Biology Reference
In-Depth Information
146. de Silva NR, Pathmeswaran A, Fernando SD, et al. Impact of mass chemotherapy for
the control of filariasis on geohelminth infections in Sri Lanka. Ann Trop Med Parasitol
2003;
97
:421
5.
147. Brooker S, Jardim-Botelho A, Quinnell RJ, et al. Age-related changes in hookworm
infection, anaemia and iron deficiency in an area of high Necator americanus hookworm
transmission in south-eastern Brazil. Trans R Soc Trop Med Hyg 2007;
101
:146
e
54.
148. Flohr C, Tuyen LN, Lewis S, et al. Low efficacy of mebendazole against hookworm in
Vietnam: two randomized controlled trials. Am J Trop Med Hyg 2007;
76
:732
e
6.
149. Lambert D. Zero-inflated Poisson regression, with an application to defects in
manufacturing. Technometrics 1992;
34
:1
e
14.
150. Mullahy J. Specification and testing of some modified count data models. J Econom
1986;
33
:341
e
65.
151. Heilbron DC. Zero-altered and other regression models for count data with added
zeros. Biom J 1994;
36
:531
e
47.
152. Greene W. Accounting for excess zeros and sample selection in Poisson and negative binomial
regression models. Working Paper EC-94
e
e
10,. Department of Economics, New York
University; 1994.
153. Ridout M, Dem´trio CGB, Hinde J. Models for count data with many zeros. Cape Town:
International Biometric Conference; 1998.
154. Vounatsou P, Raso G, Tanner M, N'goran EK, Utzinger J. Bayesian geostatistical
modelling for mapping schistosomiasis transmission. Parasitology 2009;
136
:1695
705.
155. Soares Magalh˜es RJ, Biritwum N-K, Gyapong JO, et al. Mapping helminth
co-infection and co-intensity: geostatistical prediction in Ghana. PLoS Negl Trop Dis
2011;
5
:e1200.
156. Paterson S, Lello J. Mixed models: getting the best use of parasitological data. Trends
Parasitol 2003;
19
:370
e
5.
157. Clayton DG. Generalized linear mixed models. In: Markov Chain Monte Carlo in Practice.
London: Chapman & Hall; 1996.
158. Bundy DAP, Cooper ES, Thompson DE, Didier JM, Simmons I. Epidemiology and
population dynamics of Ascaris lumbricoides and Trichuris trichiura infection in the same
community. Trans R Soc Trop Med Hyg 1987;
81
:987
e
93.
159. Koukounari A, Sacko M, Keita AD, et al. Assessment of ultrasound morbidity
indicators of schistosomiasis in the context of large-scale programs illustrated with
experiences from Malian children. Am J Trop Med Hyg 2006;
75
:1042
e
52.
160. Booth JG, Casella G, Friedl H, Hobert JP. Negative binomial loglinear mixed models.
Stat Modelling 2003;
3
:179
e
91.
161. Hall DB. Zero-inflated Poisson and binomial regression with random effects: a case
study. Biometrics 2000;
56
:1030
e
9.
162. Min Y, Agresti A. Random effect models for repeated measures of zero-inflated count
data. Stat Modelling 2005;
5
:1
e
19.
163. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis. London: Chapman &
Hall; 2003.
164. Gilks WR, Richardson S, Spiegelhalter DJ. Introducing Markov chain Monte Carlo. In:
Gilks WR, Richardson S, Spiegelhalter DJ, editors. Markov Chain Monte Carlo in Practice.
London: Chapman & Hall; 1996. p. 1
e
20.
165. Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS
e
a Bayesian modelling
framework: concepts, structure, and extensibility. Stat Comput 2000;
10
:325
e
37.
166. Plummer M. JAGS: a program for analysis of Bayesian graphical models using Gibbs
sampling. Vienna: Proceedings of the 3rd International Workshop on Distributed
Statistical Computing; 2003.
167. Brooker S, Clements ACA, Bundy DAP. Global epidemiology, ecology and control of
soil-transmitted helminth infections. Adv Parasitol 2006;
62
:221
e
e
61.
