days in the year that below the thermal threshold (Figure 4). The prevalence of A. lumbricoides and T. trichiura was generally low in locations where temperatures fall below the thermal threshold for less than 35–40 days, and increased with increasing number of days. Hookworms, however, required a much smaller (8 day) window of thermal suitability for transmission and so were able to persist even when the period available for development was of the order of 10 days. Finally, a potentially very important factor is the longevity of the adult worm, since the location inside the host is essentially a refuge 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 102030405060 Days below thermal threshold Proportion infected Hookworm A. lumbricoides T. trichiura 0 10 20 30 40 50 25 30 35 40 45 50 Mean Land Surface Temperature (°C) No. weeks under thermal threshold Figure 4 Relationship betw een the proportion of humans infected with worms and the number of days during which temperature was less than 40 1C, which is suitable for survival of free-living STH infective stages: based on observed data for 601 locations from nationwide surveys in Cameroon (Ratard et al., 1991, 1992), Chad (Brooker et al., 2002a) and Uganda (Brooker et al., 2004b). Data were collected in cross-sectional school surveys using similar diagnostic technique (Kato–Katz method) and sampling de- signs (stratified, random), and encompass a broad range of infection rates. Insert: Relationship between mean LST and number of weeks temperature falls below 40 1C(y ¼À3.006x+147.8, r ¼ 0.94, po0.001). Estimates were derived for each survey location and locations with the same number of days under the thermal threshold were averaged for presentation. S. BROOKER ET AL.230 Table 2 Prevalence of STH infections among schoolchildren in urban and rural communities in developing countries. Included studies sought to restrict investigation to areas of similar socio-economic characteristics Setting Sample size A. lumbricoides T. trichiura Hookworm Reference Urban Rural Urban Rural Urban Rural Blantyre, Malawi 553 children (3–14 years old) 15.4 0.7 — — 0.4 2.1 Phiri et al. (2000) Pemba, Tanzania 256 children (3–14 years old) 60.6 63.6 100 100 97.6 94.6 Albonico et al. (1997) Buea, Cameroon 211 children (8–15 years old) 33.9 56.4 32.3 59 0 5.1 Ndenecho et al. (2002) Rolandia, Brazil 236 children (5–15 years old) 6.1 1.3 0.7 0.1 4.3 4.4 Giraldi et al. (2001) Malaysia 3073 children (o15 years old) 51.7 21.2 65.3 29.1 5.7 5.9 Kan et al. (1989) Penang, Malaysia 192 children (7–12 years old) 37.4 33.4 100 92 18.7 19.7 Rahman (1998) GLOBAL HELMINTH ECOLOGY 233 Table 3 Estimated numbers of STH infection among school-aged children in SSA by country, 2005 Country School-aged population (1000s) Estimated numbers of infections (1000s) Estimated numbers requiring mass treatment based on 50% threshold (1000s) Total annual per treatment cost (US$ 1000s) b A. lumbricoides T. trichiura Hookworm All STH species Angola 3666 610 712 1048 1896 2031 122–183 Benin 1737 256 352 399 831 836 50–75 Botswana 422 8 10 121 113 — — Burkina Faso 3222 30 35 759 671 — — Burundi 1768 309 341 583 956 753 45–68 Cameroon 4091 1227 1490 1059 2728 3017 181–272 Cape Verde 111 7 13 44 50 22 1–2 CAR a 1014 200 227 256 555 640 38–58 Chad 2186 25 27 529 474 — — Comoros 146 72 81 45 131 147 9–13 DRC a 14151 4111 4710 3790 9643 13410 805–1207 Republic of Con- go 839 242 310 201 574 828 50–75 Cote d’ Ivoire 4399 1400 1783 044 3189 3967 238–357 Djibouti 170 1 1 41 14 — — Equatorial Gui- nea 125 70 81 37 118 126 8–11 Eritrea 1020 11 14 324 281 4 0.3–0.4 Ethiopia 17424 1653 1641 6085 7362 4476 269–403 Gabon 332 155 191 9 295 331 20–30 Gambia 353 15 21 99 112 41 2–4 Ghana 5326 1330 1699 1300 3284 3551 213–320 Guinea 2226 505 622 584 1325 1450 87–131 Guinea Bissau 330 47 66 84 161 105 6–9 Kenya 8385 1134 1158 2682 3921 3661 220–330 S. BROOKER ET AL.248 Lesotho 549 175 217 206 423 553 33–50 Liberia 807 401 485 185 734 813 49–73 Madagascar 4413 1538 1739 1224 3146 3312 199–298 Malawi 3113 471 548 857 1525 1308 79–118 Mali 3160 41 49 750 688 — — Mauritania 337 5 11 75 76 — — Mauritius 292 168 190 84 277 295 18–27 Mozambique 5006 995 1188 1216 2767 3319 199–299 Namibia 473 7 9 140 126 — — Niger 3034 14 9 725 613 — — Nigeria 31742 4690 6346 8332 15193 14286 857–1286 Rwanda 2105 474 526 714 1272 1326 80–119 Re ´ union 192 116 128 61 185 194 12–17 Senegal 2574 424 495 589 1023 560 34–50 Seychelles 22 7 8 16 19 22 1–2 Sierra Leone 1224 475 590 319 1004 1234 74–111 Somalia 2458 136 155 799 803 74 16–25 South Africa 11787 3436 4123 3358 7779 7770 466–699 Sudan 8571 86 104 2140 1897 — — Swaziland 256 84 99 66 189 258 15–23 Sa ˜ o Tome ´ & Prı ´ ncipe 37 26 29 9 37 37 2–3 Tanzania 9628 1385 1542 2839 4591 3906 234–352 Togo 1250 149 213 294 550 348 21–31 Uganda 6540 1324 1388 1977 3630 3498 210–315 Zambia 2853 382 423 840 1334 1044 63–94 Zimbabwe 3434 289 322 1002 1312 302 18–27 Total 179308 30710 36530 50010 89900 84056 5043–7565 a CAR, Central African Republic; DRC, Democratic Republic of Congo. b Range based on delivery costs of US$0.03–0.04 per child and drug costs US$0.03–0.05 per child. GLOBAL HELMINTH ECOLOGY 249 Plate 7.2 Predicted prevalence of (A) A. lumbricoides, (B) T. trichiura and (C) hookworm, based on relationships between observed prevalence of infection among school-aged children (insert) and AVHRR satellite data (see Hay et al., this volume for details) and elevation obtained from an interpolated digital elevation model from the Global Land Information System (GLIS) of the United States Geological Survey (http://ed- cwww.cr.usgs.gov/landdaac/gtopo30/). Prevalence data are available for 1172 sites across SSA including 84 412 children. All surveys were conducted using similar diagnostic techniques (direct smear, typically using the Kato–Katz method) and based on random samples of children in areas where no control measures have previously been undertaken. Due to non-linear relationships between observed prevalence and predictor variables, the predictors were categorized before being entered into the models. The coefficients from these models were then applied to the categories of the predictor variables to generate a predicted prevalence of infection. Validation statistics including area under the curve (AUC), optimal prediction threshold and sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) at the optimal prediction threshold are presented for observed prevalence thresholds of 5% and 50%. (A) Validation statistic Prevalence threshold 5% 50% AUC 0.90 0.91 Optimal prediction threshold 0.13 0.28 Sensitivity (%) 86.2 89.5 Specificity (%) 80.9 81.9 PPV (%) 79.9 60.0 NPV (%) 86.9 96.3 (B) Validation statistic 5% 50% AUC 0.88 0.92 Optimal prediction threshold 0.12 0.32 Sensitivity (%) 84.8 87.7 Specificity (%) 77.5 85.0 PPV (%) 76.0 68.0 NPV (%) 85.9 95.0 (C) Validation statistic 5% 50% AUC 0.76 0.70 Optimal prediction threshold 0.31 0.36 Sensitivity (%) 78.6 67.7 Specificity (%) 79.7 68.5 PPV (%) 88.7 50.8 NPV (%) 68.9 81.5 C Plate 7.2 (continued) Plate 7.6 (continued) Plate 7.6 (A) Risk prediction surface for prevalence of S. haematobium infection in northwest Tanzania. Values presented are interpolated median posterior risk es- timates from a Bayesian geostatistical binomial logistic regression model. Model parameters were: a (intercept) ¼ 2.3 (95% Bayesian CI À0.7 to 5.9), k (smoothing parameter) ¼ 0.9 (95% Bayesian CI 0.6 – 1.2), j (decay of spatial correlation) ¼ 0.2 (95%Bayesian CI 0.1 – 0.5) and s (overall variance) ¼ 4.8 (95%Bayesian CI 2.7 – 7.6). (B) Risk prediction surface for prevalence of S. mansoni infection in northwest Tanzania. Values presented are interpolated median posterior risk estimates from the Bayesian geostatistical binomial logistic regression model. Model parameters were: a (intercept) ¼À12.3 (95%Bayesian CI À18.8 to À4.5), coefficient for distance to perennial water body, o0.04 decimal degrees ¼ 4.1 (95%Bayesian CI 2.8 – 5.4), coefficient for distance to perennial water body, 0.04 – 0.1 decimal degrees ¼ 2.3 (95%Bayesian CI 1.2 – 3.4), coefficient for distance to perennial water body, 0.1 – 0.4 decimal degrees ¼ 1.1 (95%Bayesian CI 0.1 – 2.0), coefficient for annual minimum temperature ¼ 0.4 (95%Bayesian CI 0.0 – 0.8), k (smoothing parameter) ¼ 0.8 (95%Bayesian CI 0.5 – 1.3) ¼ j (decay of spatial correlation) ¼ 2.8 (95%Bayesian CI 1.0 – 5.7) and s (overall variance) ¼ 1.2 (95%Bayesian CI 0.8 – 1.9). (C) Inter- vention contour map overlying districts of northwest Tanzania. Areas outside the 0.1 risk contour will be excluded from the mass treatment programme and PQZ will be made available in health centres. Areas between the 0.1 and 0.5 risk contour will receive mass treatment, targeted at school-age children. Areas within the 0.5 risk contour are priority areas where mass treatment will be targeted at school-age chil- dren and other high-risk groups. Tick-borne Disease Systems: Mapping Geographic and Phylogenetic Space S.E. Randolph 1 and D.J. Rogers 2 1 Oxford Tick Research Group, Tinbergen Building, Department of Zoology, South Parks Road, Oxford OX1 3PS, UK 2 TALA Research Group, Tinbergen Building, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK Abstract 263 1. Predicting Changing Risk of Infection on Evolutionary Time Scales 264 1.1. Evolutionary Emergence of Vector-Borne Pathogens 265 2. The Evolutionary Time Scale for Vector-Borne Flaviviruses . . . 267 3. Correl ates of Phylogenetic Patterns 269 3.1. Biotic Selectors . . 269 3.2. Geography and Phylogeny . . 270 3.3. Biotic Liberators, Abiotic Constraints . . . 272 4. Testin g the Role of Climate in the Evolution of Tick-borne Flaviviruses. 276 4.1. Constructing Phenetic Eco-Climatic Trees for Viruses . . . 277 4.2. Data Quantity and Quality. . . 279 4.3. Composite Predictive Map for Six Viruses. 281 4.4. Congruence between Phylogenetic and Eco-Climatic Trees? 283 Acknowledgements . . . 285 References . . 285 ABSTRACT Evidence is presented that the evolution of the tick-borne flaviviruses is driven by biotic factors, principally the exploitation of new hosts as transmission routes. Because vector-borne diseases are limited by ADVANCES IN PARASITOLOGY VOL 62 ISSN: 0065-308X $35.00 DOI: 10.1016/S0065-308X(05)62008-8 Copyright r 2006 Elsevier Ltd. All rights of reproduction in any form reserved . 34–50 Seychelles 22 7 8 16 19 22 1–2 Sierra Leone 1224 475 590 319 1004 1234 74 –111 Somalia 2458 136 155 79 9 803 74 16–25 South Africa 1 178 7 3436 4123 3358 77 79 77 70 466–699 Sudan 8 571 86 104 2140 18 97 — — Swaziland. 3113 471 548 8 57 1525 1308 79 –118 Mali 3160 41 49 75 0 688 — — Mauritania 3 37 5 11 75 76 — — Mauritius 292 168 190 84 277 295 18– 27 Mozambique 5006 995 1188 1216 276 7 3319 199–299 Namibia 473 7 9. 9643 13410 805–12 07 Republic of Con- go 839 242 310 201 574 828 50 75 Cote d’ Ivoire 4399 1400 178 3 044 3189 39 67 238–3 57 Djibouti 170 1 1 41 14 — — Equatorial Gui- nea 125 70 81 37 118 126 8–11 Eritrea