Enabling targeted mass drug administration for schistosomiasis in north-western Tanzania: Exploring the use of geostatistical modeling to inform planning at sub-district level.

INTRODUCTION: Schistosomiasis is a parasitic disease in Tanzania affecting over 50% of the population. Current control strategies involve mass drug administration (MDA) campaigns at the district level, which have led to problems of over- and under-treatment in different areas. WHO guidelines have called for more targeted MDA to circumvent these problems, however a scarcity of prevalence data inhibits decision makers from prioritizing sub-district areas for MDA. This study demonstrated how geostatistics can be used to inform planning for targeted MDA. METHODS: Geostatistical sub-district (ward-level) prevalence estimates were generated through combining a zero-inflated poisson model and kriging approach (regression kriging). To make predictions, the model used prevalence survey data collected in 2021 of 17,400 school children in six regions of Tanzania, along with several open source ecological and socio-demographic variables with known associations with schistosomiasis. RESULTS: The model results show that regression kriging can be used to effectively predict the ward level parasite prevalence of the two species of Schistosoma endemic to the study area. Kriging was found to further improve the regression model fit, with an adjusted R-squared value of 0.51 and 0.32 for intestinal and urogenital schistosomiasis, respectively. Targeted treatment based on model predictions would represent a shift in treatment away from 193 wards estimated to be over-treated to 149 wards that would have been omitted from the district level MDA. CONCLUSIONS: Geostatistical models can help to support NTD program efficiency and reduce disease transmission by facilitating WHO recommended targeted MDA treatment through provision of prevalence estimates where data is scarce.

Nodding Syndrome: Clinical Characteristics, Risks Factors, Access to Treatment, and Perceptions in the Greater Mundri Area, South Sudan.

We conducted a house-to-house survey in the Mundri, Western Equatoria state of South Sudan to investigate the clinical characteristics, risk factors, access to treatment and perceptions about nodding syndrome (NS). In total, 224 NS cases with median age of seizure onset of 10 years were identified. Head nodding only was reported in 50 (22.3%) cases, and head nodding plus other types of seizures in 174 (77.7%) cases. Wasting, stunted growth, delayed sexual development and speech and behavioral abnormalities were observed in 17 (23.6%), 16 (22.2%), 9 (17.3%), 14 (19.4%) and 4 (5.6%) cases, respectively. The consumption of rat meat, but not other bushmeat was associated with an increased risk of NS (OR 9.31, 95% CI 1.27-406.51). Children with NS were more likely to have taken ivermectin in the last 5 years (OR 2.40, 95% CI 1.33-4.43). NS cases were less likely to share a bedroom with other children (OR 0.06, 95% CI 0.02-0.16) or adults (OR 0.27, 95% CI 0.13-0.56). In conclusion, rat meat consumption is an unlikely risk factor for NS, and ivermectin intake was more common among NS cases than controls. Importantly, we documented that children with NS are stigmatized because of the misconception that NS is transmitted through direct contact.

Epidemiology of nodding syndrome in the Greater Mundri area, South Sudan: Prevalence, spatial pattern and environmental risk factors.

BACKGROUND: Nodding syndrome (NS) is a progressive neurological disease that has been described in several sub-Saharan African counties, but South Sudan is considered the most affected. However, knowledge about the exact burden and the epidemiological risk factors of NS in South Sudan is lacking. OBJECTIVE: To determine the prevalence, distribution and epidemiological risk factors of NS in the Greater Mundri area, the epicenter of NS in South Sudan. METHODS: A NS prevalence house-to-house survey was conducted in multiple villages between February 2018 and November 2019. Geographical distribution and clustering of NS cases was identified using spatial and binomial regression analysis. Epidemiological risk factors of NS were identified using univariate and multivariate models. RESULTS: Of the 22,411 persons surveyed in 92 villages, 607 (2.7%) persons with NS were identified, of which 114 (19%) were new-onset cases. The highest prevalence was found in Diko village with a prevalence of 13.7%. NS showed a significant spatial pattern with clustering of cases between adjacent households and along rivers. Risks factors for NS include all behaviors around rivers (drinking, cooking, handwashing and bathing) and exposure to poultry. On the other hand, ownership of mobile phone decreased the risk of NS. Many other factors, including prior ivermectin treatment and internal displacement were not associated with NS. CONCLUSION: Our study demonstrates a very high burden of the NS disease in the Greater Mundri area, strengthens the association with rivers, and identified possible new clues for an underlying cause.

Environmental risk of leptospirosis infections in the Netherlands: Spatial modelling of environmental risk factors of leptospirosis in the Netherlands.

Leptospirosis is a globally emerging zoonotic disease, associated with various climatic, biotic and abiotic factors. Mapping and quantifying geographical variations in the occurrence of leptospirosis and the surrounding environment offer innovative methods to study disease transmission and to identify associations between the disease and the environment. This study aims to investigate geographic variations in leptospirosis incidence in the Netherlands and to identify associations with environmental factors driving the emergence of the disease. Individual case data derived over the period 1995-2012 in the Netherlands were geocoded and aggregated by municipality. Environmental covariate data were extracted for each municipality and stored in a spatial database. Spatial clusters were identified using kernel density estimations and quantified using local autocorrelation statistics. Associations between the incidence of leptospirosis and the local environment were determined using Simultaneous Autoregressive Models (SAR) explicitly modelling spatial dependence of the model residuals. Leptospirosis incidence rates were found to be spatially clustered, showing a marked spatial pattern. Fitting a spatial autoregressive model significantly improved model fit and revealed significant association between leptospirosis and the coverage of arable land, built up area, grassland and sabulous clay soils. The incidence of leptospirosis in the Netherlands could effectively be modelled using a combination of soil and land-use variables accounting for spatial dependence of incidence rates per municipality. The resulting spatially explicit risk predictions provide an important source of information which will benefit clinical awareness on potential leptospirosis infections in endemic areas.

Population status of a cryptic top predator: an island-wide assessment of tigers in Sumatran rainforests.

Large carnivores living in tropical rainforests are under immense pressure from the rapid conversion of their habitat. In response, millions of dollars are spent on conserving these species. However, the cost-effectiveness of such investments is poorly understood and this is largely because the requisite population estimates are difficult to achieve at appropriate spatial scales for these secretive species. Here, we apply a robust detection/non-detection sampling technique to produce the first reliable population metric (occupancy) for a critically endangered large carnivore; the Sumatran tiger (Panthera tigris sumatrae). From 2007-2009, seven landscapes were surveyed through 13,511 km of transects in 394 grid cells (17×17 km). Tiger sign was detected in 206 cells, producing a naive estimate of 0.52. However, after controlling for an unequal detection probability (where p = 0.13±0.017; ±S.E.), the estimated tiger occupancy was 0.72±0.048. Whilst the Sumatra-wide survey results gives cause for optimism, a significant negative correlation between occupancy and recent deforestation was found. For example, the Northern Riau landscape had an average deforestation rate of 9.8%/yr and by far the lowest occupancy (0.33±0.055). Our results highlight the key tiger areas in need of protection and have led to one area (Leuser-Ulu Masen) being upgraded as a 'global priority' for wild tiger conservation. However, Sumatra has one of the highest global deforestation rates and the two largest tiger landscapes identified in this study will become highly fragmented if their respective proposed roads networks are approved. Thus, it is vital that the Indonesian government tackles these threats, e.g. through improved land-use planning, if it is to succeed in meeting its ambitious National Tiger Recovery Plan targets of doubling the number of Sumatran tigers by 2022.