Ono Recipe

We've had a big of mac nut collection from the front yard this season, as well as a few mangoes. The local fisherman still sells the days's catch from giant ice chests on the back of his truck a couple time a week. So, with a hat tip for the original recipe by Bev Gannon of Maui, here is a tasty recipe for cooking at home.

Mac Nut Rice Flour Crusted Ono

Use a cast iron skillet for frying and to hold baking fish.

Preheat oven to 425 degrees.

Ingredients

Fish: 2 lbs thick ono fillets

Sauce:

1/2 cup chopped fresh mango

1/2 cup white wine or grapes

1/3 cup sugar

1 tablespoon minced ginger

1/4 cup lime juice

Bring to a boil, add 1/4 cup sour cream and 1/4 cup butter, mix in blender.

Breading:

Salt / pepper / garlic seasoning

1/2 cup macadamia nuts, whole or pieces

1 cup rice flour

1/2 cup fresh basil (or other green herb seasoning

Set seasoning aside for now. Grind nuts, flour, and herbs in blender until fully mixed.

COOKING 1: Coat fish with seasoning. Place ono skin side down in lightly oiled frying pan and lightly fry skin side.

Undercoat:

1/2 cup good-quality mayonnaise

2 teaspoons Sriracha

Mix mayo and sriracha and spread on the skinless side of ono fillets.

Add rice flour mixture so that the spicy mayo sticks the flour mixture to the ono fillets.

COOKING 2: Flip ono flour side down in pan and brown flour side.

COOKING 3: Transfer to hot (425 degrees) oven and bake for 8

minutes, then shut off oven, vent, and allow fish to sit warmly in oven til time

to serve (will finish cooking during this time).

Serve flour side up with the sauce to ladle as a gravy.

Covid-19 Modeling for the Big Island

Extrapolations, anyone? Here are some of mine (see bottom for sources):

HERE, from Plos One, is a good discussion of why extrapolations such as the above are often very wrong, just published last week:

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ABSTRACT

Forecasting the novel coronavirus COVID-19

Fotios Petropoulos ,Spyros Makridakis

Published: March 31, 2020https://doi.org/10.1371/journal.pone.0231236

Abstract

What will be the global impact of the novel coronavirus (COVID-19)? Answering this question requires accurate forecasting the spread of confirmed cases as well as analysis of the number of deaths and recoveries. Forecasting, however, requires ample historical data. At the same time, no prediction is certain as the future rarely repeats itself in the same way as the past. Moreover, forecasts are influenced by the reliability of the data, vested interests, and what variables are being predicted. Also, psychological factors play a significant role in how people perceive and react to the danger from the disease and the fear that it may affect them personally. This paper introduces an objective approach to predicting the continuation of the COVID-19 using a simple, but powerful method to do so. Assuming that the data used is reliable and that the future will continue to follow the past pattern of the disease, our forecasts suggest a continuing increase in the confirmed COVID-19 cases with sizable associated uncertainty. The risks are far from symmetric as underestimating its spread like a pandemic and not doing enough to contain it is much more severe than overspending and being over careful when it will not be needed. This paper describes the timeline of a live forecasting exercise with massive potential implications for planning and decision making and provides objective forecasts for the confirmed cases of COVID-19.

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FOOTNOTE


The origin of the R0 of 0.1 in the curve above is based on the following analysis:

# Simplistic Julia extrapolation model to fit world data totals

using LsqFit

const K = 7_800_000_000  # approximate world population
const n0 = 27  # starting at day 0 with 27 Chinese cases

""" The model for logistic regression with a given r0 """
@. model(t, r) = (n0 * exp(r * t)) / (( 1 + n0 * (exp(r * t) - 1) / K))

# Source: https://ourworldindata.org/coronavirus#all-charts-preview
# Daily world totals, including China and all countries
ydata = [
27, 27, 27, 44, 44, 59, 59, 59, 59, 59, 59, 59, 59, 60, 60,
61, 61, 66, 83, 219, 239, 392, 534, 631, 897, 1350, 2023,
2820, 4587, 6067, 7823, 9826, 11946, 14554, 17372, 20615,
24522, 28273, 31491, 34933, 37552, 40540, 43105, 45177,
60328, 64543, 67103, 69265, 71332, 73327, 75191, 75723,
76719, 77804, 78812, 79339, 80132, 80995, 82101, 83365,
85203, 87024, 89068, 90664, 93077, 95316, 98172, 102133,
105824, 109695, 114232, 118610, 125497, 133852, 143227,
151367, 167418, 180096, 194836, 213150, 242364, 271106,
305117, 338133, 377918, 416845, 468049, 527767, 591704,
656866, 715353, 777796, 851308, 928436, 1000249, 1082054,
1174652,
]
# The initial data is for 97 days starting with day 0 = 31 December 2019
tdata = collect(LinRange(0.0, 96, 97))

# starting approximation for r of 1/2
rparam = [0.5]

fit = curve_fit(model, tdata, ydata, rparam)

# Our answer for r given the world data and simplistic model
r = fit.param
println("The logistic r for the world data is: ", r)
println("The confidence interval at 5% significance is: ",
    confidence_interval(fit, 0.05))
println("Since R0 is about exp(G * r), and G is about 12, R0 ≈ ", exp(12r[1]))

# Output: 
# The logistic r for the world data is: [0.11230217572265622]
# The confidence interval at 5% significance is: [(0.11199074156706985, 0.11261360987824258)]
# Since R0 is about exp(G * r), and G is about 12, R0 ≈ 3.8482792820761063