Schistosomiasis is a snail-borne disease endemic in sub-Saharan Africa transmitted by freshwater snails. The distribution of schistosomiasis coincides with that of the intermediate hosts as determined by climatic and environmental factors. The aim of this paper was to model the spatial and seasonal distribution of suitable habitats for Bulinus globosus and Biomphalaria pfeifferi snail species (intermediate hosts for Schistosoma haematobium and Schistosoma mansoni, respectively) in the Ndumo area of uMkhanyakude district, South Africa. Maximum Entropy (Maxent) modelling technique was used to predict the distribution of suitable habitats for B. globosus and B. pfeifferi using presence-only datasets with ≥ 5 and ≤ 12 sampling points in different seasons. Precipitation, maximum and minimum temperatures, Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), pH, slope and Enhanced Vegetation Index (EVI) were the background variables in the Maxent models. The models were validated using the area under the curve (AUC) and omission rate. The predicted suitable habitats for intermediate snail hosts varied with seasons. The AUC for models in all seasons ranged from 0.71 to 1 and the prediction rates were between 0.8 and 0.9. Although B. globosus was found at more localities in the Ndumo area, there was also evidence of cohabiting with B. pfiefferi at some of the locations. NDWI had significant contribution to the models in all seasons. The Maxent model is robust in snail habitat suitability modelling even with small dataset of presence-only sampling sites. Application of the methods and design used in this study may be useful in developing a control and management programme for schistosomiasis in the Ndumo area.

During the last 30 years, the development of geographical information systems and satellites for Earth observation has made important progress in the monitoring of the weather, climate, environmental and anthropogenic factors that influence the reduction or the reemergence of vector-borne diseases. Analyses resulting from the combination of geographical information systems (GIS) and remote sensing have improved knowledge of climatic, environmental, and biodiversity factors influencing vector-borne diseases (VBDs) such as malaria, visceral leishmaniasis, dengue, Rift Valley fever, schistosomiasis, Chagas disease and leptospirosis. These knowledge and products developed using remotely sensed data helped and continue to help decision makers to better allocate limited resources in the fight against VBDs. Because VBDs are linked to climate and environment, we present here our experience during the last four years working with the projects under the, World Health Organization (WHO)/ The Special Programme for Research and Training in Tropical Diseases (TDR)-International Development Research Centre (IDRC) Research Initiative on VBDs and Climate Change to integrate climate and environmental information into research and decision-making processes. The following sections present the methodology we have developed, which uses remote sensing to monitor climate variability, environmental conditions, and their impacts on the dynamics of infectious diseases. We then show how remotely sensed data can be accessed and evaluated and how they can be integrated into research and decision-making processes for mapping risks, and creating Early Warning Systems, using two examples from the WHO TDR projects based on schistosomiasis analysis in South Africa and Trypanosomiasis in Tanzania. The tools presented in this article have been successfully used by the projects under the WHO/TDR-IDRC Research Initiative on VBDs and Climate Change. Combined with capacity building, they are an important piece of work which can significantly contribute to the goals of WHO Global Vector Control Response and to the Sustainable Development Goals especially those on health and climate action.

Despite its low cure rates and possible resistance, praziquantel (PZQ) is the only drug available for schistosomiasis treatment. Hence, monitoring its efficacy is crucial. This study assessed the efficacy of PZQ, determined re-infection and incidence rates of Schistosoma haematobium infection among school-going children in the Ndumo area, KwaZulu-Natal. A cohort of 320 school-going children (10 – 15 years) in 10 primary schools was screened for S. haematobium infection using the filtration technique. Infected children were treated at different times and hence were divided into two sub-cohorts; A1 and A2. Non-infected children constituted the sub-cohort B. Children who continued excreting viable eggs 4 weeks post-treatment received a second dose of PZQ. Re-infection rates were determined in sub-cohort A1 and A2 at 28 and 20 weeks post-treatment, respectively. Cure rates (CR) and egg reduction rates (ERR) were calculated. Incidence rate was assessed 28 weeks post baseline survey using children that were negative for schistosome eggs at that survey. Analysis of data was done using the Chi square and the Wilcoxon rank test. A 95% confidence interval with a P-value < 0.05 determined significance. At baseline, 120 (37.5%) of the 320 study participants were found infected with Schistosoma haematobium. Heavy infections accounted for 36.7%. The calculated cure rates were 88.07% and 82.92% for females and males, respectively. Egg Reduction Rates of 80% and 64% for females and males were observed 4 weeks after the initial treatment. After the second treatment, CR was 100% in females and 50% in males with an ERR of 100% in females and 70% in males. At 20 and 28 weeks post treatment, reinfection rates of 8.03% and 8.00% were observed, respectively, giving an overall rate of 8.1%. An incidence rate of 4.1% was observed 28 weeks after the baseline screening. The study indicated high CR while the ERR was low suggesting a reduced PZQ efficacy. The efficacy improved among females after the second dose. Re-infection rates at 20 and 28 weeks post-treatment were low. The study also indicated a low incidence rate for the 28 weeks period.

Background: Schsistosomiasis is endemic in sub-Saharan Africa. It is transmitted by intermediate host snails such as Bulinus and Biomphalaria. An understanding of the abundance and distribution of snail vectors is important in designing control strategies. This study describes the spatial and seasonal variation of B. globosus and Bio. pfeifferi and their schistosome infection rates between May 2014 and May 2015 in Ingwavuma, uMkhanyakude district, KwaZulu-Natal province, South Africa. Methods: Snail sampling was done on 16 sites once every month by two people for 30 min at each site using the scooping and handpicking methods. Snails collected from each site were screened for schistosome mammalian cercariae by the shedding method. The negative binomial generalised linear mixed model (glmm) was used to determine the relationship between abundances of the intermediate host snails and climatic factors [rainfall, land surface temperatures (LST), seasons, habitats, sampling sites and water physico-chemical parameters including pH and dissolved oxygen (DO)]. Results: In total, 1846 schistosomiasis intermediate host snails were collected during the study period. Biompharia pfeifferi was more abundant (53.36%, n = 985) compared to B. globosus (46.64%, n = 861). Bulinus globosus was recorded at 12 sites (75%) and Bio. pfeifferi was present at 7 sites (43.8%). Biompharia pfeifferi cohabited with B. globosus at all the sites it was present. High numbers of Bio. pfeifferi (n = 872, 88.5%) and B. globosus (n = 705, 81.9%) were found between winter and mid-spring. Monthly rainfall showed a statistically significant negative relationship with the abundance of B. globosus (p < 0.05). Dissolved oxygen (DO) had a statistically significant positive relationship with the abundance of Bio. pfeifferi (p < 0.05) while (LST) had a statistically significant negative relationship (p < 0.05). More B. globosus (8.9%, n = 861) were shedding schistosome mammalian cercariae compared to Bio. pfeifferi (0.1%, n = 985) confirming the already documented high prevalence of S. haematobium in Ingwavuma compared to S. mansoni. Conclusion: Results of this study provide updated information on the distribution of schistosomiasis intermediate host snails in the study area and contributes towards the understanding of the transmission dynamics of schistosomiasis at the micro-geographical scale in this area. Graphical

Although there has been a decline in the number of malaria cases in Zimbabwe since 2010, the disease remains the biggest public health threat in the country. Gwanda district, located in Matabeleland South Province of Zimbabwe has progressed to the malaria pre-elimination phase. The aim of this study was to determine the spatial distribution of malaria incidence at ward level for improving the planning and implementation of malaria elimination in the district. The Poisson purely spatial model was used to detect malaria clusters and their properties, including relative risk and significance levels at ward level. The geographically weighted Poisson regression (GWPR) model was used to explore the potential role and significance of environmental variables [rainfall, minimum and maximum temperature, altitude, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), rural/urban] and malaria control strategies [indoor residual spraying (IRS) and long-lasting insecticide-treated nets (LLINs)] on the spatial patterns of malaria incidence at ward level. Two significant clusters (p < 0.05) of malaria cases were identified: (1) ward 24 south of Gwanda district and (2) ward 9 in the urban municipality, with relative risks of 5.583 and 4.316, respectively. The semiparametric-GWPR model with both local and global variables had higher performance based on AICc (70.882) compared to global regression (74.390) and GWPR which assumed that all variables varied locally (73.364). The semiparametric-GWPR captured the spatially non-stationary relationship between malaria cases and minimum temperature, NDVI, NDWI, and altitude at the ward level. The influence of LLINs, IRS and rural or urban did not vary and remained in the model as global terms. NDWI (positive coefficients) and NDVI (range from negative to positive coefficients) showed significant association with malaria cases in some of the wards. The IRS had a protection effect on malaria incidence as expected. Malaria incidence is heterogeneous even in low-transmission zones including those in pre-elimination phase. The relationship between malaria cases and NDWI, NDVI, altitude, and minimum temperature may vary at local level. The results of this study can be used in planning and implementation of malaria control strategies at district and ward levels.