Schistosoma Transmission in a Dynamic Seasonal Environment and its Impact on the Effectiveness of Disease Control

We used mathematical models to evaluate the impact of seasonality on Schistosoma transmission. We found the seasonal variation makes Schistosoma transmission less sustainable, and intraseasonal timing of interventions could improve the long-term outcomes of control measures in such communities. Background A seasonal transmission environment including seasonal variation of snail population density and human-snail contact patterns can affect the dynamics of Schistosoma infection and the success of control interventions. In projecting control outcomes, conventional modeling approaches have often ignored seasonality by using simplified intermediate-host modeling, or by restricting seasonal effects through use of yearly averaging. Methods We used mathematical analysis and numerical simulation to estimate the impact of seasonality on disease dynamics and control outcomes, and to evaluate whether seasonal averaging or intermediate-host reduction can provide reliable predictions of control outcomes. We also examined whether seasonality could be used as leverage in creation of effective control strategies. Results We found models that used seasonal averaging could grossly overestimate infection burden and underestimate control outcomes in highly seasonal environments. We showed that proper intraseasonal timing of control measures could make marked improvement on the long-term burden reduction for Schistosoma transmission control, and we identified the optimal timing for each intervention. Seasonal snail control, implemented alone, was less effective than mass drug administration, but could provide additive impact in reaching control and elimination targets. Conclusions Seasonal variation makes Schistosoma transmission less sustainable and easier to control than predicted by earlier modeling studies.

Automated summary

The study used mathematical models to assess the impact of seasonality on Schistosoma transmission, finding that seasonal variation makes transmission less sustainable and that intraseasonal timing of interventions can improve long-term control outcomes. This research showed that modeling approaches that ignore seasonality can lead to overestimations of infection burden and underestimations of control outcomes in highly seasonal environments. Proper timing of control measures within the season can greatly enhance the effectiveness of Schistosoma transmission control.