An Ecological Model for Quantitative Risk Assessment for Schistosomiasis: The Case of a Patchy Environment in the Coastal Tropical Area of Northeastern Brazil

Abstract

We developed a stochastic model for quantitative risk assessment for the Schistosoma mansoni (SM) parasite, which causes an endemic disease of public concern. The model provides answers in a useful format for public health decisions, uses data and expert opinion, and can be applied to any landscape where the snail Biomphalaria glabrata is the main intermediate host (South and Central America, the Caribbean, and Africa). It incorporates several realistic and case-specific features: stage-structured parasite populations, periodic praziquantel (PZQ) drug treatment for humans, density dependence, extreme events (prolonged rainfall), site-specific sanitation quality, environmental stochasticity, monthly rainfall variation, uncertainty in parameters, and spatial dynamics. We parameterize the model through a real-world application in the district of Porto de Galinhas (PG), one of the main touristic destinations in Brazil, where previous studies identified four parasite populations within the metapopulation. The results provide a good approximation of the dynamics of the system and are in agreement with our field observations, i.e., the lack of basic infrastructure (sanitation level and health programs) makes PG a suitable habitat for the persistence and growth of a parasite metapopulation. We quantify the risk of SM metapopulation explosion and quasi-extinction and the time to metapopulation explosion and quasi-extinction. We evaluate the sensitivity of the results under varying scenarios of future periodic PZQ treatment (based on the Brazilian Ministry of Health's plan) and sanitation quality. We conclude that the plan might be useful to slow SM metapopulation growth but not to control it. Additional investments in better sanitation are necessary.

Faecal Pathogen Flows and Their Public Health Risks in Urban Environments: A Proposed Approach to Inform Sanitation Planning

“…The six exposure points are: (i) household environment (hands, fomite); (ii) groundwater (drinking water); (iii) local drain (flooding, children playing); (iv) community drains and downstream waterways (washing, bathing, swimming); (v) agricultural reuse (fresh produce); and (vi) farm land (accidental ingestion). While vector, skin and aerosol exposure pathways are also important, there are limited quantitative approaches to estimate the health risk from pathogens in the environment [ 54 ], and these pathways therefore have not been considered at this stage. Adult and child exposures were considered separately, as the transmission pathways and exposures can vary significantly in low-income urban environments [ 18 ].…”

The realities of risk-cost-benefit analysis

“…Examples of the progress possible when assumptions are clearly documented can be found in the peer-reviewed assessments of microbial risks in a recent issue of Risk Analysis. Results there include the possibility of treating municipal wastewater well enough to use for irrigating vegetables (29) and the impossibility of treating victims as a way to eliminate the parasite responsible for schistosomiasis (30). Clear documentation can also reveal fundamental flaws, as when an external review (31) concluded that the Department of Homeland Security's 2006 Bioterrorism Risk Assessment (32) "should not be used "(italics in original), given such problems as using estimates "not supported by any existing data," omitting "economic loss and environmental and agricultural effects," and lacking "a realistic representation of the behavior of an intelligent adversary."…”

Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence

“…Sources of uncertainty that need to be accounted for in the modelling process include differences in variable selection criteria, statistical methods used, selected spatial and temporal scales of analysis [16], sampling design, sensitivity and specificity of diagnostic techniques as well as the quality of the spatial data used.…”