Kunal K Dayal

John E Cater

Michael J Kingan

Gilles D Bellon

Rajnish N Sharma

An analysis of the Butoni wind farm in Fiji

Keywords: Fiji, wind farm, wind characteristics, WAsP. 1

This study carries out an analysis of the Butoni wind farm in Fiji using 6 years of uninterrupted near-surface wind observations (2013 -2018) from a nearby Automatic Weather Station (AWS). The standard wind-industry software, Wind Atlas Analysis and Application Program (WAsP) is used to analyse and evaluate the wind characteristics of the wind farm and the surrounding areas. In addition, the modelled and operational annual energy production (AEP) are discussed with the related economic analysis together with the main causes for the under-performance of the wind farm. The results revealed that the mean wind speed, power density and the AEP at the Butoni wind farm are 3.5 m/s, 68 W/m 2 and 130 MWh/year/turbine, which are below the utility-scale standard of 6.4 m/s, 300 W/m 2 and 500 MWh/year/turbine respectively, at 55 m above ground level (AGL). The main reason for the under-performance of the wind farm is that it was commissioned for a low mean wind speed regime of 3.1 m/s to 4.0 m/s. The wind farm has a lower than expected capacity factor of 5.4 % and a higher wind shear coefficient of 0.35. An economic analysis revealed that the payback time is 24.5 years and the cost of energy generation is FJD $0.55/kWh.

Introduction

Fiji is a small island developing state (SIDS) located in the tropical Southwest Pacific region between the latitudes of 12 °S -22 °S and longitudes of 177 °E -178 °W. The 332 islands in the country cover a total land area of 18,333 km 2 with 110 islands being inhabited. The two largest islands of Viti Levu and Vanua Levu occupy around 87 % of the total land area with 10,400 km 2 and 5,540 km 2 respectively. The islands are mountainous with a maximum peak of 1300 m above mean sea level and are of volcanic origin. The islands of Fiji experience a tropical marine climate, which consists of two climatic seasons, namely austral summer (wet season) extending from November to April and austral winter (dry season) from May to October. The islands are also prone to tropical cyclones with a frequency of on average 2 tropical cyclones forecasted during austral summer for the Fijian waters [START_REF] Sturman | Climate[END_REF].

Energy Fiji Limited (EFL) (formerly known as Fiji Electricity Authority -FEA) is responsible for the generation, transmission and distribution of grid electricity in Fiji.

Electricity generation in Fiji is a mixture of renewable (hydro, biomass and wind) and conventional (industrial diesel and heavy fuel oil) power stations. In the year 2019, 1060 GWh of electricity was generated with 57.7 % (610.8 GWh) from renewable sources and 42.4 % (449.7 GWh) from conventional power plants. The renewable energy-based power plants contributed 52.7 % (559.3 GWh) from hydropower, 4.6 % (48.8 GWh) from biomassbased independent power producers and 0.25 % (2.7 GWh) from wind [START_REF] Efl | Energy Fiji Limited[END_REF].

An assessment of the wind resources is required to determine the potential for wind power generation at any scale, whether for grid-connected or stand-alone systems. In addition, siting of wind turbines is important for the optimal utilization of the available wind resources and the viability of a wind power project [START_REF] Sathyajith | Wind Energy: Fundamentals, Resource Analysis and Economics[END_REF][START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF]. Several wind-resource assessment studies have been conducted in Fiji using the Wind Atlas Analysis and Application Program (WAsP) [5 -12] and WindPRO [START_REF] Dayal | Offshore wind resource assessment, site suitability and Technology selection for Bligh Waters Fiji using WindPRO[END_REF], with mean wind speed, power density and Weibull A and k parameters ranging from 4.6 m/s -6.5 m/s, 139 W/m 2 -241 W/m 2 , 5.2 m/s -7.35 m/s and 1.68 -2.63, respectively at 10 m elevation, but none have led to developing new wind farms to support the electricity grid. However, a recent study identified three potential wind farm sites that can be further investigated for utility-scale wind power applications with each site capable of incorporating a minimum installed capacity of 10 MW using Vergnet 275-kW wind turbines [START_REF] Dayal | Wind resource assessment and energy potential of selected locations in Fiji[END_REF].

Fiji has a grid-connected 10 MW wind farm situated in the Butoni hills of Sigatoka. The wind farm consists of Vergnet wind turbines, which are connected to the EFL grid network. Table 1 presents the electricity generation statistics, contribution to grid and the capacity factor (CF) of the Butoni wind farm since its installation in 2007. The wind farm is operating below the operational capacity of current utility-scale wind farms around the world and in the Oceania region, which have capacity factors ranging from 0.20 -0.54 and 0.31 -0.48, respectively [START_REF]Renewables 2019 Global Status Report[END_REF]. The power production ranges from a minimum of 2.1 GWh in 2017 to a maximum of 7.2 GWh in 2009 with a minimum and maximum contribution of 0.21 % and 0.93 % to the annual electricity generation within Fiji over a 13-year period, respectively.

The Butoni wind farm in Fiji is reported to be under-performing due to a lack of analysis prior to installation [START_REF]$34 M Failure: FEA admits lack of study in the wind farm project[END_REF]. To date, no research has been carried out to analyse the wind resources and to determine the main causes for the under-performance of the wind farm. This study aims to carry out an analysis of the Butoni wind farm to evaluate the wind characteristics in terms of mean wind speed, dominant wind direction, mean power density, diurnal and annual wind speed patterns, capacity factor, average wind shear and the energy potential via resource mapping of a 275-kW Vergnet wind turbine using measured data from a nearby located Automatic Weather Station (AWS) in WAsP. In addition, the analysis compares simulated yearly results with actual operational production from the wind farm and discusses the related economic return together with the causes for the under-performance of the wind farm.

Section 2 of this work provides the background for the analysis, section 3 describes the methodology, and the results are presented in section 4 and then discussed in section 5. The conclusions are presented in section 6.

Background

The key wind resource parameters computed by WAsP [START_REF] Mortensen | Wind Atlas Analysis and Application Program: WAsP 12 Help Facility[END_REF] include the Weibull distribution, power and energy production from a wind turbine and the mean wind speed. The wind shear coefficient, capacity factor and the economic viability parameters are computed using the WAsP results.

Weibull Distribution

In most locations surface wind speed distribution is well approximated by a Weibull distribution [START_REF] Sathyajith | Wind Energy: Fundamentals, Resource Analysis and Economics[END_REF][START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF][START_REF] Mortensen | Wind Atlas Analysis and Application Program: WAsP 12 Help Facility[END_REF]. Fitting the Weibull distribution is therefore the method most frequently used in wind energy studies to obtain a smooth distribution despite under-sampling of wind speed due to limited observation periods. The Weibull distribution is a two-parameter function, mathematically represented by while the cumulative distribution function is represented by : where is the reference wind speed at the height of measurement, the non-dimensional shape parameter, and the scale parameter whose dimensions coincide with that of .

. The Weibull shape parameter indicates how peaked the distribution is, the Weibull parameter indicates on average how windy the site is and the Weibull mean indicates the long-term mean wind speed at the site. A higher Weibull parameter also indicates an increase in the dispersion of the wind speed around the mean wind speed and a higher Weibull parameter indicates that the variation of mean wind speed is small [START_REF] Sathyajith | Wind Energy: Fundamentals, Resource Analysis and Economics[END_REF][START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF].

Power and Energy Production from a Wind Turbine

The power production (in units of ) from a wind turbine is estimated using the wind turbine power curve and the probability density function of the wind speed at the hub height of the wind turbine as follows:

The annual energy output from a wind turbine E (in units of ) is estimated using the following equation:

where is the number of hours in one year and is the energy conversion efficiency of the wind turbine [START_REF] Mortensen | Wind Atlas Analysis and Application Program: WAsP 12 Help Facility[END_REF].

Wind Speed calculation using WAsP

WAsP is a linear wind simulation model, based on linear models of the fluid flow equations and considers the following fundamental factors when calculating wind speed:

1. The geostrophic balance, where the geostrophic drag law gives the geostrophic wind speed 'G':

where , are dimensionless constants, is the friction velocity, is the dimensionless von Karman's constant , is the surface roughness length and is the Coriolis parameter.

2. The modified logarithmic wind profile.

where is the wind speed at height , is the stability dependent function which is positive for unstable and negative for stable conditions, and is the Monin-Obukhov Length [START_REF] Mortensen | Wind Atlas Analysis and Application Program: WAsP 12 Help Facility[END_REF].

3. A specific (but uniform) stability, roughness variations and elevation variations from the topographical data.

Vertical Profile of Wind Speed and Wind Shear

A power law is often used to describe the wind velocity profile near the surface and is commonly used to model the vertical profile of wind speed over regions of homogeneous, flat terrain and prairies. The power law is expressed as: where is the reference wind speed at height and is the power law exponent, which is the estimated wind shear coefficient at the site.

The wind shear is the vertical variation of wind speed with height. It results from the surface drag which slows down the wind at the surface. It depends on how the effect of this drag is transported upwards by turbulence, and on the surface roughness, which modulates turbulence. It is an important parameter not only for power production from wind turbines, but also for aerodynamic loadings on the wind turbine blades. The wind shear coefficient can be computed using known wind speeds at two elevations, as in equation ( 8):

Many researchers have used a one-seventh power law where for relatively flat terrain.

Capacity Factor

The capacity factor (CF) is the ratio of the energy output ( ) produced by a wind power system to the output that would have been generated if the system operated at its rated power ( ) over a given time period ( ). The dimensionless value of CF is given by:

Payback Period and Cost of Energy Generation

In a simple payback analysis, the revenue is compared with the costs and the length of time required for recovering the initial investment costs. The payback period in years equals to the total capital costs of the wind energy system divided by the annual revenue generated from the energy produced [START_REF] Sathyajith | Wind Energy: Fundamentals, Resource Analysis and Economics[END_REF][START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF]. The simple payback period is expressed as:

where is the simple payback period, is the total installation cost ($), is the average annual return ($/year), is the annual energy production (kWh/year) and is the feed-intariff for electricity ($/kWh).

The cost of energy generation is the unit cost required to produce energy in $/kWh from a power generation system, it is expressed as: where is the average annual operation and maintenance cost ($/year) and is the fixed charge rate ($/year), which refers to the value of interest one pays and or an average annual charge used to account for debt, equity costs and taxes etc.

Methodology

Fiji Meteorological Services (FMS) provided 6 years of wind data from 2013 to 2018 for the Sigatoka Automatic Weather Station (AWS) in Fiji since its installation. The average wind speed and wind direction dataset was recorded with a temporal resolution of 10 minutes using mechanical anemometers and wind vanes at a height of 10 m above ground level (AGL) as per the World Meteorological Organisation (WMO) requirements.

Table 2 shows the station details in terms of the latitude, longitude, altitude, observation period and the total number of observations and the data availability. The AWS site has a data availability of 95.4 % over the study period. of wind data. The accuracy of the predictions are subject to the assumption that there is the same overall weather regime for the reference and predicted sites, the weather conditions are close to neutrally stable, the reference wind data is reliable, the surrounding terrain is sufficiently smooth, and the topographical inputs are adequate and reliable.

WAsP Climate Analyst 3.1 generated an estimate of the wind climatology for the Sigatoka AWS location using the raw data provided by Fiji Meteorological Services. The estimated wind is presented sector-wise in the form of a wind rose and a Weibull distribution function.

The wind rose indicates the relative frequency of occurrence of wind direction grouped in 12 sectors. Equation [START_REF] Kumar | Wind characteristics and energy potentials at Wainiyaku Taveuni, Fiji[END_REF], is used to compute the wind shear coefficient using the mean wind speeds at 10 m and 55 m above ground level (AGL) as the reference and calculation elevations for wind shear coefficient, respectively. These heights have been used because 10 m wind speed is available from the automatic weather station, while WAsP modelled the wind speed at 55 m AGL, and measurements are not available at any other heights.

The topography and the land roughness maps were imported from the GWA-Elevation

Database and GWA-Roughness-Global-Cover Databases (https://globalwindatlas.info/) at a horizontal resolution of 3 arc seconds (approximately 90 m) and 10 arc seconds (approximately 300 m), respectively. Both the topography and the land roughness maps were joined into one map using the WAsP Map Editor 12.1.

WAsP 12.1 was then used to analyse the wind energy resource in terms of mean wind speed, power density, orography, and the AEP using a Vergnet -kW wind turbine on a resource grid of at a height of 55 m AGL. The obstacles and sheltering effects from nearby vegetation and buildings were considered in the WAsP analysis. High-resolution micro-scale resource maps for the resource grid of at 50 m horizontal resolution were developed for each resource parameter. A turbine cluster of Vergnet wind turbines were placed at the same locations as in the Butoni wind farm. Annual energy production (AEP) was computed using WAsP 12.1 and the wake losses were modelled using the Park 2 model [START_REF] Mortensen | Wind Atlas Analysis and Application Program: WAsP 12 Help Facility[END_REF].

The Vergnet 275-kW wind turbine has been used at the Butoni wind farm as these turbines can be lowered to the ground and secured in place during extreme wind conditions, such as tropical cyclones, which are not uncommon in Fiji. Vergnet wind turbines are in use at existing wind farms in other Southwest Pacific island countries prone to tropical cyclones such as Samoa, Vanuatu and New Caledonia. Table 3 presents the specifications of the Vergnet 275-kW wind turbine. and 50 m, respectively) and AEP of 0.500 GWh/year/turbine or higher are considered feasible for utility-scale wind power development [START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF][START_REF] Liu | Dynamical Downscaling of Climate Change Impacts on Wind Energy Resources in the Contiguous United States by Using a Limited-Area Model with Scale-Selective Data Assimilation[END_REF]19]. The individual wind turbine and the overall wind farm capacity factor is calculated using Equation 9. The payback period and the cost of energy generation are computed using Equations 10 and 11, respectively.

Results

This section presents the results as follows: wind speed frequency and wind direction analysis, diurnal and annual wind speed patterns, wind shear coefficient and resource maps of mean wind speed, wind power density and annual energy production (AEP). Also presented is a comparison between the AEP calculated using WAsP and the actual AEP of the Butoni wind farm and the related wind farm economics.

Wind Speed Frequency and Wind Direction

The wind speed frequency distribution provides information about the site-specific Weibull wind resource parameters ( and ) and the average power density at the height of the measurement mast. Similarly, a wind rose provides site specific sector-wise wind direction information. Figure 3 presents the wind speed frequency distribution and the wind direction analysis of the Sigatoka AWS location. The major reason for the larger differences observed between the Sigatoka AWS and other stations is the geographical location, as the wind resource parameters vary in space depending on the location, surrounding terrain and topography.

Wind Speed Patterns

The diurnal wind speed pattern represents the composite long-term averaged hourly variation of wind speed during a 24-hour period. The 6-year diurnal wind speed patterns and the average observed at the Sigatoka AWS location is presented in those reported in previous studies [5 -14] in Fiji, but with lower magnitude. On average the minimum diurnal wind speed is lower by 272 %, while the maximum diurnal wind speed is lower by 91 % at the Sigatoka AWS in comparison with the other wind energy studies done in Fiji [5 -14]. The magnitude of the annual wind speed variations at Sigatoka AWS are lower than those reported in previous studies [5 -14], but similar behaviour is observed with higher wind speeds during austral winter (May -Oct) and lower wind speeds during austral summer (Nov -Apr). On average the minimum annual wind speed is lower by 100 % while the maximum wind speed is lower by 219 % at the Sigatoka AWS in comparison with wind energy studies done in Fiji [5 -14].

Wind Shear

The mean wind shear coefficient for the Sigatoka area is 0.35 as presented in Table 5. It represents a terrain type of a small town with few trees and shrubs [START_REF] Patel | Wind and Solar Power Systems: Design, Analysis and Operation[END_REF]. For the normal wind turbine operation for all wind power classes, a wind shear coefficient of is required [START_REF] Smith | Evaluation of Wind Shear Patterns at Midwest Wind Energy Facilities[END_REF], the Sigatoka wind shear coefficient is outside this range. with an average wind farm capacity factor of 5.4 %. The net AEP for the individual wind turbines is significantly below 500 MWh/year, which is considered feasible for utility-scale wind power applications [START_REF] Manwell | Wind Energy Explained: Theory, Design and Application[END_REF][START_REF] Liu | Dynamical Downscaling of Climate Change Impacts on Wind Energy Resources in the Contiguous United States by Using a Limited-Area Model with Scale-Selective Data Assimilation[END_REF]19]. The capacity factor is also below the operational capacity of current utility-scale wind farms around the world and in the Oceania region, which have capacity factors ranging from 0.20 -0.54 and 0.31 -0.48, respectively [START_REF]Renewables 2019 Global Status Report[END_REF]. Table 7 presents the WAsP average Weibull ( and ) parameters, mean wind speed and the power density of individual wind turbines for the study period of 2013 -2018 at the hubheight of 55 m AGL. The Weibull scale parameter and the shape parameter varies from a minimum to maximum of 3.4 m/s for turbine 1 to 4.5 m/s for turbine 5, and 1.42 for turbine 2 to 1.65 for turbine 14, with an average of 3.9 m/s and 1.51 for the entire wind farm, respectively. The mean wind speed and power density varies from a minimum to maximum of 3.1 m/s for turbine 1 to 4.04 m/s for turbine 5, 46 W/m 2 for turbine 1 to 106 W/m 2 for turbine 5, with an average of 3.51 m/s and 68 W/m 2 for the whole wind farm, respectively.

The Weibull parameter is less than 5 m/s, which indicates that the mean wind speed is lower at the wind farm site [3 -14]. The Weibull parameter is less then 2.0, which indicates a greater deviation from the mean wind speed. The mean wind speed and the power density are also less than 6.4 m/s and 300 W/m 2 (Wind Power Class 3), which are the minimum requirements for utility-scale wind power applications using the 50 m AGL reference from NREL [19]. It is concluded that the whole of Sigatoka and Butoni wind farm area are poor candidate sites to commission a utility-scale wind farm. Other sites that have used fewer wind turbines and target rural electrification have a higher wind resource potential then the current Butoni wind farm site [5 -12]. Other work which has recommended utility-scale wind power applications also have a higher wind resource potential [START_REF] Dayal | Offshore wind resource assessment, site suitability and Technology selection for Bligh Waters Fiji using WindPRO[END_REF][START_REF] Dayal | Wind resource assessment and energy potential of selected locations in Fiji[END_REF]. AEP/year is taken as the average of the 13 years of operation of the wind farm which amounts to 4.621 GWh. The cost of energy generation is calculated to be FJD $0.55/kWh, which is higher than the cost of electricity generation from other sources of generation in Fiji (Hydro-power -FJD $0.20/kWh, Oil-power -FJD $0.39/kWh, Bagasse -FJD $0.28/kWh and Biomass -FJD $0.23/kWh) [START_REF] Dornan | Electricity Generation in Fiji: Assessing the Impact of Renewable Technologies on Costs and Financial Risk[END_REF]. Using the wind farm capital costs, the average annual return from the wind farm and the feed-in-tariff, the payback period for the Butoni wind farm is computed to be 24.5 years, which is greater than the standard wind farm lifetime of 20 years. The cost of energy generation and the payback period in the current study is much higher in comparison with those computed in other wind energy studies in Fiji [5 -14], which are of the order of less than FJD $0.20/kWh and 10 years, respectively.

Comparison of AEP

Discussion

The standard utility-scale wind farm application benchmarks from NREL [19] and the relevant literature [5 -14], identifies that the Butoni wind farm is situated in a weak wind speed regime of Wind Power Class 1, which is not suitable for utility-scale wind power applications. No other sites in the greater Sigatoka area, analysed by WAsP using the resource grid show potential wind resources that could be utilized for viable wind farm applications. Another problem is the higher wind shear coefficient of 0.35 at the Butoni wind farm, which is 75 % higher than the normal working standard (0.20) of wind turbines, and contributes towards the aerodynamic loadings on the wind turbine blades and may be one of the reasons for the higher turbine downtime reported earlier. The economics suggest that the Butoni wind farm project is not an economically viable wind power project in term of payback period (> 20 years), capacity factor (> 0.20) and the cost of energy generation exceeding other renewable and conventional power generations methods in Fiji.

It is recommended that EFL transfer the poorly operating wind turbines to other locations.

Possible other sites have been identified as Rakiraki, Nabouwalu and Udu [START_REF] Dayal | Wind resource assessment and energy potential of selected locations in Fiji[END_REF]. These three sites have been found to be potential wind farm sites with 5 -6 years of wind data confirming the consistency of the potential of the wind resources [START_REF] Dayal | Wind resource assessment and energy potential of selected locations in Fiji[END_REF]. This transfer is required to optimally utilize the wind turbines within their standard available lifetime of 20 years, out of which 7 years remain.

The main limitation of this work is that it is based on the measurements from a nearby AWS as no time series wind data measurements could be obtained from within the wind farm. For a further study, it would be interesting to use the 10 years of model simulation results at the Butoni wind farm location from the recent mesoscale wind resource mapping study for the three domains of , and and investigate how the wind resource parameters vary.

Conclusions

This study completed an analysis of the Butoni wind farm in Fiji using 6 years of This research promotes the idea that wind farm project planning via wind resource and economic viability assessments are fundamental prior to the installation of a grid-connected wind farm project in the SIDSs of the Pacific, if they intend to optimally utilize the wind resources to ease its dependence on diesel-based power generation.

Figure 1

 1 Figure 1 shows the map of Fiji with the study area and an enlarged spatial map with the location of the Sigatoka AWS (denoted by X) and the wind farm site (bounded by the rectangular box). In the enlarged spatial map, the contours are at intervals of 10 m, the roughness is represented by polygonal shapes and the rectangular box outlines the location of the wind farm and the 37 Vergnet 275 kW wind turbines located on the Butoni hills.

Figure 1 .

 1 Figure 1. Map of Fiji showing the location of the AWS and the wind farm site (Original Map Source: Google Earth).

Figure 2

 2 Figure 2 presents the elevation and the polygonal land roughness grid maps of Sigatoka with a contour interval of 10 m. The elevation varies from sea level to 380 m.

Figure 2 .

 2 Figure 2. Elevation map of Sigatoka (x denotes the location of the AWS).

Figure 3 .

 3 Figure 3. Wind rose and wind speed frequency distribution at Sigatoka AWS for 2013-2018 at 10 m AGL.

Figure 4 .

 4 The average diurnal wind speed varies from a minimum of 1.1 m/s at midnight to a maximum of 3.4 m/s at 13:00 and 14:00 hours, at a height of 10 m. The diurnal variation of wind speed also falls in the category of Wind Power Class 1 [19]. The average diurnal wind speed pattern is similar to

Figure 4 .

 4 Figure 4. Diurnal wind speed patterns at 10 m AGL at Sigatoka AWS.

Figure 5 .

 5 Figure 5. Annual wind speed patterns at 10 m AGL at Sigatoka AWS.

Figures 6 - 8

 68 Figures 6 -8 present the high-resolution ( ) spatial distribution of the mean wind speed, wind power density and the AEP using the Vergnet 275-kW wind turbines, respectively. These are generated using WAsP on a domain, surrounding the Sigatoka AWS and covering the Butoni wind farm and the greater Sigatoka area at 55 m AGL. The wind farm area is bounded by the rectangular box shown in Figure 6.

Figure 6 .

 6 Figure 6. Mean wind speed map of the Sigatoka area at 55 m AGL.

Figure 7 .

 7 Figure 7. Wind-power density map of the Sigatoka area at 55 m AGL.

Figure 8 .

 8 Figure 8. AEP map of the Sigatoka area at 55 m AGL.

Figure 9

 9 Figure 9 presents a graphical comparison of the measured AEP from the Butoni wind farm site to that computed with WAsP. Since measured wind data is only available from the years 2013 -2018, the average of 6 years of WAsP AEP is used for the year 2019 for comparison.

Figure 9 .

 9 Figure 9. AEP comparison of the Butoni wind farm [EFL, 2019].

A recent 10 -

 10 year (2009 -2018) high-resolution mesoscale wind resource mapping study of the SIDS of Fiji using the Weather Research and Forecasting (WRF) model at a resolution of [25], also indicates that the wind speed and the power density at the Butoni wind farm location ranges from 3.5 m/s to 4 m/s and 50 W/m 2 to 100 W/m 2 , respectively at 55 m elevation. The mesoscale model results are similar to the overall results obtained with WAsP using 6 years of measured wind data from a nearby AWS, indicating that the wind resource falls in category of Wind Power Class 1.

  uninterrupted near-surface wind observations (2013 -2018) from a nearby Automatic Weather Station (AWS). The standard wind-industry software, Wind Atlas Analysis and Application Program (WAsP) is used to analyse and evaluate the wind characteristics of the wind farm and the surrounding areas. In addition, the modelled and operational annual energy production (AEP) are discussed with the related economic analysis together with the main causes for the under-performance of the wind farm.The results revealed that the average wind speed, power density and the AEP at the Butoni wind farm are 3.5 m/s, 68 W/m 2 and 129.7 MWh/year/turbine, respectively. These wind resource parameters are below the standard wind speed of 6.4 m/s, power density of 300 W/m 2 and AEP of 500 MWh/year/turbine for utility-scale wind power applications at 55 m AGL. The main reason for the under-performance of the wind farm is that it is commissioned for a weak mean wind speed regime of 3.1 m/s to 4.0 m/s. The wind farm has a low capacity factor of 5.38 % and a high wind shear coefficient of 0.35, which are also contributing factors to the wind farm underperformance. An approximate economic analysis revealed that the payback time for the Butoni wind farm is 24.5 years and the cost of energy generation is FJD $0.55/kWh.

Table 1 .

 1 Butoni Wind Farm Operational Statistics

	Year	Production (GWh)	Contribution to grid (%)	Capacity Factor (%)
	2007	3.35	0.44	3.83
	2008	4.60	0.60	5.26
	2009	7.21	0.93	8.23
	2010	6.42	0.77	7.33
	2011	4.98	0.62	5.68
	2012	6.81	0.85	7.77
	2013	5.35	0.62	6.11
	2014	4.27	0.50	4.87
	2015	5.67	0.64	6.48
	2016	3.63	0.39	4.15
	2017	2.08	0.21	2.38
	2018	2.56	0.26	2.92
	2019	3.15	0.25	3.59

Table 2 .

 2 Location and Data Records for the Sigatoka AWS

	Station	Latitude Longitude	Height	Elevation	Data	# of	Availability

Table 3 .

 3 Vergnet 275-kW turbine specifications

	Rotor	Hub	Cut-in	Cut-out	Rotation	Rated	Rated wind
	Diameter	Height	speed	speed	rate	Power	speed
	32 m	55 m	3.5 m/s	25 m/s	31 rpm	275-kW	12 m/s

Table 4 .

 4 Details of the wind turbines in the Butoni wind farm

	Site	X-location	Y-location	Elevation a.s.l.
	description	[m]	[m]	[m]
	WTG	553245.7	7996709	139.1
	WTG	553066.1	7996809	150.1
	WTG	553278.1	7996930	184.2
	WTG	553352.5	7997063	213.7
	WTG	553395.1	7997173	239.2
	WTG	553501.1	7997239	238.7
	WTG	553427.5	7997394	241.3
	WTG	553533.4	7997461	213.6
	WTG	553692.3	7997515	208.9
	WTG	553787.6	7997548	200.7
	WTG	553798.6	7997692	179.4
	WTG	553989.4	7997824	199.4
	WTG	554116.2	7997780	193.2
	WTG	553979.2	7997979	188.6
	WTG	553968.9	7998079	180.8
	WTG	554053.8	7998156	170.8
	WTG	554045.1	7998853	202.9
	WTG	554077.3	7998997	224.2
	WTG	554193.8	7999052	226.1
	WTG	554236.4	7999129	219.9
	WTG	554289.5	7999218	213.1
	WTG	554205.1	7999317	218
	WTG	554173.6	7999406	217.3
	WTG	554248	7999517	229.8
	WTG	554290.6	7999594	230
	WTG	554343.8	7999704	221.7
	WTG	554312.4	7999848	210.5
	WTG	553691.5	7997217	204.6
	WTG	553924	7997150	185.2
	WTG	553754.6	7997095	193.9
	WTG	554017.7	7996596	170.2
	WTG	554112.7	7996518	153.9
	WTG	554027.9	7996463	157
	WTG	554017.1	7996386	148.3
	WTG	553953.5	7996331	142.6
	WTG	553910.9	7996242	134.5

Table 5 .

 5 Mean wind shear coefficient of Sigatoka area

		Location	Wind speed @ 10 m	Wind speed @ 55 m	Mean wind shear coefficient
			[m/s]	[m/s]	[α]
		Butoni	1.94	3.50	0.35
	4.4	Wind Farm Statistics
	Table 6 presents the WAsP calculated average statistics of the individual wind turbines in the
	Butoni wind farm for the study period of 2013 -2018. The net AEP varies from a minimum
	of 62.6 MWh for turbine 15 to a maximum of 215 MWh for turbine 6 with an average of
	129.7 MWh for the 37 turbines in the wind farm. The wake losses vary from a minimum of

1.1 % (1.4 MWh) for turbine 37 to a maximum of 40 % (43.6 MWh) for turbine 23 with an average of 13.1 % (18.4 MWh) for the entire wind farm. The capacity factor of the wind

Table 6 .

 6 Wind Turbines in the Butoni Wind Farm 2013 -2018

	Turbine Site	Gross AEP (MWh)	Net AEP [MWh]	Wake Loss (MWh)	Wake loss [%]	Capacity Factor (%)
	WTG 1	97.0	94.7	2.3	2.4	3.9
	WTG 2	142.8	139.0	3.9	2.7	5.8
	WTG 3	153.2	140.0	13.2	8.6	5.8
	WTG 4	186.6	175.7	10.8	5.8	7.3
	WTG 5	232.1	210.7	21.4	9.2	8.8
	WTG 6	228.5	215.0	13.5	5.9	8.9
	WTG 7	227.8	177.3	50.5	22.2	7.4
	WTG 8	198.9	169.5	29.4	14.8	7.0
	WTG 9	189.8	168.9	20.8	11.0	7.0
	WTG	167.4	152.3	15.1	9.0	6.3
	WTG	127.7	108.8	18.8	14.8	4.5
	WTG	161.3	149.2	12.0	7.5	6.1
	WTG	178.8	172.9	5.9	3.3	7.1
	WTG	100.4	76.7	23.7	23.6	3.2
	WTG	92.2	62.6	29.7	32.2	2.6
	WTG	98.5	87.0	11.6	11.7	3.6
	WTG	118.5	110.3	8.1	6.9	4.6
	WTG	158.3	137.5	20.8	13.2	5.7
	WTG	155.6	146.5	9.2	5.9	6.1
	WTG	131.9	119.4	12.6	9.5	5.0
	WTG	114.1	105.8	8.4	7.3	4.4
	WTG	103.4	69.8	33.6	32.5	2.9
	WTG	109.4	65.8	43.6	39.8	2.7
	WTG	138.5	110.0	28.6	20.6	4.6
	WTG	155.7	132.9	22.8	14.6	5.5
	WTG	138.1	123.8	14.3	10.4	5.1
	WTG	94.7	63.7	31.0	32.7	2.6
	WTG	168.9	141.6	27.3	16.2	5.9
	WTG	146.7	135.3	11.5	7.8	5.6
	WTG	151.2	140.0	11.2	7.4	5.8
	WTG	160.2	109.2	51.0	31.8	4.5
	WTG	157.9	154.6	3.3	2.1	6.4
	WTG	154.6	115.6	39.0	25.2	4.8
	WTG	149.8	145.0	4.8	3.2	6.0
	WTG	138.9	131.4	7.5	5.4	5.5
	WTG	128.9	119.2	9.7	7.5	5.0
	WTG	121.7	120.3	1.4	1.1	5.0
	Mean	148.1	129.7	18.4	13.1	5.4

Table 7 .

 7 Wind Climate Statistics 2013 -2018

	Turbine Site	[m/s]		[m/s]	[W/m²]
	WTG 1	3.4	1.50	3.10	46
	WTG 2	3.7	1.42	3.35	64
	WTG 3	3.9	1.47	3.50	68
	WTG 4	4.1	1.48	3.75	83
	WTG 5	4.5	1.46	4.04	106
	WTG 6	4.4	1.46	4.02	105
	WTG 7	4.4	1.47	4.00	103
	WTG 8	4.1	1.44	3.76	88
	WTG 9	4.1	1.44	3.70	83
	WTG 10	4.0	1.47	3.58	74
	WTG 11	3.7	1.48	3.31	58
	WTG 12	4.0	1.51	3.62	72
	WTG 13	4.1	1.47	3.71	81
	WTG 14	3.7	1.65	3.30	49
	WTG 15	3.6	1.64	3.22	46
	WTG 16	3.6	1.58	3.21	48
	WTG 17	3.7	1.56	3.35	55
	WTG 18	4.0	1.52	3.61	72
	WTG 19	4.0	1.53	3.60	71
	WTG 20	3.9	1.56	3.47	61
	WTG 21	3.8	1.60	3.37	54
	WTG 22	3.7	1.62	3.31	50
	WTG 23	3.7	1.58	3.32	52
	WTG 24	3.9	1.54	3.50	64
	WTG 25	4.0	1.51	3.58	71
	WTG 26	3.8	1.52	3.47	64
	WTG 27	3.6	1.59	3.20	47
	WTG 28	4.0	1.45	3.63	78
	WTG 29	3.9	1.47	3.51	69

Table 8

 8

	presents a numerical comparison of the AEP from the Butoni wind farm to that
	calculated using WAsP from 2013 -2018 with differences in AEP and possible explanations
	for the differences observed.

Table 8 .

 8 Comparison of WAsP and Measured Production from the Butoni Wind Farm

		7
	(GWh)	5 6
	Annual AEP	1 2 3 4
		0
		2013	2014	2015	2016	2017	2018	2019
				Years
			Measured AEP (GWh)	WAsP AEP (GWh)
					%
		WAsP AEP	Measured	Difference	Differenc	Explanations
	Year	(MWh)	AEP (MWh)	(MWh)	e
	201
	3	5119.1	5348	-228.9	-4.3	5 WTG Partially down
	201
	4	5182.9	4269	913.9	21.4
						4 WTGs fully down and 5
						Partially down
	201	5720.4	5674	46.4	0.81
	5					8 WTGs fully down and 3
						Partially down

Table 9 .

 9 Economic Analysis of the Butoni Wind Farm

	Energy Economics Parameter
	Installed Capacity	10 MW
	Cost per MW	$3,400,000
	O&M Costs per kWh	$0.10
	AEP in kWh/year	4621923
	Feed-in-tariff / kWh	$0.30
	FCR	0.0609
	Capital Costs	$34,000,000
	Annual Average Return	$1,386,576.90
	O&M Costs	$462,192.30
	Simple Payback Period	24.5
	Cost of Energy per kWh	$0.55

Acknowledgments

This study is part of a PhD research project, for which the lead author is the recipient of scholarship funding from the New Zealand Ministry of Foreign Affairs and Trade (NZMFAT). The authors gratefully acknowledge the Fiji Meteorological Services for providing the ground-based wind data measurements, Mr. Om Dutt Sharma of Energy Fiji Limited (EFL) for providing production data from the Butoni wind farm and the Department of Wind Energy of the Denmark Technical University for providing a student license for the wind simulation software package WAsP.

Wind Farm Economics

Wind farm economics include important monetary information about the economic viability of a wind farm site or wind power project. Table 9 presents an approximate economic analysis of the Butoni wind farm in Fiji with all costs in Fijian Dollars (FJD). The total cost of the 10 MW Butoni wind farm was reported to be FJD $34 M [START_REF]$34 M Failure: FEA admits lack of study in the wind farm project[END_REF], the operational and maintenance (O&M) costs are adopted from operational wind farms in the Oceania region [START_REF]Renewables 2019 Global Status Report[END_REF], the feed-in-tariff is the price set by the Fiji Commerce Commission [START_REF]Authorization of Independent Power Producer (IPP) Rates -2 nd Phase[END_REF] for renewable energy based power plants feeding the generated electricity to the national electricity grid network, which amounts to FJD $0.30/kWh (fixed rate over the period) and a fixed charge rate (FCR) of 6.09 % (10-year average) is adopted from the Reserve Bank of Fiji [START_REF] Rbf | Reserve Bank of Fiji[END_REF]. The


Erwan Sallard

François-Xavier Lescure

Yazdan Yazdanpanah

France Mentré

Nathan Peiffer-Smadja
email: nathan.peiffer-smadja@inserm.fr

France Mentre

Type 1 interferons as a potential treatment against COVID-19

Keywords: interferon, COVID-19, SARS-CoV-2

Main Text

SARS-CoV-2 is a human coronavirus causing the COVID-19 disease. It emerged in China in December 2019 and rapidly propagated in numerous countries, having contaminated more than one million people and killing more than 55,000 up to April 3, 2020. Antiviral treatments are warranted to contain the epidemics. Several candidates are already being investigated, including type 1 interferon (IFN-I) [START_REF] Martinez | Compounds with therapeutic potential against novel respiratory 2019 coronavirus[END_REF][START_REF] Belhadi | A brief review of antiviral drugs evaluated in registered clinical trials for COVID-19[END_REF] . Indeed, in the context of emerging viral infections, IFN-I are often evaluated (usually in combination with other drugs) before specific treatments are developed, due to their unspecific antiviral effects [START_REF] Gao | A randomized controlled trial of low-dose recombinant human interferons α-2b nasal spray to prevent acute viral respiratory infections in military recruits[END_REF][START_REF] Loutfy | Interferon Alfacon-1 Plus Corticosteroids in Severe Acute Respiratory Syndrome: A Preliminary Study[END_REF][START_REF] Omrani | Ribavirin and interferon alfa-2a for severe Middle East respiratory syndrome coronavirus infection: A retrospective cohort study[END_REF] . We aimed to review the evidence supporting the evaluation of IFN-1 in the treatment of coronaviruses and to discuss its potential in SARS-CoV-2.

Type 1 interferons (IFN-I) designate a group of cytokines comprising the ubiquitous α and β subtypes (themselves subdivided in several isoforms), as well as the ε, ω and κ subtypes [START_REF] Samuel | Antiviral actions of interferons[END_REF] . They are secreted by various cell types, notably plasmacytoid dendritic cells, upon recognition of viral components by pattern recognition receptors (PRR) [START_REF] Liu | IPC: Professional Type 1 Interferon-Producing Cells and Plasmacytoid Dendritic Cell Precursors[END_REF] . IFN-I are thus among the first cytokines produced during a viral infection. They are recognized by the IFNAR receptor present at the plasma membrane in most cell types. Interferon fixation on IFNAR induces the phosphorylation of transcriptional factors such as STAT1 and their relocalization to the nucleus, where they activate interferon-stimulated genes (ISG). Most ISGs are involved in inflammation, signaling and immunomodulation. They interfere with viral replication and spread by several mechanisms such as a slowdown of cell metabolism or secretion of cytokines which promote the activation of the adaptive immunity. ISGs include PRRs, which further sensitize the cell to pathogens, proteins which decrease membrane fluidity, preventing viral egress or membrane fusion, and antivirals that specifically inhibit one step of the viral cycle [START_REF] Schneider | Interferon-Stimulated Genes: A Complex Web of Host Defenses[END_REF][START_REF] Totura | SARS coronavirus pathogenesis: Host innate immune responses and viral antagonism of interferon[END_REF] . IFN-I thus play a major role in antiviral immunity. Because of their immunomodulatory properties, IFN-I are used in the treatment of numerous diseases: for example, subcutaneous injections of IFNβ have been used for more than 20 years for the treatment of patients with multiple sclerosis. The role of IFNβ in the treatment of multiple sclerosis is still debated and likely results partly from the downregulation of the major histocompatibility complex (MHC) class II expression in antigenpresenting cells, the induction of IL-10 secretion and the inhibition of T-cell migration [START_REF] Jakimovski | Interferon β for Multiple Sclerosis[END_REF] .

MERS-CoV and SARS-CoV are coronaviruses closely linked with SARS-CoV-2 and presenting similar properties, despite differences in their epidemiology, pathology and in several of their proteins [START_REF] Lai | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges[END_REF] . IFN-I treatment has been studied against MERS-CoV and SARS-CoV (reviewed in [START_REF] Stockman | SARS: Systematic review of treatment effects[END_REF] in numerous experiments, both in vitro and in vivo, and in combination or not with lopinavir/ritonavir [START_REF] Chan | Treatment with lopinavir/ritonavir or interferon-β1b improves outcome of MERSCoV infection in a nonhuman primate model of common marmoset[END_REF][START_REF] Sheahan | Comparative therapeutic efficacy of remdesivir and combination lopinavir, ritonavir, and interferon beta against MERS-CoV[END_REF] , ribavirin [START_REF] Chen | In vitro susceptibility of 10 clinical isolates of SARS coronavirus to selected antiviral compounds[END_REF][START_REF] Morgenstern | Ribavirin and interferon-β synergistically inhibit SARS-associated coronavirus replication in animal and human cell lines[END_REF][START_REF] Omrani | Ribavirin and interferon alfa-2a for severe Middle East respiratory syndrome coronavirus infection: A retrospective cohort study[END_REF] , remdesivir, corticosteroids [START_REF] Loutfy | Interferon Alfacon-1 Plus Corticosteroids in Severe Acute Respiratory Syndrome: A Preliminary Study[END_REF] or IFNγ [START_REF] Sainz | Interferon-beta and interferon-gamma synergistically inhibit the replication of severe acute respiratory syndrome-associated coronavirus (SARS-CoV)[END_REF][START_REF] Scagnolari | Increased sensitivity of SARS-coronavirus to a combination of human type I and type II interferons[END_REF] . IFNα and β were systematically relatively efficient in vitro and succeeded in certain animal models [START_REF] Chan | Treatment with lopinavir/ritonavir or interferon-β1b improves outcome of MERSCoV infection in a nonhuman primate model of common marmoset[END_REF] , but generally failed to significantly improve the disease in humans [START_REF] Stockman | SARS: Systematic review of treatment effects[END_REF] . For example, a combination of IFNβ with lopinavir/ritonavir against MERS-CoV improved pulmonary function but did not significantly reduce virus replication or lung pathology severity [START_REF] Sheahan | Comparative therapeutic efficacy of remdesivir and combination lopinavir, ritonavir, and interferon beta against MERS-CoV[END_REF] , while a combination of IFNα2a with ribavirin delayed mortality without decreasing it on the long run [START_REF] Omrani | Ribavirin and interferon alfa-2a for severe Middle East respiratory syndrome coronavirus infection: A retrospective cohort study[END_REF] .. Similarly, the combination of IFNα2b with ribavirin gave excellent results in the rhesus macaque [START_REF] Falzarano | Inhibition of novel β coronavirus replication by a combination of interferon-α2b and ribavirin[END_REF] , but was inconclusive in human [START_REF] Arabi | Effect of Ribavirin and Interferon on the Outcome of Critically Ill Patients with MERS[END_REF] . The lack of significant disease improvement with IFN-I treatment in numerous studies can be explained by the mechanisms of inhibition of the IFN signaling pathway used by MERS-CoV and SARS-CoV, by the limited number of patients or animals used in the studies, or by the difficulty to decipher whether disease improvements were caused by IFN-I or the drugs used in combination with it. In addition, results often differ substantially between studies because of inconsistencies in the experimental settings or the clinical conditions [START_REF] Stockman | SARS: Systematic review of treatment effects[END_REF] : for example, a study on SARS-CoV revealed a positive effect of IFN-I treatment [START_REF] Loutfy | Interferon Alfacon-1 Plus Corticosteroids in Severe Acute Respiratory Syndrome: A Preliminary Study[END_REF] , while another study with a larger cohort did not detect any significant effect [START_REF] Zhao | Description and clinical treatment of an early outbreak of severe acute respiratory syndrome (SARS) in Guangzhou, PR China[END_REF] . It has also been proposed that interferon was efficient in patients only if they lacked comorbidities [START_REF] Al-Tawfiq | Ribavirin and interferon therapy in patients infected with the Middle East respiratory syndrome coronavirus: An observational study[END_REF][START_REF] Shalhoub | IFN-α2a or IFN-β1a in combination with ribavirin to treat Middle East respiratory syndrome coronavirus pneumonia: A retrospective study[END_REF] . Subtype diversity could be another explanation of inconsistencies between studies. It was repeatedly shown that IFNβ is a more potent inhibitor of coronaviruses than IFNα [START_REF] Scagnolari | Increased sensitivity of SARS-coronavirus to a combination of human type I and type II interferons[END_REF][START_REF] Stockman | SARS: Systematic review of treatment effects[END_REF] : depending on the studies, IFNβ1b or IFNβ1a were the most potent IFN-I subtype in the inhibition of SARS-CoV [START_REF] Hensley | Interferon-β 1a and SARS Coronavirus Replication[END_REF] and MERS-CoV [START_REF] Chan | Broad-spectrum antivirals for the emerging Middle East respiratory syndrome coronavirus[END_REF][START_REF] Dong | Discovering drugs to treat coronavirus disease 2019 (COVID-19)[END_REF][START_REF] Hart | Interferon-β and mycophenolic acid are potent inhibitors of middle east respiratory syndrome coronavirus in cell-based assays[END_REF] . Consequently, IFNβ1 appears to be most relevant interferon to treat coronavirus infections. This fact can be related to the protective activity of IFNβ1 in the lung: it up-regulates cluster of differentiation 73 (CD73) in pulmonary endothelial cells, resulting in the secretion of antiinflammatory adenosine and the maintenance of endothelial barrier function. This process explains why clinical data indicate a reduction of vascular leakage in acute respiratory distress syndrome (ARDS) with IFNβ1a treatment [START_REF] Bellingan | The effect of intravenous interferon-beta-1a (FP-1201) on lung CD73 expression and on acute respiratory distress syndrome mortality: An open-label study[END_REF] . However, this effect is insufficient to decrease ARDS mortality [START_REF] Ranieri | Effect of Intravenous Interferon β-1a on Death and Days Free from Mechanical Ventilation among Patients with Moderate to Severe Acute Respiratory Distress Syndrome: A Randomized Clinical Trial[END_REF] . It has been suggested from in vivo studies in mice that the timing of IFN-I administration plays a crucial role: positive effects were observed if IFN-I was administered shortly after infection, but IFN-I failed to inhibit viral replication and had side-effects when administered later [START_REF] Channappanavar | IFN-I response timing relative to virus replication determines MERS coronavirus infection outcomes[END_REF] . Following a study showing that IFNβ1b was as efficient as lopinavir/ritonavir against MERS-CoV in marmosets [START_REF] Chan | Treatment with lopinavir/ritonavir or interferon-β1b improves outcome of MERSCoV infection in a nonhuman primate model of common marmoset[END_REF] , the combination of IFNβ1b (injected intravenously) and lopinavir/ritonavir is currently investigated in a clinical trial in Saudi Arabia [START_REF] Arabi | β1b (MIRACLE trial): Study protocol for a randomized controlled[END_REF] . This is to our knowledge the only clinical trial against MERS-CoV.

The knowledge gained from experiments of IFN-I treatment against SARS-CoV and MERS-CoV is valuable in the selection of potential treatments against SARS-CoV-2. SARS-CoV and MERS-CoV are able to disrupt the interferon signaling pathway. For example, the Orf6 protein of SARS-CoV disrupts karyopherin transport [START_REF] Frieman | Severe Acute Respiratory Syndrome Coronavirus ORF6 Antagonizes STAT1 Function by Sequestering Nuclear Import Factors on the Rough Endoplasmic Reticulum/Golgi Membrane[END_REF][START_REF] Kopecky-Bromberg | Severe Acute Respiratory Syndrome Coronavirus Open Reading Frame (ORF) 3b, ORF 6, and Nucleocapsid Proteins Function as Interferon Antagonists[END_REF] and consequently inhibits the import in the nucleus of transcriptional factors such as STAT1, resulting in the interferon response. Similarly, the Orf3b protein of SARS-CoV inhibits the phosphorylation of IRF3 [START_REF] Kopecky-Bromberg | Severe Acute Respiratory Syndrome Coronavirus Open Reading Frame (ORF) 3b, ORF 6, and Nucleocapsid Proteins Function as Interferon Antagonists[END_REF] , a protein involved in the activation of IFN expression. However, the Orf6 and Orf3b proteins of SARS-CoV-2 are truncated [START_REF] Lokugamage | SARS-CoV-2 sensitive to type I interferon pretreatment[END_REF] and may have lost their anti-interferon functions. It could explain why SARS-CoV-2 displays in vitro a substantial sensitivity to IFNα [START_REF] Lokugamage | SARS-CoV-2 sensitive to type I interferon pretreatment[END_REF]: although SARS-CoV-2 replication is not entirely suppressed by interferons, viral titers are decreased by several orders of magnitude. SARS-CoV2 is substantially more sensitive to IFN-I than SARS-CoV, which suggests that IFN-I treatment should be at least as effective for the former than for the latter. Supporting this hypothesis, it was shown that IFNα2b sprays can reduce the infection rate of SARS-CoV-2 [START_REF] Shen | Diagnosis and treatment of 2019 novel coronavirus infection in children: a pressing issue[END_REF] . This study shows that IFN-I can be used as a prophylaxis against SARS-CoV-2, which is confirmed by the in vitro efficacy of interferon pretreatment against the virus [START_REF] Lokugamage | SARS-CoV-2 sensitive to type I interferon pretreatment[END_REF], while the replication of MERS-CoV [START_REF] Sheahan | Comparative therapeutic efficacy of remdesivir and combination lopinavir, ritonavir, and interferon beta against MERS-CoV[END_REF] and SARS-CoV [START_REF] Menachery | Attenuation and Restoration of Severe Acute Respiratory Syndrome Coronavirus Mutant Lacking 2'-O-Methyltransferase Activity[END_REF][START_REF] Thiel | Interferon and cytokine responses to SARS-coronavirus infection[END_REF] was reported to be indifferent to IFN-I prophylaxis.

From the data presented above, IFN-I might be a safe and efficient treatment against SARS-CoV-2. Knowledge acquired during studies on MERS-CoV or SARS-CoV would be critical assets in that perspective: for example, they indicate that IFNβ should be the most relevant interferon subtype, and that IFN-I should be administered as early as possible to optimize antiviral therapy and avoid adverse events [START_REF] Channappanavar | IFN-I response timing relative to virus replication determines MERS coronavirus infection outcomes[END_REF] . Furthermore, COVID-19 pathology, mainly consisting in pulmonary lesions, presents similar characteristics with interferonopathies: it may suggest that SARS-CoV-2 induces an excessive IFN-I mediated antiviral response, leading to tissue damage. IFN-I treatment should be limited to the early phases of the infection if this hypothesis is confirmed, as suggested in [START_REF] Siddiqi | COVID-19 Illness in Native and Immunosuppressed States: A Clinical-Therapeutic Staging Proposal[END_REF] and by early clinical data showing that inflammatory biomarkers are associated with increased mortality [START_REF] Zhou | Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study[END_REF]. In the late phases, it is even possible that anti-interferon drugs should be used to mitigate the pathology [START_REF] Zhang | The use of anti-inflammatory drugs in the treatment of people with severe coronavirus disease 2019 (COVID-19): the perspectives of clinical immunologists from China[END_REF].

In China, the guidelines for the treatment of COVID-19 recommend to administer 5 million U of IFNα by vapor inhalation twice a day to the patients, in combination with ribavirin [START_REF] Dong | Discovering drugs to treat coronavirus disease 2019 (COVID-19)[END_REF][START_REF] Lu | Drug treatment options for the 2019-new coronavirus (2019-nCoV)[END_REF]. Clinical trials have been recently registered to evaluate a combination of lopinavir/ritonavir and IFNα2b (ChiCTR2000029387) or a combination of lopinavir/ritonavir with ribavirin and IFNß1b administered subcutanously (NCT04276688) for the treatment of COVID-19. The administration by vapor inhalation currently performed in China offers the advantage of targeting specifically the respiratory tract; however, to the best of our knowledge, the pharmacodynamics and pharmacokinetics of this mode of administration have never been assessed. On the contrary, the intravenous and subcutaneous modes of administration are well-described, have already proven safe in several clinical trials, and have similar pharmacodynamics and pharmacokinetics [START_REF] Mager | Receptor-Mediated Pharmacokinetic/ Pharmacodynamic Model of Interferon-β1a in Humans[END_REF]. The combination of IFN-I with lopinavir/ritonavir, ribavirin or remdesivir could improve its efficacy, because of the efficiency of such combinations observed in vitro in other coronaviruses [START_REF] Sheahan | Comparative therapeutic efficacy of remdesivir and combination lopinavir, ritonavir, and interferon beta against MERS-CoV[END_REF] . It might also be relevant to evaluate type III IFN for the treatment of COVID-19 [START_REF] Lokugamage | SARS-CoV-2 sensitive to type I interferon pretreatment[END_REF], because of the protective effects of this interferon type in the respiratory tract. Subcutaneous IFNß1a in combination with lopinavir/ritonavir is compared to lopinavir/ritonavir alone, hydroxychloroquine, and remdesivir in the DisCoVeRy trial (NCT04315948), which is the first clinical trial of the WHO Solidarity consortium of clinical trials.

In conclusion, IFNß1 may account for a safe and easy to upscale treatment against COVID-19 in the early stages of infection. Similar treatments had a mixed efficiency against MERS-CoV and SARS-CoV viruses, but in vitro studies suggest that SARS-CoV-2 could be substantially more sensitive to IFN-I than other coronaviruses. The current lack of animal model for COVID-19 should not prevent the clinical evaluation of IFN-I treatment, since its safety has already been assessed in numerous independent clinical trials. Publications of data about IFNbased COVID-19 treatment performed in China in early 2020, expected in a near future, should give more accurate information on the relevance of this therapy.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declarations of interests: none