Frekvenser af esperantotalende mennesker i alle lande

English  Esperanto

Opdatering: Jeg har tilføjet konfidensintervaller på alle estimater. 

Esperanto

I August 2009 læste jeg en populærvidenskabelig bog, som betød meget for mig. Det var “Det Virkelige Menneske” af Dennis Nørmark og Lars Andreassen, og bogen forklarede mange sider af den menneskelige kultur ved hjælp af biologi og evolutionsteori. Om sprog skrev de:

At sproget er den bedste indikator på, hvem, der er inde, og hvem, der ude, kan måske forklare, hvorfor de fleste sprog indeholder meget kompliceret og som oftest helt overflødige grammatiske regler. […] For dummer du dig i grammatikken, afsløres det øjeblikkeligt, at du ikke hører til gruppen, og jo mere kompliceret et sprog er, jo nemmere bliver det at afsløre afvigere.

Tekststykket italesætter et af de sekundære formål ved sprog, nemlig at afgrænse fællesskabet og afsløre fremmede. Esperanto er et sprog uden det formål. Det er meget nemmere for udefrakommende på grund af dets nemme udtale, logiske grammatik og modulære ordopbygning. Esperanto blev konstrueret i 1887 af LL Zamenhof med formålet at forene og bringe fred til verden. ‘Fred’ er ikke den bedste beskrivelse af de efterfølgende 100 år, men sproget fejler ikke noget. En visionær drøm for mig og de fleste esperantotalende mennesker, er at gøre esperanto til et globalt andetsprog, for det ville gøre verden mere optimal og mere lige. Jeg har kigget på esperantos udbredelse.

Esperanto demografi

Takket være tilgængeligt data fra UEA, esperantujo.directory, edukado, Pasporta Servo, og Lernu! (og Wikipedia) har jeg samlet et datasæt bestående af esperantomedlemstal fra de fleste verden i landen.

befolkning UEA Lernu! esp.dir pasporto edukado nat. org
Afghanistan 27.70 1 48 0 0 0 0
Albania 2.80 14 64 0 1 5 33
Algeria 39.20 6 503 1 1 8 0
Andorra 0.08 0 167 0 8 0
Angola 21.50 2 42 1 0 0
Argentina 41.50 29 1965 27 16 61 140
Armenia 3.00 12 92 0 0 1 27
Australia 23.10 40 2568 23 16 52 130
Austria 8.60 44 780 4 8 23 79
Zimbabwe 14.20 1 30 0 1 1 0

Se metodesektionen for beskrivelse af kolonnerne. Jeg fittede en model til dette data og brugte den til at estimere den relative frekvens af esperantotalende mennesker i hvert land. For at få de totale antal blev de relative frekvenser skaleret med folketællingerne fra Litauen, Estland, Rusland, New Zealand (men ikke Ungarn). Det samlede antal esperantotalende mennesker i hele verden estimeres til 62983.9 med konfidensinterval på [59077,68176]/[31460,183420](Se Konfidensintervaller for forklaring).

datamapsco-5
Hvert land er farvet efter frekvensen af esperantotalende mennesker pr. indbygger. Kvadratrodstransformationen betyder kun noget for det visuelle. De lyserøde lande er lande uden data.
europeesperanto
Frekvensen af esperantotalende mennesker i Europa.

Tallene bag kortene findes i en stor tabel i bunden af postet. Det kan ikke ses på kortet, men Andorra har den højeste koncentration af esperanto. Landene med de største antal esperantotalende mennesker er (rangeret) Brasilien, Frankrig, USA, Tyskland, Rusland, Polen og Spanien.

Modellen antager at antallet af medlemmer af en esperantoorganisation er proportionelt til antallet af esperantotalende mennesker i det pågældende land. Det er ikke altid sandt, fordi organisationerne ikke er lige populære i alle lande. Modellen prøver at modvirke dette ved at tillade en del afvigelse i nogle af organisationerne, uden at det ændrer frekvensen. Hvis alle organisationer derimod er underrepræsenterede i et land, vil den samlede frekvens også blive underestimeret. Ungarn er sådan et tilfælde, for modellen estimerer antallet af esperantotalende mennesker til 1997.5, mens en nylig folketælling fandt, at der var 8397. Afvigelsen skyldes nok, at man kan tage esperanto som valgfag i det ungarnske skolesystem, hvilket producerer esperantotalende ungarere som ikke bruger internationale esperantoorganisationer.

Definitionen af et esperantotalende menneske er en person, som i en folketælling opgiver esperanto som et talt sprog.

Metoder

Model

Jeg vil give en intuition for modellen ved at bruge Litauen som eksempel. Lituaen har medlemstal

pop UEA Lernu! esp.dir paspo. edukado nat. org.
Lithuania 3 million 43 5127 8 13 32 960

Ifølge UEA-kolonnen har Litauen 43 af de i alt 5501 medlemmer af UEA. Det tyder på, at 0.78% af alle esperantotalende mennesker kommer fra Litauen. På den anden side indikerer Lernu!-kolonnen at 2.88% af alle esperantotalende mennesker kommer fra Litauen. I stedet for at hver kolonne giver deres eget estimat, vil vi gerne have et enkelt tal, som tager brug af alle kolonnerne. En måde at få sådan et tal på kunne være gennemsnittet (0.78+2.88+\cdots + 5.8)/6, men så bestemmer Lernu! lige så meget som de andre kolonner, selvom den indeholder langt flere observationer. Et andet valg kunne være gennemsnittet (43+5127+\cdots + 960)/206886, men det ligger altid meget tæt på Lernu!-gennemsnittet fordi Lernu! har langt størstedelen af observationerne. Jeg ønsker et estimat imellem disse to. Et, der lytter ekstra til kategorierne med mange observationer og samtidig bruger de andre kategorier.

Det føres ud i livet ved at sige

  • Antallet af medlemmer fra organisation j i land i er N_i \cdot p_i \cdot \alpha_{j} \cdot w_{ij} hvor
    • N_i er antallet af indbyggere i land i.
    • p_i er den relative frekvens af esperantotalende mennesker i land i.
    • \alpha_j er det totale antal mennesker i organisation j.
    • w_{ij} er et tal som vælges for at få ligningen til at gå op. Modellen går efter at få dem tæt på 1.
  • Hvert lands p_i vælges sådan at w_{ij}‘erne ikke er ‘langt væk’ fra 1. Definition af ‘langt væk’ er et valg i modellen.
    • ‘langt væk’ defineres sådan at det både opfylder nogle gode matematiske egenskaber og reducerer afstanden mellem w_{ij}‘erne og 1.
  • Lad m_i være det faktiske antal esperantotalende mennesker i land i, hvilket vi kender for Estland, Litauen, Rusland og New Zealand. Ud fra disse tal kan vi udregne antallet af esperantotalende mennesker per enhed af p_i per indbygger.
    • Udregn b_i=m_i/(p_i \cdot N_i) for de fire nævnte lande.
    • Udregn gennemsnittet \bar b=(b_{\textup{Estonia}}+\cdots+b_{\textup{New Zealand}})/4
    • Antallet af esperantotalende mennesker i land i estimeres til \bar b \cdot N_i \cdot p_i

Rigid matematisk forklaring af modellen

Lad x_{ij} være antallet af medlemmer af organisation j fra land i. Lad N_i være befolkningstallet for land i. Lad \alpha_j=\sum_i x_{ij}/\sum_{i,j} x_{ij}. Modellen er

\displaystyle x_{ij} \sim b^-(\kappa=\kappa_j, \mu= N_i\cdot \alpha_{j}\cdot p_i),\quad p_i\sim U(0,1), \kappa_j \sim \Gamma(2,2)

hvor der er uafhængighed mellem x_{ij}‘erne (betinget på p_i‘erne), imellem p_i‘erne, og imellem \kappa_j‘s. Frekvenserne, p_i, estimeres med

\displaystyle \textup{argmax}_{p_1, \dots, p_n}\Bigl\{ \max_{\kappa_1, \dots, \kappa_6} P\bigl(p_1, \dots, p_n, \kappa_1,\dots, \kappa_6  \mid (x_{ij})_{i,j=1,1}^{n,6} \bigr) \Bigr\}.

I praksis bruger jeg betingelsen \kappa_1=\kappa_3=\kappa_4=\kappa_5 på grund af konvergensproblemer.

Konfidensintervallerne blev udregnet med Bayesian bootstrap. I den første type konfidensinterval antoges det, at \bar{b} var kendt. I den anden type konfidensinterval blev det i stedet antaget at folketællingstallene var tilfældige og dermed blev de også ‘bootstrappet’.

Konfidensintervaller

Vi ville ikke blive overraskede, hvis det sande antal af esperanto talende mennesker ikke var præcist 62983.9. Vi tror derimod at det sande antal ligger i et område omkring 62983.9. Et konfidensinterval speciferer sådan et område ved hjælp af statistik. Man må tro på hver værdi i et konfidensinterval uden at være uenig med modellens antagelser.

Jeg udregnede konfidensintervaller på to måder; Den første antager at skaleringsfaktoren er kendt. Skaleringsfaktoren er antallet af esperantotalende mennesker i en folketælling per esperantotalende mennesker ‘på nettet'(se \bar b). Den anden måde antager at skaleringsfaktoren er ukendt og inkluderer usikkerheden af skaleringsfaktoren. De intervaller, som jeg anbefaler at man bruger, har jeg skrevet i fed skrift. Hvor de to konfidensintervaller er identiske, har jeg kun skrevet den ene.

Data

Datasættet er indsamlet fra de følgende hjemmesider.

  • UEA er en international interesseorganisation, hvis mål er at sprede esperanto og lighed mellem sprog. De har lagt deres medlemstal på nettet, men det kræver en del klik at indsamle dem.
  • lernu.net er den største portal for at lære esperanto. Mange profiler er inaktive og tilhører folk, som kun var interesseret i esperanto kortvarigt. Derfor kan antallet af esperantotalende mennesker godt være lavere end antallet af profiler.
  • Esperantujo.directory er et online adressekartotek over esperantotalende mennesker Jeg er ikke i den (endnu)
  • Pasporta Servo er en side, hvor man kan tilbyde at lade esperantotalende mennesker bo gratis hos sig. Tallene i kolonnen angiver antallet af værter i det pågældende land.
  • Edukado er en anden læringsportal, men mere seriøs end Lernu!.
  • National organization sizes er størrelserne på de nationale esperantoorganisationer, som er associeret med UEA.

Der er manglende værdier i datasættet. Andorra har for eksempel en manglende værdi på esperantujo.directory, fordi landet er for småt til at være med på deres kort. Angola har heller ingen værdi for national organization size, fordi de har en national esperanto organisation, hvis medlemstal ikke er angivet på UEA’s hjemmeside. De manglende værdier giver ikke problemer for modellen.

Kilder

Data og mine R-scripts findes på github. Jeg har brugt datamaps.co til at tegne kortene.

Resultater

Nedenunder ses alle lande rangeret efter deres frekvens af esperantotalende mennesker.   Frequency er antallet af esperantotalende mennesker per 1 million indbyggere. Total er det samlede antal esperantotalende mennesker. Proportion er hvor stor en andel et land har af den samlede mængde esperantotalende mennesker.

Rank Frequency Total Proportion
Andorra 1 [1,2] 620.03 [277.3,1039.9]/ [180.4,2246.5] 48.4 [22,81]/ [14,175] 0.0008 [0.0003,0.0013]
Lithuania 2 [1,6] 249.32 [156.7,482.6]/ [119.2,936.9] 748 [470,1448]/ [357,2811] 0.0119 [0.0072,0.0225]
Iceland 3 [2,8] 210.07 [109.7,375]/ [71.5,729.9] 67.9 [35,121]/ [23,236] 0.0011 [0.0006,0.0019]
Hungary 4 [2,6] 203.82 [151.1,283.8]/ [98.3,695.9] 1997.5 [1481,2781]/ [963,6820] 0.0317 [0.0242,0.0449]
Luxembourg 5 [2,10] 196.82 [93.9,285.1]/ [60.4,681.1] 112.2 [53,163]/ [34,388] 0.0018 [0.0008,0.0026]
New Caledonia 6 [3,16] 122.85 [64.1,234.2]/ [47.2,450.3] 32.9 [17,63]/ [13,121] 0.0005 [0.0003,0.001]
Belgium 7 [6,12] 109.48 [77.4,135.8]/ [38.1,320.4] 1237.1 [875,1535]/ [430,3621] 0.0196 [0.0132,0.0239]
Finland 8 [5,15] 105.89 [69.9,155.2]/ [48.7,331.7] 582.4 [384,854]/ [268,1824] 0.0092 [0.006,0.0137]
France 9 [5,17] 104.01 [61.4,132.8]/ [44.2,327.8] 6906.6 [4078,8816]/ [2933,21763] 0.1097 [0.067,0.1381]
Denmark 10 [5,16] 97.34 [65.7,145]/ [44.6,321.7] 554.8 [375,827]/ [254,1834] 0.0088 [0.0061,0.013]
Sweden 11 [8,16] 91.99 [67.8,117.3]/ [40.2,265.1] 910.7 [671,1162]/ [398,2625] 0.0145 [0.0109,0.0183]
Switzerland 12 [8,20] 89.44 [55.6,115.4]/ [36.2,270.9] 724.4 [451,935]/ [293,2195] 0.0115 [0.0071,0.0152]
Netherlands 13 [7,19] 84.87 [60.9,115.1]/ [37.2,267.3] 1442.8 [1036,1956]/ [632,4544] 0.0229 [0.0159,0.0304]
Czech Republic 14 [10,19] 76.23 [59,90.3]/ [34.9,245.3] 800.4 [619,948]/ [366,2575] 0.0127 [0.0095,0.0154]
Estonia 15 [7,26] 74.02 [41.9,117.6]/ [34.3,165.2] 96.2 [55,153]/ [45,215] 0.0015 [0.0009,0.0024]
Norway 16 [10,23] 68.99 [50,96.3]/ [30.9,226.5] 358.7 [260,501]/ [160,1178] 0.0057 [0.0041,0.0079]
Slovenia 17 [10,32] 60.79 [33.2,95.7]/ [22.3,230.3] 127.7 [70,201]/ [47,484] 0.002 [0.0011,0.0032]
Liechtenstein 18 [14,62] 59.67 [11.1,74.2]/ [8.3,199] 2.2 [0,3]/ [0,7] 0 [0,0]
Slovakia 19 [10,28] 59.4 [39.2,81.3]/ [23.7,182.7] 320.8 [212,439]/ [128,986] 0.0051 [0.0034,0.007]
Poland 20 [16,24] 57.55 [48.9,65.5]/ [28.6,154.9] 2215.9 [1882,2524]/ [1100,5965] 0.0352 [0.0294,0.0404]
Croatia 21 [14,28] 55.09 [39,71.3]/ [24.4,175.7] 231.4 [164,300]/ [102,738] 0.0037 [0.0026,0.0048]
Israel 22 [15,34] 50.56 [31.2,70.6]/ [20.8,174.8] 409.5 [253,572]/ [169,1416] 0.0065 [0.004,0.0091]
Latvia 23 [13,35] 49.01 [30.8,80.3]/ [21.1,159.9] 98 [62,161]/ [42,320] 0.0016 [0.0009,0.0025]
Germany 24 [19,30] 47.51 [36.1,58.5]/ [18.6,138.1] 3871.8 [2942,4769]/ [1519,11256] 0.0615 [0.0475,0.0754]
Spain 25 [20,28] 47.38 [39.5,54.3]/ [21.2,133.4] 2198.5 [1835,2519]/ [985,6192] 0.0349 [0.0292,0.0399]
New Zealand 26 [18,33] 46.42 [31.4,58.1]/ [21.8,141.4] 208.9 [141,261]/ [98,636] 0.0033 [0.0022,0.0043]
Bulgaria 27 [18,38] 46.12 [28.1,63.3]/ [19,124] 332 [202,455]/ [136,893] 0.0053 [0.0032,0.0072]
Austria 28 [22,36] 38.77 [29.4,48.5]/ [18.1,121] 333.4 [252,417]/ [155,1041] 0.0053 [0.004,0.0066]
Brazil 29 [24,39] 36.5 [27.5,47.6]/ [16.3,104.5] 7314 [5509,9545]/ [3276,20939] 0.1161 [0.0887,0.148]
Malta 30 [16,45] 35.25 [22.3,66.3]/ [17.5,144.4] 15 [9,28]/ [7,61] 0.0002 [0.0002,0.0005]
Greenland 31 [26,93] 34.24 [3.9,42]/ [4.4,98.8] 1.9 [0,2]/ [0,6] 0 [0,0]
Mongolia 32 [19,63] 34.05 [11.1,61.6]/ [10.1,118.6] 95.3 [31,172]/ [28,332] 0.0015 [0.0005,0.0026]
Australia 33 [28,41] 31.92 [26.2,36.6]/ [15.5,98.7] 737.4 [605,845]/ [358,2281] 0.0117 [0.0092,0.0136]
Bosnia and Herzegovina 34 [27,47] 30.7 [18.7,44]/ [13.9,81.5] 116.7 [71,167]/ [53,310] 0.0019 [0.0011,0.0027]
Republic of Serbia 35 [27,49] 29.7 [18,40.8]/ [11.8,98.6] 210.9 [128,290]/ [84,700] 0.0033 [0.002,0.0046]
Canada 36 [30,41] 29.36 [25,34.7]/ [14.7,88.8] 1033.4 [880,1220]/ [518,3128] 0.0164 [0.0141,0.0199]
Costa Rica 37 [28,45] 28.99 [21.7,40.9]/ [12.4,80] 142.1 [106,200]/ [61,392] 0.0023 [0.0017,0.0032]
Uruguay 38 [29,46] 28.9 [22,37.9]/ [13.3,94] 98.3 [75,129]/ [45,320] 0.0016 [0.0012,0.0021]
Ireland 39 [27,48] 28.88 [19.3,41.8]/ [13.5,91.6] 132.8 [89,192]/ [62,421] 0.0021 [0.0014,0.0031]
Italy 40 [33,46] 26.13 [21.1,31.5]/ [12.2,71.1] 1562.5 [1259,1882]/ [730,4250] 0.0248 [0.0198,0.0295]
Togo 41 [27,63] 25.9 [10.8,39.3]/ [8.9,79] 183.9 [77,279]/ [63,561] 0.0029 [0.0012,0.0045]
Cyprus 42 [35,56] 25.58 [14,30.5]/ [8.8,79.7] 20.5 [11,24]/ [7,64] 0.0003 [0.0002,0.0004]
United Kingdom 43 [35,47] 24.5 [21,30.1]/ [13.5,72.3] 1595.3 [1367,1961]/ [879,4709] 0.0253 [0.0214,0.0318]
Albania 44 [23,62] 24.26 [11.7,47.6]/ [7.5,80.5] 67.9 [33,133]/ [21,225] 0.0011 [0.0005,0.0021]
Cuba 45 [34,66] 22.08 [10.9,31.7]/ [6.8,72.9] 249.5 [124,358]/ [76,823] 0.004 [0.002,0.0058]
Portugal 46 [34,54] 20.64 [15.6,30.5]/ [10,67.3] 212.6 [161,315]/ [103,693] 0.0034 [0.0025,0.0047]
Nicaragua 47 [39,61] 19.16 [12.5,27.6]/ [8.5,57.6] 116.9 [76,168]/ [52,352] 0.0019 [0.0012,0.0027]
Macedonia 48 [38,63] 18.54 [10.9,27.1]/ [8.3,61.5] 38.9 [23,57]/ [17,129] 0.0006 [0.0004,0.0009]
United States of America 49 [39,60] 18.34 [12.8,26.2]/ [7.4,63.6] 5847.1 [4079,8367]/ [2360,20290] 0.0928 [0.0674,0.1335]
Hong Kong 50 [28,84] 17.81 [5.6,39.5]/ [5,92.4] 131.8 [42,292]/ [37,684] 0.0021 [0.0007,0.0047]
Argentina 51 [45,62] 17.58 [12.5,22.4]/ [8,54.5] 729.6 [520,929]/ [332,2260] 0.0116 [0.0081,0.0144]
The Bahamas 52 [27,73] 17.39 [7.7,41.1]/ [6.4,78.1] 6.6 [3,16]/ [2,30] 0.0001 [0,0.0003]
Ukraine 53 [46,61] 16.19 [13.1,22.6]/ [7.8,51.5] 694.7 [562,970]/ [336,2209] 0.011 [0.0087,0.015]
Armenia 54 [32,71] 16.06 [8.1,33.9]/ [5.4,67.8] 48.2 [24,102]/ [16,203] 0.0008 [0.0004,0.0016]
Russia 55 [48,63] 15.96 [11.6,19.8]/ [7.8,50.5] 2298.1 [1667,2852]/ [1130,7276] 0.0365 [0.0272,0.0468]
Belarus 56 [48,63] 15.71 [11.7,18.4]/ [7.8,48.5] 149.3 [111,175]/ [74,460] 0.0024 [0.0018,0.0028]
Puerto Rico 57 [41,81] 15.53 [5.8,26.4]/ [5.2,57.5] 54.3 [20,92]/ [18,201] 0.0009 [0.0003,0.0014]
Chile 58 [44,69] 14.45 [8.5,23.3]/ [6.5,53.8] 254.4 [150,410]/ [114,948] 0.004 [0.0024,0.0063]
Colombia 59 [50,70] 13.09 [9.2,19.4]/ [6.3,48.3] 616.7 [433,916]/ [298,2275] 0.0098 [0.0069,0.0147]
Benin 60 [44,90] 12.5 [4.7,23.6]/ [3.8,46.3] 132.5 [50,250]/ [40,490] 0.0021 [0.0008,0.0041]
Iran 61 [47,74] 12.5 [7.7,20.3]/ [4.9,44.4] 968.7 [598,1571]/ [383,3437] 0.0154 [0.0095,0.0246]
Belize 62 [46,90] 12.24 [4.2,20.4]/ [2.6,38.8] 4.7 [2,8]/ [1,15] 0.0001 [0,0.0001]
Romania 63 [53,73] 11.87 [8.5,16.9]/ [4.7,35.4] 235.1 [168,335]/ [93,700] 0.0037 [0.0024,0.0055]
South Korea 64 [55,75] 11.67 [7.6,16]/ [5,38.1] 585.8 [383,803]/ [251,1911] 0.0093 [0.0061,0.0129]
Japan 65 [43,87] 11.31 [5,25.5]/ [4,42.6] 1439.9 [637,3243]/ [513,5420] 0.0229 [0.0102,0.0489]
Taiwan 66 [49,82] 10.53 [6,18.9]/ [4.2,38.4] 247.5 [140,444]/ [99,902] 0.0039 [0.0022,0.0072]
Cape Verde 67 [55,115] 10.45 [1.6,14.2]/ [2.3,34.4] 5.5 [1,8]/ [1,18] 0.0001 [0,0.0001]
East Timor 68 [43,100] 10.44 [3.1,23.1]/ [2.7,40.9] 12.5 [4,28]/ [3,49] 0.0002 [0.0001,0.0004]
Venezuela 69 [57,83] 9.66 [5.5,14.8]/ [3.4,30.9] 293.5 [168,449]/ [103,938] 0.0047 [0.0026,0.0069]
Burundi 70 [56,112] 9.6 [2.2,14.7]/ [2.4,32.7] 96.9 [22,148]/ [25,331] 0.0015 [0.0004,0.0024]
Greece 71 [61,76] 9.46 [7.2,11.9]/ [4.5,31.1] 102.2 [77,129]/ [48,336] 0.0016 [0.0012,0.0021]
Mexico 72 [59,82] 8.74 [5.7,12.6]/ [3.6,28.1] 1069 [696,1539]/ [435,3438] 0.017 [0.0114,0.0249]
Peru 73 [54,90] 8.44 [4.4,14.8]/ [3.2,29.3] 256.6 [133,451]/ [98,891] 0.0041 [0.002,0.0072]
El Salvador 74 [60,91] 8.2 [4,12.8]/ [2.6,29.7] 51.6 [25,81]/ [16,187] 0.0008 [0.0004,0.0013]
Georgia 75 [60,82] 8.19 [5.5,13.3]/ [3.7,25.6] 30.3 [20,49]/ [14,95] 0.0005 [0.0003,0.0008]
Panama 76 [55,111] 8.07 [1.8,16.2]/ [1.5,39.8] 31.5 [7,63]/ [6,155] 0.0005 [0.0001,0.001]
Brunei 77 [67,125] 7.97 [1.1,10.1]/ [1.2,24.6] 3.3 [0,4]/ [0,10] 0.0001 [0,0.0001]
Moldova 78 [66,90] 7.92 [3.9,10.5]/ [3.2,24.2] 28.5 [14,38]/ [11,87] 0.0005 [0.0002,0.0006]
Dominican Republic 79 [57,100] 7.49 [2.7,14.7]/ [2,23.3] 77.9 [28,153]/ [21,242] 0.0012 [0.0005,0.0024]
Montenegro 80 [63,93] 6.69 [3.6,11.3]/ [2,24.1] 4.2 [2,7]/ [1,15] 0.0001 [0,0.0001]
Ecuador 81 [63,92] 6.66 [3.8,11.4]/ [2.9,26.2] 104.6 [60,178]/ [46,412] 0.0017 [0.001,0.0029]
Senegal 82 [65,96] 6.55 [3.2,11.3]/ [2.5,21.5] 96.9 [48,167]/ [37,318] 0.0015 [0.0008,0.0027]
Kazakhstan 83 [77,93] 5.34 [3.8,6.9]/ [2.7,16.6] 90.8 [65,118]/ [45,282] 0.0014 [0.001,0.0018]
Guatemala 84 [72,107] 5.18 [2.5,8.4]/ [2.6,18.5] 80.2 [39,131]/ [41,287] 0.0013 [0.0006,0.0021]
Nepal 85 [72,106] 5.09 [2.4,8.9]/ [1.4,18.2] 157.9 [74,277]/ [43,563] 0.0025 [0.0011,0.0045]
Honduras 86 [77,110] 5.04 [2.2,7.3]/ [1.9,14.5] 40.8 [18,59]/ [15,117] 0.0006 [0.0003,0.0009]
Bolivia 87 [74,107] 5.03 [2.4,8.1]/ [1.6,18.3] 55.3 [26,89]/ [18,202] 0.0009 [0.0004,0.0014]
United Arab Emirates 88 [79,106] 4.69 [2.5,6.8]/ [2.5,16.2] 46.4 [25,67]/ [25,161] 0.0007 [0.0004,0.0011]
Swaziland 89 [56,117] 4.54 [1.8,13.7]/ [1.3,24.5] 5.9 [2,18]/ [2,32] 0.0001 [0,0.0003]
Trinidad and Tobago 90 [79,140] 4.51 [0.7,6.4]/ [0.7,15.8] 6.3 [1,9]/ [1,22] 0.0001 [0,0.0002]
Vanuatu 91 [34,113] 4.39 [2,35.6]/ [1.5,53.6] 1.2 [1,10]/ [0,15] 0 [0,0.0002]
Vietnam 92 [81,105] 4.34 [2.4,6.1]/ [1.8,12.9] 389.7 [218,551]/ [162,1158] 0.0062 [0.0034,0.0087]
Maurtitius 93 [79,141] 4.27 [0.7,6]/ [0.6,14.8] 5.5 [1,8]/ [1,19] 0.0001 [0,0.0001]
Morocco 94 [75,118] 3.81 [1.5,8.8]/ [0.9,17.3] 125.7 [51,291]/ [31,569] 0.002 [0.0008,0.0045]
Tunisia 95 [81,118] 3.78 [1.5,6]/ [1.5,11.5] 42.3 [16,67]/ [17,129] 0.0007 [0.0003,0.0011]
Qatar 96 [85,111] 3.6 [2,4.9]/ [1.2,13.3] 9.4 [5,13]/ [3,35] 0.0001 [0.0001,0.0002]
Paraguay 97 [81,134] 3.57 [1,6.3]/ [0.8,15.6] 24.3 [7,43]/ [5,106] 0.0004 [0.0001,0.0007]
Suriname 98 [89,132] 3.54 [0.9,4.4]/ [0.5,10.8] 1.9 [1,2]/ [0,6] 0 [0,0]
Turkey 99 [86,115] 3.42 [1.7,5.1]/ [1.1,12.1] 256.5 [128,385]/ [82,904] 0.0041 [0.002,0.0062]
Maldives 100 [89,141] 3.37 [0.7,4]/ [0.7,10] 1.2 [0,1]/ [0,3] 0 [0,0]
Lebanon 101 [89,122] 3.23 [1.3,4.7]/ [0.9,11.1] 19.4 [8,28]/ [6,67] 0.0003 [0.0001,0.0005]
Tajikistan 102 [82,130] 3.18 [1,5.5]/ [0.8,11.7] 27.4 [9,47]/ [7,100] 0.0004 [0.0001,0.0007]
Malaysia 103 [82,123] 2.94 [1.2,5.8]/ [0.8,9.9] 93.6 [39,184]/ [24,314] 0.0015 [0.0006,0.0029]
Azerbaijan 104 [94,117] 2.78 [1.6,3.6]/ [1.1,9.5] 27.2 [16,35]/ [11,93] 0.0004 [0.0002,0.0006]
South Africa 105 [86,123] 2.77 [1.3,5.2]/ [0.9,10.2] 146.9 [69,273]/ [47,543] 0.0023 [0.0011,0.0043]
Bhutan 106 [96,144] 2.7 [0.6,3.3]/ [0.5,9.2] 2.2 [0,3]/ [0,7] 0 [0,0]
Madagascar 107 [93,117] 2.66 [1.6,4.1]/ [1.1,8.1] 60.8 [37,95]/ [25,186] 0.001 [0.0006,0.0015]
Kyrgyzstan 108 [95,116] 2.6 [1.7,3.7]/ [1,8.2] 15.9 [10,22]/ [6,50] 0.0003 [0.0002,0.0004]
Gabon 109 [97,147] 2.55 [0.5,3.5]/ [0.4,9.3] 4.6 [1,6]/ [1,17] 0.0001 [0,0.0001]
Algeria 110 [92,115] 2.52 [1.8,3.9]/ [1.2,9.4] 98.8 [70,151]/ [46,368] 0.0016 [0.0011,0.0023]
Democratic Republic of the Congo 111 [94,127] 2.51 [1.1,3.4]/ [0.8,7.5] 169.3 [76,232]/ [57,504] 0.0027 [0.0013,0.0038]
Fiji 112 [98,136] 2.45 [0.8,3]/ [0.5,8.1] 2.2 [1,3]/ [0,7] 0 [0,0]
Djibouti 113 [99,153] 2.4 [0.4,3]/ [0.4,7.3] 2.2 [0,3]/ [0,7] 0 [0,0]
Jamaica 114 [98,155] 2.27 [0.3,3.4]/ [0.3,7.8] 6.1 [1,9]/ [1,21] 0.0001 [0,0.0001]
Pakistan 115 [90,136] 2.24 [0.8,4]/ [0.6,8.4] 408.3 [137,734]/ [105,1537] 0.0065 [0.0022,0.0119]
Kuwait 116 [96,144] 2.22 [0.6,3.4]/ [0.5,7.3] 9.3 [3,14]/ [2,31] 0.0001 [0,0.0002]
Namibia 117 [100,157] 2.17 [0.3,3.1]/ [0.3,7.6] 5 [1,7]/ [1,17] 0.0001 [0,0.0001]
Central African Republic 118 [89,154] 1.89 [0.4,4.9]/ [0.2,7.9] 9.5 [2,24]/ [1,39] 0.0002 [0,0.0004]
Republic of the Congo 119 [88,138] 1.64 [0.7,4.6]/ [0.5,10.2] 7.7 [4,21]/ [2,48] 0.0001 [0.0001,0.0003]
Philippines 120 [102,138] 1.57 [0.7,2.8]/ [0.6,5.2] 154.1 [69,271]/ [60,515] 0.0024 [0.0011,0.0043]
Jordan 121 [108,152] 1.49 [0.4,2.3]/ [0.4,5.3] 14.6 [4,22]/ [4,52] 0.0002 [0.0001,0.0004]
Ghana 122 [105,139] 1.42 [0.8,2.6]/ [0.5,5.1] 40.1 [22,73]/ [14,144] 0.0006 [0.0003,0.0012]
Solomon Islands 123 [116,159] 1.35 [0.3,1.6]/ [0.3,3.7] 0.9 [0,1]/ [0,2] 0 [0,0]
Guinea Bissau 124 [117,158] 1.28 [0.3,1.6]/ [0.4,3.7] 1.9 [0,2]/ [1,6] 0 [0,0]
Thailand 125 [118,135] 1.22 [0.8,1.6]/ [0.6,3.6] 81.9 [56,107]/ [38,242] 0.0013 [0.0009,0.0017]
China 126 [115,135] 1.2 [0.9,1.7]/ [0.6,3.8] 1626.6 [1236,2365]/ [778,5185] 0.0258 [0.02,0.0362]
Equatorial Guinea 127 [119,166] 1.17 [0.2,1.4]/ [0.2,3.1] 1.4 [0,2]/ [0,4] 0 [0,0]
Guyana 128 [119,161] 1.16 [0.2,1.4]/ [0.3,3.2] 0.9 [0,1]/ [0,2] 0 [0,0]
Mauritania 129 [118,169] 1.07 [0.1,1.4]/ [0.2,3.7] 4 [0,5]/ [1,14] 0.0001 [0,0.0001]
Haiti 130 [115,163] 1.03 [0.2,1.8]/ [0.2,3.6] 11.5 [2,20]/ [3,39] 0.0002 [0,0.0003]
Rwanda 131 [105,152] 1.02 [0.4,2.8]/ [0.4,4] 11.9 [5,33]/ [4,46] 0.0002 [0.0001,0.0005]
Gambia 132 [124,158] 1.01 [0.3,1.3]/ [0.2,2.9] 1.9 [1,2]/ [0,6] 0 [0,0]
Syria 133 [118,153] 1 [0.4,1.5]/ [0.4,3.1] 18.7 [7,27]/ [8,57] 0.0003 [0.0001,0.0004]
Botswana 134 [124,161] 0.98 [0.2,1.2]/ [0.2,2.9] 2.2 [0,3]/ [0,6] 0 [0,0]
Ivory Coast 135 [112,150] 0.98 [0.4,2]/ [0.3,4.2] 22.2 [10,47]/ [8,95] 0.0004 [0.0002,0.0008]
Cambodia 136 [121,145] 0.91 [0.6,1.4]/ [0.4,3.1] 14.3 [10,22]/ [6,49] 0.0002 [0.0002,0.0004]
Zimbabwe 137 [120,147] 0.85 [0.6,1.3]/ [0.4,2.8] 12.1 [8,19]/ [6,40] 0.0002 [0.0001,0.0003]
Indonesia 138 [123,150] 0.84 [0.5,1.2]/ [0.3,2.9] 209.9 [119,304]/ [77,718] 0.0033 [0.0018,0.005]
Saudi Arabia 139 [118,170] 0.84 [0.1,1.6]/ [0.1,3.5] 24.1 [3,46]/ [2,102] 0.0004 [0.0001,0.0007]
Niger 140 [119,151] 0.81 [0.5,1.5]/ [0.3,3.3] 16.7 [9,31]/ [7,69] 0.0003 [0.0001,0.0005]
Cameroon 141 [119,151] 0.79 [0.4,1.4]/ [0.3,3] 17.9 [9,32]/ [8,69] 0.0003 [0.0001,0.0005]
Libya 142 [127,168] 0.78 [0.1,1]/ [0.2,2.4] 5 [1,7]/ [1,15] 0.0001 [0,0.0001]
United Republic of Tanzania 143 [120,165] 0.7 [0.2,1.4]/ [0.2,2.4] 38.8 [11,79]/ [11,133] 0.0006 [0.0002,0.0013]
Angola 144 [130,162] 0.69 [0.2,1.1]/ [0.2,2] 14.9 [5,23]/ [4,43] 0.0002 [0.0001,0.0004]
Uzbekistan 145 [135,150] 0.68 [0.5,0.9]/ [0.3,2.2] 21.6 [16,28]/ [9,68] 0.0003 [0.0003,0.0004]
Malawi 146 [116,166] 0.67 [0.2,1.6]/ [0.2,3] 11.3 [3,27]/ [4,51] 0.0002 [0,0.0004]
Laos 147 [134,164] 0.64 [0.2,0.9]/ [0.1,2.1] 4.2 [1,6]/ [1,14] 0.0001 [0,0.0001]
Egypt 148 [125,164] 0.64 [0.2,1.3]/ [0.2,2.8] 52.5 [14,104]/ [15,229] 0.0008 [0.0002,0.0017]
Sri Lanka 149 [126,159] 0.58 [0.3,1.2]/ [0.2,2.2] 12.3 [6,25]/ [5,46] 0.0002 [0.0001,0.0004]
Zambia 150 [122,161] 0.55 [0.2,1.4]/ [0.1,2.3] 8.7 [4,22]/ [2,36] 0.0001 [0.0001,0.0003]
Uganda 151 [121,165] 0.54 [0.2,1.4]/ [0.1,3] 20 [7,53]/ [5,113] 0.0003 [0.0001,0.0008]
Liberia 152 [141,170] 0.53 [0.1,0.7]/ [0.1,1.8] 2.2 [0,3]/ [0,7] 0 [0,0]
Kenya 153 [139,164] 0.48 [0.2,0.7]/ [0.2,1.5] 21.1 [9,32]/ [7,65] 0.0003 [0.0001,0.0005]
Mali 154 [145,158] 0.45 [0.4,0.6]/ [0.2,1.4] 8.3 [7,10]/ [4,25] 0.0001 [0.0001,0.0002]
Oman 155 [147,171] 0.42 [0.1,0.5]/ [0.1,1.3] 1.9 [0,2]/ [0,6] 0 [0,0]
Afghanistan 156 [144,167] 0.4 [0.1,0.6]/ [0.1,1.4] 11.2 [4,16]/ [3,38] 0.0002 [0.0001,0.0002]
Iraq 157 [134,168] 0.4 [0.1,0.9]/ [0.1,1.5] 14.6 [4,34]/ [3,57] 0.0002 [0.0001,0.0005]
Nigeria 158 [142,163] 0.39 [0.2,0.6]/ [0.2,1.6] 67.7 [38,112]/ [27,272] 0.0011 [0.0006,0.0018]
Sierra Leone 159 [148,172] 0.37 [0.1,0.5]/ [0,1.2] 2.6 [0,3]/ [0,8] 0 [0,0.0001]
Eritrea 160 [150,172] 0.36 [0.1,0.4]/ [0.1,1.2] 1.9 [0,2]/ [0,6] 0 [0,0]
Turkmenistan 161 [154,173] 0.29 [0.1,0.4]/ [0.1,0.9] 1.4 [0,2]/ [0,4] 0 [0,0]
Chad 162 [122,173] 0.28 [0,1.2]/ [0,2.6] 4.1 [1,18]/ [1,38] 0.0001 [0,0.0003]
Burkina Faso 163 [149,171] 0.26 [0.1,0.5]/ [0.1,0.8] 5 [2,10]/ [2,15] 0.0001 [0,0.0002]
Yemen 164 [155,173] 0.23 [0,0.3]/ [0,0.8] 6.3 [1,9]/ [1,22] 0.0001 [0,0.0001]
Sudan 165 [157,171] 0.22 [0.1,0.3]/ [0.1,0.7] 9 [2,12]/ [2,31] 0.0001 [0,0.0002]
Guinea 166 [159,173] 0.2 [0.1,0.3]/ [0,0.7] 2.6 [1,3]/ [0,9] 0 [0,0.0001]
Myanmar 167 [158,168] 0.2 [0.1,0.3]/ [0.1,0.6] 11 [8,16]/ [5,31] 0.0002 [0.0001,0.0002]
India 168 [159,170] 0.17 [0.1,0.3]/ [0.1,0.6] 219 [134,348]/ [79,755] 0.0035 [0.002,0.0056]
Lesotho 169 [162,173] 0.16 [0,0.2]/ [0,0.4] 0.3 [0,0]/ [0,1] 0 [0,0]
Mozambique 170 [165,173] 0.13 [0,0.2]/ [0,0.5] 3.5 [1,5]/ [1,12] 0.0001 [0,0.0001]
Ethiopia 171 [167,173] 0.1 [0.1,0.1]/ [0,0.3] 10.3 [5,14]/ [4,35] 0.0002 [0.0001,0.0002]
Bangladesh 172 [168,173] 0.1 [0.1,0.1]/ [0,0.3] 15.1 [10,19]/ [6,48] 0.0002 [0.0002,0.0003]
Somalia 173 [169,173] 0.08 [0,0.1]/ [0,0.2] 0.9 [0,1]/ [0,3] 0 [0,0]
South Sudan 174 [173,174] 0.02 [0,0]/ [0,0.1] 0.3 [0,0]/ [0,1] 0 [0,0]
Papua New Guinea 175 [175,175] 0 [0,0]/ [0,0] 0 [0,0]/ [0,0] 0 [0,0]
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