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Shilu Tong - One of the best experts on this subject based on the ideXlab platform.
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Risk factor analysis and spatiotemporal CART model of Cryptosporidiosis in Queensland, Australia
2010Co-Authors: Kerrie Mengersen, Shilu TongAbstract:Background: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for Cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on Cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia. ----- ----- Methods: Data on weather variables, notified Cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of Cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of Cryptosporidiosis in Queensland, Australia. ----- ----- Results: The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of Cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of Cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of Cryptosporidiosis. When SEIFA was less than 892 the RR of Cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the Cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models. ----- ----- Conclusions: The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of Cryptosporidiosis in Queensland, Australia.
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Risk factor analysis and spatiotemporal CART model of Cryptosporidiosis in Queensland
2010Co-Authors: Kerrie Mengersen, Shilu TongAbstract:Background: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for Cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on Cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia. Methods: Data on weather variables, notified Cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of Cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of Cryptosporidiosis in Queensland, Australia. Results: The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of Cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of Cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of Cryptosporidiosis. When SEIFA was less than 892 the RR of Cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the Cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models. Conclusions: The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of Cryptosporidiosis in Queensland, Australia.
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Spatial analysis of notified Cryptosporidiosis infections in Brisbane, Australia.
Annals of Epidemiology, 2009Co-Authors: Kerrie Mengersen, Shilu TongAbstract:Purpose: This study explored the spatial distribution of notified Cryptosporidiosis cases and identified major socioeconomic factors associated with the transmission of Cryptosporidiosis in Brisbane, Australia. Methods: We obtained the computerized data sets on the notified Cryptosporidiosis cases and their key socioeconomic factors by statistical local area (SLA) in Brisbane for the period of 1996 to 2004 from the Queensland Department of Health and Australian Bureau of Statistics, respectively. We used spatial empirical Bayes rates smoothing to estimate the spatial distribution of Cryptosporidiosis cases. A spatial classification and regression tree (CART) model was developed to explore the relationship between socioeconomic factors and the incidence rates of Cryptosporidiosis. Results: Spatial empirical Bayes analysis reveals that the Cryptosporidiosis infections were primarily concentrated in the northwest and southeast of Brisbane. A spatial CART model shows that the relative risk for Cryptosporidiosis transmission was 2.4 when the value of the social economic index for areas (SEIFA) was over 1028 and the proportion of residents with low educational attainment in an SLA exceeded 8.8%. Conclusions: There was remarkable variation in spatial distribution of Cryptosporidiosis infections in Brisbane. Spatial pattern of Cryptosporidiosis seems to be associated with SEIFA and the proportion of residents with low education attainment.
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weather variability and the incidence of Cryptosporidiosis comparison of time series poisson regression and sarima models
Annals of Epidemiology, 2007Co-Authors: Shilu Tong, Kerrie Mengersen, Desley ConnellAbstract:Purpose Few studies have examined the relationship between weather variables and Cryptosporidiosis in Australia. This paper examines the potential impact of weather variability on the transmission of Cryptosporidiosis and explores the possibility of developing an empirical forecast system. Methods Data on weather variables, notified Cryptosporidiosis cases, and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics for the period of January 1, 1996-December 31, 2004, respectively. Time series Poisson regression and seasonal auto-regression integrated moving average (SARIMA) models were performed to examine the potential impact of weather variability on the transmission of Cryptosporidiosis. Results Both the time series Poisson regression and SARIMA models show that seasonal and monthly maximum temperature at a prior moving average of 1 and 3 months were significantly associated with Cryptosporidiosis disease. It suggests that there may be 50 more cases a year for an increase of 1°C maximum temperature on average in Brisbane. Model assessments indicated that the SARIMA model had better predictive ability than the Poisson regression model (SARIMA: root mean square error (RMSE): 0.40, Akaike information criterion (AIC): −12.53; Poisson regression: RMSE: 0.54, AIC: −2.84). Furthermore, the analysis of residuals shows that the time series Poisson regression appeared to violate a modeling assumption, in that residual autocorrelation persisted. Conclusions The results of this study suggest that weather variability (particularly maximum temperature) may have played a significant role in the transmission of Cryptosporidiosis. A SARIMA model may be a better predictive model than a Poisson regression model in the assessment of the relationship between weather variability and the incidence of Cryptosporidiosis.
Kerrie Mengersen - One of the best experts on this subject based on the ideXlab platform.
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Risk factor analysis and spatiotemporal CART model of Cryptosporidiosis in Queensland, Australia
2010Co-Authors: Kerrie Mengersen, Shilu TongAbstract:Background: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for Cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on Cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia. ----- ----- Methods: Data on weather variables, notified Cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of Cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of Cryptosporidiosis in Queensland, Australia. ----- ----- Results: The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of Cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of Cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of Cryptosporidiosis. When SEIFA was less than 892 the RR of Cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the Cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models. ----- ----- Conclusions: The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of Cryptosporidiosis in Queensland, Australia.
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Risk factor analysis and spatiotemporal CART model of Cryptosporidiosis in Queensland
2010Co-Authors: Kerrie Mengersen, Shilu TongAbstract:Background: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for Cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on Cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia. Methods: Data on weather variables, notified Cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of Cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of Cryptosporidiosis in Queensland, Australia. Results: The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of Cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of Cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of Cryptosporidiosis. When SEIFA was less than 892 the RR of Cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the Cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models. Conclusions: The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of Cryptosporidiosis in Queensland, Australia.
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Spatial analysis of notified Cryptosporidiosis infections in Brisbane, Australia.
Annals of Epidemiology, 2009Co-Authors: Kerrie Mengersen, Shilu TongAbstract:Purpose: This study explored the spatial distribution of notified Cryptosporidiosis cases and identified major socioeconomic factors associated with the transmission of Cryptosporidiosis in Brisbane, Australia. Methods: We obtained the computerized data sets on the notified Cryptosporidiosis cases and their key socioeconomic factors by statistical local area (SLA) in Brisbane for the period of 1996 to 2004 from the Queensland Department of Health and Australian Bureau of Statistics, respectively. We used spatial empirical Bayes rates smoothing to estimate the spatial distribution of Cryptosporidiosis cases. A spatial classification and regression tree (CART) model was developed to explore the relationship between socioeconomic factors and the incidence rates of Cryptosporidiosis. Results: Spatial empirical Bayes analysis reveals that the Cryptosporidiosis infections were primarily concentrated in the northwest and southeast of Brisbane. A spatial CART model shows that the relative risk for Cryptosporidiosis transmission was 2.4 when the value of the social economic index for areas (SEIFA) was over 1028 and the proportion of residents with low educational attainment in an SLA exceeded 8.8%. Conclusions: There was remarkable variation in spatial distribution of Cryptosporidiosis infections in Brisbane. Spatial pattern of Cryptosporidiosis seems to be associated with SEIFA and the proportion of residents with low education attainment.
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weather variability and the incidence of Cryptosporidiosis comparison of time series poisson regression and sarima models
Annals of Epidemiology, 2007Co-Authors: Shilu Tong, Kerrie Mengersen, Desley ConnellAbstract:Purpose Few studies have examined the relationship between weather variables and Cryptosporidiosis in Australia. This paper examines the potential impact of weather variability on the transmission of Cryptosporidiosis and explores the possibility of developing an empirical forecast system. Methods Data on weather variables, notified Cryptosporidiosis cases, and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics for the period of January 1, 1996-December 31, 2004, respectively. Time series Poisson regression and seasonal auto-regression integrated moving average (SARIMA) models were performed to examine the potential impact of weather variability on the transmission of Cryptosporidiosis. Results Both the time series Poisson regression and SARIMA models show that seasonal and monthly maximum temperature at a prior moving average of 1 and 3 months were significantly associated with Cryptosporidiosis disease. It suggests that there may be 50 more cases a year for an increase of 1°C maximum temperature on average in Brisbane. Model assessments indicated that the SARIMA model had better predictive ability than the Poisson regression model (SARIMA: root mean square error (RMSE): 0.40, Akaike information criterion (AIC): −12.53; Poisson regression: RMSE: 0.54, AIC: −2.84). Furthermore, the analysis of residuals shows that the time series Poisson regression appeared to violate a modeling assumption, in that residual autocorrelation persisted. Conclusions The results of this study suggest that weather variability (particularly maximum temperature) may have played a significant role in the transmission of Cryptosporidiosis. A SARIMA model may be a better predictive model than a Poisson regression model in the assessment of the relationship between weather variability and the incidence of Cryptosporidiosis.
R. Heru Prasetyo - One of the best experts on this subject based on the ideXlab platform.
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Cryptosporidiosis PARU DI PENDERITA TBC
INDONESIAN JOURNAL OF CLINICAL PATHOLOGY AND MEDICAL LABORATORY, 2016Co-Authors: R. Heru PrasetyoAbstract:The pulmonary Cryptosporidiosis cases had been reported for immunocompromised persons, most all of whom were secunder infected with HIV and AIDS patients. Tuberculosis is a chronic respiratory disease and tending to cause a weakened immune system (immunocompromised). However, pulmonary Cryptosporidiosis has not been previously reported as secunder infection in tuberculosis patients. The objective of this study was to know the prevalence of pulmonary Cryptosporidiosis determination in tuberculosis patients. This research was carried out by a cross sectional study utilitzing waste sputum samples from tuberculosis patients. The detection of Cryptosporidium oocyst used modified version acid fast stain of Ziehl Neelsen technique. Three sputum samples among 44 sputum samples (6.8%) had Cryptosporidium oocyst positive. These findings suggest that there was a potential for respiratory transmission of Cryptosporidiosis. Although the prevalence of pulmonary Cryptosporidiosis in tuberculosis patients are low, the researcher suggest that the possibility of pulmonary Cryptosporidiosis as a secondary infection in tuberculosis patients existed, and there for a laboratory examination of pulmonary Cryptosporidiosis becoming routinely laboratory for tuberculosis patients.
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Cryptosporidiosis Paru di HIV dan AIDS
INDONESIAN JOURNAL OF CLINICAL PATHOLOGY AND MEDICAL LABORATORY, 2016Co-Authors: Js. Hutagalung, R. Heru Prasetyo, Erwin Astha TriyonoAbstract:Although the prevalence of intestinal Cryptosporidiosis in Indonesian HIV and AIDS patients were high, however the prevalence of pulmonary Cryptosporidiosis have not been previously reported. The objective of this study was to know the determination of the pulmonary Cryptosporidiosis prevalence in HIV and AIDS patients with pulmonary symptom that was treated in Dr. Soetomo General Hospital Surabaya. The detection of Cryptosporidium in sputum samples used modified versien acid fast stain of Ziehl Neelsen technique. In this study was found that three (3) of the eight (8) sputum samples (37.5%) of ≥55 years old and CD4≤70 HIV and AIDS patients were Cryptosporidium positive. Based on this study the HIV and AIDS patients with pulmonary symptoms should be suspect having the possibility of pulmonary Cryptosporidiosis beside suffered tuberculosis.
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Pulmonary Cryptosporidiosis in TBC Patients
Indonesian Journal of Clinical Pathology and Medical Laboratory, 2012Co-Authors: R. Heru PrasetyoAbstract:The pulmonary Cryptosporidiosis cases had been reported for immunocompromised persons, most all of whom were secunder infected with HIV and AIDS patients. Tuberculosis is a chronic respirtory disease and tending to cause a weakened immune system (immunocompromised). However, pulmonary Cryptosporidiosis has not been previously reported as secunder infection in tuberculosis patients. The objective of this study was to know the prevalence of pulmonary Cryptosporidiosis determination in tuberculosis patients. This research was carried out by a cross sectional study utilitzing waste sputum samples from tuberculosis patients. The detection of Cryptosporidium oocyst used modified version acid fast stain of Ziehl Neelsen technique. Three sputum samples among 44 sputum samples (6.8%) had Cryptosporidium oocyst positive. These findings suggest that there was a potential for respiratory transmission of Cryptosporidiosis. Although the prevalence of pulmonary Cryptosporidiosis in tuberculosis patients are low, the researcher suggest that the possibility of pulmonary Cryptosporidiosis as a secondary infection in tuberculosis patients existed, and there for a laboratory examination of pulmonary Cryptosporidiosis becoming routinely laboratory for tuberculosis patients. Beberapa kasus Cryptosporidiosis paru sudah dilaporkan dapat terjadi di penderita dengan immunocompromised (penurunan kekebalan tubuh), hampir semua yang dilaporkan merupakan infeksi sekunder pada penderita HIV dan AIDS. Tuberkulosis adalah penyakit sistem pernafasan kronis dan cenderung menyebabkan melemahnya sistem kekebalan tubuh (immunocompromised). Namun sampai saat ini Cryptosporidiosis paru sebagai infeksi sekunder di penderita tuberkulosis belum pernah dilaporkan. Tujuan penelitian ini adalah untuk menentukan jumlah penderita penyakit Cryptosporidiosis paru di penderita tuberkulosis. Penelitian dilakukan secara kajian potong silang dengan memanfaatkan sampel dahak sisa periksaan penderita tuberkulosis. Temuan ookista Cryptosporidium dalam sampel dahak dilakukan dengan pengecatan modifikasi Ziehl Neelsen. Tiga dari 44 sampel dahak yang diperiksa (6,8%) menunjukkan positif ookista Cryptosporidium. Penemuan ini harus diwaspadai bahwa kemungkinan penularan Cryptosporidiosis melalui jalan pernapasan. Meskipun jumlah penderita penyakit Cryptosporidiosis paru di antara penderita tuberkulosis rendah, tetapi setiap penderita tersebut harus dicurigai kemungkinan terjadi infeksi sekunder Cryptosporidiosis, sehingga pemeriksaan laboratorik Cryptosporidiosis paru perlu disertakan sebagai pemeriksaan rutin pada penderita tuberkulosis.
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Pulmonary Cryptosporidiosis in HIV and AIDS
Indonesian Journal of Clinical Pathology and Medical Laboratory, 2012Co-Authors: Js. Hutagalung, R. Heru Prasetyo, Erwin Astha TriyonoAbstract:Although the prevalence of intestinal Cryptosporidiosis in Indonesian HIV and AIDS patients were high, however the prevalence of pulmonary Cryptosporidiosis have not been previously reported. The objective of this study was to know the determination of the pulmonary Cryptosporidiosis prevalence in HIV and AIDS patients with pulmonary symptom that was treated in Dr.Soetomo General Hospital Surabaya. The detection of Cryptosporidium in sputum samples used modified versien acid fast stain of Ziehl Neelsen technique. In this study was found that three (3) of the eight (8) sputum samples (37.5%) of ≥ 55 years old and CD4 ≤ 70 HIV and AIDS patients were Cryptosporidium positive. Based on this study the HIV and AIDS patients with pulmonary symptoms should be suspect having the possibility of pulmonary Cryptosporidiosis beside suffered tuberculosis. Jumlah penderita penyakit Cryptosporidiosis usus di penderita HIV dan AIDS di Indonesia tinggi, tetapi jumlah penderita penyakit Cryptosporidiosis paru di penderita sejenis belum pernah dilaporkan. Tujuan penelitian ini adalah untuk mengetahui jumlah penderita penyakit Cryptosporidiosis paru di penderita HIV dan AIDS dengan keluhan paru yang sedang rawat inap di RSUD Dr. Soetomo Surabaya. Pemeriksaan Cryptosporidium di sampel dahak dilakukan dengan cara pengecatan tahan asam modifikasi Zeihl Neelsen. Pada penelitian ini ditemukan tiga dari delapan (8) sampel dahak (37,5%) berasal dari penderita yang berumur ≥55 th dan jumlah CD4-nya ≤ 70 menunjukkan Cryptosporidium positif. Didasari penelitian ini dapat disimpulkan bahwa penderita HIV dan AIDS dengan keluhan paru, selain dicurigai tuberkulosis harus juga diduga kemungkinan mengidap Cryptosporidiosis paru.
Sarah J Obrien - One of the best experts on this subject based on the ideXlab platform.
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can syndromic surveillance data detect local outbreaks of communicable disease a model using a historical Cryptosporidiosis outbreak
Epidemiology and Infection, 2005Co-Authors: D L Cooper, Neville Q Verlander, G E Smith, A Charlett, E Gerard, L Willocks, Sarah J ObrienAbstract:A national UK surveillance system currently uses data from a health helpline (NHS Direct) in an attempt to provide early warning of a bio-terrorist attack, or an outbreak caused by a more common infection. To test this syndromic surveillance system we superimposed data from a historical outbreak of Cryptosporidiosis onto a statistical model of NHS Direct call data. We modelled whether calls about diarrhoea (a proxy for Cryptosporidiosis) exceeded a statistical threshold, thus alerting the surveillance team to the outbreak. On the date that the public health team were first notified of the outbreak our model predicted a 4% chance of detection when we assumed that one-twentieth of Cryptosporidiosis cases telephoned the helpline. This rose to a 72% chance when we assumed nine-tenths of cases telephoned. The NHS Direct surveillance system is currently unlikely to detect an event similar to the Cryptosporidiosis outbreak used here and may be most suited to detecting more widespread rises in syndromes in the community, as previously demonstrated. However, the expected rise in NHS Direct call rates, should improve early warning of outbreaks using call data.
Saul Tzipori - One of the best experts on this subject based on the ideXlab platform.
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The Evolution of Respiratory Cryptosporidiosis: Evidence for Transmission by Inhalation
Clinical Microbiology Reviews, 2014Co-Authors: Jerlyn K. Sponseller, Jeffrey K. Griffiths, Saul TziporiAbstract:SUMMARY The protozoan parasite Cryptosporidium infects all major vertebrate groups and causes significant diarrhea in humans, with a spectrum of diseases ranging from asymptomatic to life-threatening. Children and immunodeficient individuals are disproportionately affected, especially in developing countries, where Cryptosporidiosis contributes substantially to morbidity and mortality in preschool-age children. Despite the enormous disease burden from Cryptosporidiosis, no antiprotozoal agent or vaccine exists for effective treatment or prevention. Cryptosporidiosis involving the respiratory tract has been described for avian species and mammals, including immunocompromised humans. Recent evidence indicates that respiratory Cryptosporidiosis may occur commonly in immunocompetent children with cryptosporidial diarrhea and unexplained cough. Findings from animal models, human case reports, and a few epidemiological studies suggest that Cryptosporidium may be transmitted via respiratory secretions, in addition to the more recognized fecal-oral route. It is postulated that transmission of Cryptosporidium oocysts may occur by inhalation of aerosolized droplets or by contact with fomites contaminated by coughing. Delineating the role of the respiratory tract in disease transmission may provide necessary evidence to establish further guidelines for prevention of Cryptosporidiosis.
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Cryptosporidiosis in children in sub saharan africa a lingering challenge
Clinical Infectious Diseases, 2008Co-Authors: Siobhan M. Mor, Saul TziporiAbstract:Hospital- and community-based studies in sub-Saharan Africa document a high prevalence of Cryptosporidiosis in children aged 6-36 months, particularly among those who are malnourished or positive for human immunodeficiency virus (HIV) infection and during rainy seasons. This is despite advances in developed countries that have curbed the incidence of Cryptosporidiosis in the general and HIV-positive populations. Transmission in sub-Saharan Africa appears to occur predominantly through an anthroponotic cycle. The preponderance of Cryptosporidium hominis, given its limited host range, and the dominance of the more ubiquitous Cryptosporidium parvum after coexposure to both species, however, suggest that the current knowledge of transmission is incomplete. Given the poor sanitation and hygiene, limited availability of antiretrovirals, and the high prevalence of Cryptosporidiosis in children-independent of HIV infection-in this region, effective control measures for Cryptosporidiosis are desperately needed. Molecular targets from the recently sequenced parasite genome should be exploited to develop an effective and safe treatment for children.