with GelmanRubin diagnostic. Our estimates are robust to the definitions of alternative distributions of the priors.
Table 12 shows our estimates of the potential number of direct deaths in the absence of restrictive measures (both for SIR and SIRDC models), when we use the infection fatality rates estimated through this Bayesian approach. As previously mentioned, this approach leads to higher infection fatality rates, which result in more potential direct deaths, also among younger age groups.
It is worth underlining here that such differences in the infection fatality rates are also reverberated in the slight discrepancies between the number of cases predicted by the SIRDC model reported in Tables 6 and 12. According to our SIRDC model, indeed, individual behavioral responses depend on the number of daily deaths. Hence, changes in the fatality rate imply differences in the intensity of reduction of the contact rate β_{a} and in the number of predicted infections.
Since an alternative infection fatality rate leads to a different number of predicted infections, in Table 13, we report the corresponding overflow deaths due to the lack of available beds in intensive care units.
Appendix 4: Results from the SIRDC model without restrictions on the time horizon
This Appendix reports the estimates derived from our SIRDC model accounting for seasonality and endogenous behavioral responses when we consider the entire time horizon until the contagion finally fades out and we do not restrict our attention only on the first 6 months after the beginning of the pandemic, before the outbreak of the second wave of infections.
Figure 6 shows that the model predicts also a second peak of infections after 200 days. The dynamics stabilizes after approximately 500 days, when 40% of Swiss individuals have been infected.
Tables 14 and 15 report the corresponding number of direct and overflow deaths by age group. In the absence of restrictions on the time horizon, our model would predict roughly 28,500 fatalities, more than twice the value over the first 6 months (see Tables 6 and 7).
Availability of data and materials
The individual data used in this paper have been provided by the Federal Office of Public Health for the purpose of academic research. Such data are not publicly available to preserve patients’ anonymity, as the detailed personal information could potentially allow to identify specific subjects. The syntax used to analyze the data and derive the estimates is available from the authors upon request.
Notes
 1.
The first official COVID19 case in Switzerland was reported on February 25 in Ticino, the most southern canton at the border with Italy.
 2.
In addition, we exploit the number of deaths in each age group by the first week of September.
 3.
The most updated value refers to year 2017 and amounts to 6.7 million Swiss Francs. More information about the Swiss VSL is provided by Ecoplan (2016).
 4.
The VSL should exhibit a humpshaped relationship with age (Aldy and Viscusi, 2008). Indeed, the VSL reflects not only life expectancy, but also other agedependent characteristics such as education and career prospects. Hence, after increasing with age, the VSL starts declining when the individual turns approximately 30.
 5.
In order to check the robustness of our results, we also estimate the agespecific infection fatality rate of COVID19 using an alternative approach based on a Bayesian model. See Appendix 3 for more details.
 6.
If R_{0}=1, the number of infected people remains constant; if R_{0}<1, the number of infected people decreases; if R_{0}>1, the number of infected people increases.
 7.
If the cumulative number of infections grows exponentially during the early stage of the epidemic, the log of the cumulative number of infections exhibits a linear growth over time.
 8.
The value of IFR_{a} is null if no deaths are reported for age group a. The youngest individual who officially died from COVID19 in Switzerland by May 15 is aged 31.
 9.
 10.
Note that this dynamics collapses to that of a basic SIR model if we aggregate R_{a},D_{a}, and C_{a}.
 11.
Agespecific mortality rates are the imputed IFRs described in Section 4.2.
 12.
The initial number of infectious individuals on March 5 includes the infections registered between March 1 and March 5, plus 80% of the infections occurred on February 29.
 13.
Hence, individuals infected before February 28, plus 20% of those infected on February 29.
 14.
The adjustment based on seroprevalence results described before is meant to obtain reliable values at this stage of the analysis, avoiding an overrepresentation of older individuals.
 15.
We recovered the lowest observed value for β from R_{0t}. Indeed, we first estimate the daily value for R_{0t}, and we recover the corresponding β_{t} from the relationship \(R_{0t} = \frac {\beta _{t}}{\gamma }\)
 16.
Indeed, 33 days pass between January 31 and March 5.
 17.
Note that IFR_{a} is null for two of the three youngest age groups, as no deaths are reported in the official data. The youngest individual who has officially died because of COVID19 in Switzerland is aged 31.
 18.
We distinguish between people needing a hospital bed, but not intensive care, and people needing intensive care.
 19.
Herd immunity is reached 46 days after March 5, i.e., on April 20.
 20.
Quite reassuringly, it is also possible to observe that the cumulated number of infections predicted by the model during the first days after March 5, when containment measures were not in place yet, are actually in line with those observed in the data.
 21.
Data provide information about gender, age group (5 years bin), and cantonal residence.
 22.
Data provide information on the total population, by gender and age.
 23.
The likelihood function is composed of 5 equations for each combination macroregion—age group, for a total of 21∗7∗8=1176 equations
Abbreviations
 COVID19:

Coronavirus disease 2019
 FOPH:

Federal Office of Public Health
 GDP:

Gross Domestic Product
 ICU:

Intensive care unit
 IFR:

Infection fatality rate
 OECD:

Organization for Economic Cooperation and Development
 SIR:

SusceptibleInfectiousRecovered
 SIRDC:

SusceptibleInfectiousResolvingDeadreCovered
 VSL:

Value of Statistical Life
References
Aldy, J.E., & Viscusi, W.K. (2008). Adjusting the value of a statistical life for age and cohort effects. The Review of Economics and Statistics, 90(3), 573–581.
Almeshal, A.M., Almazrouee, A.I., Alenizi, M.R., Alhajeri, S.N. (2020). Forecasting the spread of COVID19 in Kuwait using compartmental and logistic regression models. Applied Sciences, 10(10), 3402.
Atkeson, A. (2021). A parsimonious behavioral SEIR model of the 2020 COVID epidemic in the United States and the United Kingdom. NBER Working Papers 28434, National Bureau of Economic Research. https://doi.org/10.3386/w28434.
Cochrane, J.H. (2020). A SIR model with behavior. https://johnhcochrane.blogspot.com/2020/05/ansirmodelwithbehavior.html. Accessed 9 Mar 2021.
Cutler, D.M., & Summers, L.H. (2020). The COVID19 pandemic and the $16 trillion virus. Jama, 324(15), 1495–1496.
Daddi, E., & Giavalisco, M. (2020). Early forecasts of the evolution of the COVID19 outbreaks and quantitative assessment of the effectiveness of countering measures. arXiv preprint arXiv:2004.08365.
Deforche, K. (2020). An agestructured epidemiological model of the Belgian COVID19 epidemic. medRxiv. https://doi.org/10.1101/2020.04.23.20077115.
Ecoplan (2016). Empfehlungen zur Festlegung der Zahlungsbereitschaft für die Verminderung des Unfall und Gesundheitsrisikos (value of statistical life). Forschung und Beratung in Wirtschaft und Politik. Available at: https://www.are.admin.ch/are/de/home/suche.html#value%20of%20statistical%20life.
Eksin, C., Paarporn, K., Weitz, J.S. (2019). Systematic biases in disease forecasting–the role of behavior change. Epidemics, 27, 96–105.
European Society of Intensive Care Medicin (2020). Coronavirus – public health emergency. https://www.esicm.org/resources/coronaviruspublichealthemergency. Accessed 10 Nov 2020.
Fang, H., Wang, L., Yang, Y. (2020). Human mobility restrictions and the spread of the novel coronavirus (2019ncov) in China. Journal of Public Economics, 191, 104272.
Federal Council (2020). Bundesrat verschärft Massnahmen gegen das Coronavirus zum Schutz der Gesundheit und unterstützt betroffene Branchen. https://www.admin.ch/gov/de/start/dokumentation/medienmitteilungen/bundesrat.msgid78437.html. Accessed 17 May 2021.
Federal Office for Spatial Development (2019). Value of Statistical Life (VOSL): Empfohlener Wert der Zahlungsbereitschaft für die Verminderung des Unfall und Gesundheitsrisikos in der Schweiz. Bundesamt für Raumentwicklung. Available at: https://www.are.admin.ch/are/de/home/suche.html#value%20of%20statistical%20life.
Federal Statistical Office (2020). Medizinische Statistik der Krankenhäuser. https://www.bfs.admin.ch/bfs/de/home/statistiken/gesundheit/erhebungen/ms.html. Accessed 10 Nov 2020.
Ferguson, N., et al (2020). Report 9: impact of nonpharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. Imperial College COVID19 Response Team, London, 16 March 2020. https://www.imperial.ac.uk/mrcglobalinfectiousdiseaseanalysis/covid19/report9impactofnpisoncovid19/.
FernándezVillaverde, J., & Jones, C.I. (2020). Estimating and simulating a SIRD model of COVID19 for many countries, states and cities.. NBER Working Papers 27128. National Bureau of Economic Research. https://www.nber.org/system/files/working_papers/w27128/w27128.pdf.
Flaxman, S., et al (2020). Estimating the effects of nonpharmaceutical interventions on COVID19 in Europe. Nature, 584(7820), 257–261.
Greenstone, M., & Nigam, V. (2020). Does social distancing matter? Working Papers 202026, Becker Friedman Institute for Research in Economics. https://bfi.uchicago.edu/wpcontent/uploads/BFI_WP_202026.pdf.
Gros, D. (2020). The great lockdown: was it worth it?CEPS Policy Insights, 202011. https://www.ceps.eu/cepspublications/thegreatlockdown/.
icumonitoring.ch (2020). Nearreal time monitoring of intensive care occupancy. https://icumonitoring.ch/. Accessed 10 Nov 2020.
ILO (2020). Youth and COVID19: impacts on jobs, education, rights and mental wellbeing. Survey report 2020. ILO Global Reports. https://www.ilo.org/global/topics/youthemployment/publications/WCMS_753026/langen/index.htm.
Long, M.C., & Krause, E. (2017). Altruism by age and social proximity. PLoS ONE, 12(8), 0180411.
Massad, E., Burattini, M.N., Lopez, L.F., Coutinho, F.A. (2005). Forecasting versus projection models in epidemiology: the case of the SARS epidemics. Medical Hypotheses, 65(1), 17–22.
Mazzonna, F. (2020). Cultural differences in COVID19 spread and policy compliance: evidence from Switzerland. Covid Economics 33, 30 June 2020, (pp. 163–185): CEPR Press.
Muggeo, V., Sottile, G., Porcu, M. (2020). Modelling COVID19 outbreak: segmented regression to assess lockdown effectiveness. Research Gate. https://doi.org/10.13140/RG.2.2.32798.28485.
Murphy, K.M., & Topel, R.H. (2006). The value of health and longevity. Journal of political Economy, 114(5), 871–904.
OECD (2020a). Hospital beds. https://data.oecd.org/healtheqt/hospitalbeds.htm. Accessed 17 Oct 2020.
OECD (2020b). Youth and COVID19: response, recovery and resilience. OECD Policy Responses to Coronavirus. https://www.oecd.org/coronavirus/policyresponses/youthandcovid19responserecoveryandresiliencec40e61c6/.
Park, S., Lee, Y., Michelow, I.C., Choe, Y.J. (2020). Global seasonality of human coronaviruses: a systematic review. In Open Forum Infectious Diseases, (Vol. 7. Oxford University Press, New York, p. 443).
Pellaud, C., et al (2020). Characteristics, comorbidities, 30day outcome and inhospital mortality of patients hospitalised with COVID19 in a Swiss area – a retrospective cohort study. Swiss Medical Weekly, 150, w20314. https://doi.org/10.4414/smw.2020.20314. Accessed 09 Mar 2021.
Rhodes, A., Ferdinande, P., Flaatten, H., Guidet, B., Metnitz, P.G., Moreno, R.P. (2012). The variability of critical care bed numbers in Europe. Intensive care medicine, 38(10), 1647–1653.
Rinaldi, G., & Paradisi, M. (2020). An empirical estimate of the infection fatality rate of COVID19 from the first Italian outbreak. medRxiv. https://doi.org/10.1101/2020.04.18.20070912.
Rojas, I. (2020). On the economic benefits and costs of COVID19 mitigation measures in Mexico. Available at SSRN: https://ssrn.com/abstract=3592209; http://dx.doi.org/10.2139/ssrn.3592209.
Rowthorn, R., & Maciejowski, J. (2020). A cost–benefit analysis of the COVID19 disease. Oxford Review of Economic Policy, 36(Supplement_1), 38–55.
Stringhini, S., et al (2020). Seroprevalence of antiSARSCoV2 IgG antibodies in Geneva, Switzerland (SEROCoVPOP): a populationbased study. The Lancet. https://doi.org/10.1016/S01406736(20)313040.
Thunström, L., Newbold, S.C., Finnoff, D., Ashworth, M., Shogren, J.F. (2020). The benefits and costs of using social distancing to flatten the curve for COVID19. Journal of BenefitCost Analysis, 11(2), 1–27.
Toxvaerd, F. (2020). Equilibrium social distancing. Cambridge Working Papers in Economics 2021, Faculty of Economics, University of Cambridge. https://doi.org/10.17863/CAM.52489.
United Nations (2020). Human development indicators. http://hdr.undp.org/en/countries/profiles. Accessed 9 Mar 2021.
Verity, R., et al (2020). Estimates of the severity of coronavirus disease 2019: a modelbased analysis. The Lancet Infectious Diseases. https://doi.org/10.1016/S14733099(20)302437.
World Bank (2020). World Bank Indicators  GDP per capita. https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=CH&most_recent_value_desc=true. Accessed 9 Mar 2021.
Zhang, J., et al (2020). Changes in contact patterns shape the dynamics of the COVID19 outbreak in China. Science, 368(6498), 1481–1486. https://doi.org/10.1126/science.abb8001.
Acknowledgements
We thank the Federal Office of Public Health for providing the data and we are grateful to Prof. Fabrizio Mazzonna for his precious supervision and invaluable support. We also thank Christian Althaus, Marius Brülhart, Paolo Campli, Sara Rellstab, and the participants to the USI Brown Bag Seminars for valuable feedbacks. The views expressed in this paper are clearly those of the authors and not necessarily those of the FOPH.
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Gatti, N., Retali, B. Saving lives during the COVID19 pandemic: the benefits of the first Swiss lockdown. Swiss J Economics Statistics 157, 4 (2021). https://doi.org/10.1186/s41937021000722
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DOI: https://doi.org/10.1186/s41937021000722
Keywords
 COVID19
 Lockdown
 Saved lives
 SIRDC model
 SIR model
 Behavioral responses
JEL Classification
 I18
 D91
 H12