details the parameters, derived variables and differential equations used in the CovidSIM model. Table A1 in the Appendix provides the input parameters used in the model, as based on available publications and best estimates used in the modelling work on COVID-19 to date (as known to us on 18 March 2020).
Our results suggest that pushing the peak of the epidemic into the next year, when assuming a low basic reproduction number (R0) of only 1.5, was achievable with "general contact reduction" at levels of over 11% to over 27% (for differing time periods) (Table 1) . Similarly, it was achievable for probabilities of isolating symptomatic cases in hospital in the range of over 18% to over 41% (Table  1) . Using instead a possibly more realistic value of 2.5 for R0, the only way of shifting the epidemic peak into the next year with interventions lasting for nine months, was if: (i) over 65% of contacts were reduced; or (ii) if the probability of cases being isolated in hospitals was extremely high at over 98%. Also for R0 = 3.5, pushing the epidemic peak into the subsequent year was generally not possible, except if contact reduction was over 61% for the rest of the simulated year.
The feasibility of achieving high levels of contact reduction and case isolation is very uncertainespecially around sustaining these for long periods of time. At least in the short-term China has used intensive containment measures successfully as per the findings of the WHO-China Joint Mission Report [8] . This report stated that: "China has rolled out perhaps the most ambitious, agile, and aggressive disease containment effort in history." While it is an open question around the generalisability of the Chinese approach to other jurisdictions [9] , there is also evidence of containment success (as of late March 2020) outside mainland China, from Singapore, Hong Kong and Taiwan [10] . But for European countries currently with thousands of cases, it may be too late to adopt such intensive containment approaches and the best approach may simply be to apply less intense control measures that balance minimising health loss with minimising social and economic disruption (while also putting major resources into rapid vaccine development). Figure 1 shows the scenarios at three different levels of R0 when combined with "25% general contact reduction". At R0 = 3.5 the epidemic still peaks during the intervention period whereas for R0 = 2.5 the peak is pushed into the post-intervention period. For R0 = 1.5 the peak is nearly pushed into the subsequent year. Figure 2 again demonstrates the importance of the timing of the intervention period. A higher level of contact reduction (at 50% vs 25%) more effectively suppresses the epidemic in the intervention periodbut when the intervention period ends it causes a higher epidemic peak and a higher total health burden than for the 25% reduction (Table 2) .
Considering the three levels of R0 and the two levels of "general contact reduction" (at 25% and 50%) resulted in: 22% to 63% of the population sick, 8.8% to 25.1% seeking a medical consultation, 0.2% to 0.6% needing to be hospitalised, 5,500 to 15,682 people needing critical care (in an ICU), 2,750 to 7,841 requiring ventilators, and 6,450 to 28,200 dying (0.07% to 0.28% of the population) ( Table 2 ). The worst scenario with 0.28% of the population dying, compares to Europe in the 1918 influenza pandemic where 1.1% died (2.64 million excess deaths) [11] .
Based on the recent age distribution data from China [12], these hospitalisations and deaths from COVID-19 would particularly occur amongst older age-groups. Overall, the levels of health service demand in the more severe scenarios would be completely unprecedented for a modern European country. This is one of the first SEIR modelling studies of this new pandemic disease COVID-19 and the associated online simulation tool has advanced dashboard features and graphic visualisation of results that facilitate user engagement. Nevertheless, there are always limitations with modelling work. Some of these are discussed under issues with parameter uncertainty in Table A1, but here we  note some of the more substantive limitations:  There is still a high degree of uncertainty around many aspects of COVID-19 epidemiology. For example, the value of R0 could conceivably be higher than the highest level in the scenarios we modelled (at R0 = 3.5). Also, the case fatality risk (CFR) could be underestimated, especially if there was health system overload.  The model was deterministic and not stochastic, though we largely offset this issue with modelling a wide range of scenarios. The lack of stochastic considerations mainly translates into increased uncertainty in the very early stages of epidemic spread, which then impacts on the timing of the peak.  The model neither considers any long-term health damage to survivors (especially among risk patients) nor does it consider the hard-to-estimate health loss arising from untreated other health conditions as a result of having an overburdened health system. Likewise we do not consider the additional health harm to the health workers involved e.g., adverse mental health impacts arising from working during a pandemic [13, 14] .
Some of these issues can be addressed when improved data becomes available on the epidemiology of COVID-19. Nevertheless, public health workers can consider using this model in their own countries and adapt the parameters according to local settings and as the epidemiological characteristics of COVID-19 are better ascertained. 
Level of general contact reduction for 6 months needed to push the epidemic into the next year >27% Not possible Not possible -for 9 month intervention period (274 days) >14% >65% Not possible -for rest of the simulated year >11% >46% >61%
Probability of case isolation in hospital needed to push the epidemic into the next year (for a 6 month intervention period) is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint
The copyright holder for this this version posted March 23, 2020. . https://doi.org/10.1101/2020.03.20.20039776 doi: medRxiv preprint Table 2 : Estimated health impacts from COVID-19 in a hypothetical European country of population 10 million using CovidSIM for a range of basic reproduction number (R0) values and differing intensity of "general contact reduction" as the control measure (see Table A1 is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint Detection probability SARS-CoV-2 infections which are brought into the country may not be detected and may spread without being noticed because the symptoms of COVID-19 may easily be confused with other influenza-like illnesses (ILI). Few practitioners may decide to order a SARS-CoV-2 test for what they regard a normal ILI patient while no community-transmitted cases in the population have been reported. If we assume that fraction of pTest ILI patients who (a) seek medical help or who (b) are hospitalized or who (c) die from the disease are tested for SARS-Cov-2, then the probability that not one single test has been performed on a COVID-19 patient by time t despite the ongoing transmission in the population is given by:
The probability that at least one test has been performed (and has returned a positive result) is then is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint We assumed that the first cases with subsequent silent transmission began on 15 February when nine European countries had cases. For the whole course of the one year simulation, we assumed an average of 10 infected people per day were arriving in the hypothetical country from other countries (e.g., by legal and/or illegal crossing of land borders).
Infections that lead to sickness 67%
This figure is still uncertain but we used the same estimate as per modelling by Imperial College at "two thirds of cases being sufficiently symptomatic to self-isolate" [3] . Of note is that another modelling study used a 50% value [15] . Nevertheless, some proportion of asymptomatic cases is consistent with the findings of a very large Chinese study [12] , where 81% of cases of COVID-19 did not involve severe illness.
Sick people seek medical help (including telephone and internet consultations)
We used the default value in the CovidSIM model, which is based on medical consultations for influenza-like illness (ILI). During a pandemic there might be a shift away from face-to-face consultations with health workers, so that some of these consultations may be either telephone or internet-based. This parameter is not used for determining subsequent outcomes like hospitalisations and deaths. We further assume that cases only seek medical help once.
Sick people need hospitalisation 1% This estimate is highly uncertain. We have multiplied by 5 the percentage which has been observed for seasonal influenza in Germany (0.2%), to account for the apparent increased severity of COVID-19. The high uncertainty for this parameter is due to the likely under-diagnosis of mild cases in many settings (impacting the size of the denominator). It also may vary between countries given the use of hospital facilities to isolate mild cases. Modellers in the United Kingdom (UK) have used 4.4% (of all infected cases) [3] , and for modelling in the United States 3%, 5% and 12% have been proposed [16] . The length of hospitalisation was assumed to be 10 days which is similar to other modelling work e.g., 10.4 days for the UK [3] .
Hospitalised cases need intensive care (ICU admission)
We used the data from a very large Chinese study for the ratio of "critical" to "severe" cases (i.e., 4.7%/(13.8% + 4.7%) = 25.4%) [12] . This is similar to the Chinese case series reported by Wang et al. at 26.1% [17] . Nevertheless, it is higher than reported in a smaller case series from Singapore at 11% (2/18) [18] . A UK modelling study used a proportion of 30% "based on early reports from COVID-19 cases in the UK" [3] .
Intensive care cases requiring mechanical ventilation
We use the same value as per a US model of 50% [19] for additional calculations outside of CovidSIM. This proportion is around that reported in a Chinese study of 47% (17/36 ICU admissions) [17] but is less than in another Chinese study at 71% (37/52) [20] .
Sick people die from the disease (case fatality risk) 0.45% Given the relatively high quality of the healthcare systems in Europe, we considered the lower end of the range reported by the WHO for the infection fatality risk (IFR) of 0.3% to 1% (based on 3 publications) [21] . This IFR was then adjusted by the proportion assumed to be symptomatic (at 67%, as above) to give a case fatality risk (CFR) of 0.45% (i.e., 0.3% x 100%/67% = 0.45%). Nevertheless, we note that higher estimates exist, including a CFR for "China outside of Hubei Province" of 0.81% (95%CI: 0.67 to 0.98; and adjusted for the time delay in reporting deaths) [22] . Another CFR for "China outside of Hubei Province" was similar, at 0.9% (95% credible interval: 0.6-1.3%; also adjusted for the time delay in reporting deaths) [23] . A value . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint On 6 March 2020, the WHO reported that this number was likely to be in the range of 2.0 to 2.5 [24] . But given persisting uncertainty, we used the same three values as in the modelling work by Hellewell et al [2] . Of note is that an earlier review of 12 studies [25] , suggested estimates that ranged from 1.4 to 6.49, with a mean of 3.28, a median of 2.79 and interquartile range of 1.16. But this review also noted that in more recent studies, R₀ estimates seem to have stabilised at around 2-3. Recent UK modelling used an estimate of 2.4 (range: 2.0 to 2.6) [3] .
Relative contagiousness in the prodromal period 50%
There is uncertainty around this value but we used the same estimate as in recent UK modelling [3] . This has biological plausibility as while there is similarity in viral loads between asymptomatic and symptomatic COVID-19 patients [26] , it would be expected that those who are fully symptomatic (with a cough etc.) would be more likely to transmit infection. Of note is an estimate from the Diamond Princess cruise ship outbreak, that 17.9% of COVID-19 infections were from asymptomatic individuals (95% credible interval 15.5-20.2%) [27] . But it is unclear how generalisable this finding is given the crowded cruise ship conditions and the typically elderly nature of the passengers.
We used an average duration of 4 days as per Read et al [28] , with a standard deviation of 25% (calculated using 16 stages; Erlang distribution). This is similar to the estimate in a Chinese study which reported a median latent period of 3.69 days [29] .
Prodromal period 1 day There is as yet insufficient data on this for COVID-19, so we used an assumed value for influenza (SD = 25%, Erlang distribution).
The WHO-China Joint Mission report stated that "the median time from onset to clinical recovery for mild cases is approximately 2 weeks and is 3-6 weeks for patients with severe or critical disease" [8] . But given that mild cases may have been missed in this particular assessment, we used a slightly shorter time period of 10 days (SD = 25%, Erlang distribution). During this symptomatic period, cases were considered infectious. We note that there is evidence from COVID-19 cases of shedding of viral RNA from sputum that has outlasted the end of symptoms [30] . However, the significance of this for disease transmission is unknown. Interventions
Two scenarios (25%, 50%) and threshold analyses This variable covers the summated impact of a potentially wide variety of different interventions: people may adopt enhanced personal hygiene measures (hand washing, cough etiquette etc.); they may decide to have fewer contacts (physical distancing); and governments may close venues and schools, restrict mass transit, curtail mass gatherings, and restrict travel (within and between countries). Scenario "25%": This scenario is our approximation of a modest level of the above listed interventions. Scenario "50%": This scenario assumed an intensification of the measures being adopted (relative to the above scenario). Threshold analyses: This was where we increased the level of "general contact reduction" to a level which pushes the epidemic peak into the following year (i.e., past day 365 after the first introduction of the infection into the country).
February 2020
This date is somewhat arbitrary but does represent a time of increased awareness of the disease in Europe as there were 9 European countries having reported cases to WHO and one reporting a death (in France). It is equivalent to day 5 of the simulated year. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint This period was arbitrary and was varied in threshold analyses ( Table 1) . As further discussed in the main text the feasibility of such sustained interventions for any country is highly uncertain and may not be realistic given the adverse social and economic implications.
Variation in R₀ of 25%
Winter conditions are known to accelerate transmission of influenza and also the other coronaviruses which cause common cold like symptoms [31] . Enveloped viruses show strong seasonality with winter peaks [32] , and SARS-Cov-2 is an enveloped virus. Even though there are many uncertainties relating to seasonality and this novel coronavirus [33] , it seems prudent to assume some seasonal fluctuation so we increased the average by 25% in winter and reduced it by 25% in summer (with a sinusoidal variation throughout the simulated year), using a mid-winter peak for Europe of 15 January (i.e., day 334 of the simulation).
Case isolation (only used in the threshold analyses)
Varied in threshold analyses (Table 1) We set the following values in threshold analyses (while setting 0% for "general contact reduction"see above):  Probability that a sick person is isolated = varied in threshold analyses  Maximum capacity of isolation wards = 4 per 10,000 population (i.e., see below).  Contact reduction for cases in home isolation = 50% (this occurs while hospital isolation capacity is exceeded)  Beginning of case isolation measures = the 19 February date as above (i.e., assuming increased clinician awareness from cases in nine European countries at this time).  Duration of case isolation measures = 6 months (183 days), or 9 months (274 days) or the rest of the simulated year. For isolation capacity in hospitals we assumed that 10% of hospital beds could be converted for this use during the pandemic, with the country having a total of 44,000 hospital beds; or 44 per 10,000 population. For this we used the median hospital beds per 1,000 population for 12 European states in the OECD and in the 5 to 15 million range for total population size, i.e., using data from: Austria, Belgium, Czechia, Denmark, Greece, Hungary, Norway, Portugal, Slovakia, Sweden and Switzerland [34] . If 10% of these hospital beds were used for isolation purposes, then this is 4 beds per 10,000 population for use in the modelling process, or 4,000 in total in the country.