Frekvenser af esperantotalende mennesker i alle lande

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 i land er 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 vælges sådan at ‘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 være det faktiske antal esperantotalende mennesker i land , hvilket vi kender for Estland, Litauen, Rusland og New Zealand. Ud fra disse tal kan vi udregne antallet af esperantotalende mennesker per enhed af 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]