PS 9-105 - Optimizing Schistosomiasis treatment timing with respect to seasonal fluctuations in the force of infection

Monday, August 7, 2017
Exhibit Hall, Oregon Convention Center
Larissa Anderson, Department of Biology, University of New Mexico, Albuquerque, NM and Helen J. Wearing, Department of Biology and Department of Mathematics & Statistics, University of New Mexico, Albuquerque, NM

Schistosomiasis is a neglected parasitic disease caused by trematode species of the genus Schistosoma. Despite consistent control efforts this disease is endemic in 52 countries, with 90 percent of cases occurring in sub-Saharan Africa. A common schistosoma species, Schistosoma mansoni, utilizes humans as its obligate definitive host and freshwater planorbid snails of the genus Biomphalaria as its intermediate host. Despite relatively stable temperature regimes in sub-tropical and tropical regions, the wet and dry seasons can drastically change the aquatic environment that provide habitat for the snail intermediate host and the two free-living stages of schistosomes. The changes in water body size may also concentrate exposure in the human and snail hosts or periodically eliminate suitable habitat. Substantive variation in snail population size or infection level may impact the force of infection to humans and a reduction in this would present an opportunity to maximize the impact of current control measures. Therefore, we developed an ordinary differential equation based model of schistosomiasis transmission with seasonal snail infection, demography and population size data to examine mass drug administration timing to investigate how natural fluctuations in the force of infection can be utilized to maximize the impacts of large scale control efforts.


Snail density and infection prevalence data was collected from three regionally representative snail habitats: lake, perennial stream and an ephemeral stream in the greater Kisumu, Kenya area from July of 2015 – January 2017. Daily rainfall and temperature data from Kisumu for January 2013 –January 2017 were used to parameterize the seasonal forcing in the model. We used time-series analysis, including analysis of varying time-lags, to identify the temperature rainfall events at previous time points that were most predictive of snail density and S.mansoni infection prevalence. Seasonal patterns of rainfall and temperature were then included as periodic forcing in the ODE model and these fluctuations then impacted future birth and death rates as well as the force of infection applied to the intermediate host snail population. The resulting model allowed us to identify the times of year when interventions, either molluscicides (represented by a reduction in snail density) or chemoprophylaxis applied to the human population (represented by a reduction in force of infection to the snail population), would have the greatest impact on reducing the force of infection to the human population.