Hydroclimatological and anthropogenic factors are key drivers of waterborne disease transmission. Information on human settlements and host mobility on waterways along which pathogens and hosts disperse, and relevant hydroclimatological processes, can be acquired remotely and included in spatially explicit mathematical models of disease transmission. In the case of epidemic cholera, such models allowed the description of complex disease patterns and provided insight into the course of ongoing epidemics. The inclusion of spatial information in models of disease transmission can aid in emergency management and the assessment of alternative interventions. Here, we review the study of drivers of transmission via spatially explicit approaches and argue that, because many parasitic waterborne diseases share the same drivers as cholera, similar principles may apply. Interest in spatially explicit mathematical models of epidemic cholera has grown considerably in the recent past. These models have the potential to account for heterogeneous disease drivers and thus to portray complex patterns of disease dynamics.Current scientific and technological advancements allow for a widespread and relatively straightforward application of general spatially explicit modeling techniques. Large-scale hydroclimatological and anthropogenic drivers (or their proxies) are key to spatial descriptions of waterborne disease dynamics and can often be inferred by satellite imagery and the tracking of human mobility via mobile phones.Many parasitic waterborne infections share cholera drivers and relevant transmission processes. However, they are characterized by vastly different disease dynamics and pathogen life cycles.Lessons learned from cholera research, as suggested by recent studies on human schistosomiasis and proliferative kidney disease in fish, may be effective in guiding containment efforts for waterborne parasitic diseases.

Modeling Key Drivers of Cholera Transmission Dynamics Provides New Perspectives for Parasitology

BERTUZZO, Enrico;
2017-01-01

Abstract

Hydroclimatological and anthropogenic factors are key drivers of waterborne disease transmission. Information on human settlements and host mobility on waterways along which pathogens and hosts disperse, and relevant hydroclimatological processes, can be acquired remotely and included in spatially explicit mathematical models of disease transmission. In the case of epidemic cholera, such models allowed the description of complex disease patterns and provided insight into the course of ongoing epidemics. The inclusion of spatial information in models of disease transmission can aid in emergency management and the assessment of alternative interventions. Here, we review the study of drivers of transmission via spatially explicit approaches and argue that, because many parasitic waterborne diseases share the same drivers as cholera, similar principles may apply. Interest in spatially explicit mathematical models of epidemic cholera has grown considerably in the recent past. These models have the potential to account for heterogeneous disease drivers and thus to portray complex patterns of disease dynamics.Current scientific and technological advancements allow for a widespread and relatively straightforward application of general spatially explicit modeling techniques. Large-scale hydroclimatological and anthropogenic drivers (or their proxies) are key to spatial descriptions of waterborne disease dynamics and can often be inferred by satellite imagery and the tracking of human mobility via mobile phones.Many parasitic waterborne infections share cholera drivers and relevant transmission processes. However, they are characterized by vastly different disease dynamics and pathogen life cycles.Lessons learned from cholera research, as suggested by recent studies on human schistosomiasis and proliferative kidney disease in fish, may be effective in guiding containment efforts for waterborne parasitic diseases.
2017
33
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3689332
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